Evaluation of statistical protocols for quality control of ecosystem carbon dioxide fluxes

Treesearch

Jorge F. Perez-Quezada; Nicanor Z. Saliendra; William E. Emmerich; Emilio A. Laca

2007-01-01

The process of quality control of micrometeorological and carbon dioxide (CO2) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO2 flux...

[Analysis of variance of repeated data measured by water maze with SPSS].

PubMed

Qiu, Hong; Jin, Guo-qin; Jin, Ru-feng; Zhao, Wei-kang

2007-01-01

To introduce the method of analyzing repeated data measured by water maze with SPSS 11.0, and offer a reference statistical method to clinical and basic medicine researchers who take the design of repeated measures. Using repeated measures and multivariate analysis of variance (ANOVA) process of the general linear model in SPSS and giving comparison among different groups and different measure time pairwise. Firstly, Mauchly's test of sphericity should be used to judge whether there were relations among the repeatedly measured data. If any (P

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach.

PubMed

Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan

2016-05-01

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016. © 2016 American Institute of Chemical Engineers.

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

PubMed

Del Giudice, G; Padulano, R; Siciliano, D

2016-01-01

The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.

A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

PubMed

Buttigieg, Pier Luigi; Ramette, Alban

2014-12-01

The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.

Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing.

PubMed

Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir

2014-01-01

Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers.

Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates.

PubMed

Babamoradi, Hamid; van den Berg, Frans; Rinnan, Åsmund

2016-02-18

In Multivariate Statistical Process Control, when a fault is expected or detected in the process, contribution plots are essential for operators and optimization engineers in identifying those process variables that were affected by or might be the cause of the fault. The traditional way of interpreting a contribution plot is to examine the largest contributing process variables as the most probable faulty ones. This might result in false readings purely due to the differences in natural variation, measurement uncertainties, etc. It is more reasonable to compare variable contributions for new process runs with historical results achieved under Normal Operating Conditions, where confidence limits for contribution plots estimated from training data are used to judge new production runs. Asymptotic methods cannot provide confidence limits for contribution plots, leaving re-sampling methods as the only option. We suggest bootstrap re-sampling to build confidence limits for all contribution plots in online PCA-based MSPC. The new strategy to estimate CLs is compared to the previously reported CLs for contribution plots. An industrial batch process dataset was used to illustrate the concepts. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

NASA Astrophysics Data System (ADS)

Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

2014-12-01

The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

NASA Astrophysics Data System (ADS)

Zakour, Sihem Ben; Taleb, Hassen

2017-09-01

Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small (0.1%). It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy (OES) sensor is employed. The huge number of gathered wavelengths (profiles) is then analyzed and pre-processed using a new proposed simple algorithm named Spectra peak selection (SPS) to select the important wavelengths, then we employ wavelet analysis (WA) to enhance the performance of detection by suppressing noise and redundant information. The selected and treated OES wavelengths are then used in modified multivariate control charts (MEWMA and Hotelling) for three statistics (mean, SD and CV) and windowed polynomial regression for mean. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic. As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon.

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study.

PubMed

Catelani, Tiago A; Santos, João Rodrigo; Páscoa, Ricardo N M J; Pezza, Leonardo; Pezza, Helena R; Lopes, João A

2018-03-01

This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T 2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time. Copyright © 2017 Elsevier B.V. All rights reserved.

Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis.

PubMed

Whist, A C; Liland, K H; Jonsson, M E; Sæbø, S; Sviland, S; Østerås, O; Norström, M; Hopp, P

2014-11-01

Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population's disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling's T(2) statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds. The use of multivariate statistical process control for selection of herds will be beneficial when a diagnostic test suitable for mass screening is available and validated on the Norwegian cattle population, thus making it possible to increase the number of sampled herds. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

[Near infrared spectroscopy based process trajectory technology and its application in monitoring and controlling of traditional Chinese medicine manufacturing process].

PubMed

Li, Wen-Long; Qu, Hai-Bin

2016-10-01

In this paper, the principle of NIRS (near infrared spectroscopy)-based process trajectory technology was introduced.The main steps of the technique include:① in-line collection of the processes spectra of different technics; ② unfolding of the 3-D process spectra;③ determination of the process trajectories and their normal limits;④ monitoring of the new batches with the established MSPC (multivariate statistical process control) models.Applications of the technology in the chemical and biological medicines were reviewed briefly. By a comprehensive introduction of our feasibility research on the monitoring of traditional Chinese medicine technical process using NIRS-based multivariate process trajectories, several important problems of the practical applications which need urgent solutions are proposed, and also the application prospect of the NIRS-based process trajectory technology is fully discussed and put forward in the end. Copyright© by the Chinese Pharmaceutical Association.

A model-based approach to wildland fire reconstruction using sediment charcoal records

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; Hooten, Mevin B.; Higuera, Philip E.; Marlon, Jennifer R.; Kelly, Ryan; McLachlan, Jason S.

2017-01-01

Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history, including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate the probability of fire associated with charcoal counts from individual-lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100–350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleofire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.

1H NMR-based metabolic profiling for evaluating poppy seed rancidity and brewing.

PubMed

Jawień, Ewa; Ząbek, Adam; Deja, Stanisław; Łukaszewicz, Marcin; Młynarz, Piotr

2015-12-01

Poppy seeds are widely used in household and commercial confectionery. The aim of this study was to demonstrate the application of metabolic profiling for industrial monitoring of the molecular changes which occur during minced poppy seed rancidity and brewing processes performed on raw seeds. Both forms of poppy seeds were obtained from a confectionery company. Proton nuclear magnetic resonance (1H NMR) was applied as the analytical method of choice together with multivariate statistical data analysis. Metabolic fingerprinting was applied as a bioprocess control tool to monitor rancidity with the trajectory of change and brewing progressions. Low molecular weight compounds were found to be statistically significant biomarkers of these bioprocesses. Changes in concentrations of chemical compounds were explained relative to the biochemical processes and external conditions. The obtained results provide valuable and comprehensive information to gain a better understanding of the biology of rancidity and brewing processes, while demonstrating the potential for applying NMR spectroscopy combined with multivariate data analysis tools for quality control in food industries involved in the processing of oilseeds. This precious and versatile information gives a better understanding of the biology of these processes.

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques

NASA Astrophysics Data System (ADS)

Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein

2017-10-01

The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

PubMed

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Fault Detection and Diagnosis In Hall-Héroult Cells Based on Individual Anode Current Measurements Using Dynamic Kernel PCA

NASA Astrophysics Data System (ADS)

Yao, Yuchen; Bao, Jie; Skyllas-Kazacos, Maria; Welch, Barry J.; Akhmetov, Sergey

2018-04-01

Individual anode current signals in aluminum reduction cells provide localized cell conditions in the vicinity of each anode, which contain more information than the conventionally measured cell voltage and line current. One common use of this measurement is to identify process faults that can cause significant changes in the anode current signals. While this method is simple and direct, it ignores the interactions between anode currents and other important process variables. This paper presents an approach that applies multivariate statistical analysis techniques to individual anode currents and other process operating data, for the detection and diagnosis of local process abnormalities in aluminum reduction cells. Specifically, since the Hall-Héroult process is time-varying with its process variables dynamically and nonlinearly correlated, dynamic kernel principal component analysis with moving windows is used. The cell is discretized into a number of subsystems, with each subsystem representing one anode and cell conditions in its vicinity. The fault associated with each subsystem is identified based on multivariate statistical control charts. The results show that the proposed approach is able to not only effectively pinpoint the problematic areas in the cell, but also assess the effect of the fault on different parts of the cell.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

PubMed

Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

2017-02-01

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Testing for significance of phase synchronisation dynamics in the EEG.

PubMed

Daly, Ian; Sweeney-Reed, Catherine M; Nasuto, Slawomir J

2013-06-01

A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.

The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability.

PubMed

Marković, Snežana; Kerč, Janez; Horvat, Matej

2017-03-01

We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach.

PubMed

Moya, Claudio E; Raiber, Matthias; Taulis, Mauricio; Cox, Malcolm E

2015-03-01

The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na-Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na-HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous-Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls. Copyright © 2014 Elsevier B.V. All rights reserved.

Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

ERIC Educational Resources Information Center

Thompson, Bruce

Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

Diagnosis of abnormal patterns in multivariate microclimate monitoring: a case study of an open-air archaeological site in Pompeii (Italy).

PubMed

Merello, Paloma; García-Diego, Fernando-Juan; Zarzo, Manuel

2014-08-01

Chemometrics has been applied successfully since the 1990s for the multivariate statistical control of industrial processes. A new area of interest for these tools is the microclimatic monitoring of cultural heritage. Sensors record climatic parameters over time and statistical data analysis is performed to obtain valuable information for preventive conservation. A case study of an open-air archaeological site is presented here. A set of 26 temperature and relative humidity data-loggers was installed in four rooms of Ariadne's house (Pompeii). If climatic values are recorded versus time at different positions, the resulting data structure is equivalent to records of physical parameters registered at several points of a continuous chemical process. However, there is an important difference in this case: continuous processes are controlled to reach a steady state, whilst open-air sites undergo tremendous fluctuations. Although data from continuous processes are usually column-centred prior to applying principal components analysis, it turned out that another pre-treatment (row-centred data) was more convenient for the interpretation of components and to identify abnormal patterns. The detection of typical trajectories was more straightforward by dividing the whole monitored period into several sub-periods, because the marked climatic fluctuations throughout the year affect the correlation structures. The proposed statistical methodology is of interest for the microclimatic monitoring of cultural heritage, particularly in the case of open-air or semi-confined archaeological sites. Copyright © 2014 Elsevier B.V. All rights reserved.

Analyzing Faculty Salaries When Statistics Fail.

ERIC Educational Resources Information Center

Simpson, William A.

The role played by nonstatistical procedures, in contrast to multivariant statistical approaches, in analyzing faculty salaries is discussed. Multivariant statistical methods are usually used to establish or defend against prima facia cases of gender and ethnic discrimination with respect to faculty salaries. These techniques are not applicable,…

Moving Average Models with Bivariate Exponential and Geometric Distributions.

DTIC Science & Technology

1985-03-01

ordinary time series and of point processes. Developments in Statistics, Vol. 1, P.R. Krishnaiah , ed. Academic Press, New York. [9] Esary, J.D. and...valued and discrete - valued time series with ARMA correlation structure. Multivariate Analysis V, P.R. Krishnaiah , ed. North-Holland. 151-166. [28

Artificial Neural Networks in Policy Research: A Current Assessment.

ERIC Educational Resources Information Center

Woelfel, Joseph

1993-01-01

Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

Parsimonious Use of Indicators for Evaluating Sustainability Systems with Multivariate Statistical Analyses

EPA Science Inventory

Indicators are commonly used for evaluating relative sustainability for competing products and processes. When a set of indicators is chosen for a particular system of study, it is important to ensure that they are variable independently of each other. Often the number of indicat...

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

Multivariate statistical process control in product quality review assessment - A case study.

PubMed

Kharbach, M; Cherrah, Y; Vander Heyden, Y; Bouklouze, A

2017-11-01

According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T 2 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. Copyright © 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.

Multivariate Relationships between Statistics Anxiety and Motivational Beliefs

ERIC Educational Resources Information Center

Baloglu, Mustafa; Abbassi, Amir; Kesici, Sahin

2017-01-01

In general, anxiety has been found to be associated with motivational beliefs and the current study investigated multivariate relationships between statistics anxiety and motivational beliefs among 305 college students (60.0% women). The Statistical Anxiety Rating Scale, the Motivated Strategies for Learning Questionnaire, and a set of demographic…

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Post-processing of multi-hydrologic model simulations for improved streamflow projections

NASA Astrophysics Data System (ADS)

khajehei, sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

2016-04-01

Hydrologic model outputs are prone to bias and uncertainty due to knowledge deficiency in model and data. Uncertainty in hydroclimatic projections arises due to uncertainty in hydrologic model as well as the epistemic or aleatory uncertainties in GCM parameterization and development. This study is conducted to: 1) evaluate the recently developed multi-variate post-processing method for historical simulations and 2) assess the effect of post-processing on uncertainty and reliability of future streamflow projections in both high-flow and low-flow conditions. The first objective is performed for historical period of 1970-1999. Future streamflow projections are generated for 10 statistically downscaled GCMs from two widely used downscaling methods: Bias Corrected Statistically Downscaled (BCSD) and Multivariate Adaptive Constructed Analogs (MACA), over the period of 2010-2099 for two representative concentration pathways of RCP4.5 and RCP8.5. Three semi-distributed hydrologic models were employed and calibrated at 1/16 degree latitude-longitude resolution for over 100 points across the Columbia River Basin (CRB) in the pacific northwest USA. Streamflow outputs are post-processed through a Bayesian framework based on copula functions. The post-processing approach is relying on a transfer function developed based on bivariate joint distribution between the observation and simulation in historical period. Results show that application of post-processing technique leads to considerably higher accuracy in historical simulations and also reducing model uncertainty in future streamflow projections.

Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

PubMed

Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

2018-02-01

Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical mixture design and multivariate analysis of inkjet printed a-WO3/TiO2/WOX electrochromic films.

PubMed

Wojcik, Pawel Jerzy; Pereira, Luís; Martins, Rodrigo; Fortunato, Elvira

2014-01-13

An efficient mathematical strategy in the field of solution processed electrochromic (EC) films is outlined as a combination of an experimental work, modeling, and information extraction from massive computational data via statistical software. Design of Experiment (DOE) was used for statistical multivariate analysis and prediction of mixtures through a multiple regression model, as well as the optimization of a five-component sol-gel precursor subjected to complex constraints. This approach significantly reduces the number of experiments to be realized, from 162 in the full factorial (L=3) and 72 in the extreme vertices (D=2) approach down to only 30 runs, while still maintaining a high accuracy of the analysis. By carrying out a finite number of experiments, the empirical modeling in this study shows reasonably good prediction ability in terms of the overall EC performance. An optimized ink formulation was employed in a prototype of a passive EC matrix fabricated in order to test and trial this optically active material system together with a solid-state electrolyte for the prospective application in EC displays. Coupling of DOE with chromogenic material formulation shows the potential to maximize the capabilities of these systems and ensures increased productivity in many potential solution-processed electrochemical applications.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

PubMed

Kamal, Ghulam Mustafa; Wang, Xiaohua; Bin Yuan; Wang, Jie; Sun, Peng; Zhang, Xu; Liu, Maili

2016-09-01

Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. Copyright © 2016 Elsevier B.V. All rights reserved.

Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power generation, Applied Energy, 96, 12-20, DOI: 10.1016/j.apenergy.2011.11.004. Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28, 616-640, DOI: 10.1214/13-STS443.

Big-Data RHEED analysis for understanding epitaxial film growth processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence.more » This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.« less

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

PubMed

Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

Harnessing Multivariate Statistics for Ellipsoidal Data in Structural Geology

NASA Astrophysics Data System (ADS)

Roberts, N.; Davis, J. R.; Titus, S.; Tikoff, B.

2015-12-01

Most structural geology articles do not state significance levels, report confidence intervals, or perform regressions to find trends. This is, in part, because structural data tend to include directions, orientations, ellipsoids, and tensors, which are not treatable by elementary statistics. We describe a full procedural methodology for the statistical treatment of ellipsoidal data. We use a reconstructed dataset of deformed ooids in Maryland from Cloos (1947) to illustrate the process. Normalized ellipsoids have five degrees of freedom and can be represented by a second order tensor. This tensor can be permuted into a five dimensional vector that belongs to a vector space and can be treated with standard multivariate statistics. Cloos made several claims about the distribution of deformation in the South Mountain fold, Maryland, and we reexamine two particular claims using hypothesis testing: 1) octahedral shear strain increases towards the axial plane of the fold; 2) finite strain orientation varies systematically along the trend of the axial trace as it bends with the Appalachian orogen. We then test the null hypothesis that the southern segment of South Mountain is the same as the northern segment. This test illustrates the application of ellipsoidal statistics, which combine both orientation and shape. We report confidence intervals for each test, and graphically display our results with novel plots. This poster illustrates the importance of statistics in structural geology, especially when working with noisy or small datasets.

A simple rapid approach using coupled multivariate statistical methods, GIS and trajectory models to delineate areas of common oil spill risk

NASA Astrophysics Data System (ADS)

Guillen, George; Rainey, Gail; Morin, Michelle

2004-04-01

Currently, the Minerals Management Service uses the Oil Spill Risk Analysis model (OSRAM) to predict the movement of potential oil spills greater than 1000 bbl originating from offshore oil and gas facilities. OSRAM generates oil spill trajectories using meteorological and hydrological data input from either actual physical measurements or estimates generated from other hydrological models. OSRAM and many other models produce output matrices of average, maximum and minimum contact probabilities to specific landfall or target segments (columns) from oil spills at specific points (rows). Analysts and managers are often interested in identifying geographic areas or groups of facilities that pose similar risks to specific targets or groups of targets if a spill occurred. Unfortunately, due to the potentially large matrix generated by many spill models, this question is difficult to answer without the use of data reduction and visualization methods. In our study we utilized a multivariate statistical method called cluster analysis to group areas of similar risk based on potential distribution of landfall target trajectory probabilities. We also utilized ArcView™ GIS to display spill launch point groupings. The combination of GIS and multivariate statistical techniques in the post-processing of trajectory model output is a powerful tool for identifying and delineating areas of similar risk from multiple spill sources. We strongly encourage modelers, statistical and GIS software programmers to closely collaborate to produce a more seamless integration of these technologies and approaches to analyzing data. They are complimentary methods that strengthen the overall assessment of spill risks.

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Profiling and multivariate statistical analysis of Panax ginseng based on ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

PubMed

Wu, Wei; Sun, Le; Zhang, Zhe; Guo, Yingying; Liu, Shuying

2015-03-25

An ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) method was developed for the detection and structural analysis of ginsenosides in white ginseng and related processed products (red ginseng). Original neutral, malonyl, and chemically transformed ginsenosides were identified in white and red ginseng samples. The aglycone types of ginsenosides were determined by MS/MS as PPD (m/z 459), PPT (m/z 475), C-24, -25 hydrated-PPD or PPT (m/z 477 or m/z 493), and Δ20(21)-or Δ20(22)-dehydrated-PPD or PPT (m/z 441 or m/z 457). Following the structural determination, the UHPLC-Q-TOF-MS-based chemical profiling coupled with multivariate statistical analysis method was applied for global analysis of white and processed ginseng samples. The chemical markers present between the processed products red ginseng and white ginseng could be assigned. Process-mediated chemical changes were recognized as the hydrolysis of ginsenosides with large molecular weight, chemical transformations of ginsenosides, changes in malonyl-ginsenosides, and generation of 20-(R)-ginsenoside enantiomers. The relative contents of compounds classified as PPD, PPT, malonyl, and transformed ginsenosides were calculated based on peak areas in ginseng before and after processing. This study provides possibility to monitor multiple components for the quality control and global evaluation of ginseng products during processing. Copyright © 2014 Elsevier B.V. All rights reserved.

Automated information and control complex of hydro-gas endogenous mine processes

NASA Astrophysics Data System (ADS)

Davkaev, K. S.; Lyakhovets, M. V.; Gulevich, T. M.; Zolin, K. A.

2017-09-01

The automated information and control complex designed to prevent accidents, related to aerological situation in the underground workings, accounting of the received and handed over individual devices, transmission and display of measurement data, and the formation of preemptive solutions is considered. Examples for the automated workplace of an airgas control operator by individual means are given. The statistical characteristics of field data characterizing the aerological situation in the mine are obtained. The conducted studies of statistical characteristics confirm the feasibility of creating a subsystem of controlled gas distribution with an adaptive arrangement of points for gas control. The adaptive (multivariant) algorithm for processing measuring information of continuous multidimensional quantities and influencing factors has been developed.

mvMapper: statistical and geographical data exploration and visualization of multivariate analysis of population structure

USDA-ARS?s Scientific Manuscript database

Characterizing population genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata is not always easily integrated into t...

Monitoring of an antigen manufacturing process.

PubMed

Zavatti, Vanessa; Budman, Hector; Legge, Raymond; Tamer, Melih

2016-06-01

Fluorescence spectroscopy in combination with multivariate statistical methods was employed as a tool for monitoring the manufacturing process of pertactin (PRN), one of the virulence factors of Bordetella pertussis utilized in whopping cough vaccines. Fluorophores such as amino acids and co-enzymes were detected throughout the process. The fluorescence data collected at different stages of the fermentation and purification process were treated employing principal component analysis (PCA). Through PCA, it was feasible to identify sources of variability in PRN production. Then, partial least square (PLS) was employed to correlate the fluorescence spectra obtained from pure PRN samples and the final protein content measured by a Kjeldahl test from these samples. In view that a statistically significant correlation was found between fluorescence and PRN levels, this approach could be further used as a method to predict the final protein content.

Multivariate statistical analysis of a high rate biofilm process treating kraft mill bleach plant effluent.

PubMed

Goode, C; LeRoy, J; Allen, D G

2007-01-01

This study reports on a multivariate analysis of the moving bed biofilm reactor (MBBR) wastewater treatment system at a Canadian pulp mill. The modelling approach involved a data overview by principal component analysis (PCA) followed by partial least squares (PLS) modelling with the objective of explaining and predicting changes in the BOD output of the reactor. Over two years of data with 87 process measurements were used to build the models. Variables were collected from the MBBR control scheme as well as upstream in the bleach plant and in digestion. To account for process dynamics, a variable lagging approach was used for variables with significant temporal correlations. It was found that wood type pulped at the mill was a significant variable governing reactor performance. Other important variables included flow parameters, faults in the temperature or pH control of the reactor, and some potential indirect indicators of biomass activity (residual nitrogen and pH out). The most predictive model was found to have an RMSEP value of 606 kgBOD/d, representing a 14.5% average error. This was a good fit, given the measurement error of the BOD test. Overall, the statistical approach was effective in describing and predicting MBBR treatment performance.

[Rank distributions in community ecology from the statistical viewpoint].

PubMed

Maksimov, V N

2004-01-01

Traditional statistical methods for definition of empirical functions of abundance distribution (population, biomass, production, etc.) of species in a community are applicable for processing of multivariate data contained in the above quantitative indices of the communities. In particular, evaluation of moments of distribution suffices for convolution of the data contained in a list of species and their abundance. At the same time, the species should be ranked in the list in ascending rather than descending population and the distribution models should be analyzed on the basis of the data on abundant species only.

Profiling and analysis of multiple constituents in Baizhu Shaoyao San before and after processing by stir-frying using UHPLC/Q-TOF-MS/MS coupled with multivariate statistical analysis.

PubMed

Xu, Yangyang; Cai, Hao; Cao, Gang; Duan, Yu; Pei, Ke; Tu, Sicong; Zhou, Jia; Xie, Li; Sun, Dongdong; Zhao, Jiayu; Liu, Jing; Wang, Xiaoqi; Shen, Lin

2018-04-15

Baizhu Shaoyao San (BSS) is a famous traditional Chinese medicinal formula widely used for the treatment of painful diarrhea, intestinal inflammation, and diarrhea-predominant irritable bowel syndrome. According to clinical medication, three medicinal herbs (Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, and Citri Reticulatae Pericarpium) included in BSS must be processed using some specific methods of stir-frying. On the basis of the classical theories of traditional Chinese medicine, the therapeutic effects of BSS would be significantly enhanced after processing. Generally, the changes of curative effects mainly result from the variations of inside chemical basis caused by the processing procedure. To find out the corresponding changes of chemical compositions in BSS after processing and to elucidate the material basis of the changed curative effects, an optimized ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry in positive and negative ion modes coupled with multivariate statistical analyses were developed. As a result, a total of 186 compounds were ultimately identified in crude and processed BSS, in which 62 marker compounds with significant differences between crude and processed BSS were found by principal component analysis and t-test. Compared with crude BSS, the contents of 23 compounds were remarkably decreased and the contents of 39 compounds showed notable increase in processed BSS. The transformation mechanisms of some changed compounds were appropriately inferred from the results. Furthermore, compounds with extremely significant differences might strengthen the effects of the whole herbal formula. Copyright © 2018 Elsevier B.V. All rights reserved.

One Hundred Ways to be Non-Fickian - A Rigorous Multi-Variate Statistical Analysis of Pore-Scale Transport

NASA Astrophysics Data System (ADS)

Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

2015-04-01

Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where the validity of yet-unexplored non-Gaussian-but-Markovian random walks start. 2) The distance where bivariate statistical dependence simplifies to a multi-Gaussian dependence based on simple linear correlation (validity of correlated PTRW/CTRW). 3) The distance of complete statistical independence (validity of classical PTRW/CTRW). The second objective is to reveal characteristic dependencies influencing transport the most. Those dependencies can be very complex. Copulas are highly capable of representing linear dependence as well as non-linear dependence. With that tool we are able to detect persistent characteristics dominating transport even across different scales. The results derived from our experimental data set suggest that there are many more non-Fickian aspects of pore-scale transport than the univariate statistics of longitudinal displacements. Non-Fickianity can also be found in transverse displacements, and in the relations between increments at different time steps. Also, the found dependence is non-linear (i.e. beyond simple correlation) and persists over long distances. Thus, our results strongly support the further refinement of techniques like correlated PTRW or correlated CTRW towards non-linear statistical relations.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

A Civilian/Military Trauma Institute: National Trauma Coordinating Center

DTIC Science & Technology

2015-12-01

zip codes was used in “proximity to violence” analysis. Data were analyzed using SPSS (version 20.0, SPSS Inc., Chicago, IL). Multivariable linear...number of adverse events and serious events was not statistically higher in one group, the incidence of deep venous thrombosis (DVT) was statistically ...subjects the lack of statistical difference on multivariate analysis may be related to an underpowered sample size. It was recommended that the

A new test of multivariate nonlinear causality

PubMed Central

Bai, Zhidong; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power. PMID:29304085

A new test of multivariate nonlinear causality.

PubMed

Bai, Zhidong; Hui, Yongchang; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power.

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Nursing Homes Appeals of Deficiency Citations: The Informal Dispute Resolution Process

PubMed Central

Mukamel, Dana B.; Weimer, David L.; Li, Yue; Bailey, Lauren; Spector, William D.; Harrington, Charlene

2012-01-01

Objective Nursing homes found to be not meeting quality standards are cited for deficiencies. Before 1995, their only recourse was a formal appeal process, which is lengthy and costly. In 1995, the Centers for Medicare & Medicaid Services (CMS) instituted the Informal Dispute Resolution (IDR) process. This study presents for the first time national statistics about the IDR process and an analysis of the factors that influence nursing homes’ decisions to request an IDR. Design Retrospective study including descriptive statistics and multivariate logistic hierarchical models. Setting U.S. nursing homes in 2005 to 2008. Participant 15,916 Medicaid and Medicare certified nursing homes nationally, with 94,188 surveys and 9,388 IDRs. Measures The unit of observation was an annual survey or a complaint survey that generated at least one deficiency. The dependent variable was dichotomous and indicated whether the annual or a complaint survey triggered an IDR request. Independent variables included characteristics of the nursing home, the deficiency, the market, and the state regulatory environment. Results Ten percent of all annual surveys and complaint surveys resulted in IDRs. There was substantial variation across states, which persisted over time. Multivariate results suggest that nursing homes’ decisions to request an IDR depend on their assessment of the probability of success and assessment of the benefits of the submission. Conclusions Nursing homes avail themselves of the IDR process. Their propensity to do so depends on a number of factors, including the state regulatory system and the market environment in which they operate. PMID:22402171

Analysis of the dependence of extreme rainfalls

NASA Astrophysics Data System (ADS)

Padoan, Simone; Ancey, Christophe; Parlange, Marc

2010-05-01

The aim of spatial analysis is to quantitatively describe the behavior of environmental phenomena such as precipitation levels, wind speed or daily temperatures. A number of generic approaches to spatial modeling have been developed[1], but these are not necessarily ideal for handling extremal aspects given their focus on mean process levels. The areal modelling of the extremes of a natural process observed at points in space is important in environmental statistics; for example, understanding extremal spatial rainfall is crucial in flood protection. In light of recent concerns over climate change, the use of robust mathematical and statistical methods for such analyses has grown in importance. Multivariate extreme value models and the class of maxstable processes [2] have a similar asymptotic motivation to the univariate Generalized Extreme Value (GEV) distribution , but providing a general approach to modeling extreme processes incorporating temporal or spatial dependence. Statistical methods for max-stable processes and data analyses of practical problems are discussed by [3] and [4]. This work illustrates methods to the statistical modelling of spatial extremes and gives examples of their use by means of a real extremal data analysis of Switzerland precipitation levels. [1] Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York. [2] de Haan, L and Ferreria A. (2006). Extreme Value Theory An Introduction. Springer, USA. [3] Padoan, S. A., Ribatet, M and Sisson, S. A. (2009). Likelihood-Based Inference for Max-Stable Processes. Journal of the American Statistical Association, Theory & Methods. In press. [4] Davison, A. C. and Gholamrezaee, M. (2009), Geostatistics of extremes. Journal of the Royal Statistical Society, Series B. To appear.

Host-Based Multivariate Statistical Computer Operating Process Anomaly Intrusion Detection System (PAIDS)

DTIC Science & Technology

2009-03-01

viii 3.2.3 Sub7 ...from TaskInfo in Excel Format. 3.2.3 Sub7 Also known as SubSeven, this is one of the best known, most widely distributed backdoor programs on the...engineering the spread of viruses, worms, backdoors and other malware. The Sub7 Trojan establishes a server on the victim computer that

Analyzing developmental processes on an individual level using nonstationary time series modeling.

PubMed

Molenaar, Peter C M; Sinclair, Katerina O; Rovine, Michael J; Ram, Nilam; Corneal, Sherry E

2009-01-01

Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data.

Metal and physico-chemical variations at a hydroelectric reservoir analyzed by Multivariate Analyses and Artificial Neural Networks: environmental management and policy/decision-making tools.

PubMed

Cavalcante, Y L; Hauser-Davis, R A; Saraiva, A C F; Brandão, I L S; Oliveira, T F; Silveira, A M

2013-01-01

This paper compared and evaluated seasonal variations in physico-chemical parameters and metals at a hydroelectric power station reservoir by applying Multivariate Analyses and Artificial Neural Networks (ANN) statistical techniques. A Factor Analysis was used to reduce the number of variables: the first factor was composed of elements Ca, K, Mg and Na, and the second by Chemical Oxygen Demand. The ANN showed 100% correct classifications in training and validation samples. Physico-chemical analyses showed that water pH values were not statistically different between the dry and rainy seasons, while temperature, conductivity, alkalinity, ammonia and DO were higher in the dry period. TSS, hardness and COD, on the other hand, were higher during the rainy season. The statistical analyses showed that Ca, K, Mg and Na are directly connected to the Chemical Oxygen Demand, which indicates a possibility of their input into the reservoir system by domestic sewage and agricultural run-offs. These statistical applications, thus, are also relevant in cases of environmental management and policy decision-making processes, to identify which factors should be further studied and/or modified to recover degraded or contaminated water bodies. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate analysis, mass balance techniques, and statistical tests as tools in igneous petrology: application to the Sierra de las Cruces volcanic range (Mexican Volcanic Belt).

PubMed

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Big-data reflection high energy electron diffraction analysis for understanding epitaxial film growth processes.

PubMed

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P; Kalinin, Sergei V

2014-10-28

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED images, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the data set are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of a RHEED image sequence. This approach is illustrated for growth of La(x)Ca(1-x)MnO(3) films grown on etched (001) SrTiO(3) substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the asymmetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Optimization of Sinter Plant Operating Conditions Using Advanced Multivariate Statistics: Intelligent Data Processing

NASA Astrophysics Data System (ADS)

Fernández-González, Daniel; Martín-Duarte, Ramón; Ruiz-Bustinza, Íñigo; Mochón, Javier; González-Gasca, Carmen; Verdeja, Luis Felipe

2016-08-01

Blast furnace operators expect to get sinter with homogenous and regular properties (chemical and mechanical), necessary to ensure regular blast furnace operation. Blends for sintering also include several iron by-products and other wastes that are obtained in different processes inside the steelworks. Due to their source, the availability of such materials is not always consistent, but their total production should be consumed in the sintering process, to both save money and recycle wastes. The main scope of this paper is to obtain the least expensive iron ore blend for the sintering process, which will provide suitable chemical and mechanical features for the homogeneous and regular operation of the blast furnace. The systematic use of statistical tools was employed to analyze historical data, including linear and partial correlations applied to the data and fuzzy clustering based on the Sugeno Fuzzy Inference System to establish relationships among the available variables.

Integration of ecological indices in the multivariate evaluation of an urban inventory of street trees

Treesearch

J. Grabinsky; A. Aldama; A. Chacalo; H. J. Vazquez

2000-01-01

Inventory data of Mexico City's street trees were studied using classical statistical arboricultural and ecological statistical approaches. Multivariate techniques were applied to both. Results did not differ substantially and were complementary. It was possible to reduce inventory data and to group species, boroughs, blocks, and variables.

The effect of normalization of Partial Directed Coherence on the statistical assessment of connectivity patterns: a simulation study.

PubMed

Toppi, J; Petti, M; Vecchiato, G; Cincotti, F; Salinari, S; Mattia, D; Babiloni, F; Astolfi, L

2013-01-01

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis. Results of the statistical analysis revealed an effect of PDC normalization only on the percentages of type I and type II errors occurred by using Shuffling procedure for the assessment of connectivity patterns. No effects of the PDC formulation resulted on the performances achieved during the validation process executed instead by means of Asymptotic Statistic approach. Moreover, the percentages of both false positives and false negatives committed by Asymptotic Statistic are always lower than those achieved by Shuffling procedure for each type of normalization.

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

The Effect of the Multivariate Box-Cox Transformation on the Power of MANOVA.

ERIC Educational Resources Information Center

Kirisci, Levent; Hsu, Tse-Chi

Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A comparison of ensemble post-processing approaches that preserve correlation structures

NASA Astrophysics Data System (ADS)

Schefzik, Roman; Van Schaeybroeck, Bert; Vannitsem, Stéphane

2016-04-01

Despite the fact that ensemble forecasts address the major sources of uncertainty, they exhibit biases and dispersion errors and therefore are known to improve by calibration or statistical post-processing. For instance the ensemble model output statistics (EMOS) method, also known as non-homogeneous regression approach (Gneiting et al., 2005) is known to strongly improve forecast skill. EMOS is based on fitting and adjusting a parametric probability density function (PDF). However, EMOS and other common post-processing approaches apply to a single weather quantity at a single location for a single look-ahead time. They are therefore unable of taking into account spatial, inter-variable and temporal dependence structures. Recently many research efforts have been invested in designing post-processing methods that resolve this drawback but also in verification methods that enable the detection of dependence structures. New verification methods are applied on two classes of post-processing methods, both generating physically coherent ensembles. A first class uses the ensemble copula coupling (ECC) that starts from EMOS but adjusts the rank structure (Schefzik et al., 2013). The second class is a member-by-member post-processing (MBM) approach that maps each raw ensemble member to a corrected one (Van Schaeybroeck and Vannitsem, 2015). We compare variants of the EMOS-ECC and MBM classes and highlight a specific theoretical connection between them. All post-processing variants are applied in the context of the ensemble system of the European Centre of Weather Forecasts (ECMWF) and compared using multivariate verification tools including the energy score, the variogram score (Scheuerer and Hamill, 2015) and the band depth rank histogram (Thorarinsdottir et al., 2015). Gneiting, Raftery, Westveld, and Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., {133}, 1098-1118. Scheuerer and Hamill, 2015. Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities. Mon. Wea. Rev. {143},1321-1334. Schefzik, Thorarinsdottir, Gneiting. Uncertainty quantification in complex simulation models using ensemble copula coupling. Statistical Science {28},616-640, 2013. Thorarinsdottir, M. Scheuerer, and C. Heinz, 2015. Assessing the calibration of high-dimensional ensemble forecasts using rank histograms, arXiv:1310.0236. Van Schaeybroeck and Vannitsem, 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

Quality control for quantitative PCR based on amplification compatibility test.

PubMed

Tichopad, Ales; Bar, Tzachi; Pecen, Ladislav; Kitchen, Robert R; Kubista, Mikael; Pfaffl, Michael W

2010-04-01

Quantitative qPCR is a routinely used method for the accurate quantification of nucleic acids. Yet it may generate erroneous results if the amplification process is obscured by inhibition or generation of aberrant side-products such as primer dimers. Several methods have been established to control for pre-processing performance that rely on the introduction of a co-amplified reference sequence, however there is currently no method to allow for reliable control of the amplification process without directly modifying the sample mix. Herein we present a statistical approach based on multivariate analysis of the amplification response data generated in real-time. The amplification trajectory in its most resolved and dynamic phase is fitted with a suitable model. Two parameters of this model, related to amplification efficiency, are then used for calculation of the Z-score statistics. Each studied sample is compared to a predefined reference set of reactions, typically calibration reactions. A probabilistic decision for each individual Z-score is then used to identify the majority of inhibited reactions in our experiments. We compare this approach to univariate methods using only the sample specific amplification efficiency as reporter of the compatibility. We demonstrate improved identification performance using the multivariate approach compared to the univariate approach. Finally we stress that the performance of the amplification compatibility test as a quality control procedure depends on the quality of the reference set. Copyright 2010 Elsevier Inc. All rights reserved.

Geochemical processes controlling water salinization in an irrigated basin in Spain: identification of natural and anthropogenic influence.

PubMed

Merchán, D; Auqué, L F; Acero, P; Gimeno, M J; Causapé, J

2015-01-01

Salinization of water bodies represents a significant risk in water systems. The salinization of waters in a small irrigated hydrological basin is studied herein through an integrated hydrogeochemical study including multivariate statistical analyses and geochemical modeling. The study zone has two well differentiated geologic materials: (i) Quaternary sediments of low salinity and high permeability and (ii) Tertiary sediments of high salinity and very low permeability. In this work, soil samples were collected and leaching experiments conducted on them in the laboratory. In addition, water samples were collected from precipitation, irrigation, groundwater, spring and surface waters. The waters show an increase in salinity from precipitation and irrigation water to ground- and, finally, surface water. The enrichment in salinity is related to the dissolution of soluble mineral present mainly in the Tertiary materials. Cation exchange, precipitation of calcite and, probably, incongruent dissolution of dolomite, have been inferred from the hydrochemical data set. Multivariate statistical analysis provided information about the structure of the data, differentiating the group of surface waters from the groundwaters and the salinization from the nitrate pollution processes. The available information was included in geochemical models in which hypothesis of consistency and thermodynamic feasibility were checked. The assessment of the collected information pointed to a natural control on salinization processes in the Lerma Basin with minimal influence of anthropogenic factors. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparative statistical analysis of chrome and vegetable tanning effluents and their effects on related soil.

PubMed

Tariq, Saadia R; Shah, Munir H; Shaheen, Nazia

2009-09-30

Two tanning units of Pakistan, namely, Kasur and Mian Channun were investigated with respect to the tanning processes (chrome and vegetable, respectively) and the effects of the tanning agents on the quality of soil in vicinity of tanneries were evaluated. The effluent and soil samples from 16 tanneries each of Kasur and Mian Channun were collected. The levels of selected metals (Na, K, Ca, Mg, Fe, Cr, Mn, Co, Cd, Ni, Pb and Zn) were determined by using flame atomic absorption spectrophotometer under optimum analytical conditions. The data thus obtained were subjected to univariate and multivariate statistical analyses. Most of the metals exhibited considerably higher concentrations in the effluents and soils of Kasur compared with those of Mian Channun. It was observed that the soil of Kasur was highly contaminated by Na, K, Ca and Mg emanating from various processes of leather manufacture. Furthermore, the levels of Cr were also present at much enhanced levels than its background concentration due to the adoption of chrome tanning. The levels of Cr determined in soil samples collected from the vicinity of Mian Channun tanneries were almost comparable to the background levels. The soil of this city was found to have contaminated only by the metals originating from pre-tanning processes. The apportionment of selected metals in the effluent and soil samples was determined by a multivariate cluster analysis, which revealed significant differences in chrome and vegetable tanning processes.

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Assessment of data pre-processing methods for LC-MS/MS-based metabolomics of uterine cervix cancer.

PubMed

Chen, Yanhua; Xu, Jing; Zhang, Ruiping; Shen, Guoqing; Song, Yongmei; Sun, Jianghao; He, Jiuming; Zhan, Qimin; Abliz, Zeper

2013-05-07

A metabolomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS) and multivariate statistics has been implemented to identify potential biomarkers in uterine cervix cancer. Due to the importance of the data pre-processing method, three popular software packages have been compared. Then they have been used to acquire respective data matrices from the same LC-MS/MS data. Multivariate statistics was subsequently used to identify significantly changed biomarkers for uterine cervix cancer from the resulting data matrices. The reliabilities of the identified discriminated metabolites have been further validated on the basis of manually extracted data and ROC curves. Nine potential biomarkers have been identified as having a close relationship with uterine cervix cancer. Considering these in combination as a biomarker group, the AUC amounted to 0.997, with a sensitivity of 92.9% and a specificity of 95.6%. The prediction accuracy was 96.6%. Among these potential biomarkers, the amounts of four purine derivatives were greatly decreased, which might be related to a P2 receptor that might lead to a decrease in cell number through apoptosis. Moreover, only two of them were identified simultaneously by all of the pre-processing tools. The results have demonstrated that the data pre-processing method could seriously bias the metabolomics results. Therefore, application of two or more data pre-processing methods would reveal a more comprehensive set of potential biomarkers in non-targeted metabolomics, before a further validation with LC-MS/MS based targeted metabolomics in MRM mode could be conducted.

Atrial Electrogram Fractionation Distribution before and after Pulmonary Vein Isolation in Human Persistent Atrial Fibrillation-A Retrospective Multivariate Statistical Analysis.

PubMed

Almeida, Tiago P; Chu, Gavin S; Li, Xin; Dastagir, Nawshin; Tuan, Jiun H; Stafford, Peter J; Schlindwein, Fernando S; Ng, G André

2017-01-01

Purpose: Complex fractionated atrial electrograms (CFAE)-guided ablation after pulmonary vein isolation (PVI) has been used for persistent atrial fibrillation (persAF) therapy. This strategy has shown suboptimal outcomes due to, among other factors, undetected changes in the atrial tissue following PVI. In the present work, we investigate CFAE distribution before and after PVI in patients with persAF using a multivariate statistical model. Methods: 207 pairs of atrial electrograms (AEGs) were collected before and after PVI respectively, from corresponding LA regions in 18 persAF patients. Twelve attributes were measured from the AEGs, before and after PVI. Statistical models based on multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) have been used to characterize the atrial regions and AEGs. Results: PVI significantly reduced CFAEs in the LA (70 vs. 40%; P < 0.0001). Four types of LA regions were identified, based on the AEGs characteristics: (i) fractionated before PVI that remained fractionated after PVI (31% of the collected points); (ii) fractionated that converted to normal (39%); (iii) normal prior to PVI that became fractionated (9%) and; (iv) normal that remained normal (21%). Individually, the attributes failed to distinguish these LA regions, but multivariate statistical models were effective in their discrimination ( P < 0.0001). Conclusion: Our results have unveiled that there are LA regions resistant to PVI, while others are affected by it. Although, traditional methods were unable to identify these different regions, the proposed multivariate statistical model discriminated LA regions resistant to PVI from those affected by it without prior ablation information.

Application of multivariate statistical techniques in microbial ecology

PubMed Central

Paliy, O.; Shankar, V.

2016-01-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions, and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. In this review we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive, and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and dataset structure. PMID:26786791

Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece).

PubMed

Matiatos, Ioannis

2016-01-15

Nitrate (NO3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ(15)N-NO3 and δ(18)O-NO3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC).

Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

PubMed Central

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

Differentiation of chocolates according to the cocoa's geographical origin using chemometrics.

PubMed

Cambrai, Amandine; Marcic, Christophe; Morville, Stéphane; Sae Houer, Pierre; Bindler, Françoise; Marchioni, Eric

2010-02-10

The determination of the geographical origin of cocoa used to produce chocolate has been assessed through the analysis of the volatile compounds of chocolate samples. The analysis of the volatile content and their statistical processing by multivariate analyses tended to form independent groups for both Africa and Madagascar, even if some of the chocolate samples analyzed appeared in a mixed zone together with those from America. This analysis also allowed a clear separation between Caribbean chocolates and those from other origins. Height compounds (such as linalool or (E,E)-2,4-decadienal) characteristic of chocolate's different geographical origins were also identified. The method described in this work (hydrodistillation, GC analysis, and statistic treatment) may improve the control of the geographical origin of chocolate during its long production process.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

USGS Publications Warehouse

Baez-Cazull, S. E.; McGuire, J.T.; Cozzarelli, I.M.; Voytek, M.A.

2008-01-01

Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multichambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO4); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction. Copyright ?? 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

NASA Astrophysics Data System (ADS)

Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

2013-03-01

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

Multivariate Statistical Modelling of Drought and Heat Wave Events

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A copula is a multivariate distribution function which allows one to model the dependence structure of given variables separately from the marginal behaviour. We firstly look at the structure of soil moisture drought over the entire of France using the SAFRAN dataset between 1959 and 2009. Soil moisture is represented using the Standardised Precipitation Evapotranspiration Index (SPEI). Drought characteristics are computed at grid point scale where drought conditions are identified as those with an SPEI value below -1.0. We model the multivariate dependence structure of drought events defined by certain characteristics and compute return levels of these events. We initially find that drought characteristics such as duration, mean SPEI and the maximum contiguous area to a grid point all have positive correlations, though the degree to which they are correlated can vary considerably spatially. A spatial representation of return levels then may provide insight into the areas most prone to drought conditions. As a next step, we analyse the dependence structure between soil moisture conditions preceding the onset of a heat wave and the heat wave itself.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data.

PubMed

Adams, Dean C

2014-09-01

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (K(mult)) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of K(mult) remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K(mult) and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of K(mult) were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that K(mult) provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

NASA Astrophysics Data System (ADS)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

TU-FG-201-05: Varian MPC as a Statistical Process Control Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carver, A; Rowbottom, C

Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whethermore » or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.« less

Multivariate statistical analysis strategy for multiple misfire detection in internal combustion engines

NASA Astrophysics Data System (ADS)

Hu, Chongqing; Li, Aihua; Zhao, Xingyang

2011-02-01

This paper proposes a multivariate statistical analysis approach to processing the instantaneous engine speed signal for the purpose of locating multiple misfire events in internal combustion engines. The state of each cylinder is described with a characteristic vector extracted from the instantaneous engine speed signal following a three-step procedure. These characteristic vectors are considered as the values of various procedure parameters of an engine cycle. Therefore, determination of occurrence of misfire events and identification of misfiring cylinders can be accomplished by a principal component analysis (PCA) based pattern recognition methodology. The proposed algorithm can be implemented easily in practice because the threshold can be defined adaptively without the information of operating conditions. Besides, the effect of torsional vibration on the engine speed waveform is interpreted as the presence of super powerful cylinder, which is also isolated by the algorithm. The misfiring cylinder and the super powerful cylinder are often adjacent in the firing sequence, thus missing detections and false alarms can be avoided effectively by checking the relationship between the cylinders.

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Application of multivariate statistical techniques in microbial ecology.

PubMed

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index.

PubMed

Soltani, Shahla; Asghari Moghaddam, Asghar; Barzegar, Rahim; Kazemian, Naeimeh; Tziritis, Evangelos

2017-08-18

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO 3 , Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na + , K + , Ca 2+ , Mg 2+ , SO 4 2- , and Cl - ), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO 3 - , SiO 2 , and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayer, B. P.; Valdez, C. A.; DeHope, A. J.

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduledmore » precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.« less

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

PubMed

Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

2009-01-01

Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

Central composite design with the help of multivariate curve resolution in loadability optimization of RP-HPLC to scale-up a binary mixture.

PubMed

Taheri, Mohammadreza; Moazeni-Pourasil, Roudabeh Sadat; Sheikh-Olia-Lavasani, Majid; Karami, Ahmad; Ghassempour, Alireza

2016-03-01

Chromatographic method development for preparative targets is a time-consuming and subjective process. This can be particularly problematic because of the use of valuable samples for isolation and the large consumption of solvents in preparative scale. These processes could be improved by using statistical computations to save time, solvent and experimental efforts. Thus, contributed by ESI-MS, after applying DryLab software to gain an overview of the most effective parameters in separation of synthesized celecoxib and its co-eluted compounds, design of experiment software that relies on multivariate modeling as a chemometric approach was used to predict the optimized touching-band overloading conditions by objective functions according to the relationship between selectivity and stationary phase properties. The loadability of the method was investigated on the analytical and semi-preparative scales, and the performance of this chemometric approach was approved by peak shapes beside recovery and purity of products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluation of multivariate calibration models with different pre-processing and processing algorithms for a novel resolution and quantitation of spectrally overlapped quaternary mixture in syrup

NASA Astrophysics Data System (ADS)

Moustafa, Azza A.; Hegazy, Maha A.; Mohamed, Dalia; Ali, Omnia

2016-02-01

A novel approach for the resolution and quantitation of severely overlapped quaternary mixture of carbinoxamine maleate (CAR), pholcodine (PHL), ephedrine hydrochloride (EPH) and sunset yellow (SUN) in syrup was demonstrated utilizing different spectrophotometric assisted multivariate calibration methods. The applied methods have used different processing and pre-processing algorithms. The proposed methods were partial least squares (PLS), concentration residuals augmented classical least squares (CRACLS), and a novel method; continuous wavelet transforms coupled with partial least squares (CWT-PLS). These methods were applied to a training set in the concentration ranges of 40-100 μg/mL, 40-160 μg/mL, 100-500 μg/mL and 8-24 μg/mL for the four components, respectively. The utilized methods have not required any preliminary separation step or chemical pretreatment. The validity of the methods was evaluated by an external validation set. The selectivity of the developed methods was demonstrated by analyzing the drugs in their combined pharmaceutical formulation without any interference from additives. The obtained results were statistically compared with the official and reported methods where no significant difference was observed regarding both accuracy and precision.

Diagnostic tools for mixing models of stream water chemistry

USGS Publications Warehouse

Hooper, Richard P.

2003-01-01

Mixing models provide a useful null hypothesis against which to evaluate processes controlling stream water chemical data. Because conservative mixing of end‐members with constant concentration is a linear process, a number of simple mathematical and multivariate statistical methods can be applied to this problem. Although mixing models have been most typically used in the context of mixing soil and groundwater end‐members, an extension of the mathematics of mixing models is presented that assesses the “fit” of a multivariate data set to a lower dimensional mixing subspace without the need for explicitly identified end‐members. Diagnostic tools are developed to determine the approximate rank of the data set and to assess lack of fit of the data. This permits identification of processes that violate the assumptions of the mixing model and can suggest the dominant processes controlling stream water chemical variation. These same diagnostic tools can be used to assess the fit of the chemistry of one site into the mixing subspace of a different site, thereby permitting an assessment of the consistency of controlling end‐members across sites. This technique is applied to a number of sites at the Panola Mountain Research Watershed located near Atlanta, Georgia.

Multielement geochemistry identifies the spatial pattern of soil and sediment contamination in an urban parkland, Western Australia.

PubMed

Rate, Andrew W

2018-06-15

Urban environments are dynamic and highly heterogeneous, and multiple additions of potential contaminants are likely on timescales which are short relative to natural processes. The likely sources and location of soil or sediment contamination in urban environment should therefore be detectable using multielement geochemical composition combined with rigorously applied multivariate statistical techniques. Soil, wetland sediment, and street dust was sampled along intersecting transects in Robertson Park in metropolitan Perth, Western Australia. Samples were analysed for near-total concentrations of multiple elements (including Cd, Ce, Co, Cr, Cu, Fe, Gd, La, Mn, Nd, Ni, Pb, Y, and Zn), as well as pH, and electrical conductivity. Samples at some locations within Robertson Park had high concentrations of potentially toxic elements (Pb above Health Investigation Limits; As, Ba, Cu, Mn, Ni, Pb, V, and Zn above Ecological Investigation Limits). However, these concentrations carry low risk due to the main land use as recreational open space, the low proportion of samples exceeding guideline values, and a tendency for the highest concentrations to be located within the less accessible wetland basin. The different spatial distributions of different groups of contaminants was consistent with different inputs of contaminants related to changes in land use and technology over the history of the site. Multivariate statistical analyses reinforced the spatial information, with principal component analysis identifying geochemical associations of elements which were also spatially related. A multivariate linear discriminant model was able to discriminate samples into a-priori types, and could predict sample type with 84% accuracy based on multielement composition. The findings suggest substantial advantages of characterising a site using multielement and multivariate analyses, an approach which could benefit investigations of other sites of concern. Copyright © 2018 Elsevier B.V. All rights reserved.

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency

NASA Astrophysics Data System (ADS)

Papalexiou, Simon Michael

2018-05-01

Hydroclimatic processes come in all "shapes and sizes". They are characterized by different spatiotemporal correlation structures and probability distributions that can be continuous, mixed-type, discrete or even binary. Simulating such processes by reproducing precisely their marginal distribution and linear correlation structure, including features like intermittency, can greatly improve hydrological analysis and design. Traditionally, modelling schemes are case specific and typically attempt to preserve few statistical moments providing inadequate and potentially risky distribution approximations. Here, a single framework is proposed that unifies, extends, and improves a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific "parent" Gaussian process. A novel mathematical representation of this scheme, introducing parametric correlation transformation functions, enables straightforward estimation of the parent-Gaussian process yielding the target process after the marginal back transformation, while it provides a general description that supersedes previous specific parameterizations, offering a simple, fast and efficient simulation procedure for every stationary process at any spatiotemporal scale. This framework, also applicable for cyclostationary and multivariate modelling, is augmented with flexible parametric correlation structures that parsimoniously describe observed correlations. Real-world simulations of various hydroclimatic processes with different correlation structures and marginals, such as precipitation, river discharge, wind speed, humidity, extreme events per year, etc., as well as a multivariate example, highlight the flexibility, advantages, and complete generality of the method.

Extraction, isolation, and purification of analytes from samples of marine origin--a multivariate task.

PubMed

Liguori, Lucia; Bjørsvik, Hans-René

2012-12-01

The development of a multivariate study for a quantitative analysis of six different polybrominated diphenyl ethers (PBDEs) in tissue of Atlantic Salmo salar L. is reported. An extraction, isolation, and purification process based on an accelerated solvent extraction system was designed, investigated, and optimized by means of statistical experimental design and multivariate data analysis and regression. An accompanying gas chromatography-mass spectrometry analytical method was developed for the identification and quantification of the analytes, BDE 28, BDE 47, BDE 99, BDE 100, BDE 153, and BDE 154. These PBDEs have been used in commercial blends that were used as flame-retardants for a variety of materials, including electronic devices, synthetic polymers and textiles. The present study revealed that an extracting solvent mixture composed of hexane and CH₂Cl₂ (10:90) provided excellent recoveries of all of the six PBDEs studied herein. A somewhat lower polarity in the extracting solvent, hexane and CH₂Cl₂ (40:60) decreased the analyte %-recoveries, which still remain acceptable and satisfactory. The study demonstrates the necessity to perform an intimately investigation of the extraction and purification process in order to achieve quantitative isolation of the analytes from the specific matrix. Copyright © 2012 Elsevier B.V. All rights reserved.

EXTENDING MULTIVARIATE DISTANCE MATRIX REGRESSION WITH AN EFFECT SIZE MEASURE AND THE ASYMPTOTIC NULL DISTRIBUTION OF THE TEST STATISTIC

PubMed Central

McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.

2017-01-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. PMID:27738957

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

PubMed

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

PubMed

Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix

2014-02-15

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

qFeature

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Asymptotic Distribution of the Likelihood Ratio Test Statistic for Sphericity of Complex Multivariate Normal Distribution.

DTIC Science & Technology

1981-08-01

RATIO TEST STATISTIC FOR SPHERICITY OF COMPLEX MULTIVARIATE NORMAL DISTRIBUTION* C. Fang P. R. Krishnaiah B. N. Nagarsenker** August 1981 Technical...and their applications in time sEries, the reader is referred to Krishnaiah (1976). Motivated by the applications in the area of inference on multiple...for practical purposes. Here, we note that Krishnaiah , Lee and Chang (1976) approxi- mated the null distribution of certain power of the likeli

Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.

PubMed

Silva, Ana F T; Sarraguça, Mafalda Cruz; Ribeiro, Paulo R; Santos, Adenilson O; De Beer, Thomas; Lopes, João Almeida

2017-03-30

Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T 2 statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Anima: Modular Workflow System for Comprehensive Image Data Analysis

PubMed Central

Rantanen, Ville; Valori, Miko; Hautaniemi, Sampsa

2014-01-01

Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima. PMID:25126541

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

GAISE 2016 Promotes Statistical Literacy

ERIC Educational Resources Information Center

Schield, Milo

2017-01-01

In the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE), statistical literacy featured as a primary goal. The 2016 revision eliminated statistical literacy as a stated goal. Although this looks like a rejection, this paper argues that by including multivariate thinking and--more importantly--confounding as recommended…

Multivariate analysis techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bendavid, Josh; Fisher, Wade C.; Junk, Thomas R.

2016-01-01

The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually bothmore » be improved by separating signal events from background events with higher efficiency and purity.« less

Identification of the isomers using principal component analysis (PCA) method

NASA Astrophysics Data System (ADS)

Kepceoǧlu, Abdullah; Gündoǧdu, Yasemin; Ledingham, Kenneth William David; Kilic, Hamdi Sukur

2016-03-01

In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra. Experiments have been carried out using a linear TOF-MS coupled to a femtosecond laser system as an energy source for the ionisation processes. We have performed experiments and collected data which has been analysed and interpreted using PCA as a multivariate analysis of these spectra. This demonstrates the strength of the method to get an insight for distinguishing the isomers which cannot be identified using conventional mass analysis obtained through dissociative ionisation processes on these molecules. The PCA results dependending on the laser pulse energy and the background pressure in the spectrometers have been presented in this work.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

GC × GC-TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures.

PubMed

Stefanuto, Pierre-Hugues; Perrault, Katelynn A; Stadler, Sonja; Pesesse, Romain; LeBlanc, Helene N; Forbes, Shari L; Focant, Jean-François

2015-06-01

In forensic thanato-chemistry, the understanding of the process of soft tissue decomposition is still limited. A better understanding of the decomposition process and the characterization of the associated volatile organic compounds (VOC) can help to improve the training of victim recovery (VR) canines, which are used to search for trapped victims in natural disasters or to locate corpses during criminal investigations. The complexity of matrices and the dynamic nature of this process require the use of comprehensive analytical methods for investigation. Moreover, the variability of the environment and between individuals creates additional difficulties in terms of normalization. The resolution of the complex mixture of VOCs emitted by a decaying corpse can be improved using comprehensive two-dimensional gas chromatography (GC × GC), compared to classical single-dimensional gas chromatography (1DGC). This study combines the analytical advantages of GC × GC coupled to time-of-flight mass spectrometry (TOFMS) with the data handling robustness of supervised multivariate statistics to investigate the VOC profile of human remains during early stages of decomposition. Various supervised multivariate approaches are compared to interpret the large data set. Moreover, early decomposition stages of pig carcasses (typically used as human surrogates in field studies) are also monitored to obtain a direct comparison of the two VOC profiles and estimate the robustness of this human decomposition analog model. In this research, we demonstrate that pig and human decomposition processes can be described by the same trends for the major compounds produced during the early stages of soft tissue decomposition.

Multivariate study of trace element distribution in the geological record of Roñanzas Peat Bog (Asturias, N. Spain). Paleoenvironmental evolution and human activities over the last 8000 calyr BP.

PubMed

Gallego, José Luis R; Ortiz, José E; Sierra, Carlos; Torres, Trinidad; Llamas, J F

2013-06-01

Trace element concentrations in the Roñanzas peat bog record reveal a contribution of natural processes but the influence of anthropogenic factors predominates in the last two millenniums, particularly aerosol deposition linked to mining and industrial activities in northern Spain. We observed that the Roñanzas record can be considered a preserved environment, suitable to search for local (<50 km), regional (50-150 km) and/or long-distance human activity fingerprinting, specifically that related to the deposition of heavy metals such as Pb, Zn and Hg. We also carried out a multivariate statistical study in order to clarify the geochemical behavior of trace and major elements. Our study design represents a novel approach to assign natural vs. human contributions in peatlands. Therefore, synergies obtained by the simultaneous study of multivariate statistics and enrichment factors allow robust conclusions about paleoenvironmental evolution and human activities. Anthropogenic influence has also been reported in similar records in other parts of Europe, thereby suggesting large-scale sources for atmospheric pollution. However, here we revealed remarkable particularities, such as the association of Cd, Zn and Pb, mainly linked to regional and local factors (mining and more recently the metallurgical industry), whereas we propose that the occurrence of Hg is associated with a combination of regional factors and global atmospheric pollution. Copyright © 2013 Elsevier B.V. All rights reserved.

Multivariate statistical analysis to characterize/discriminate between anthropogenic and geogenic trace elements occurrence in the Campania Plain, Southern Italy.

PubMed

Busico, Gianluigi; Cuoco, Emilio; Kazakis, Nerantzis; Colombani, Nicolò; Mastrocicco, Micòl; Tedesco, Dario; Voudouris, Konstantinos

2018-03-01

Shallow aquifers are the most accessible reservoirs of potable groundwater; nevertheless, they are also prone to various sources of pollution and it is usually difficult to distinguish between human and natural sources at the watershed scale. The area chosen for this study (the Campania Plain) is characterized by high spatial heterogeneities both in geochemical features and in hydraulic properties. Groundwater mineralization is driven by many processes such as, geothermal activity, weathering of volcanic products and intense human activities. In such a landscape, multivariate statistical analysis has been used to differentiate among the main hydrochemical processes occurring in the area, using three different approaches of factor analysis: (i) major elements, (ii) trace elements, (iii) both major and trace elements. The elaboration of the factor analysis approaches has revealed seven distinct hydrogeochemical processes: i) Salinization (Cl - , Na + ); ii) Carbonate rocks dissolution; iii) Anthropogenic inputs (NO 3 - , SO 4 2- , U, V); iv) Reducing conditions (Fe 2+ , Mn 2+ ); v) Heavy metals contamination (Cr and Ni); vi) Geothermal fluids influence (Li + ); and vii) Volcanic products contribution (As, Rb). Results from this study highlight the need to separately apply factor analysis when a large data set of trace elements is available. In fact, the impact of geothermal fluids in the shallow aquifer was identified from the application of the factor analysis using only trace elements. This study also reveals that the factor analysis of major and trace elements can differentiate between anthropogenic and geogenic sources of pollution in intensively exploited aquifers. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Effect of vinegar-processed Curcumae Rhizoma on bile metabolism in rats].

PubMed

Gu, Wei; Lu, Tu-Lin; Li, Jin-Ci; Wang, Qiao-Han; Pan, Zi-Hao; Ji, De; Li, Lin; Zhang, Ji; Mao, Chun-Qin

2016-04-01

To explore the effect of vinegar-processed Curcumae Rhizoma on endogenous metabolites in bile by investigating the endogenous metabolites difference in bile before and after Curcumae Rhizoma was processed with vinegar. Alcohol extracts of crude and vinegar-processed Curcumae Rhizoma, as well as normal saline were prepared respectively, which were then given to the rats by intragastric administration for 0.5 h. Then common bile duct intubation drainage was conducted to collect 12 h bile of the rats. UPLC-TOF-MS analysis of bile samples was applied after 1∶3 acetonitrile protein precipitation; unidimensional statistics were combined with multivariate statistics and PeakView software was compared with network database to identify the potential biomarkers. Vinegar-processed Curcumae Rhizoma extracts had significant effects on metabolites spectrum in bile of the rats. With the boundaries of P<0.05, 13 metabolites with significant differences were found in bile of crude and vinegar-processed Curcumae Rhizoma groups, and 8 of them were identified when considering the network database. T-test unidimensional statistical analysis was applied between administration groups and blank group to obtain 7 metabolites with significant differences and identify them as potential biomarkers. 6 of the potential biomarkers were up-regulated in vinegar-processed group, which were related to the metabolism regulation of phospholipid metabolism, fat metabolism, bile acid metabolism, and N-acylethanolamine hydrolysis reaction balance, indicating the mechanism of vinegar-processed Curcumae Rhizoma on endogenous metabolites in bile of the rats. Copyright© by the Chinese Pharmaceutical Association.

Multivariate statistical analysis of low-voltage EDS spectrum images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

Quick Overview Scout 2008 Version 1.0

EPA Science Inventory

The Scout 2008 version 1.0 statistical software package has been updated from past DOS and Windows versions to provide classical and robust univariate and multivariate graphical and statistical methods that are not typically available in commercial or freeware statistical softwar...

LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Stassun, K.; Brunner, R. J.; Djorgovski, S. G.; Graham, M.; Hakkila, J.; Mahabal, A.; Paegert, M.; Pesenson, M.; Ptak, A.; Scargle, J.; Informatics, LSST; Statistics Team

2011-01-01

The LSST Informatics and Statistics Science Collaboration (ISSC) focuses on research and scientific discovery challenges posed by the very large and complex data collection that LSST will generate. Application areas include astroinformatics, machine learning, data mining, astrostatistics, visualization, scientific data semantics, time series analysis, and advanced signal processing. Research problems to be addressed with these methodologies include transient event characterization and classification, rare class discovery, correlation mining, outlier/anomaly/surprise detection, improved estimators (e.g., for photometric redshift or early onset supernova classification), exploration of highly dimensional (multivariate) data catalogs, and more. We present sample science results from these data-oriented approaches to large-data astronomical research. We present results from LSST ISSC team members, including the EB (Eclipsing Binary) Factory, the environmental variations in the fundamental plane of elliptical galaxies, and outlier detection in multivariate catalogs.

The use of multivariate statistics in studies of wildlife habitat

Treesearch

David E. Capen

1981-01-01

This report contains edited and reviewed versions of papers presented at a workshop held at the University of Vermont in April 1980. Topics include sampling avian habitats, multivariate methods, applications, examples, and new approaches to analysis and interpretation.

Rejection of Multivariate Outliers.

DTIC Science & Technology

1983-05-01

available in Gnanadesikan (1977). 2 The motivation for the present investigation lies in a recent paper of Schvager and Margolin (1982) who derive a... Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. Wiley, New York. [7] Hawkins, D.M. (1980). Identification of

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

Groundwater flow and hydrogeochemical evolution in the Jianghan Plain, central China

NASA Astrophysics Data System (ADS)

Gan, Yiqun; Zhao, Ke; Deng, Yamin; Liang, Xing; Ma, Teng; Wang, Yanxin

2018-05-01

Hydrogeochemical analysis and multivariate statistics were applied to identify flow patterns and major processes controlling the hydrogeochemistry of groundwater in the Jianghan Plain, which is located in central Yangtze River Basin (central China) and characterized by intensive surface-water/groundwater interaction. Although HCO3-Ca-(Mg) type water predominated in the study area, the 457 (21 surface water and 436 groundwater) samples were effectively classified into five clusters by hierarchical cluster analysis. The hydrochemical variations among these clusters were governed by three factors from factor analysis. Major components (e.g., Ca, Mg and HCO3) in surface water and groundwater originated from carbonate and silicate weathering (factor 1). Redox conditions (factor 2) influenced the geogenic Fe and As contamination in shallow confined groundwater. Anthropogenic activities (factor 3) primarily caused high levels of Cl and SO4 in surface water and phreatic groundwater. Furthermore, the factor score 1 of samples in the shallow confined aquifer gradually increased along the flow paths. This study demonstrates that enhanced information on hydrochemistry in complex groundwater flow systems, by multivariate statistical methods, improves the understanding of groundwater flow and hydrogeochemical evolution due to natural and anthropogenic impacts.

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Outbreak of resistant Acinetobacter baumannii- measures and proposal for prevention and control.

PubMed

Romanelli, Roberta Maia de Castro; Jesus, Lenize Adriana de; Clemente, Wanessa Trindade; Lima, Stella Sala Soares; Rezende, Edna Maria; Coutinho, Rosane Luiza; Moreira, Ricardo Luiz Fontes; Neves, Francelli Aparecida Cordeiro; Brás, Nelma de Jesus

2009-10-01

Acinetobacter baumannii colonization and infection, frequent in Intensive Care Unit (ICU) patients, is commonly associated with high morbimortality. Several outbreaks due to multidrug-resistant (MDR) A. baumanii have been reported but few of them in Brazil. This study aimed to identify risk factors associated with colonization and infection by MDR and carbapenem-resistant A. baumannii strains isolated from patients admitted to the adult ICU at HC/UFMG. A case-control study was performed from January 2007 to June 2008. Cases were defined as patients colonized or infected by MDR/carbapenem-resistant A. baumannii, and controls were patients without MDR/carbapenem-resistant A. baumannii isolation, in a 1:2 proportion. For statistical analysis, due to changes in infection control guidelines, infection criteria and the notification process, this study was divided into two periods. During the first period analyzed, from January to December 2007, colonization or infection by MDR/carbapenem-resistant A. baumannii was associated with prior infection, invasive device utilization, prior carbapenem use and clinical severity. In the multivariate analysis, prior infection and mechanical ventilation proved to be statistically significant risk factors. Carbapenem use showed a tendency towards a statistical association. During the second study period, from January to June 2008, variables with a significant association with MDR/carbapenem-resistant A. baumannii colonization/infection were catheter utilization, carbapenem and third-generation cephalosporin use, hepatic transplantation, and clinical severity. In the multivariate analysis, only CVC use showed a statistical difference. Carbapenem and third-generation cephalosporin use displayed a tendency to be risk factors. Risk factors must be focused on infection control and prevention measures considering A. baumanni dissemination.

Patient experience and process measures of quality of care at home health agencies: Factors associated with high performance.

PubMed

Smith, Laura M; Anderson, Wayne L; Lines, Lisa M; Pronier, Cristalle; Thornburg, Vanessa; Butler, Janelle P; Teichman, Lori; Dean-Whittaker, Debra; Goldstein, Elizabeth

2017-01-01

We examined the effects of provider characteristics on home health agency performance on patient experience of care (Home Health CAHPS) and process (OASIS) measures. Descriptive, multivariate, and factor analyses were used. While agencies score high on both domains, factor analyses showed that the underlying items represent separate constructs. Freestanding and Visiting Nurse Association agencies, higher number of home health aides per 100 episodes, and urban location were statistically significant predictors of lower performance. Lack of variation in composite measures potentially led to counterintuitive results for effects of organizational characteristics. This exploratory study showed the value of having separate quality domains.

High precision mass measurements for wine metabolomics

PubMed Central

Roullier-Gall, Chloé; Witting, Michael; Gougeon, Régis D.; Schmitt-Kopplin, Philippe

2014-01-01

An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS2. In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy. PMID:25431760

High precision mass measurements for wine metabolomics

NASA Astrophysics Data System (ADS)

Roullier-Gall, Chloé; Witting, Michael; Gougeon, Régis; Schmitt-Kopplin, Philippe

2014-11-01

An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS². In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

Baseline estimation in flame's spectra by using neural networks and robust statistics

NASA Astrophysics Data System (ADS)

Garces, Hugo; Arias, Luis; Rojas, Alejandro

2014-09-01

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.

Quantifying Linkages between Biogeochemical Processes in a Contaminated Aquifer-Wetland System Using Multivariate Statistics and HP1

NASA Astrophysics Data System (ADS)

Arora, B.; Mohanty, B. P.; McGuire, J. T.

2009-12-01

Fate and transport of contaminants in saturated and unsaturated zones in the subsurface is controlled by complex biogeochemical processes such as precipitation, sorption-desorption, ion-exchange, redox, etc. In dynamic systems such as wetlands and anaerobic aquifers, these processes are coupled and can interact non-linearly with each other. Variability in measured hydrological, geochemical and microbiological parameters thus corresponds to multiple processes simultaneously. To infer the contributing processes, it is important to eliminate correlations and to identify inter-linkages between factors. The objective of this study is to develop quantitative relationships between hydrological (initial and boundary conditions, hydraulic conductivity ratio, and soil layering), geochemical (mineralogy, surface area, redox potential, and organic matter) and microbiological factors (MPN) that alter the biogeochemical processes at the column scale. Data used in this study were collected from controlled flow experiments in: i) two homogeneous soil columns, ii) a layered soil column, iii) a soil column with embedded clay lenses, and iv) a soil column with embedded clay lenses and one central macropore. The soil columns represent increasing level of soil structural heterogeneity to better mimic the Norman Landfill research site. The Norman Landfill is a closed municipal facility with prevalent organic contamination. The sources of variation in the dataset were explored using multivariate statistical techniques and dominant biogeochemical processes were obtained using principal component analysis (PCA). Furthermore, artificial neural networks (ANN) coupled with HP1 was used to develop mathematical rules identifying different combinations of factors that trigger, sustain, accelerate/decelerate, or discontinue the biogeochemical processes. Experimental observations show that infiltrating water triggers biogeochemical processes in all soil columns. Similarly, slow release of water from low permeability clay lenses sustain biogeochemical cycling for a longer period of time than in homogeneous soil columns. Preliminary results indicate: i) certain variables (anion, cation concentrations, etc.) do not follow normal or lognormal distributions even at the column scale, ii) strong correlations exist between parameters related to redox geochemistry (pH with S2- concentrations), and iii) PCA can identify dominant processes (e.g. iron and sulfate reduction) occurring in the system by grouping together causative variables (e.g. dominant TEAPs).

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Modeling in the quality by design environment: Regulatory requirements and recommendations for design space and control strategy appointment.

PubMed

Djuris, Jelena; Djuric, Zorica

2017-11-30

Mathematical models can be used as an integral part of the quality by design (QbD) concept throughout the product lifecycle for variety of purposes, including appointment of the design space and control strategy, continual improvement and risk assessment. Examples of different mathematical modeling techniques (mechanistic, empirical and hybrid) in the pharmaceutical development and process monitoring or control are provided in the presented review. In the QbD context, mathematical models are predominantly used to support design space and/or control strategies. Considering their impact to the final product quality, models can be divided into the following categories: high, medium and low impact models. Although there are regulatory guidelines on the topic of modeling applications, review of QbD-based submission containing modeling elements revealed concerns regarding the scale-dependency of design spaces and verification of models predictions at commercial scale of manufacturing, especially regarding real-time release (RTR) models. Authors provide critical overview on the good modeling practices and introduce concepts of multiple-unit, adaptive and dynamic design space, multivariate specifications and methods for process uncertainty analysis. RTR specification with mathematical model and different approaches to multivariate statistical process control supporting process analytical technologies are also presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate statistical data analysis methods for detecting baroclinic wave interactions in the thermally driven rotating annulus

NASA Astrophysics Data System (ADS)

von Larcher, Thomas; Harlander, Uwe; Alexandrov, Kiril; Wang, Yongtai

2010-05-01

Experiments on baroclinic wave instabilities in a rotating cylindrical gap have been long performed, e.g., to unhide regular waves of different zonal wave number, to better understand the transition to the quasi-chaotic regime, and to reveal the underlying dynamical processes of complex wave flows. We present the application of appropriate multivariate data analysis methods on time series data sets acquired by the use of non-intrusive measurement techniques of a quite different nature. While the high accurate Laser-Doppler-Velocimetry (LDV ) is used for measurements of the radial velocity component at equidistant azimuthal positions, a high sensitive thermographic camera measures the surface temperature field. The measurements are performed at particular parameter points, where our former studies show that kinds of complex wave patterns occur [1, 2]. Obviously, the temperature data set has much more information content as the velocity data set due to the particular measurement techniques. Both sets of time series data are analyzed by using multivariate statistical techniques. While the LDV data sets are studied by applying the Multi-Channel Singular Spectrum Analysis (M - SSA), the temperature data sets are analyzed by applying the Empirical Orthogonal Functions (EOF ). Our goal is (a) to verify the results yielded with the analysis of the velocity data and (b) to compare the data analysis methods. Therefor, the temperature data are processed in a way to become comparable to the LDV data, i.e. reducing the size of the data set in such a manner that the temperature measurements would imaginary be performed at equidistant azimuthal positions only. This approach initially results in a great loss of information. But applying the M - SSA to the reduced temperature data sets enable us to compare the methods. [1] Th. von Larcher and C. Egbers, Experiments on transitions of baroclinic waves in a differentially heated rotating annulus, Nonlinear Processes in Geophysics, 2005, 12, 1033-1041, NPG Print: ISSN 1023-5809, NPG Online: ISSN 1607-7946 [2] U. Harlander, Th. von Larcher, Y. Wang and C. Egbers, PIV- and LDV-measurements of baroclinic wave interactions in a thermally driven rotating annulus, Experiments in Fluids, 2009, DOI: 10.1007/s00348-009-0792-5

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales

PubMed Central

Williams, L. Keoki; Buu, Anne

2017-01-01

We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies. PMID:28081206

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

NASA Astrophysics Data System (ADS)

Cao, Yingjie; Tang, Changyuan; Song, Xianfang; Liu, Changming; Zhang, Yinghua

2016-06-01

Two multivariate statistical technologies, factor analysis (FA) and discriminant analysis (DA), are applied to study the river and groundwater hydrochemistry and its controlling processes in the Sanjiang Plain of the northeast China. Factor analysis identifies five factors which account for 79.65 % of the total variance in the dataset. Four factors bearing specific meanings as the river and groundwater hydrochemistry controlling processes are divided into two groups, the "natural hydrochemistry evolution" group and the "pollution" group. The "natural hydrochemistry evolution" group includes the salinity factor (factor 1) caused by rock weathering and the residence time factor (factor 2) reflecting the groundwater traveling time. The "pollution" group represents the groundwater quality deterioration due to geogenic pollution caused by elevated Fe and Mn (factor 3) and elevated nitrate (NO3 -) introduced by human activities such as agriculture exploitations (factor 5). The hydrochemical difference and hydraulic connection among rivers (surface water, SW), shallow groundwater (SG) and deep groundwater (DG) group are evaluated by the factor scores obtained from FA and DA (Fisher's method). It is showed that the river water is characterized as low salinity and slight pollution, and the shallow groundwater has the highest salinity and severe pollution. The SW is well separated from SG and DG by Fisher's discriminant function, but the SG and DG can not be well separated showing their hydrochemical similarities, and emphasize hydraulic connections between SG and DG.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

PubMed

Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

2012-11-01

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2017-12-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

Permutation Tests of Hierarchical Cluster Analyses of Carrion Communities and Their Potential Use in Forensic Entomology.

PubMed

van der Ham, Joris L

2016-05-19

Forensic entomologists can use carrion communities' ecological succession data to estimate the postmortem interval (PMI). Permutation tests of hierarchical cluster analyses of these data provide a conceptual method to estimate part of the PMI, the post-colonization interval (post-CI). This multivariate approach produces a baseline of statistically distinct clusters that reflect changes in the carrion community composition during the decomposition process. Carrion community samples of unknown post-CIs are compared with these baseline clusters to estimate the post-CI. In this short communication, I use data from previously published studies to demonstrate the conceptual feasibility of this multivariate approach. Analyses of these data produce series of significantly distinct clusters, which represent carrion communities during 1- to 20-day periods of the decomposition process. For 33 carrion community samples, collected over an 11-day period, this approach correctly estimated the post-CI within an average range of 3.1 days. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Applying Sociocultural Theory to Teaching Statistics for Doctoral Social Work Students

ERIC Educational Resources Information Center

Mogro-Wilson, Cristina; Reeves, Michael G.; Charter, Mollie Lazar

2015-01-01

This article describes the development of two doctoral-level multivariate statistics courses utilizing sociocultural theory, an integrative pedagogical framework. In the first course, the implementation of sociocultural theory helps to support the students through a rigorous introduction to statistics. The second course involves students…

A review on the multivariate statistical methods for dimensional reduction studies

NASA Astrophysics Data System (ADS)

Aik, Lim Eng; Kiang, Lam Chee; Mohamed, Zulkifley Bin; Hong, Tan Wei

2017-05-01

In this research study we have discussed multivariate statistical methods for dimensional reduction, which has been done by various researchers. The reduction of dimensionality is valuable to accelerate algorithm progression, as well as really may offer assistance with the last grouping/clustering precision. A lot of boisterous or even flawed info information regularly prompts a not exactly alluring algorithm progression. Expelling un-useful or dis-instructive information segments may for sure help the algorithm discover more broad grouping locales and principles and generally speaking accomplish better exhibitions on new data set.

Generating an Empirical Probability Distribution for the Andrews-Pregibon Statistic.

ERIC Educational Resources Information Center

Jarrell, Michele G.

A probability distribution was developed for the Andrews-Pregibon (AP) statistic. The statistic, developed by D. F. Andrews and D. Pregibon (1978), identifies multivariate outliers. It is a ratio of the determinant of the data matrix with an observation deleted to the determinant of the entire data matrix. Although the AP statistic has been used…

Compositional variations of ignimbrite magmas in the Central Andes over the past 26 Ma - A multivariate statistical perspective

NASA Astrophysics Data System (ADS)

Brandmeier, M.; Wörner, G.

2016-10-01

Multivariate statistical and geospatial analyses based on a compilation of 890 geochemical and 1200 geochronological data for 194 mapped ignimbrites from the Central Andes document the compositional and temporal patterns of large-volume ignimbrites (so-called "ignimbrite flare-ups") during Neogene times. Rapid advances in computational science during the past decade led to a growing pool of algorithms for multivariate statistics for large datasets with many predictor variables. This study applies cluster analysis (CA) and linear discriminant analysis (LDA) on log-ratio transformed data with the aim of (1) testing a tool for ignimbrite correlation and (2) distinguishing compositional groups that reflect different processes and sources of ignimbrite magmatism during the geodynamic evolution of the Central Andes. CA on major and trace elements allows grouping of ignimbrites according to their geochemical characteristics into rhyolitic and dacitic "end-members" and to differentiate characteristic trace element signatures with respect to Eu anomaly, depletions in middle and heavy rare earth elements (REE) and variable enrichments in light REE. To highlight these distinct compositional signatures, we applied LDA to selected ignimbrites for which comprehensive datasets were available. In comparison to traditional geochemical parameters we found that the advantage of multivariate statistics is their capability of dealing with large datasets and many variables (elements) and to take advantage of this n-dimensional space to detect subtle compositional differences contained in the data. The most important predictors for discriminating ignimbrites are La, Yb, Eu, Al2O3, K2O, P2O5, MgO, FeOt, and TiO2. However, other REE such as Gd, Pr, Tm, Sm, Dy and Er also contribute to the discrimination functions. Significant compositional differences were found between (1) the older (> 13 Ma) large-volume plateau-forming ignimbrites in northernmost Chile and southern Peru and (2) the younger (< 10 Ma) Altiplano-Puna-Volcanic-Complex (APVC) ignimbrites that are of similar volumes. Older ignimbrites are less depleted in HREE and less radiogenic in Sr isotopes, indicating smaller crustal contributions during evolution in a thinner and thermally less evolved crust. These compositional variations indicate a relation to crustal thickening with a "transition" from plagioclase to amphibole and garnet residual mineralogy between 13 and 9 Ma. Compositional and volumetric variations correlate to the N-S passage of the Juan-Fernandéz-Ridge, crustal shortening and thickening, and increased average crustal temperatures during the past 26 Ma. Table DR2 Mapped ignimbrite sheets.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Annotating novel genes by integrating synthetic lethals and genomic information

PubMed Central

Schöner, Daniel; Kalisch, Markus; Leisner, Christian; Meier, Lukas; Sohrmann, Marc; Faty, Mahamadou; Barral, Yves; Peter, Matthias; Gruissem, Wilhelm; Bühlmann, Peter

2008-01-01

Background Large scale screening for synthetic lethality serves as a common tool in yeast genetics to systematically search for genes that play a role in specific biological processes. Often the amounts of data resulting from a single large scale screen far exceed the capacities of experimental characterization of every identified target. Thus, there is need for computational tools that select promising candidate genes in order to reduce the number of follow-up experiments to a manageable size. Results We analyze synthetic lethality data for arp1 and jnm1, two spindle migration genes, in order to identify novel members in this process. To this end, we use an unsupervised statistical method that integrates additional information from biological data sources, such as gene expression, phenotypic profiling, RNA degradation and sequence similarity. Different from existing methods that require large amounts of synthetic lethal data, our method merely relies on synthetic lethality information from two single screens. Using a Multivariate Gaussian Mixture Model, we determine the best subset of features that assign the target genes to two groups. The approach identifies a small group of genes as candidates involved in spindle migration. Experimental testing confirms the majority of our candidates and we present she1 (YBL031W) as a novel gene involved in spindle migration. We applied the statistical methodology also to TOR2 signaling as another example. Conclusion We demonstrate the general use of Multivariate Gaussian Mixture Modeling for selecting candidate genes for experimental characterization from synthetic lethality data sets. For the given example, integration of different data sources contributes to the identification of genetic interaction partners of arp1 and jnm1 that play a role in the same biological process. PMID:18194531

Multivariate statistics of the Jacobian matrices in tensor based morphometry and their application to HIV/AIDS.

PubMed

Lepore, Natasha; Brun, Caroline A; Chiang, Ming-Chang; Chou, Yi-Yu; Dutton, Rebecca A; Hayashi, Kiralee M; Lopez, Oscar L; Aizenstein, Howard J; Toga, Arthur W; Becker, James T; Thompson, Paul M

2006-01-01

Tensor-based morphometry (TBM) is widely used in computational anatomy as a means to understand shape variation between structural brain images. A 3D nonlinear registration technique is typically used to align all brain images to a common neuroanatomical template, and the deformation fields are analyzed statistically to identify group differences in anatomy. However, the differences are usually computed solely from the determinants of the Jacobian matrices that are associated with the deformation fields computed by the registration procedure. Thus, much of the information contained within those matrices gets thrown out in the process. Only the magnitude of the expansions or contractions is examined, while the anisotropy and directional components of the changes are ignored. Here we remedy this problem by computing multivariate shape change statistics using the strain matrices. As the latter do not form a vector space, means and covariances are computed on the manifold of positive-definite matrices to which they belong. We study the brain morphology of 26 HIV/AIDS patients and 14 matched healthy control subjects using our method. The images are registered using a high-dimensional 3D fluid registration algorithm, which optimizes the Jensen-Rényi divergence, an information-theoretic measure of image correspondence. The anisotropy of the deformation is then computed. We apply a manifold version of Hotelling's T2 test to the strain matrices. Our results complement those found from the determinants of the Jacobians alone and provide greater power in detecting group differences in brain structure.

A novel multivariate approach using science-based calibration for direct coating thickness determination in real-time NIR process monitoring.

PubMed

Möltgen, C-V; Herdling, T; Reich, G

2013-11-01

This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 μm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 μm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 μm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration. Copyright © 2013 Elsevier B.V. All rights reserved.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…

A Multivariate Solution of the Multivariate Ranking and Selection Problem

DTIC Science & Technology

1980-02-01

Taneja (1972)), a ’a for a vector of constants c (Krishnaiah and Rizvi (1966)), the generalized variance ( Gnanadesikan and Gupta (1970)), iegier (1976...Olk-in, I. and Sobel, M. (1977). Selecting and Ordering Populations: A New Statistical Methodology, John Wiley & Sons, Inc., New York. Gnanadesikan

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

1999-01-01

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

Optimization of hole generation in Ti/CFRP stacks

NASA Astrophysics Data System (ADS)

Ivanov, Y. N.; Pashkov, A. E.; Chashhin, N. S.

2018-03-01

The article aims to describe methods for improving the surface quality and hole accuracy in Ti/CFRP stacks by optimizing cutting methods and drill geometry. The research is based on the fundamentals of machine building, theory of probability, mathematical statistics, and experiment planning and manufacturing process optimization theories. Statistical processing of experiment data was carried out by means of Statistica 6 and Microsoft Excel 2010. Surface geometry in Ti stacks was analyzed using a Taylor Hobson Form Talysurf i200 Series Profilometer, and in CFRP stacks - using a Bruker ContourGT-Kl Optical Microscope. Hole shapes and sizes were analyzed using a Carl Zeiss CONTURA G2 Measuring machine, temperatures in cutting zones were recorded with a FLIR SC7000 Series Infrared Camera. Models of multivariate analysis of variance were developed. They show effects of drilling modes on surface quality and accuracy of holes in Ti/CFRP stacks. The task of multicriteria drilling process optimization was solved. Optimal cutting technologies which improve performance were developed. Methods for assessing thermal tool and material expansion effects on the accuracy of holes in Ti/CFRP/Ti stacks were developed.

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes.

PubMed

Harrington, Peter de Boves

2018-01-02

Validation of multivariate models is of current importance for a wide range of chemical applications. Although important, it is neglected. The common practice is to use a single external validation set for evaluation. This approach is deficient and may mislead investigators with results that are specific to the single validation set of data. In addition, no statistics are available regarding the precision of a derived figure of merit (FOM). A statistical approach using bootstrapped Latin partitions is advocated. This validation method makes an efficient use of the data because each object is used once for validation. It was reviewed a decade earlier but primarily for the optimization of chemometric models this review presents the reasons it should be used for generalized statistical validation. Average FOMs with confidence intervals are reported and powerful, matched-sample statistics may be applied for comparing models and methods. Examples demonstrate the problems with single validation sets.

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval

PubMed Central

Bonnici, Heidi M.; Richter, Franziska R.; Yazar, Yasemin

2016-01-01

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. SIGNIFICANCE STATEMENT Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. PMID:27194327

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval.

PubMed

Bonnici, Heidi M; Richter, Franziska R; Yazar, Yasemin; Simons, Jon S

2016-05-18

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. Copyright © 2016 Bonnici, Richter, et al.

Bio hydrogen production from cassava starch by anaerobic mixed cultures: Multivariate statistical modeling

NASA Astrophysics Data System (ADS)

Tien, Hai Minh; Le, Kien Anh; Le, Phung Thi Kim

2017-09-01

Bio hydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, high energy efficiency and non-polluting nature resource. In this work, the experiments have been carried out to indicate the possibility of generating bio hydrogen as well as identifying effective factors and the optimum conditions from cassava starch. Experimental design was used to investigate the effect of operating temperature (37-43 °C), pH (6-7), and inoculums ratio (6-10 %) to the yield hydrogen production, the COD reduction and the ratio of volume of hydrogen production to COD reduction. The statistical analysis of the experiment indicated that the significant effects for the fermentation yield were the main effect of temperature, pH and inoculums ratio. The interaction effects between them seem not significant. The central composite design showed that the polynomial regression models were in good agreement with the experimental results. This result will be applied to enhance the process of cassava starch processing wastewater treatment.

SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

PubMed Central

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2011-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. PMID:21479108

A Descriptive Study of Individual and Cross-Cultural Differences in Statistics Anxiety

ERIC Educational Resources Information Center

Baloglu, Mustafa; Deniz, M. Engin; Kesici, Sahin

2011-01-01

The present study investigated individual and cross-cultural differences in statistics anxiety among 223 Turkish and 237 American college students. A 2 x 2 between-subjects factorial multivariate analysis of covariance (MANCOVA) was performed on the six dependent variables which are the six subscales of the Statistical Anxiety Rating Scale.…

Using assemblage data in ecological indicators: A comparison and evaluation of commonly available statistical tools

USGS Publications Warehouse

Smith, Joseph M.; Mather, Martha E.

2012-01-01

Ecological indicators are science-based tools used to assess how human activities have impacted environmental resources. For monitoring and environmental assessment, existing species assemblage data can be used to make these comparisons through time or across sites. An impediment to using assemblage data, however, is that these data are complex and need to be simplified in an ecologically meaningful way. Because multivariate statistics are mathematical relationships, statistical groupings may not make ecological sense and will not have utility as indicators. Our goal was to define a process to select defensible and ecologically interpretable statistical simplifications of assemblage data in which researchers and managers can have confidence. For this, we chose a suite of statistical methods, compared the groupings that resulted from these analyses, identified convergence among groupings, then we interpreted the groupings using species and ecological guilds. When we tested this approach using a statewide stream fish dataset, not all statistical methods worked equally well. For our dataset, logistic regression (Log), detrended correspondence analysis (DCA), cluster analysis (CL), and non-metric multidimensional scaling (NMDS) provided consistent, simplified output. Specifically, the Log, DCA, CL-1, and NMDS-1 groupings were ≥60% similar to each other, overlapped with the fluvial-specialist ecological guild, and contained a common subset of species. Groupings based on number of species (e.g., Log, DCA, CL and NMDS) outperformed groupings based on abundance [e.g., principal components analysis (PCA) and Poisson regression]. Although the specific methods that worked on our test dataset have generality, here we are advocating a process (e.g., identifying convergent groupings with redundant species composition that are ecologically interpretable) rather than the automatic use of any single statistical tool. We summarize this process in step-by-step guidance for the future use of these commonly available ecological and statistical methods in preparing assemblage data for use in ecological indicators.

[Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].

PubMed

Han, Sheng-Nan

2014-07-01

Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.

An efficient approach to identify different chemical markers between fibrous root and rhizome of Anemarrhena asphodeloides by ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry with multivariate statistical analysis.

PubMed

Wang, Fang-Xu; Yuan, Jian-Chao; Kang, Li-Ping; Pang, Xu; Yan, Ren-Yi; Zhao, Yang; Zhang, Jie; Sun, Xin-Guang; Ma, Bai-Ping

2016-09-10

An ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry approach coupled with multivariate statistical analysis was established and applied to rapidly distinguish the chemical differences between fibrous root and rhizome of Anemarrhena asphodeloides. The datasets of tR-m/z pairs, ion intensity and sample code were processed by principal component analysis and orthogonal partial least squares discriminant analysis. Chemical markers could be identified based on their exact mass data, fragmentation characteristics, and retention times. And the new compounds among chemical markers could be isolated rapidly guided by the ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry and their definitive structures would be further elucidated by NMR spectra. Using this approach, twenty-four markers were identified on line including nine new saponins and five new steroidal saponins of them were obtained in pure form. The study validated this proposed approach as a suitable method for identification of the chemical differences between various medicinal parts in order to expand medicinal parts and increase the utilization rate of resources. Copyright © 2016 Elsevier B.V. All rights reserved.

Supervision of Ethylene Propylene Diene M-Class (EPDM) Rubber Vulcanization and Recovery Processes Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Multivariate Analysis.

PubMed

Riba Ruiz, Jordi-Roger; Canals, Trini; Cantero, Rosa

2017-01-01

Ethylene propylene diene monomer (EPDM) rubber is widely used in a diverse type of applications, such as the automotive, industrial and construction sectors among others. Due to its appealing features, the consumption of vulcanized EPDM rubber is growing significantly. However, environmental issues are forcing the application of devulcanization processes to facilitate recovery, which has led rubber manufacturers to implement strict quality controls. Consequently, it is important to develop methods for supervising the vulcanizing and recovery processes of such products. This paper deals with the supervision process of EPDM compounds by means of Fourier transform mid-infrared (FT-IR) spectroscopy and suitable multivariate statistical methods. An expedited and nondestructive classification approach was applied to a sufficient number of EPDM samples with different applied processes, that is, with and without application of vulcanizing agents, vulcanized samples, and microwave treated samples. First the FT-IR spectra of the samples is acquired and next it is processed by applying suitable feature extraction methods, i.e., principal component analysis and canonical variate analysis to obtain the latent variables to be used for classifying test EPDM samples. Finally, the k nearest neighbor algorithm was used in the classification stage. Experimental results prove the accuracy of the proposed method and the potential of FT-IR spectroscopy in this area, since the classification accuracy can be as high as 100%.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Bayesian multivariate Poisson abundance models for T-cell receptor data.

PubMed

Greene, Joshua; Birtwistle, Marc R; Ignatowicz, Leszek; Rempala, Grzegorz A

2013-06-07

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

Trbović, D.; Petronijević, R.; Đorđević, V.

2017-09-01

Milk and milk-based products are among the leading food categories according to reported cases of food adulteration. Although many authentication problems exist in all areas of the food industry, adequate control methods are required to evaluate the authenticity of milk and milk products in the dairy industry. Moreover, gas chromatography (GC) analysis of triacylglycerols (TAGs) or fatty acid (FA) profiles of milk fat (MF) in combination with multivariate statistical data processing have been used to detect adulterations of milk and dairy products with foreign fats. The adulteration of milk and butter is a major issue for the dairy industry. The major adulterants of MF are vegetable oils (soybean, sunflower, groundnut, coconut, palm and peanut oil) and animal fat (cow tallow and pork lard). Multivariate analysis enables adulterated MF to be distinguished from authentic MF, while taking into account many analytical factors. Various multivariate analysis methods have been proposed to quantitatively detect levels of adulterant non-MFs, with multiple linear regression (MLR) seemingly the most suitable. There is a need for increased use of chemometric data analyses to detect adulterated MF in foods and for their expanded use in routine quality assurance testing.

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Facilitating the Transition from Bright to Dim Environments

DTIC Science & Technology

2016-03-04

For the parametric data, a multivariate ANOVA was used in determining the systematic presence of any statistically significant performance differences...performed. All significance levels were p < 0.05, and statistical analyses were performed with the Statistical Package for Social Sciences ( SPSS ...1950. Age changes in rate and level of visual dark adaptation. Journal of Applied Physiology, 2, 407–411. Field, A. 2009. Discovering statistics

Adaptation to high throughput batch chromatography enhances multivariate screening.

PubMed

Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

2015-09-01

High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design

PubMed Central

Lu, Tsui-Shan; Longnecker, Matthew P.; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data and the general ODS design for a continuous response. While substantial work has been done for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome dependent sampling (Multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the Multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the Multivariate-ODS or the estimator from a simple random sample with the same sample size. The Multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of PCB exposure to hearing loss in children born to the Collaborative Perinatal Study. PMID:27966260

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

Chemistry of surface water at a volcanic summit area, Norikura, central Japan: multivariate statistical approach.

PubMed

Anazaw, K; Ohmori, L H

2001-11-01

Many hydrochemical studies on chemical formation of shallow ground water have been reported as results of water-rock interaction, and contamination of paleo-brine or human activities, whereas the preliminary formation of precipitation source in the recharged region has not been established yet. The purpose of this research work is to clarify the geochemical process of water formation from a water source unpolluted by seawater or human activity. Norikura volcano, located in western part of central Japan provided a suitable source for this research purpose, and hence chemical compositions of water samples from the summit and the mountainside area of Norikura volcano were determined. Most samples in the summit area showed very low electrical conductivity, and lower than 12 microS/cm. On the basis of the chemical compositions, principal component analysis (PCA) and factor analysis (FA), such as kinds of multivariate statistical techniques were used to extract geochemical factors affecting hydrochemical process. As a result, three factors were extracted. The first factor showed high loading on K+, Ca2+, SO2 and SiO2, and this factor was interpreted due to influence of the chemical interaction between acidic precipitated water and rocks. The second factor showed high loading on Na+ and Cl-, and it was assumed to be an influence of seawater salt. The third factor showed loading on NO3-, and it was interpreted to be caused by biochemical effect of vegetation. The proportionate contributions of these factors to the evolution of water chemical composition were found to be 45%, 20%, and 10% for factors 1, 2 and 3, respectively. The same exploration at the mountainside of Norikura volcano revealed that the chemical variances of the non-geothermal water samples were highly influenced by water-rock interactions. The silicate dissolution showed 45% contribution for all chemical variances, while the adsorption of Ca2+ and Mg2+ by precipitation or ion exchange showed 20% contribution. The seawater salt influence or biochemical effect was statistically negligible in this area. The clear differentiation of geochemical process on water formation was found between the summit area and the mountainside area.

NONPARAMETRIC MANOVA APPROACHES FOR NON-NORMAL MULTIVARIATE OUTCOMES WITH MISSING VALUES

PubMed Central

He, Fanyin; Mazumdar, Sati; Tang, Gong; Bhatia, Triptish; Anderson, Stewart J.; Dew, Mary Amanda; Krafty, Robert; Nimgaonkar, Vishwajit; Deshpande, Smita; Hall, Martica; Reynolds, Charles F.

2017-01-01

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the nonparametric multivariate Kruskal-Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially-observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete-case analyses. PMID:29416225

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Attitudes toward Advanced and Multivariate Statistics When Using Computers.

ERIC Educational Resources Information Center

Kennedy, Robert L.; McCallister, Corliss Jean

This study investigated the attitudes toward statistics of graduate students who studied advanced statistics in a course in which the focus of instruction was the use of a computer program in class. The use of the program made it possible to provide an individualized, self-paced, student-centered, and activity-based course. The three sections…

Statistics Anxiety and Worry: The Roles of Worry Beliefs, Negative Problem Orientation, and Cognitive Avoidance

ERIC Educational Resources Information Center

Williams, Amanda S.

2015-01-01

Statistics anxiety is a common problem for graduate students. This study explores the multivariate relationship between a set of worry-related variables and six types of statistics anxiety. Canonical correlation analysis indicates a significant relationship between the two sets of variables. Findings suggest that students who are more intolerant…

Automating Geospatial Visualizations with Smart Default Renderers for Data Exploration Web Applications

NASA Astrophysics Data System (ADS)

Ekenes, K.

2017-12-01

This presentation will outline the process of creating a web application for exploring large amounts of scientific geospatial data using modern automated cartographic techniques. Traditional cartographic methods, including data classification, may inadvertently hide geospatial and statistical patterns in the underlying data. This presentation demonstrates how to use smart web APIs that quickly analyze the data when it loads, and provides suggestions for the most appropriate visualizations based on the statistics of the data. Since there are just a few ways to visualize any given dataset well, it is imperative to provide smart default color schemes tailored to the dataset as opposed to static defaults. Since many users don't go beyond default values, it is imperative that they are provided with smart default visualizations. Multiple functions for automating visualizations are available in the Smart APIs, along with UI elements allowing users to create more than one visualization for a dataset since there isn't a single best way to visualize a given dataset. Since bivariate and multivariate visualizations are particularly difficult to create effectively, this automated approach removes the guesswork out of the process and provides a number of ways to generate multivariate visualizations for the same variables. This allows the user to choose which visualization is most appropriate for their presentation. The methods used in these APIs and the renderers generated by them are not available elsewhere. The presentation will show how statistics can be used as the basis for automating default visualizations of data along continuous ramps, creating more refined visualizations while revealing the spread and outliers of the data. Adding interactive components to instantaneously alter visualizations allows users to unearth spatial patterns previously unknown among one or more variables. These applications may focus on a single dataset that is frequently updated, or configurable for a variety of datasets from multiple sources.

Statistical Model of Dynamic Markers of the Alzheimer's Pathological Cascade.

PubMed

Balsis, Steve; Geraci, Lisa; Benge, Jared; Lowe, Deborah A; Choudhury, Tabina K; Tirso, Robert; Doody, Rachelle S

2018-05-05

Alzheimer's disease (AD) is a progressive disease reflected in markers across assessment modalities, including neuroimaging, cognitive testing, and evaluation of adaptive function. Identifying a single continuum of decline across assessment modalities in a single sample is statistically challenging because of the multivariate nature of the data. To address this challenge, we implemented advanced statistical analyses designed specifically to model complex data across a single continuum. We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1,056), focusing on indicators from the assessments of magnetic resonance imaging (MRI) volume, fluorodeoxyglucose positron emission tomography (FDG-PET) metabolic activity, cognitive performance, and adaptive function. Item response theory was used to identify the continuum of decline. Then, through a process of statistical scaling, indicators across all modalities were linked to that continuum and analyzed. Findings revealed that measures of MRI volume, FDG-PET metabolic activity, and adaptive function added measurement precision beyond that provided by cognitive measures, particularly in the relatively mild range of disease severity. More specifically, MRI volume, and FDG-PET metabolic activity become compromised in the very mild range of severity, followed by cognitive performance and finally adaptive function. Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.

Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring

PubMed Central

Moorman, J. Randall; Delos, John B.; Flower, Abigail A.; Cao, Hanqing; Kovatchev, Boris P.; Richman, Joshua S.; Lake, Douglas E.

2014-01-01

We have applied principles of statistical signal processing and non-linear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This review focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician. PMID:22026974

Assessing the effects of habitat patches ensuring propagule supply and different costs inclusion in marine spatial planning through multivariate analyses.

PubMed

Appolloni, L; Sandulli, R; Vetrano, G; Russo, G F

2018-05-15

Marine Protected Areas are considered key tools for conservation of coastal ecosystems. However, many reserves are characterized by several problems mainly related to inadequate zonings that often do not protect high biodiversity and propagule supply areas precluding, at the same time, economic important zones for local interests. The Gulf of Naples is here employed as a study area to assess the effects of inclusion of different conservation features and costs in reserve design process. In particular eight scenarios are developed using graph theory to identify propagule source patches and fishing and exploitation activities as costs-in-use for local population. Scenarios elaborated by MARXAN, software commonly used for marine conservation planning, are compared using multivariate analyses (MDS, PERMANOVA and PERMDISP) in order to assess input data having greatest effects on protected areas selection. MARXAN is heuristic software able to give a number of different correct results, all of them near to the best solution. Its outputs show that the most important areas to be protected, in order to ensure long-term habitat life and adequate propagule supply, are mainly located around the Gulf islands. In addition through statistical analyses it allowed us to prove that different choices on conservation features lead to statistically different scenarios. The presence of propagule supply patches forces MARXAN to select almost the same areas to protect decreasingly different MARXAN results and, thus, choices for reserves area selection. The multivariate analyses applied here to marine spatial planning proved to be very helpful allowing to identify i) how different scenario input data affect MARXAN and ii) what features have to be taken into account in study areas characterized by peculiar biological and economic interests. Copyright © 2018 Elsevier Ltd. All rights reserved.

Interfaces between statistical analysis packages and the ESRI geographic information system

NASA Technical Reports Server (NTRS)

Masuoka, E.

1980-01-01

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

PubMed

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Alejandro Q; Musolf, Anthony; Matise, Tara C; Finch, Stephen J; Gordon, Derek

2012-01-01

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci. Copyright © 2013 S. Karger AG, Basel.

Single variant and multi-variant trend tests for genetic association with next generation sequencing that are robust to sequencing error

PubMed Central

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Andrew; Musolf, Anthony; Matise, Tara C.; Finch, Stephen J.; Gordon, Derek

2013-01-01

As with any new technology, next generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model, based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to that data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p-value, no matter how many loci. PMID:23594495

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Departure from Normality in Multivariate Normative Comparison: The Cramer Alternative for Hotelling's "T[squared]"

ERIC Educational Resources Information Center

Grasman, Raoul P. P. P.; Huizenga, Hilde M.; Geurts, Hilde M.

2010-01-01

Crawford and Howell (1998) have pointed out that the common practice of z-score inference on cognitive disability is inappropriate if a patient's performance on a task is compared with relatively few typical control individuals. Appropriate univariate and multivariate statistical tests have been proposed for these studies, but these are only valid…

Applied statistics in agricultural, biological, and environmental sciences.

USDA-ARS?s Scientific Manuscript database

Agronomic research often involves measurement and collection of multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate statistical methods encompass the simultaneous analysis of all random variables measured on each experimental or s...

Application of a quality by design approach to the cell culture process of monoclonal antibody production, resulting in the establishment of a design space.

PubMed

Nagashima, Hiroaki; Watari, Akiko; Shinoda, Yasuharu; Okamoto, Hiroshi; Takuma, Shinya

2013-12-01

This case study describes the application of Quality by Design elements to the process of culturing Chinese hamster ovary cells in the production of a monoclonal antibody. All steps in the cell culture process and all process parameters in each step were identified by using a cause-and-effect diagram. Prospective risk assessment using failure mode and effects analysis identified the following four potential critical process parameters in the production culture step: initial viable cell density, culture duration, pH, and temperature. These parameters and lot-to-lot variability in raw material were then evaluated by process characterization utilizing a design of experiments approach consisting of a face-centered central composite design integrated with a full factorial design. Process characterization was conducted using a scaled down model that had been qualified by comparison with large-scale production data. Multivariate regression analysis was used to establish statistical prediction models for performance indicators and quality attributes; with these, we constructed contour plots and conducted Monte Carlo simulation to clarify the design space. The statistical analyses, especially for raw materials, identified set point values, which were most robust with respect to the lot-to-lot variability of raw materials while keeping the product quality within the acceptance criteria. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Measuring the Processes of Change From the Transtheoretical Model for Physical Activity and Exercise in Overweight and Obese Adults.

PubMed

Romain, Ahmed Jerôme; Bernard, Paquito; Hokayem, Marie; Gernigon, Christophe; Avignon, Antoine

2016-03-01

This study aimed to test three factorial structures conceptualizing the processes of change (POC) from the transtheoretical model and to examine the relationships between the POC and stages of change (SOC) among overweight and obese adults. Cross-sectional study. This study was conducted at the University Hospital of Montpellier, France. A sample of 289 overweight or obese participants (199 women) was enrolled in the study. Participants completed the POC and SOC questionnaires during a 5-day hospitalization for weight management. Structural equation modeling was used to compare the different factorial structures. The unweighted least-squares method was used to identify the best-fit indices for the five fully correlated model (goodness-of-fit statistic = .96; adjusted goodness-of-fit statistic = .95; standardized root mean residual = .062; normed-fit index = .95; parsimonious normed-fit index = .83; parsimonious goodness-of-fit statistic = .78). The multivariate analysis of variance was significant (p < .001). A post hoc test showed that individuals in advanced SOC used more of both experiential and behavioral POC than those in preaction stages, with effect sizes ranging from .06 to .29. This study supports the validity of the factorial structure of POC concerning physical activity and confirms the assumption that, in this context, people with excess weight use both experiential and behavioral processes. These preliminary results should be confirmed in a longitudinal study. © The Author(s) 2016.

Assessing groundwater vulnerability to agrichemical contamination in the Midwest US

USGS Publications Warehouse

Burkart, M.R.; Kolpin, D.W.; James, D.E.

1999-01-01

Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.

Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

PubMed

Selvarasu, Suresh; Kim, Do Yun; Karimi, Iftekhar A; Lee, Dong-Yup

2010-10-01

We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies. Copyright © 2010 Elsevier B.V. All rights reserved.

Multivariate model of female black bear habitat use for a Geographic Information System

USGS Publications Warehouse

Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.

1993-01-01

Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

NASA Astrophysics Data System (ADS)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

PubMed

Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P; Gonzalez-Zacarias, Clio; Zhao, Lu; D'Arcy, Mike; Kesselman, Carl; Herting, Megan M; Dinov, Ivo D; Toga, Arthur W; Clark, Kristi A

2018-05-15

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases). Copyright © 2018 Elsevier Inc. All rights reserved.

Multivariate Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.

A model of the human observer and decision maker

NASA Technical Reports Server (NTRS)

Wewerinke, P. H.

1981-01-01

The decision process is described in terms of classical sequential decision theory by considering the hypothesis that an abnormal condition has occurred by means of a generalized likelihood ratio test. For this, a sufficient statistic is provided by the innovation sequence which is the result of the perception an information processing submodel of the human observer. On the basis of only two model parameters, the model predicts the decision speed/accuracy trade-off and various attentional characteristics. A preliminary test of the model for single variable failure detection tasks resulted in a very good fit of the experimental data. In a formal validation program, a variety of multivariable failure detection tasks was investigated and the predictive capability of the model was demonstrated.

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

Reflectance of vegetation, soil, and water

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator)

1973-01-01

There are no author-identified significant results in this report. This report deals with the selection of the best channels from the 24-channel aircraft data to represent crop and soil conditions. A three-step procedure has been developed that involves using univariate statistics and an F-ratio test to indicate the best 14 channels. From the 14, the 10 best channels are selected by a multivariate stochastic process. The third step involves the pattern recognition procedures developed in the data analysis plan. Indications are that the procedures in use are satsifactory and will extract the desired information from the data.

Trace element fingerprinting of jewellery rubies by external beam PIXE

NASA Astrophysics Data System (ADS)

Calligaro, T.; Poirot, J.-P.; Querré, G.

1999-04-01

External beam PIXE analysis allows the non-destructive in situ characterisation of gemstones mounted on jewellery pieces. This technique was used for the determination of the geographical origin of 64 rubies set on a high-valued necklace. The trace element content of these gemstones was measured and compared to that of a set of rubies of known sources. Multivariate statistical processing of the results allowed us to infer the provenance of rubies : one comes from Thailand/Cambodia deposit while the remaining are attributed to Burma. This highlights the complementary capabilities of PIXE and conventional gemological observations.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

ERIC Educational Resources Information Center

Yuan, Ke-Hai

2008-01-01

In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

Some Tests of Randomness with Applications

DTIC Science & Technology

1981-02-01

freedom. For further details, the reader is referred to Gnanadesikan (1977, p. 169) wherein other relevant tests are also given, Graphical tests, as...sample from a gamma distri- bution. J. Am. Statist. Assoc. 71, 480-7. Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

Statistical polarization in greenhouse gas emissions: Theory and evidence.

PubMed

Remuzgo, Lorena; Trueba, Carmen

2017-11-01

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990-2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design.

PubMed

Lu, Tsui-Shan; Longnecker, Matthew P; Zhou, Haibo

2017-03-15

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data, and the general ODS design for a continuous response. While substantial work has been carried out for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome-dependent sampling (multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the multivariate-ODS or the estimator from a simple random sample with the same sample size. The multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of polychlorinated biphenyl exposure to hearing loss in children born to the Collaborative Perinatal Study. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Identification of Enterococcus, Streptococcus, and Staphylococcus by Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

PubMed Central

Bourne, Roger; Himmelreich, Uwe; Sharma, Ansuiya; Mountford, Carolyn; Sorrell, Tania

2001-01-01

A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy (1H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate. PMID:11474013

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Identification of Differential Item Functioning in Multiple-Group Settings: A Multivariate Outlier Detection Approach

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2011-01-01

We focus on the identification of differential item functioning (DIF) when more than two groups of examinees are considered. We propose to consider items as elements of a multivariate space, where DIF items are outlying elements. Following this approach, the situation of multiple groups is a quite natural case. A robust statistics technique is…

Uses of Multivariate Analytical Techniques in Online and Blended Business Education: An Assessment of Current Practice and Recommendations for Future Research

ERIC Educational Resources Information Center

Arbaugh, J. B.; Hwang, Alvin

2013-01-01

Seeking to assess the analytical rigor of empirical research in management education, this article reviews the use of multivariate statistical techniques in 85 studies of online and blended management education over the past decade and compares them with prescriptions offered by both the organization studies and educational research communities.…

On Some Multiple Decision Problems

DTIC Science & Technology

1976-08-01

parameter space. Some recent results in the area of subset selection formulation are Gnanadesikan and Gupta [28], Gupta and Studden [43], Gupta and...York, pp. 363-376. [27) Gnanadesikan , M. (1966). Some Selection and Ranking Procedures for Multivariate Normal Populations. Ph.D. Thesis. Dept. of...Statist., Purdue Univ., West Lafayette, Indiana 47907. [28) Gnanadesikan , M. and Gupta, S. S. (1970). Selection procedures for multivariate normal

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

PubMed

Yang, James J; Williams, L Keoki; Buu, Anne

2017-08-24

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

PubMed Central

Preisser, John S.; Sen, Pranab K.; Offenbacher, Steven

2011-01-01

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones. Additionally, there is a need for statistical methods that control an overall error rate for multiple hypothesis testing. We review univariate and multivariate nonparametric hypothesis tests and apply them to longitudinal data to assess changes over time in 31 biomarkers measured from the gingival crevicular fluid in 22 subjects whereby gingivitis was induced by temporarily withholding tooth brushing. To identify biomarkers that can be induced to change, multivariate Wilcoxon signed rank tests for a set of four summary measures based upon area under the curve are applied for each biomarker and compared to their univariate counterparts. Multiple hypothesis testing methods with choice of control of the false discovery rate or strong control of the family-wise error rate are examined. PMID:21984957

An introduction to statistical process control in research proteomics.

PubMed

Bramwell, David

2013-12-16

Statistical process control is a well-established and respected method which provides a general purpose, and consistent framework for monitoring and improving the quality of a process. It is routinely used in many industries where the quality of final products is critical and is often required in clinical diagnostic laboratories [1,2]. To date, the methodology has been little utilised in research proteomics. It has been shown to be capable of delivering quantitative QC procedures for qualitative clinical assays [3] making it an ideal methodology to apply to this area of biological research. To introduce statistical process control as an objective strategy for quality control and show how it could be used to benefit proteomics researchers and enhance the quality of the results they generate. We demonstrate that rules which provide basic quality control are easy to derive and implement and could have a major impact on data quality for many studies. Statistical process control is a powerful tool for investigating and improving proteomics research work-flows. The process of characterising measurement systems and defining control rules forces the exploration of key questions that can lead to significant improvements in performance. This work asserts that QC is essential to proteomics discovery experiments. Every experimenter must know the current capabilities of their measurement system and have an objective means for tracking and ensuring that performance. Proteomic analysis work-flows are complicated and multi-variate. QC is critical for clinical chemistry measurements and huge strides have been made in ensuring the quality and validity of results in clinical biochemistry labs. This work introduces some of these QC concepts and works to bridge their use from single analyte QC to applications in multi-analyte systems. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics. Copyright © 2013 The Author. Published by Elsevier B.V. All rights reserved.

Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Ultimate compression after impact load prediction in graphite/epoxy coupons using neural network and multivariate statistical analyses

NASA Astrophysics Data System (ADS)

Gregoire, Alexandre David

2011-07-01

The goal of this research was to accurately predict the ultimate compressive load of impact damaged graphite/epoxy coupons using a Kohonen self-organizing map (SOM) neural network and multivariate statistical regression analysis (MSRA). An optimized use of these data treatment tools allowed the generation of a simple, physically understandable equation that predicts the ultimate failure load of an impacted damaged coupon based uniquely on the acoustic emissions it emits at low proof loads. Acoustic emission (AE) data were collected using two 150 kHz resonant transducers which detected and recorded the AE activity given off during compression to failure of thirty-four impacted 24-ply bidirectional woven cloth laminate graphite/epoxy coupons. The AE quantification parameters duration, energy and amplitude for each AE hit were input to the Kohonen self-organizing map (SOM) neural network to accurately classify the material failure mechanisms present in the low proof load data. The number of failure mechanisms from the first 30% of the loading for twenty-four coupons were used to generate a linear prediction equation which yielded a worst case ultimate load prediction error of 16.17%, just outside of the +/-15% B-basis allowables, which was the goal for this research. Particular emphasis was placed upon the noise removal process which was largely responsible for the accuracy of the results.

Dental erosion and its association with diet in Libyan schoolchildren.

PubMed

Huew, R; Waterhouse, P J; Moynihan, P J; Kometa, S; Maguire, A

2011-10-01

To investigate any association between dental erosion and its potential dietary risk factors in a group of schoolchildren in Benghazi, Libya. A cross-sectional observational study. A random sample of 12-year-old schoolchildren in 36 randomly selected schools completed a questionnaire to provide dietary data and underwent dental examination. Dental erosion was assessed using UK National Diet and Nutrition Survey (2000) criteria. Associations between erosion and dietary variables under study were investigated through processes of bivariate and multivariate analyses. Of 791 schoolchildren dentally examined, 40.8% had dental erosion; erosion into enamel affecting 32.5%, into dentine affecting 8% and into pulp affecting 0.3% of subjects. Bivariate analysis showed frequency of fruit-based sugary drink intake was statistically significantly and positively associated with erosion (p=0.006, Odds Ratio; 1.498, 95% CI; 1.124, 1.996) as was the length of time taken to consume acidic drinks (p≠0.005, Odds Ratio; 1.593, 95%CI; 1.161, 2.186). Additionally, multivariate analysis showed frequency of consumption of fruit other than bananas, sugared tea with milk and flavoured milk to also be positively associated with erosion (p=<0.05). In this group of Libyan 12-year-olds, frequency of consumption of fruit-based sugary drinks and length of time taken to consume acidic drinks were the primary statistically significant positive risk factors for dental erosion.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Analysis of river pollution data from low-flow period by means of multivariate techniques: a case study from the oil-shale industry region, northeastern Estonia.

PubMed

Truu, Jaak; Heinaru, Eeva; Talpsep, Ene; Heinaru, Ain

2002-01-01

The oil-shale industry has created serious pollution problems in northeastern Estonia. Untreated, phenol-rich leachate from semi-coke mounds formed as a by-product of oil-shale processing is discharged into the Baltic Sea via channels and rivers. An exploratory analysis of water chemical and microbiological data sets from the low-flow period was carried out using different multivariate analysis techniques. Principal component analysis allowed us to distinguish different locations in the river system. The riverine microbial community response to water chemical parameters was assessed by co-inertia analysis. Water pH, COD and total nitrogen were negatively related to the number of biodegradative bacteria, while oxygen concentration promoted the abundance of these bacteria. The results demonstrate the utility of multivariate statistical techniques as tools for estimating the magnitude and extent of pollution based on river water chemical and microbiological parameters. An evaluation of river chemical and microbiological data suggests that the ambient natural attenuation mechanisms only partly eliminate pollutants from river water, and that a sufficient reduction of more recalcitrant compounds could be achieved through the reduction of wastewater discharge from the oil-shale chemical industry into the rivers.

Statistical optimization of arsenic biosorption by microbial enzyme via Ca-alginate beads.

PubMed

Banerjee, Suchetana; Banerjee, Anindita; Sarkar, Priyabrata

2018-04-16

Bioremediation of arsenic using green technology via microbial enzymes has attracted scientists due to its simplicity and cost effectiveness. Statistical optimization of arsenate bioremediation was conducted by the enzyme arsenate reductase extracted from arsenic tolerant bacterium Pseudomonas alcaligenes. Response surface methodology based on Box-Behnken design matrix was performed to determine the optimal operational conditions of a multivariable system and their interactive effects on the bioremediation process. The highest biosorptive activity of 96.2 µg gm -1 of beads was achieved under optimized conditions (pH = 7.0; As (V) concentration = 1000 ppb; time = 2 h). SEM analysis showed the morphological changes on the surface of enzyme immobilized gluteraldehyde crosslinked Ca-alginate beads. The immobilized enzyme retained its activity for 8 cycles. ANOVA with a high correlation coefficient (R 2 > 0.99) and lower "Prob > F"value (<0.0001) corroborated the second-order polynomial model for the biosorption process. This study on the adsorptive removal of As (V) by enzyme-loaded biosorbent revealed a possible way of its application in large scale treatment of As (V)-contaminated water bodies.

Importance of the Correlation between Width and Length in the Shape Analysis of Nanorods: Use of a 2D Size Plot To Probe Such a Correlation.

PubMed

Zhao, Zhihua; Zheng, Zhiqin; Roux, Clément; Delmas, Céline; Marty, Jean-Daniel; Kahn, Myrtil L; Mingotaud, Christophe

2016-08-22

Analysis of nanoparticle size through a simple 2D plot is proposed in order to extract the correlation between length and width in a collection or a mixture of anisotropic particles. Compared to the usual statistics on the length associated with a second and independent statistical analysis of the width, this simple plot easily points out the various types of nanoparticles and their (an)isotropy. For each class of nano-objects, the relationship between width and length (i.e., the strong or weak correlations between these two parameters) may suggest information concerning the nucleation/growth processes. It allows one to follow the effect on the shape and size distribution of physical or chemical processes such as simple ripening. Various electron microscopy pictures from the literature or from the authors' own syntheses are used as examples to demonstrate the efficiency and simplicity of the proposed 2D plot combined with a multivariate analysis. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

NASA Astrophysics Data System (ADS)

Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

2018-03-01

The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

Application of multivariate statistical analysis and hydrochemical and isotopic investigations for evaluation of groundwater quality and its suitability for drinking and agriculture purposes: case of Oum Ali-Thelepte aquifer, central Tunisia.

PubMed

Hassen, Imen; Hamzaoui-Azaza, Fadoua; Bouhlila, Rachida

2016-03-01

Groundwater plays a dominant role in arid regions; it is among the most available water resources in Tunisia. Located in northwestern Tunisia, Oum Ali-Thelepte is a deep Miocene sedimentary aquifer, where groundwater is the most important source of water supply. The aim of the study is to investigate the hydrochemical processes leading to mineralization and to assess water quality with respect to agriculture and drinking for a better management of groundwater resources. To achieve such objectives, water analysis was carried out on 16 groundwater samples collected during January-February 2014. Stable isotopes and 26 hydrochemical parameters were examined. The interpretation of these analytical data showed that the concentrations of major and trace elements were within the permissible level for human use. The distribution of mineral processes in this aquifer was identified using conventional classification techniques, suggesting that the water facies gradually changes from Ca-HCO3 to Mg-SO4 type and are controlled by water-rock interaction. These results were endorsed using multivariate statistical methods such as principal component analysis and cluster analysis. The sustainability of groundwater for drinking and irrigation was assessed based on the water quality index (WQI) and on Wilcox and Richards's diagrams. This aquifer has been classified as "excellent water" serving good irrigation in the area. As for the stable isotope, the measurements showed that groundwater samples lay between global meteoric water line (GMWL) and LMWL; hence, this arrangement signifies that the recharge of the Oum Ali-Thelepte aquifer is ensured by rainwater infiltration through mountains in the border of the aquifer without evaporation effects.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, S. George

1994-01-01

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Comparative Research of Navy Voluntary Education at Operational Commands

DTIC Science & Technology

2017-03-01

return on investment, ROI, logistic regression, multivariate analysis, descriptive statistics, Markov, time-series, linear programming 15. NUMBER...21  B.  DESCRIPTIVE STATISTICS TABLES ...............................................25  C.  PRIVACY CONSIDERATIONS...THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF TABLES Table 1.  Variables and Descriptions . Adapted from NETC (2016). .......................21

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

Establishing Benchmarks for Outcome Indicators: A Statistical Approach to Developing Performance Standards.

ERIC Educational Resources Information Center

Henry, Gary T.; And Others

1992-01-01

A statistical technique is presented for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit (a school district in the example), a unique comparison group is selected. (SLD)

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Challenging Conventional Wisdom for Multivariate Statistical Models with Small Samples

ERIC Educational Resources Information Center

McNeish, Daniel

2017-01-01

In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…

Multivariate Models of Adult Pacific Salmon Returns

PubMed Central

Burke, Brian J.; Peterson, William T.; Beckman, Brian R.; Morgan, Cheryl; Daly, Elizabeth A.; Litz, Marisa

2013-01-01

Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586

Accumulation risk assessment for the flooding hazard

NASA Astrophysics Data System (ADS)

Roth, Giorgio; Ghizzoni, Tatiana; Rudari, Roberto

2010-05-01

One of the main consequences of the demographic and economic development and of markets and trades globalization is represented by risks cumulus. In most cases, the cumulus of risks intuitively arises from the geographic concentration of a number of vulnerable elements in a single place. For natural events, risks cumulus can be associated, in addition to intensity, also to event's extension. In this case, the magnitude can be such that large areas, that may include many regions or even large portions of different countries, are stroked by single, catastrophic, events. Among natural risks, the impact of the flooding hazard cannot be understated. To cope with, a variety of mitigation actions can be put in place: from the improvement of monitoring and alert systems to the development of hydraulic structures, throughout land use restrictions, civil protection, financial and insurance plans. All of those viable options present social and economic impacts, either positive or negative, whose proper estimate should rely on the assumption of appropriate - present and future - flood risk scenarios. It is therefore necessary to identify proper statistical methodologies, able to describe the multivariate aspects of the involved physical processes and their spatial dependence. In hydrology and meteorology, but also in finance and insurance practice, it has early been recognized that classical statistical theory distributions (e.g., the normal and gamma families) are of restricted use for modeling multivariate spatial data. Recent research efforts have been therefore directed towards developing statistical models capable of describing the forms of asymmetry manifest in data sets. This, in particular, for the quite frequent case of phenomena whose empirical outcome behaves in a non-normal fashion, but still maintains some broad similarity with the multivariate normal distribution. Fruitful approaches were recognized in the use of flexible models, which include the normal distribution as a special or limiting case (e.g., the skew-normal or skew-t distributions). The present contribution constitutes an attempt to provide a better estimation of the joint probability distribution able to describe flood events in a multi-site multi-basin fashion. This goal will be pursued through the multivariate skew-t distribution, which allows to analytically define the joint probability distribution. Performances of the skew-t distribution will be discussed with reference to the Tanaro River in Northwestern Italy. To enhance the characteristics of the correlation structure, both nested and non-nested gauging stations will be selected, with significantly different contributing areas.

The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

PubMed

Menditto, Anthony A; Linhorst, Donald M; Coleman, James C; Beck, Niels C

2006-04-01

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.

Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis.

PubMed

Tiwari, P K; Awasthi, S; Kumar, R; Anand, R K; Rai, P K; Rai, A K

2018-02-01

Type 2 diabetes drug tablets containing voglibose having dose strengths of 0.2 and 0.3 mg of various brands have been examined, using laser-induced breakdown spectroscopy (LIBS) technique. The statistical methods such as the principal component analysis (PCA) and the partial least square regression analysis (PLSR) have been employed on LIBS spectral data for classifying and developing the calibration models of drug samples. We have developed the ratio-based calibration model applying PLSR in which relative spectral intensity ratios H/C, H/N and O/N are used. Further, the developed model has been employed to predict the relative concentration of element in unknown drug samples. The experiment has been performed in air and argon atmosphere, respectively, and the obtained results have been compared. The present model provides rapid spectroscopic method for drug analysis with high statistical significance for online control and measurement process in a wide variety of pharmaceutical industrial applications.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Defining the ecological hydrology of Taiwan Rivers using multivariate statistical methods

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Wu, Tzu-Ching; Tsai, Wen-Ping; Herricks, Edwin E.

2009-09-01

SummaryThe identification and verification of ecohydrologic flow indicators has found new support as the importance of ecological flow regimes is recognized in modern water resources management, particularly in river restoration and reservoir management. An ecohydrologic indicator system reflecting the unique characteristics of Taiwan's water resources and hydrology has been developed, the Taiwan ecohydrological indicator system (TEIS). A major challenge for the water resources community is using the TEIS to provide environmental flow rules that improve existing water resources management. This paper examines data from the extensive network of flow monitoring stations in Taiwan using TEIS statistics to define and refine environmental flow options in Taiwan. Multivariate statistical methods were used to examine TEIS statistics for 102 stations representing the geographic and land use diversity of Taiwan. The Pearson correlation coefficient showed high multicollinearity between the TEIS statistics. Watersheds were separated into upper and lower-watershed locations. An analysis of variance indicated significant differences between upstream, more natural, and downstream, more developed, locations in the same basin with hydrologic indicator redundancy in flow change and magnitude statistics. Issues of multicollinearity were examined using a Principal Component Analysis (PCA) with the first three components related to general flow and high/low flow statistics, frequency and time statistics, and quantity statistics. These principle components would explain about 85% of the total variation. A major conclusion is that managers must be aware of differences among basins, as well as differences within basins that will require careful selection of management procedures to achieve needed flow regimes.

Mathematical background and attitudes toward statistics in a sample of Spanish college students.

PubMed

Carmona, José; Martínez, Rafael J; Sánchez, Manuel

2005-08-01

To examine the relation of mathematical background and initial attitudes toward statistics of Spanish college students in social sciences the Survey of Attitudes Toward Statistics was given to 827 students. Multivariate analyses tested the effects of two indicators of mathematical background (amount of exposure and achievement in previous courses) on the four subscales. Analysis suggested grades in previous courses are more related to initial attitudes toward statistics than the number of mathematics courses taken. Mathematical background was related with students' affective responses to statistics but not with their valuing of statistics. Implications of possible research are discussed.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Characterizations of linear sufficient statistics

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Reoner, R.; Decell, H. P., Jr.

1977-01-01

A surjective bounded linear operator T from a Banach space X to a Banach space Y must be a sufficient statistic for a dominated family of probability measures defined on the Borel sets of X. These results were applied, so that they characterize linear sufficient statistics for families of the exponential type, including as special cases the Wishart and multivariate normal distributions. The latter result was used to establish precisely which procedures for sampling from a normal population had the property that the sample mean was a sufficient statistic.

Use of multivariate statistics to identify unreliable data obtained using CASA.

PubMed

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Coupling Spatiotemporal Community Assembly Processes to Changes in Microbial Metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graham, Emily B.; Crump, Alex R.; Resch, Charles T.

Community assembly processes govern shifts in species abundances in response to environmental change, yet our understanding of assembly remains largely decoupled from ecosystem function. Here, we test hypotheses regarding assembly and function across space and time using hyporheic microbial communities as a model system. We pair sampling of two habitat types through hydrologic fluctuation with null modeling and multivariate statistics. We demonstrate that dual selective pressures assimilate to generate compositional changes at distinct timescales among habitat types, resulting in contrasting associations of Betaproteobacteria and Thaumarchaeota with selection and with seasonal changes in aerobic metabolism. Our results culminate in a conceptualmore » model in which selection from contrasting environments regulates taxon abundance and ecosystem function through time, with increases in function when oscillating selection opposes stable selective pressures. Our model is applicable within both macrobial and microbial ecology and presents an avenue for assimilating community assembly processes into predictions of ecosystem function.« less

Testing the causality of Hawkes processes with time reversal

NASA Astrophysics Data System (ADS)

Cordi, Marcus; Challet, Damien; Muni Toke, Ioane

2018-03-01

We show that univariate and symmetric multivariate Hawkes processes are only weakly causal: the true log-likelihoods of real and reversed event time vectors are almost equal, thus parameter estimation via maximum likelihood only weakly depends on the direction of the arrow of time. In ideal (synthetic) conditions, tests of goodness of parametric fit unambiguously reject backward event times, which implies that inferring kernels from time-symmetric quantities, such as the autocovariance of the event rate, only rarely produce statistically significant fits. Finally, we find that fitting financial data with many-parameter kernels may yield significant fits for both arrows of time for the same event time vector, sometimes favouring the backward time direction. This goes to show that a significant fit of Hawkes processes to real data with flexible kernels does not imply a definite arrow of time unless one tests it.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Statistical Knowledge for Teaching: Exploring it in the Classroom

ERIC Educational Resources Information Center

Burgess, Tim

2009-01-01

This paper first reports on the methodology of a study of teacher knowledge for statistics, conducted in a classroom at the primary school level. The methodology included videotaping of a sequence of lessons that involved students in investigating multivariate data sets, followed up by audiotaped interviews with each teacher. These stimulated…

Performance of the S - [chi][squared] Statistic for Full-Information Bifactor Models

ERIC Educational Resources Information Center

Li, Ying; Rupp, Andre A.

2011-01-01

This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

Conventional statistical significance tests do not inform the researcher regarding the likelihood that results will replicate. One strategy for evaluating result replication is to use a "bootstrap" resampling of a study's data so that the stability of results across numerous configurations of the subjects can be explored. This paper…

Spatial Statistical Model and Optimal Survey Design for Rapid Geophysical Characterization of UXO Sites

DTIC Science & Technology

2003-07-01

4, Gnanadesikan , 1977). An entity whose measured features fall into one of the regions is classified accordingly. For the approaches we discuss here... Gnanadesikan , R. 1977. Methods for Statistical Data Analysis of Multivariate Observations. John Wiley & Sons, New York. Hassig, N. L., O’Brien, R. F

Conceptual and statistical problems associated with the use of diversity indices in ecology.

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. There is often no a single statistical method that retains all information needed to answer even a simple question. However, multivariate analyses could be used instead of diversity indices, such as cluster analyses or multiple regressions. More complex multivariate analyses, such as Canonical Correspondence Analysis, provide very valuable information on environmental variables associated to the presence and abundance of the species in a community. In addition, particular hypotheses associated to changes in species richness across localities, or change in abundance of one, or a group of species can be tested using univariate, bivariate, and/or rarefaction statistical tests. The rarefaction method has proved to be robust to standardize all samples to a common size. Even the simplest method as reporting the number of species per taxonomic category possibly provides more information than a diversity index value.

Arsenic distribution and valence state variation studied by fast hierarchical length-scale morphological, compositional, and speciation imaging at the Nanoscopium, Synchrotron Soleil

NASA Astrophysics Data System (ADS)

Somogyi, Andrea; Medjoubi, Kadda; Sancho-Tomas, Maria; Visscher, P. T.; Baranton, Gil; Philippot, Pascal

2017-09-01

The understanding of real complex geological, environmental and geo-biological processes depends increasingly on in-depth non-invasive study of chemical composition and morphology. In this paper we used scanning hard X-ray nanoprobe techniques in order to study the elemental composition, morphology and As speciation in complex highly heterogeneous geological samples. Multivariate statistical analytical techniques, such as principal component analysis and clustering were used for data interpretation. These measurements revealed the quantitative and valance state inhomogeneity of As and its relation to the total compositional and morphological variation of the sample at sub-μm scales.

Contribution of artificial intelligence to the knowledge of prognostic factors in Hodgkin's lymphoma.

PubMed

Buciński, Adam; Marszałł, Michał Piotr; Krysiński, Jerzy; Lemieszek, Andrzej; Załuski, Jerzy

2010-07-01

Hodgkin's lymphoma is one of the most curable malignancies and most patients achieve a lasting complete remission. In this study, artificial neural network (ANN) analysis was shown to provide significant factors with regard to 5-year recurrence after lymphoma treatment. Data from 114 patients treated for Hodgkin's disease were available for evaluation and comparison. A total of 31 variables were subjected to ANN analysis. The ANN approach as an advanced multivariate data processing method was shown to provide objective prognostic data. Some of these prognostic factors are consistent or even identical to the factors evaluated earlier by other statistical methods.

Quality assessment of raw and processed Arctium lappa L. through multicomponent quantification, chromatographic fingerprint, and related chemometric analysis.

PubMed

Qin, Kunming; Wang, Bin; Li, Weidong; Cai, Hao; Chen, Danni; Liu, Xiao; Yin, Fangzhou; Cai, Baochang

2015-05-01

In traditional Chinese medicine, raw and processed herbs are used to treat different diseases. Suitable quality assessment methods are crucial for the discrimination between raw and processed herbs. The dried fruit of Arctium lappa L. and their processed products are widely used in traditional Chinese medicine, yet their therapeutic effects are different. In this study, a novel strategy using high-performance liquid chromatography and diode array detection coupled with multivariate statistical analysis to rapidly explore raw and processed Arctium lappa L. was proposed and validated. Four main components in a total of 30 batches of raw and processed Fructus Arctii samples were analyzed, and ten characteristic peaks were identified in the fingerprint common pattern. Furthermore, similarity evaluation, principal component analysis, and hierachical cluster analysis were applied to demonstrate the distinction. The results suggested that the relative amounts of the chemical components of raw and processed Fructus Arctii samples are different. This new method has been successfully applied to detect the raw and processed Fructus Arctii in marketed herbal medicinal products. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Exploratory Multivariate Analysis. A Graphical Approach.

DTIC Science & Technology

1981-01-01

Gnanadesikan , 1977) but we feel that these should be used with great caution unless one really has good reason to believe that the data came from such a...are referred to Gnanadesikan (1977). The present author hopes that the convenience of a single summary or significance level will not deter his readers...fit of a harmonic model to meteorological data. (In preparation). Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

The intervals method: a new approach to analyse finite element outputs using multivariate statistics

PubMed Central

De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

2017-01-01

Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Environmental assessment of Al-Hammar Marsh, Southern Iraq.

PubMed

Al-Gburi, Hind Fadhil Abdullah; Al-Tawash, Balsam Salim; Al-Lafta, Hadi Salim

2017-02-01

(a) To determine the spatial distributions and levels of major and minor elements, as well as heavy metals, in water, sediment, and biota (plant and fish) in Al-Hammar Marsh, southern Iraq, and ultimately to supply more comprehensive information for policy-makers to manage the contaminants input into the marsh so that their concentrations do not reach toxic levels. (b) to characterize the seasonal changes in the marsh surface water quality. (c) to address the potential environmental risk of these elements by comparison with the historical levels and global quality guidelines (i.e., World Health Organization (WHO) standard limits). (d) to define the sources of these elements (i.e., natural and/or anthropogenic) using combined multivariate statistical techniques such as Principal Component Analysis (PCA) and Agglomerative Hierarchical Cluster Analysis (AHCA) along with pollution analysis (i.e., enrichment factor analysis). Water, sediment, plant, and fish samples were collected from the marsh, and analyzed for major and minor ions, as well as heavy metals, and then compared to historical levels and global quality guidelines (WHO guidelines). Then, multivariate statistical techniques, such as PCA and AHCA, were used to determine the element sourcing. Water analyses revealed unacceptable values for almost all physio-chemical and biological properties, according to WHO standard limits for drinking water. Almost all major ions and heavy metal concentrations in water showed a distinct decreasing trend at the marsh outlet station compared to other stations. In general, major and minor ions, as well as heavy metals exhibit higher concentrations in winter than in summer. Sediment analyses using multivariate statistical techniques revealed that Mg, Fe, S, P, V, Zn, As, Se, Mo, Co, Ni, Cu, Sr, Br, Cd, Ca, N, Mn, Cr, and Pb were derived from anthropogenic sources, while Al, Si, Ti, K, and Zr were primarily derived from natural sources. Enrichment factor analysis gave results compatible with multivariate statistical techniques findings. Analysis of heavy metals in plant samples revealed that there is no pollution in plants in Al-Hammar Marsh. However, the concentrations of heavy metals in fish samples showed that all samples were contaminated by Pb, Mn, and Ni, while some samples were contaminated by Pb, Mn, and Ni. Decreasing of Tigris and Euphrates discharges during the past decades due to drought conditions and upstream damming, as well as the increasing stress of wastewater effluents from anthropogenic activities, led to degradation of the downstream Al-Hammar Marsh water quality in terms of physical, chemical, and biological properties. As such properties were found to consistently exceed the historical and global quality objectives. However, element concentration decreasing trend at the marsh outlet station compared to other stations indicate that the marsh plays an important role as a natural filtration and bioremediation system. Higher element concentrations in winter were due to runoff from the washing of the surrounding Sabkha during flooding by winter rainstorms. Finally, the high concentrations of heavy metals in fish samples can be attributed to bioaccumulation and biomagnification processes.

Post-processing of multi-model ensemble river discharge forecasts using censored EMOS

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2014-05-01

When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a daily basis over a period of three years. For the two catchments considered, this resulted in well calibrated and sharp forecast distributions over all lead-times from 1 to 114 h. Training observations tended to be better indicators for the dependence structure than the raw ensemble.

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gunn, Andrew J., E-mail: agunn@uabmc.edu; Sheth, Rahul A.; Luber, Brandon

2017-01-15

PurposeThe purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC).Materials and methodsHospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which,more » if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP).Results75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6–24.8) and 9.8 months (95 % CI 7.1–21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51–0.58 in the univariate model; range: 0.54–0.58 in the multivariate model) or TTP (C-statistic range: 0.55–0.59 in the univariate model; range: 0.57–0.61 in the multivariate model).ConclusionCurrent response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.« less

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria.

PubMed

Gunn, Andrew J; Sheth, Rahul A; Luber, Brandon; Huynh, Minh-Huy; Rachamreddy, Niranjan R; Kalva, Sanjeeva P

2017-01-01

The purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC). Hospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which, if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP). 75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6-24.8) and 9.8 months (95 % CI 7.1-21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51-0.58 in the univariate model; range: 0.54-0.58 in the multivariate model) or TTP (C-statistic range: 0.55-0.59 in the univariate model; range: 0.57-0.61 in the multivariate model). Current response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

Socio-Demographic and Clinical Characteristics are Not Clinically Useful Predictors of Refill Adherence in Patients with Hypertension

PubMed Central

Steiner, John F.; Ho, P. Michael; Beaty, Brenda L.; Dickinson, L. Miriam; Hanratty, Rebecca; Zeng, Chan; Tavel, Heather M.; Havranek, Edward P.; Davidson, Arthur J.; Magid, David J.; Estacio, Raymond O.

2009-01-01

Background Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in two health care delivery systems. Methods and Results Retrospective cohort studies of hypertension registries in an inner-city health care delivery system (N = 17176) and a health maintenance organization (N = 94297) in Denver, Colorado. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than non-adherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed less than three antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from non-adherent patients (C-statistic = 0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from non-adherent individuals (C-statistic = 0.576). Conclusions Although certain socio-demographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment. PMID:20031876

Publication of statistically significant research findings in prosthodontics & implant dentistry in the context of other dental specialties.

PubMed

Papageorgiou, Spyridon N; Kloukos, Dimitrios; Petridis, Haralampos; Pandis, Nikolaos

2015-10-01

To assess the hypothesis that there is excessive reporting of statistically significant studies published in prosthodontic and implantology journals, which could indicate selective publication. The last 30 issues of 9 journals in prosthodontics and implant dentistry were hand-searched for articles with statistical analyses. The percentages of significant and non-significant results were tabulated by parameter of interest. Univariable/multivariable logistic regression analyses were applied to identify possible predictors of reporting statistically significance findings. The results of this study were compared with similar studies in dentistry with random-effects meta-analyses. From the 2323 included studies 71% of them reported statistically significant results, with the significant results ranging from 47% to 86%. Multivariable modeling identified that geographical area and involvement of statistician were predictors of statistically significant results. Compared to interventional studies, the odds that in vitro and observational studies would report statistically significant results was increased by 1.20 times (OR: 2.20, 95% CI: 1.66-2.92) and 0.35 times (OR: 1.35, 95% CI: 1.05-1.73), respectively. The probability of statistically significant results from randomized controlled trials was significantly lower compared to various study designs (difference: 30%, 95% CI: 11-49%). Likewise the probability of statistically significant results in prosthodontics and implant dentistry was lower compared to other dental specialties, but this result did not reach statistical significant (P>0.05). The majority of studies identified in the fields of prosthodontics and implant dentistry presented statistically significant results. The same trend existed in publications of other specialties in dentistry. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

Edward J. Wolfrum | NREL

Science.gov Websites

. Another project used multivariate statistics to develop a novel device to non-invasively measure hydrogen Cellulosic Ethanol Production due to Experimental Measurement Uncertainty," Biotechnology for Biofuels

Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

DTIC Science & Technology

2014-09-01

approaches. Ecological Modelling Volume 200, Issues 1–2, 10, pp 1–19. Buhlmann, Kurt A ., Thomas S.B. Akre , John B. Iverson, Deno Karapatakis, Russell A ...statistical multivariate analysis to define the current and projected future range probability for species of interest to Army land managers. A software...15 Figure 4. RCW omission rate and predicted area as a function of the cumulative threshold

Deterministic annealing for density estimation by multivariate normal mixtures

NASA Astrophysics Data System (ADS)

Kloppenburg, Martin; Tavan, Paul

1997-03-01

An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

A Note on Asymptotic Joint Distribution of the Eigenvalues of a Noncentral Multivariate F Matrix.

DTIC Science & Technology

1984-11-01

Krishnaiah (1982). Now, let us consider the samples drawn from the k multivariate normal popuiejons. Let (Xlt....Xpt) denote the mean vector of the t...to maltivariate problems. Sankh-ya, 4, 381-39(s. (71 KRISHNAIAH , P. R. (1982). Selection of variables in discrimlnant analysis. In Handbook of...Statistics, Volume 2 (P. R. Krishnaiah , editor), 805-820. North-Holland Publishing Company. 6. Unclassifie INSTRUCTIONS REPORT DOCUMENTATION PAGE

LIKELIHOOD RATIO TESTS OF HYPOTHESES ON MULTIVARIATE POPULATIONS, VOLUME II, TEST OF HYPOTHESIS--STATISTICAL MODELS FOR THE EVALUATION AND INTERPRETATION OF EDUCATIONAL CRITERIA. PART 4.

ERIC Educational Resources Information Center

SAW, J.G.

THIS PAPER DEALS WITH SOME TESTS OF HYPOTHESIS FREQUENTLY ENCOUNTERED IN THE ANALYSIS OF MULTIVARIATE DATA. THE TYPE OF HYPOTHESIS CONSIDERED IS THAT WHICH THE STATISTICIAN CAN ANSWER IN THE NEGATIVE OR AFFIRMATIVE. THE DOOLITTLE METHOD MAKES IT POSSIBLE TO EVALUATE THE DETERMINANT OF A MATRIX OF HIGH ORDER, TO SOLVE A MATRIX EQUATION, OR TO…

Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria.

DTIC Science & Technology

1983-06-16

has been advocated by Gnanadesikan and 𔃾ilk (1969), and others in the literature. This suggests that, if we use the formal signficance test type...American Statistical Asso., 62, 1159-1178. Gnanadesikan , R., and Wilk, M..B. (1969). Data Analytic Methods in Multi- variate Statistical Analysis. In

What is a good index? Problems with statistically based indicators and the Malmquist index as alternative

USDA-ARS?s Scientific Manuscript database

Conventional multivariate statistical methods have been used for decades to calculate environmental indicators. These methods generally work fine if they are used in a situation where the method can be tailored to the data. But there is some skepticism that the methods might fail in the context of s...

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

Allen, J. I.; Somerfield, P. J.; Gilbert, F. J.

2007-01-01

Marine ecosystem models are becoming increasingly complex and sophisticated, and are being used to estimate the effects of future changes in the earth system with a view to informing important policy decisions. Despite their potential importance, far too little attention has been, and is generally, paid to model errors and the extent to which model outputs actually relate to real-world processes. With the increasing complexity of the models themselves comes an increasing complexity among model results. If we are to develop useful modelling tools for the marine environment we need to be able to understand and quantify the uncertainties inherent in the simulations. Analysing errors within highly multivariate model outputs, and relating them to even more complex and multivariate observational data, are not trivial tasks. Here we describe the application of a series of techniques, including a 2-stage self-organising map (SOM), non-parametric multivariate analysis, and error statistics, to a complex spatio-temporal model run for the period 1988-1989 in the Southern North Sea, coinciding with the North Sea Project which collected a wealth of observational data. We use model output, large spatio-temporally resolved data sets and a combination of methodologies (SOM, MDS, uncertainty metrics) to simplify the problem and to provide tractable information on model performance. The use of a SOM as a clustering tool allows us to simplify the dimensions of the problem while the use of MDS on independent data grouped according to the SOM classification allows us to validate the SOM. The combination of classification and uncertainty metrics allows us to pinpoint the variables and associated processes which require attention in each region. We recommend the use of this combination of techniques for simplifying complex comparisons of model outputs with real data, and analysis of error distributions.

Effect of sexual steroids on boar kinematic sperm subpopulations.

PubMed

Ayala, E M E; Aragón, M A

2017-11-01

Here, we show the effects of sexual steroids, progesterone, testosterone, or estradiol on motility parameters of boar sperm. Sixteen commercial seminal doses, four each of four adult boars, were analyzed using computer assisted sperm analysis (CASA). Mean values of motility parameters were analyzed by bivariate and multivariate statistics. Principal component analysis (PCA), followed by hierarchical clustering, was applied on data of motility parameters, provided automatically as intervals by the CASA system. Effects of sexual steroids were described in the kinematic subpopulations identified from multivariate statistics. Mean values of motility parameters were not significantly changed after addition of sexual steroids. Multivariate graphics showed that sperm subpopulations were not sensitive to the addition of either testosterone or estradiol, but sperm subpopulations responsive to progesterone were found. Distribution of motility parameters were wide in controls but sharpened at distinct concentrations of progesterone. We conclude that kinematic sperm subpopulations responsive to progesterone are present in boar semen, and these subpopulations are masked in evaluations of mean values of motility parameters. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Compound-Specific Isotope Analysis of Diesel Fuels in a Forensic Investigation

NASA Astrophysics Data System (ADS)

Muhammad, Syahidah; Frew, Russell; Hayman, Alan

2015-02-01

Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ13C and δ2H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin i.e. the very subtle differences in isotopic values between the samples.

Multivariate statistical analysis of radiological data of building materials used in Tiruvannamalai, Tamilnadu, India.

PubMed

Ravisankar, R; Vanasundari, K; Suganya, M; Raghu, Y; Rajalakshmi, A; Chandrasekaran, A; Sivakumar, S; Chandramohan, J; Vijayagopal, P; Venkatraman, B

2014-02-01

Using γ spectrometry, the concentration of the naturally occurring radionuclides (226)Ra, (232)Th and (40)K has been measured in soil, sand, cement, clay and bricks, which are used as building materials in Tiruvannamalai, Tamilnadu, India. The radium equivalent activity (Raeq), the criterion formula (CF), indoor gamma absorbed dose rate (DR), annual effective dose (HR), activity utilization index (AUI), alpha index (Iα), gamma index (Iγ), external radiation hazard index (Hex), internal radiation hazard index (Hin), representative level index (RLI), excess lifetime cancer risk (ELCR) and annual gonadal dose equivalent (AGDE) associated with the natural radionuclides are calculated to assess the radiation hazard of the natural radioactivity in the building materials. From the analysis, it is found that these materials used for the construction of dwellings are safe for the inhabitants. The radiological data were processed using multivariate statistical methods to determine the similarities and correlation among the various samples. The frequency distributions for all radionuclides were analyzed. The data set consisted of 15 measured variables. The Pearson correlation coefficient reveals that the (226)Ra distribution in building materials is controlled by the variation of the (40)K concentration. Principal component analysis (PCA) yields a two-component representation of the acquired data from the building materials in Tiruvannamalai, wherein 94.9% of the total variance is explained. The resulting dendrogram of hierarchical cluster analysis (HCA) classified the 30 building materials into four major groups using 15 variables. Copyright © 2013 Elsevier Ltd. All rights reserved.

Compound-specific isotope analysis of diesel fuels in a forensic investigation

PubMed Central

Muhammad, Syahidah A.; Frew, Russell D.; Hayman, Alan R.

2015-01-01

Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ13C and δ2H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin, i.e., the very subtle differences in isotopic values between the samples. PMID:25774366

Implementation of quality by design principles in the development of microsponges as drug delivery carriers: Identification and optimization of critical factors using multivariate statistical analyses and design of experiments studies.

PubMed

Simonoska Crcarevska, Maja; Dimitrovska, Aneta; Sibinovska, Nadica; Mladenovska, Kristina; Slavevska Raicki, Renata; Glavas Dodov, Marija

2015-07-15

Microsponges drug delivery system (MDDC) was prepared by double emulsion-solvent-diffusion technique using rotor-stator homogenization. Quality by design (QbD) concept was implemented for the development of MDDC with potential to be incorporated into semisolid dosage form (gel). Quality target product profile (QTPP) and critical quality attributes (CQA) were defined and identified, accordingly. Critical material attributes (CMA) and Critical process parameters (CPP) were identified using quality risk management (QRM) tool, failure mode, effects and criticality analysis (FMECA). CMA and CPP were identified based on results obtained from principal component analysis (PCA-X&Y) and partial least squares (PLS) statistical analysis along with literature data, product and process knowledge and understanding. FMECA identified amount of ethylcellulose, chitosan, acetone, dichloromethane, span 80, tween 80 and water ratio in primary/multiple emulsions as CMA and rotation speed and stirrer type used for organic solvent removal as CPP. The relationship between identified CPP and particle size as CQA was described in the design space using design of experiments - one-factor response surface method. Obtained results from statistically designed experiments enabled establishment of mathematical models and equations that were used for detailed characterization of influence of identified CPP upon MDDC particle size and particle size distribution and their subsequent optimization. Copyright © 2015 Elsevier B.V. All rights reserved.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

PubMed

Heidema, A Geert; Thissen, Uwe; Boer, Jolanda M A; Bouwman, Freek G; Feskens, Edith J M; Mariman, Edwin C M

2009-06-01

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

Extracting chemical information from high-resolution Kβ X-ray emission spectroscopy

NASA Astrophysics Data System (ADS)

Limandri, S.; Robledo, J.; Tirao, G.

2018-06-01

High-resolution X-ray emission spectroscopy allows studying the chemical environment of a wide variety of materials. Chemical information can be obtained by fitting the X-ray spectra and observing the behavior of some spectral features. Spectral changes can also be quantified by means of statistical parameters calculated by considering the spectrum as a probability distribution. Another possibility is to perform statistical multivariate analysis, such as principal component analysis. In this work the performance of these procedures for extracting chemical information in X-ray emission spectroscopy spectra for mixtures of Mn2+ and Mn4+ oxides are studied. A detail analysis of the parameters obtained, as well as the associated uncertainties is shown. The methodologies are also applied for Mn oxidation state characterization of double perovskite oxides Ba1+xLa1-xMnSbO6 (with 0 ≤ x ≤ 0.7). The results show that statistical parameters and multivariate analysis are the most suitable for the analysis of this kind of spectra.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE PAGES

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

2014-12-02

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified bymore » their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Circularly-symmetric complex normal ratio distribution for scalar transmissibility functions. Part I: Fundamentals

NASA Astrophysics Data System (ADS)

Yan, Wang-Ji; Ren, Wei-Xin

2016-12-01

Recent advances in signal processing and structural dynamics have spurred the adoption of transmissibility functions in academia and industry alike. Due to the inherent randomness of measurement and variability of environmental conditions, uncertainty impacts its applications. This study is focused on statistical inference for raw scalar transmissibility functions modeled as complex ratio random variables. The goal is achieved through companion papers. This paper (Part I) is dedicated to dealing with a formal mathematical proof. New theorems on multivariate circularly-symmetric complex normal ratio distribution are proved on the basis of principle of probabilistic transformation of continuous random vectors. The closed-form distributional formulas for multivariate ratios of correlated circularly-symmetric complex normal random variables are analytically derived. Afterwards, several properties are deduced as corollaries and lemmas to the new theorems. Monte Carlo simulation (MCS) is utilized to verify the accuracy of some representative cases. This work lays the mathematical groundwork to find probabilistic models for raw scalar transmissibility functions, which are to be expounded in detail in Part II of this study.

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Evaluating the role of admixture in cancer therapy via in vitro drug response and multivariate genome-wide associations

PubMed Central

Jack, John; Havener, Tammy M; McLeod, Howard L; Motsinger-Reif, Alison A; Foster, Matthew

2015-01-01

Aim: We investigate the role of ethnicity and admixture in drug response across a broad group of chemotherapeutic drugs. Also, we generate hypotheses on the genetic variants driving differential drug response through multivariate genome-wide association studies. Methods: Immortalized lymphoblastoid cell lines from 589 individuals (Hispanic or non-Hispanic/Caucasian) were used to investigate dose-response for 28 chemotherapeutic compounds. Univariate and multivariate statistical models were used to elucidate associations between genetic variants and differential drug response as well as the role of ethnicity in drug potency and efficacy. Results & Conclusion: For many drugs, the variability in drug response appears to correlate with self-reported race and estimates of genetic ancestry. Additionally, multivariate genome-wide association analyses offered interesting hypotheses governing these differential responses. PMID:26314407

The critical role of NIR spectroscopy and statistical process control (SPC) strategy towards captopril tablets (25 mg) manufacturing process understanding: a case study.

PubMed

Curtivo, Cátia Panizzon Dal; Funghi, Nathália Bitencourt; Tavares, Guilherme Diniz; Barbosa, Sávio Fujita; Löbenberg, Raimar; Bou-Chacra, Nádia Araci

2015-05-01

In this work, near-infrared spectroscopy (NIRS) method was used to evaluate the uniformity of dosage units of three captopril 25 mg tablets commercial batches. The performance of the calibration method was assessed by determination of Q value (0.9986), standard error of estimation (C-set SEE = 1.956), standard error of prediction (V-set SEP = 2.076) as well as the consistency (106.1%). These results indicated the adequacy of the selected model. The method validation revealed the agreement of the reference high pressure liquid chromatography (HPLC) and NIRS methods. The process evaluation using the NIRS method showed that the variability was due to common causes and delivered predictable results consistently. Cp and Cpk values were, respectively, 2.05 and 1.80. These results revealed a non-centered process in relation to the average target (100% w/w), in the specified range (85-115%). The probability of failure was 21:100 million tablets of captopril. The NIRS in combination with the method of multivariate calibration, partial least squares (PLS) regression, allowed the development of methodology for the uniformity of dosage units evaluation of captopril tablets 25 mg. The statistical process control strategy associated with NIRS method as PAT played a critical role in understanding of the sources and degree of variation and its impact on the process. This approach led towards a better process understanding and provided the sound scientific basis for its continuous improvement.

Probabilistic modelling of flood events using the entropy copula

NASA Astrophysics Data System (ADS)

Li, Fan; Zheng, Qian

2016-11-01

The estimation of flood frequency is vital for the flood control strategies and hydraulic structure design. Generating synthetic flood events according to statistical properties of observations is one of plausible methods to analyze the flood frequency. Due to the statistical dependence among the flood event variables (i.e. the flood peak, volume and duration), a multidimensional joint probability estimation is required. Recently, the copula method is widely used for multivariable dependent structure construction, however, the copula family should be chosen before application and the choice process is sometimes rather subjective. The entropy copula, a new copula family, employed in this research proposed a way to avoid the relatively subjective process by combining the theories of copula and entropy. The analysis shows the effectiveness of the entropy copula for probabilistic modelling the flood events of two hydrological gauges, and a comparison of accuracy with the popular copulas was made. The Gibbs sampling technique was applied for trivariate flood events simulation in order to mitigate the calculation difficulties of extending to three dimension directly. The simulation results indicate that the entropy copula is a simple and effective copula family for trivariate flood simulation.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

Multivariate Tensor-based Morphometry on Surfaces: Application to Mapping Ventricular Abnormalities in HIV/AIDS

PubMed Central

Wang, Yalin; Zhang, Jie; Gutman, Boris; Chan, Tony F.; Becker, James T.; Aizenstein, Howard J.; Lopez, Oscar L.; Tamburo, Robert J.; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics - these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. PMID:19900560

Statistical analysis and interpolation of compositional data in materials science.

PubMed

Pesenson, Misha Z; Suram, Santosh K; Gregoire, John M

2015-02-09

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material; that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

Geographic variation in forest composition and precipitation predict the synchrony of forest insect outbreaks

Treesearch

Kyle J. Haynes; Andrew M. Liebhold; Ottar N. Bjørnstad; Andrew J. Allstadt; Randall S. Morin

2018-01-01

Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth outbreaks. Regression...

Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches.

PubMed

Varekar, Vikas; Karmakar, Subhankar; Jha, Ramakar

2016-02-01

The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders approach outperforms FA/PCA when limited water quality and extensive watershed information is available. The available water quality dataset is limited and FA/PCA-based approach fails to identify monitoring locations with higher variation, as these multivariate statistical approaches are data-driven. The priority/hierarchy and number of sampling sites designed by modified Sanders approach are well justified by the land use practices and observed river basin characteristics of the study area.

Information trimming: Sufficient statistics, mutual information, and predictability from effective channel states

NASA Astrophysics Data System (ADS)

James, Ryan G.; Mahoney, John R.; Crutchfield, James P.

2017-06-01

One of the most basic characterizations of the relationship between two random variables, X and Y , is the value of their mutual information. Unfortunately, calculating it analytically and estimating it empirically are often stymied by the extremely large dimension of the variables. One might hope to replace such a high-dimensional variable by a smaller one that preserves its relationship with the other. It is well known that either X (or Y ) can be replaced by its minimal sufficient statistic about Y (or X ) while preserving the mutual information. While intuitively reasonable, it is not obvious or straightforward that both variables can be replaced simultaneously. We demonstrate that this is in fact possible: the information X 's minimal sufficient statistic preserves about Y is exactly the information that Y 's minimal sufficient statistic preserves about X . We call this procedure information trimming. As an important corollary, we consider the case where one variable is a stochastic process' past and the other its future. In this case, the mutual information is the channel transmission rate between the channel's effective states. That is, the past-future mutual information (the excess entropy) is the amount of information about the future that can be predicted using the past. Translating our result about minimal sufficient statistics, this is equivalent to the mutual information between the forward- and reverse-time causal states of computational mechanics. We close by discussing multivariate extensions to this use of minimal sufficient statistics.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

PubMed Central

Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P

1999-01-01

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149

Trace elements and common ions in southeastern Idaho snow: Regional air pollutant tracers for source area emissions

USGS Publications Warehouse

Abbott, M.; Einerson, J.; Schuster, P.; Susong, D.; Taylor, Howard E.; ,

2004-01-01

Snow sampling and analysis methods which produce accurate and ultra-low measurements of trace elements and common ion concentration in southeastern Idaho snow, were developed. Snow samples were collected over two winters to assess trace elements and common ion concentrations in air pollutant fallout across the southeastern Idaho. The area apportionment of apportionment of fallout concentrations measured at downwind location were investigated using pattern recognition and multivariate statistical technical techniques. Results show a high level of contribution from phosphates processing facilities located outside Pocatello in the southern portion of the Eastern Snake River Plain, and no obvious source area profiles other than at Pocatello.

The Raman spectrum character of skin tumor induced by UVB

NASA Astrophysics Data System (ADS)

Wu, Shulian; Hu, Liangjun; Wang, Yunxia; Li, Yongzeng

2016-03-01

In our study, the skin canceration processes induced by UVB were analyzed from the perspective of tissue spectrum. A home-made Raman spectral system with a millimeter order excitation laser spot size combined with a multivariate statistical analysis for monitoring the skin changed irradiated by UVB was studied and the discrimination were evaluated. Raman scattering signals of the SCC and normal skin were acquired. Spectral differences in Raman spectra were revealed. Linear discriminant analysis (LDA) based on principal component analysis (PCA) were employed to generate diagnostic algorithms for the classification of skin SCC and normal. The results indicated that Raman spectroscopy combined with PCA-LDA demonstrated good potential for improving the diagnosis of skin cancers.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

PubMed

Park, Eun Sug; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford

2015-06-01

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

Multivariate methods to visualise colour-space and colour discrimination data.

PubMed

Hastings, Gareth D; Rubin, Alan

2015-01-01

Despite most modern colour spaces treating colour as three-dimensional (3-D), colour data is usually not visualised in 3-D (and two-dimensional (2-D) projection-plane segments and multiple 2-D perspective views are used instead). The objectives of this article are firstly, to introduce a truly 3-D percept of colour space using stereo-pairs, secondly to view colour discrimination data using that platform, and thirdly to apply formal statistics and multivariate methods to analyse the data in 3-D. This is the first demonstration of the software that generated stereo-pairs of RGB colour space, as well as of a new computerised procedure that investigated colour discrimination by measuring colour just noticeable differences (JND). An initial pilot study and thorough investigation of instrument repeatability were performed. Thereafter, to demonstrate the capabilities of the software, five colour-normal and one colour-deficient subject were examined using the JND procedure and multivariate methods of data analysis. Scatter plots of responses were meaningfully examined in 3-D and were useful in evaluating multivariate normality as well as identifying outliers. The extent and direction of the difference between each JND response and the stimulus colour point was calculated and appreciated in 3-D. Ellipsoidal surfaces of constant probability density (distribution ellipsoids) were fitted to response data; the volumes of these ellipsoids appeared useful in differentiating the colour-deficient subject from the colour-normals. Hypothesis tests of variances and covariances showed many statistically significant differences between the results of the colour-deficient subject and those of the colour-normals, while far fewer differences were found when comparing within colour-normals. The 3-D visualisation of colour data using stereo-pairs, as well as the statistics and multivariate methods of analysis employed, were found to be unique and useful tools in the representation and study of colour. Many additional studies using these methods along with the JND and other procedures have been identified and will be reported in future publications. © 2014 The Authors Ophthalmic & Physiological Optics © 2014 The College of Optometrists.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

PubMed Central

Jia, Erik; Chen, Tianlu

2018-01-01

Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp. PMID:29385130

[Statistical prediction methods in violence risk assessment and its application].

PubMed

Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

2013-06-01

It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

[Monitoring method for macroporous resin column chromatography process of salvianolic acids based on near infrared spectroscopy].

PubMed

Hou, Xiang-Mei; Zhang, Lei; Yue, Hong-Shui; Ju, Ai-Chun; Ye, Zheng-Liang

2016-07-01

To study and establish a monitoring method for macroporous resin column chromatography process of salvianolic acids by using near infrared spectroscopy (NIR) as a process analytical technology (PAT).The multivariate statistical process control (MSPC) model was developed based on 7 normal operation batches, and 2 test batches (including one normal operation batch and one abnormal operation batch) were used to verify the monitoring performance of this model. The results showed that MSPC model had a good monitoring ability for the column chromatography process. Meanwhile, NIR quantitative calibration model was established for three key quality indexes (rosmarinic acid, lithospermic acid and salvianolic acid B) by using partial least squares (PLS) algorithm. The verification results demonstrated that this model had satisfactory prediction performance. The combined application of the above two models could effectively achieve real-time monitoring for macroporous resin column chromatography process of salvianolic acids, and can be used to conduct on-line analysis of key quality indexes. This established process monitoring method could provide reference for the development of process analytical technology for traditional Chinese medicines manufacturing. Copyright© by the Chinese Pharmaceutical Association.

High-throughput manufacturing of size-tuned liposomes by a new microfluidics method using enhanced statistical tools for characterization.

PubMed

Kastner, Elisabeth; Kaur, Randip; Lowry, Deborah; Moghaddam, Behfar; Wilkinson, Alexander; Perrie, Yvonne

2014-12-30

Microfluidics has recently emerged as a new method of manufacturing liposomes, which allows for reproducible mixing in miliseconds on the nanoliter scale. Here we investigate microfluidics-based manufacturing of liposomes. The aim of these studies was to assess the parameters in a microfluidic process by varying the total flow rate (TFR) and the flow rate ratio (FRR) of the solvent and aqueous phases. Design of experiment and multivariate data analysis were used for increased process understanding and development of predictive and correlative models. High FRR lead to the bottom-up synthesis of liposomes, with a strong correlation with vesicle size, demonstrating the ability to in-process control liposomes size; the resulting liposome size correlated with the FRR in the microfluidics process, with liposomes of 50 nm being reproducibly manufactured. Furthermore, we demonstrate the potential of a high throughput manufacturing of liposomes using microfluidics with a four-fold increase in the volumetric flow rate, maintaining liposome characteristics. The efficacy of these liposomes was demonstrated in transfection studies and was modelled using predictive modeling. Mathematical modelling identified FRR as the key variable in the microfluidic process, with the highest impact on liposome size, polydispersity and transfection efficiency. This study demonstrates microfluidics as a robust and high-throughput method for the scalable and highly reproducible manufacture of size-controlled liposomes. Furthermore, the application of statistically based process control increases understanding and allows for the generation of a design-space for controlled particle characteristics. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

Connectopic mapping with resting-state fMRI.

PubMed

Haak, Koen V; Marquand, Andre F; Beckmann, Christian F

2018-04-15

Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or 'connectopies' in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity 'fingerprints' with a novel approach to spatial statistical inference. We apply the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Sharpening method of satellite thermal image based on the geographical statistical model

NASA Astrophysics Data System (ADS)

Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng

2016-04-01

To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Evaluation of Facility Management by Multivariate Statistics - Factor Analysis

NASA Astrophysics Data System (ADS)

Singovszki, Miloš; Vranayová, Zuzana

2013-06-01

Facility management is evolving, there is no exact than other sciences, although its development is fast forward. The knowledge and practical skills in facility management is not replaced, on the contrary, they complement each other. The existing low utilization of science in the field of facility management is mainly caused by the management of support activities are many variables and prevailing immediate reaction to the extraordinary situation arising from motives of those who have substantial experience and years of proven experience. Facility management is looking for a system that uses organized knowledge and will form the basis, which grows from a wide range of disciplines. Significant influence on its formation as a scientific discipline is the "structure, which follows strategy". The paper deals evaluate technology building as part of an facility management by multivariate statistic - factor analysis.

Multivariate statistics applied to the reaction of common bean plants to parasitism by Meloidogyne javanica.

PubMed

Santos, L N S; Cabral, P D S; Neves, G A R; Alves, F R; Teixeira, M B; Cunha, F N; Silva, N F

2017-03-16

The availability of common bean cultivars tolerant to Meloidogyne javanica is limited in Brazil. Thus, the present study aimed to evaluate the reactions of 33 common bean genotypes (23 landrace, 8 commercial, 1 susceptible standard and 1 resistant standard) to M. javanica, employing multivariate statistics to discriminate the reaction of the genotypes. The experiment was conducted in a greenhouse using a completely randomized design with seven replicates. The seeds were sown in 1-L pots containing autoclaved soil and sand in a 1:1 ratio (v:v). On day 19, after emergence of the seedlings, the plants were treated with inoculum containing 4000 eggs + second-stage juveniles (J2). At 60 days after inoculation, the seedlings were evaluated based on biometric and parasitism-related traits, such as number of galls, final nematode population per root system, reproduction factor, and percent reduction in the reproduction factor of the nematode (%RRF). The data were subjected to analysis of variance using the F-test. The Mahalanobis generalized distance was used to obtain the dissimilarity matrix, and the average linkage between groups was used for clustering. The use of multivariate statistics allowed groups to be separated according to the resistance levels of genotypes, as observed in the %RRF. The landrace genotypes FORT-09, FORT-17, FORT-31, FORT-32, FORT-34 and FORT-36 presented resistance to M. javanica; thus, these genotypes can be considered potential sources of resistance.

Peripheral vascular damage in systemic lupus erythematosus: data from LUMINA, a large multi-ethnic U.S. cohort (LXIX).

PubMed

Burgos, P I; Vilá, L M; Reveille, J D; Alarcón, G S

2009-12-01

To determine the factors associated with peripheral vascular damage in systemic lupus erythematosus patients and its impact on survival from Lupus in Minorities, Nature versus Nurture, a longitudinal US multi-ethnic cohort. Peripheral vascular damage was defined by the Systemic Lupus International Collaborating Clinics Damage Index (SDI). Factors associated with peripheral vascular damage were examined by univariable and multi-variable logistic regression models and its impact on survival by a Cox multi-variable regression. Thirty-four (5.3%) of 637 patients (90% women, mean [SD] age 36.5 [12.6] [16-87] years) developed peripheral vascular damage. Age and the SDI (without peripheral vascular damage) were statistically significant (odds ratio [OR] = 1.05, 95% confidence interval [CI] 1.01-1.08; P = 0.0107 and OR = 1.30, 95% CI 0.09-1.56; P = 0.0043, respectively) in multi-variable analyses. Azathioprine, warfarin and statins were also statistically significant, and glucocorticoid use was borderline statistically significant (OR = 1.03, 95% CI 0.10-1.06; P = 0.0975). In the survival analysis, peripheral vascular damage was independently associated with a diminished survival (hazard ratio = 2.36; 95% CI 1.07-5.19; P = 0.0334). In short, age was independently associated with peripheral vascular damage, but so was the presence of damage in other organs (ocular, neuropsychiatric, renal, cardiovascular, pulmonary, musculoskeletal and integument) and some medications (probably reflecting more severe disease). Peripheral vascular damage also negatively affected survival.

Evaluation of dissolution profile similarity - Comparison between the f2, the multivariate statistical distance and the f2 bootstrapping methods.

PubMed

Paixão, Paulo; Gouveia, Luís F; Silva, Nuno; Morais, José A G

2017-03-01

A simulation study is presented, evaluating the performance of the f 2 , the model-independent multivariate statistical distance and the f 2 bootstrap methods in the ability to conclude similarity between two dissolution profiles. Different dissolution profiles, based on the Noyes-Whitney equation and ranging from theoretical f 2 values between 100 and 40, were simulated. Variability was introduced in the dissolution model parameters in an increasing order, ranging from a situation complying with the European guidelines requirements for the use of the f 2 metric to several situations where the f 2 metric could not be used anymore. Results have shown that the f 2 is an acceptable metric when used according to the regulatory requirements, but loses its applicability when variability increases. The multivariate statistical distance presented contradictory results in several of the simulation scenarios, which makes it an unreliable metric for dissolution profile comparisons. The bootstrap f 2 , although conservative in its conclusions is an alternative suitable method. Overall, as variability increases, all of the discussed methods reveal problems that can only be solved by increasing the number of dosage form units used in the comparison, which is usually not practical or feasible. Additionally, experimental corrective measures may be undertaken in order to reduce the overall variability, particularly when it is shown that it is mainly due to the dissolution assessment instead of being intrinsic to the dosage form. Copyright © 2016. Published by Elsevier B.V.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Towards better process understanding: chemometrics and multivariate measurements in manufacturing of solid dosage forms.

PubMed

Matero, Sanni; van Den Berg, Frans; Poutiainen, Sami; Rantanen, Jukka; Pajander, Jari

2013-05-01

The manufacturing of tablets involves many unit operations that possess multivariate and complex characteristics. The interactions between the material characteristics and process related variation are presently not comprehensively analyzed due to univariate detection methods. As a consequence, current best practice to control a typical process is to not allow process-related factors to vary i.e. lock the production parameters. The problem related to the lack of sufficient process understanding is still there: the variation within process and material properties is an intrinsic feature and cannot be compensated for with constant process parameters. Instead, a more comprehensive approach based on the use of multivariate tools for investigating processes should be applied. In the pharmaceutical field these methods are referred to as Process Analytical Technology (PAT) tools that aim to achieve a thorough understanding and control over the production process. PAT includes the frames for measurement as well as data analyzes and controlling for in-depth understanding, leading to more consistent and safer drug products with less batch rejections. In the optimal situation, by applying these techniques, destructive end-product testing could be avoided. In this paper the most prominent multivariate data analysis measuring tools within tablet manufacturing and basic research on operations are reviewed. Copyright © 2013 Wiley Periodicals, Inc.

Sample processing, protocol, and statistical analysis of the time-of-flight secondary ion mass spectrometry (ToF-SIMS) of protein, cell, and tissue samples.

PubMed

Barreto, Goncalo; Soininen, Antti; Sillat, Tarvo; Konttinen, Yrjö T; Kaivosoja, Emilia

2014-01-01

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is increasingly being used in analysis of biological samples. For example, it has been applied to distinguish healthy and osteoarthritic human cartilage. This chapter discusses ToF-SIMS principle and instrumentation including the three modes of analysis in ToF-SIMS. ToF-SIMS sets certain requirements for the samples to be analyzed; for example, the samples have to be vacuum compatible. Accordingly, sample processing steps for different biological samples, i.e., proteins, cells, frozen and paraffin-embedded tissues and extracellular matrix for the ToF-SIMS are presented. Multivariate analysis of the ToF-SIMS data and the necessary data preprocessing steps (peak selection, data normalization, mean-centering, and scaling and transformation) are discussed in this chapter.

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

NASA Technical Reports Server (NTRS)

Djorgovski, S. G.

1994-01-01

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication.

PubMed

Riedl, Janet; Esslinger, Susanne; Fauhl-Hassek, Carsten

2015-07-23

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for "good practice" shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control. Copyright © 2015 Elsevier B.V. All rights reserved.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Assessing the impacts of socio-economic and hydrological factors on urban water demand: A multivariate statistical approach

NASA Astrophysics Data System (ADS)

Panagopoulos, George P.

2014-10-01

The multivariate statistical techniques conducted on quarterly water consumption data in Mytilene reveal valuable tools that could help the local authorities in assigning strategies aimed at the sustainable development of urban water resources. The proposed methodology is an innovative approach, applied for the first time in the international literature, to handling urban water consumption data in order to analyze statistically the interrelationships among the determinants of urban water use. Factor analysis of demographic, socio-economic and hydrological variables shows that total water consumption in Mytilene is the combined result of increases in (a) income, (b) population, (c) connections and (d) climate parameters. On the other hand, the per connection water demand is influenced by variations in water prices but with different consequences in each consumption class. Increases in water prices are faced by large consumers; they then reduce their consumption rates and transfer to lower consumption blocks. These shifts are responsible for the increase in the average consumption values in the lower blocks despite the increase in the marginal prices.

Statistical methods in personality assessment research.

PubMed

Schinka, J A; LaLone, L; Broeckel, J A

1997-06-01

Emerging models of personality structure and advances in the measurement of personality and psychopathology suggest that research in personality and personality assessment has entered a stage of advanced development, in this article we examine whether researchers in these areas have taken advantage of new and evolving statistical procedures. We conducted a review of articles published in the Journal of Personality, Assessment during the past 5 years. Of the 449 articles that included some form of data analysis, 12.7% used only descriptive statistics, most employed only univariate statistics, and fewer than 10% used multivariate methods of data analysis. We discuss the cost of using limited statistical methods, the possible reasons for the apparent reluctance to employ advanced statistical procedures, and potential solutions to this technical shortcoming.

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

Fatty acid methyl ester analysis to identify sources of soil in surface water.

PubMed

Banowetz, Gary M; Whittaker, Gerald W; Dierksen, Karen P; Azevedo, Mark D; Kennedy, Ann C; Griffith, Stephen M; Steiner, Jeffrey J

2006-01-01

Efforts to improve land-use practices to prevent contamination of surface waters with soil are limited by an inability to identify the primary sources of soil present in these waters. We evaluated the utility of fatty acid methyl ester (FAME) profiles of dry reference soils for multivariate statistical classification of soils collected from surface waters adjacent to agricultural production fields and a wooded riparian zone. Trials that compared approaches to concentrate soil from surface water showed that aluminum sulfate precipitation provided comparable yields to that obtained by vacuum filtration and was more suitable for handling large numbers of samples. Fatty acid methyl ester profiles were developed from reference soils collected from contrasting land uses in different seasons to determine whether specific fatty acids would consistently serve as variables in multivariate statistical analyses to permit reliable classification of soils. We used a Bayesian method and an independent iterative process to select appropriate fatty acids and found that variable selection was strongly impacted by the season during which soil was collected. The apparent seasonal variation in the occurrence of marker fatty acids in FAME profiles from reference soils prevented preparation of a standardized set of variables. Nevertheless, accurate classification of soil in surface water was achieved utilizing fatty acid variables identified in seasonally matched reference soils. Correlation analysis of entire chromatograms and subsequent discriminant analyses utilizing a restricted number of fatty acid variables showed that FAME profiles of soils exposed to the aquatic environment still had utility for classification at least 1 wk after submersion.

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

PubMed Central

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-01-01

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition. PMID:24961428

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition.

PubMed

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-05-28

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China.

PubMed

Guan, Qingyu; Wang, Feifei; Xu, Chuanqi; Pan, Ninghui; Lin, Jinkuo; Zhao, Rui; Yang, Yanyan; Luo, Haiping

2018-02-01

Hexi Corridor is the most important base of commodity grain and producing area for cash crops. However, the rapid development of agriculture and industry has inevitably led to heavy metal contamination in the soils. Multivariate statistical analysis, GIS-based geostatistical methods and Positive Matrix Factorization (PMF) receptor modeling techniques were used to understand the levels of heavy metals and their source apportionment for agricultural soil in Hexi Corridor. The results showed that the average concentrations of Cr, Cu, Ni, Pb and Zn were lower than the secondary standard of soil environmental quality; however, the concentrations of eight metals (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn) were higher than background values, and their corresponding enrichment factor values were significantly greater than 1. Different degrees of heavy metal pollution occurred in the agricultural soils; specifically, Ni had the most potential for impacting human health. The results from the multivariate statistical analysis and GIS-based geostatistical methods indicated both natural sources (Co and W) and anthropogenic sources (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn). To better identify pollution sources of heavy metals in the agricultural soils, the PMF model was applied. Further source apportionment revealed that enrichments of Pb and Zn were attributed to traffic sources; Cr and Ni were closely related to industrial activities, including mining, smelting, coal combustion, iron and steel production and metal processing; Zn and Cu originated from agricultural activities; and V, Ti and Mn were derived from oil- and coal-related activities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, Ana; Hegermiller, Christie A.; Antolinez, Jose A. A.; Camus, Paula; Vitousek, Sean; Ruggiero, Peter; Barnard, Patrick L.; Erikson, Li H.; Tomás, Antonio; Mendez, Fernando J.

2017-02-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Multiscale Climate Emulator of Multimodal Wave Spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, A.; Hegermiller, C.; Alvarez Antolinez, J. A.; Camus, P.; Vitousek, S.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Tomas, A.; Mendez, F. J.

2016-12-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this problem complex yet tractable using computationally-expensive numerical models. So far, the skill of statistical-downscaling models based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical-downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the Southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Stratification of Recanalization for Patients with Endovascular Treatment of Intracranial Aneurysms

PubMed Central

Ogilvy, Christopher S.; Chua, Michelle H.; Fusco, Matthew R.; Reddy, Arra S.; Thomas, Ajith J.

2015-01-01

Background With increasing utilization of endovascular techniques in the treatment of both ruptured and unruptured intracranial aneurysms, the issue of obliteration efficacy has become increasingly important. Objective Our goal was to systematically develop a comprehensive model for predicting retreatment with various types of endovascular treatment. Methods We retrospectively reviewed medical records that were prospectively collected for 305 patients who received endovascular treatment for intracranial aneurysms from 2007 to 2013. Multivariable logistic regression was performed on candidate predictors identified by univariable screening analysis to detect independent predictors of retreatment. A composite risk score was constructed based on the proportional contribution of independent predictors in the multivariable model. Results Size (>10 mm), aneurysm rupture, stent assistance, and post-treatment degree of aneurysm occlusion were independently associated with retreatment while intraluminal thrombosis and flow diversion demonstrated a trend towards retreatment. The Aneurysm Recanalization Stratification Scale was constructed by assigning the following weights to statistically and clinically significant predictors. Aneurysm-specific factors: Size (>10 mm), 2 points; rupture, 2 points; presence of thrombus, 2 points. Treatment-related factors: Stent assistance, -1 point; flow diversion, -2 points; Raymond Roy 2 occlusion, 1 point; Raymond Roy 3 occlusion, 2 points. This scale demonstrated good discrimination with a C-statistic of 0.799. Conclusion Surgical decision-making and patient-centered informed consent require comprehensive and accessible information on treatment efficacy. We have constructed the Aneurysm Recanalization Stratification Scale to enhance this decision-making process. This is the first comprehensive model that has been developed to quantitatively predict the risk of retreatment following endovascular therapy. PMID:25621984

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

NASA Astrophysics Data System (ADS)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Weighing of risk factors for penetrating keratoplasty graft failure: application of Risk Score System.

PubMed

Tourkmani, Abdo Karim; Sánchez-Huerta, Valeria; De Wit, Guillermo; Martínez, Jaime D; Mingo, David; Mahillo-Fernández, Ignacio; Jiménez-Alfaro, Ignacio

2017-01-01

To analyze the relationship between the score obtained in the Risk Score System (RSS) proposed by Hicks et al with penetrating keratoplasty (PKP) graft failure at 1y postoperatively and among each factor in the RSS with the risk of PKP graft failure using univariate and multivariate analysis. The retrospective cohort study had 152 PKPs from 152 patients. Eighteen cases were excluded from our study due to primary failure (10 cases), incomplete medical notes (5 cases) and follow-up less than 1y (3 cases). We included 134 PKPs from 134 patients stratified by preoperative risk score. Spearman coefficient was calculated for the relationship between the score obtained and risk of failure at 1y. Univariate and multivariate analysis were calculated for the impact of every single risk factor included in the RSS over graft failure at 1y. Spearman coefficient showed statistically significant correlation between the score in the RSS and graft failure ( P <0.05). Multivariate logistic regression analysis showed no statistically significant relationship ( P >0.05) between diagnosis and lens status with graft failure. The relationship between the other risk factors studied and graft failure was significant ( P <0.05), although the results for previous grafts and graft failure was unreliable. None of our patients had previous blood transfusion, thus, it had no impact. After the application of multivariate analysis techniques, some risk factors do not show the expected impact over graft failure at 1y.

Weighing of risk factors for penetrating keratoplasty graft failure: application of Risk Score System

PubMed Central

Tourkmani, Abdo Karim; Sánchez-Huerta, Valeria; De Wit, Guillermo; Martínez, Jaime D.; Mingo, David; Mahillo-Fernández, Ignacio; Jiménez-Alfaro, Ignacio

2017-01-01

AIM To analyze the relationship between the score obtained in the Risk Score System (RSS) proposed by Hicks et al with penetrating keratoplasty (PKP) graft failure at 1y postoperatively and among each factor in the RSS with the risk of PKP graft failure using univariate and multivariate analysis. METHODS The retrospective cohort study had 152 PKPs from 152 patients. Eighteen cases were excluded from our study due to primary failure (10 cases), incomplete medical notes (5 cases) and follow-up less than 1y (3 cases). We included 134 PKPs from 134 patients stratified by preoperative risk score. Spearman coefficient was calculated for the relationship between the score obtained and risk of failure at 1y. Univariate and multivariate analysis were calculated for the impact of every single risk factor included in the RSS over graft failure at 1y. RESULTS Spearman coefficient showed statistically significant correlation between the score in the RSS and graft failure (P<0.05). Multivariate logistic regression analysis showed no statistically significant relationship (P>0.05) between diagnosis and lens status with graft failure. The relationship between the other risk factors studied and graft failure was significant (P<0.05), although the results for previous grafts and graft failure was unreliable. None of our patients had previous blood transfusion, thus, it had no impact. CONCLUSION After the application of multivariate analysis techniques, some risk factors do not show the expected impact over graft failure at 1y. PMID:28393027

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

Performance evaluation of a hybrid-passive landfill leachate treatment system using multivariate statistical techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wallace, Jack, E-mail: jack.wallace@ce.queensu.ca; Champagne, Pascale, E-mail: champagne@civil.queensu.ca; Monnier, Anne-Charlotte, E-mail: anne-charlotte.monnier@insa-lyon.fr

Highlights: • Performance of a hybrid passive landfill leachate treatment system was evaluated. • 33 Water chemistry parameters were sampled for 21 months and statistically analyzed. • Parameters were strongly linked and explained most (>40%) of the variation in data. • Alkalinity, ammonia, COD, heavy metals, and iron were criteria for performance. • Eight other parameters were key in modeling system dynamics and criteria. - Abstract: A pilot-scale hybrid-passive treatment system operated at the Merrick Landfill in North Bay, Ontario, Canada, treats municipal landfill leachate and provides for subsequent natural attenuation. Collected leachate is directed to a hybrid-passive treatment system,more » followed by controlled release to a natural attenuation zone before entering the nearby Little Sturgeon River. The study presents a comprehensive evaluation of the performance of the system using multivariate statistical techniques to determine the interactions between parameters, major pollutants in the leachate, and the biological and chemical processes occurring in the system. Five parameters (ammonia, alkalinity, chemical oxygen demand (COD), “heavy” metals of interest, with atomic weights above calcium, and iron) were set as criteria for the evaluation of system performance based on their toxicity to aquatic ecosystems and importance in treatment with respect to discharge regulations. System data for a full range of water quality parameters over a 21-month period were analyzed using principal components analysis (PCA), as well as principal components (PC) and partial least squares (PLS) regressions. PCA indicated a high degree of association for most parameters with the first PC, which explained a high percentage (>40%) of the variation in the data, suggesting strong statistical relationships among most of the parameters in the system. Regression analyses identified 8 parameters (set as independent variables) that were most frequently retained for modeling the five criteria parameters (set as dependent variables), on a statistically significant level: conductivity, dissolved oxygen (DO), nitrite (NO{sub 2}{sup −}), organic nitrogen (N), oxidation reduction potential (ORP), pH, sulfate and total volatile solids (TVS). The criteria parameters and the significant explanatory parameters were most important in modeling the dynamics of the passive treatment system during the study period. Such techniques and procedures were found to be highly valuable and could be applied to other sites to determine parameters of interest in similar naturalized engineered systems.« less

Quantifying long-term human impact in contrasting environments: Statistical analysis of modern and fossil pollen records

NASA Astrophysics Data System (ADS)

Broothaerts, Nils; López-Sáez, José Antonio; Verstraeten, Gert

2017-04-01

Reconstructing and quantifying human impact is an important step to understand human-environment interactions in the past. Quantitative measures of human impact on the landscape are needed to fully understand long-term influence of anthropogenic land cover changes on the global climate, ecosystems and geomorphic processes. Nevertheless, quantifying past human impact is not straightforward. Recently, multivariate statistical analysis of fossil pollen records have been proposed to characterize vegetation changes and to get insights in past human impact. Although statistical analysis of fossil pollen data can provide useful insights in anthropogenic driven vegetation changes, still it cannot be used as an absolute quantification of past human impact. To overcome this shortcoming, in this study fossil pollen records were included in a multivariate statistical analysis (cluster analysis and non-metric multidimensional scaling (NMDS)) together with modern pollen data and modern vegetation data. The information on the modern pollen and vegetation dataset can be used to get a better interpretation of the representativeness of the fossil pollen records, and can result in a full quantification of human impact in the past. This methodology was applied in two contrasting environments: SW Turkey and Central Spain. For each region, fossil pollen data from different study sites were integrated, together with modern pollen data and information on modern vegetation. In this way, arboreal cover, grazing pressure and agricultural activities in the past were reconstructed and quantified. The data from SW Turkey provides new integrated information on changing human impact through time in the Sagalassos territory, and shows that human impact was most intense during the Hellenistic and Roman Period (ca. 2200-1750 cal a BP) and decreased and changed in nature afterwards. The data from central Spain shows for several sites that arboreal cover decreases bellow 5% from the Feudal period onwards (ca. 850 cal a BP) related to increasing human impact in the landscape. At other study sites arboreal cover remained above 25% beside significant human impact. Overall, the presented examples from two contrasting environments shows how cluster analysis and NMDS of modern and fossil pollen data can help to provide quantitative insights in anthropogenic land cover changes. Our study extensively discuss and illustrate the possibilities and limitations of statistical analysis of pollen data to quantify human induced land use changes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Piepel, Gregory F.; Pasquini, Benedetta; Cooley, Scott K.

In recent years, multivariate optimization has played an increasing role in analytical method development. ICH guidelines recommend using statistical design of experiments to identify the design space, in which multivariate combinations of composition variables and process variables have been demonstrated to provide quality results. Considering a microemulsion electrokinetic chromatography method (MEEKC), the performance of the electrophoretic run depends on the proportions of mixture components (MCs) of the microemulsion and on the values of process variables (PVs). In the present work, for the first time in the literature, a mixture-process variable (MPV) approach was applied to optimize a MEEKC method formore » the analysis of coenzyme Q10 (Q10), ascorbic acid (AA), and folic acid (FA) contained in nutraceuticals. The MCs (buffer, surfactant-cosurfactant, oil) and the PVs (voltage, buffer concentration, buffer pH) were simultaneously changed according to a MPV experimental design. A 62-run MPV design was generated using the I-optimality criterion, assuming a 46-term MPV model allowing for special-cubic blending of the MCs, quadratic effects of the PVs, and some MC-PV interactions. The obtained data were used to develop MPV models that express the performance of an electrophoretic run (measured as peak efficiencies of Q10, AA, and FA) in terms of the MCs and PVs. Contour and perturbation plots were drawn for each of the responses. Finally, the MPV models and criteria for the peak efficiencies were used to develop the design space and an optimal subregion (i.e., the settings of the mixture MCs and PVs that satisfy the respective criteria), as well as a unique optimal combination of MCs and PVs.« less

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Prognostic Significance of POLE Proofreading Mutations in Endometrial Cancer

PubMed Central

Church, David N.; Stelloo, Ellen; Nout, Remi A.; Valtcheva, Nadejda; Depreeuw, Jeroen; ter Haar, Natalja; Noske, Aurelia; Amant, Frederic; Wild, Peter J.; Lambrechts, Diether; Jürgenliemk-Schulz, Ina M.; Jobsen, Jan J.; Smit, Vincent T. H. B. M.; Creutzberg, Carien L.; Bosse, Tjalling

2015-01-01

Background: Current risk stratification in endometrial cancer (EC) results in frequent over- and underuse of adjuvant therapy, and may be improved by novel biomarkers. We examined whether POLE proofreading mutations, recently reported in about 7% of ECs, predict prognosis. Methods: We performed targeted POLE sequencing in ECs from the PORTEC-1 and -2 trials (n = 788), and analyzed clinical outcome according to POLE status. We combined these results with those from three additional series (n = 628) by meta-analysis to generate multivariable-adjusted, pooled hazard ratios (HRs) for recurrence-free survival (RFS) and cancer-specific survival (CSS) of POLE-mutant ECs. All statistical tests were two-sided. Results: POLE mutations were detected in 48 of 788 (6.1%) ECs from PORTEC-1 and-2 and were associated with high tumor grade (P < .001). Women with POLE-mutant ECs had fewer recurrences (6.2% vs 14.1%) and EC deaths (2.3% vs 9.7%), though, in the total PORTEC cohort, differences in RFS and CSS were not statistically significant (multivariable-adjusted HR = 0.43, 95% CI = 0.13 to 1.37, P = .15; HR = 0.19, 95% CI = 0.03 to 1.44, P = .11 respectively). However, of 109 grade 3 tumors, 0 of 15 POLE-mutant ECs recurred, compared with 29 of 94 (30.9%) POLE wild-type cancers; reflected in statistically significantly greater RFS (multivariable-adjusted HR = 0.11, 95% CI = 0.001 to 0.84, P = .03). In the additional series, there were no EC-related events in any of 33 POLE-mutant ECs, resulting in a multivariable-adjusted, pooled HR of 0.33 for RFS (95% CI = 0.12 to 0.91, P = .03) and 0.26 for CSS (95% CI = 0.06 to 1.08, P = .06). Conclusion: POLE proofreading mutations predict favorable EC prognosis, independently of other clinicopathological variables, with the greatest effect seen in high-grade tumors. This novel biomarker may help to reduce overtreatment in EC. PMID:25505230

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

PubMed

Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

2018-04-26

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants. Copyright © 2018. Published by Elsevier Ltd.

MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals.

PubMed

Azami, Hamed; Smith, Keith; Escudero, Javier

2016-08-01

Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the relative instability of mvSE for short signals remains a problem. Here, we address this issue by proposing the multivariate fuzzy entropy (mvFE) with a new fuzzy membership function. The results using white Gaussian noise show that the mvFE leads to more reliable and stable results, especially for short signals, in comparison with mvSE. Accordingly, we propose MEMD-enhanced mvFE to quantify the complexity of signals. The characteristics of brain regions influenced by partial epilepsy are investigated by focal and non-focal electroencephalogram (EEG) time series. In this sense, the proposed MEMD-enhanced mvFE and mvSE are employed to discriminate focal EEG signals from non-focal ones. The results demonstrate the MEMD-enhanced mvFE values have a smaller coefficient of variation in comparison with those obtained by the MEMD-enhanced mvSE, even for long signals. The results also show that the MEMD-enhanced mvFE has better performance to quantify focal and non-focal signals compared with multivariate multiscale permutation entropy.

Multivariate evoked response detection based on the spectral F-test.

PubMed

Rocha, Paulo Fábio F; Felix, Leonardo B; Miranda de Sá, Antonio Mauricio F L; Mendes, Eduardo M A M

2016-05-01

Objective response detection techniques, such as magnitude square coherence, component synchrony measure, and the spectral F-test, have been used to automate the detection of evoked responses. The performance of these detectors depends on both the signal-to-noise ratio (SNR) and the length of the electroencephalogram (EEG) signal. Recently, multivariate detectors were developed to increase the detection rate even in the case of a low signal-to-noise ratio or of short data records originated from EEG signals. In this context, an extension to the multivariate case of the spectral F-test detector is proposed. The performance of this technique is assessed using Monte Carlo. As an example, EEG data from 12 subjects during photic stimulation is used to demonstrate the usefulness of the proposed detector. The multivariate method showed detection rates consistently higher than those ones when only one signal was used. It is shown that the response detection in EEG signals with the multivariate technique was statistically significant if two or more EEG derivations were used. Copyright © 2016 Elsevier B.V. All rights reserved.

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements.

PubMed

Burggraeve, A; Van den Kerkhof, T; Hellings, M; Remon, J P; Vervaet, C; De Beer, T

2011-04-18

Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate statistical characterization of charged and uncharged domain walls in multiferroic hexagonal YMnO3 single crystal visualized by a spherical aberration-corrected STEM.

PubMed

Matsumoto, Takao; Ishikawa, Ryo; Tohei, Tetsuya; Kimura, Hideo; Yao, Qiwen; Zhao, Hongyang; Wang, Xiaolin; Chen, Dapeng; Cheng, Zhenxiang; Shibata, Naoya; Ikuhara, Yuichi

2013-10-09

A state-of-the-art spherical aberration-corrected STEM was fully utilized to directly visualize the multiferroic domain structure in a hexagonal YMnO3 single crystal at atomic scale. With the aid of multivariate statistical analysis (MSA), we obtained unbiased and quantitative maps of ferroelectric domain structures with atomic resolution. Such a statistical image analysis of the transition region between opposite polarizations has confirmed atomically sharp transitions of ferroelectric polarization both in antiparallel (uncharged) and tail-to-tail 180° (charged) domain boundaries. Through the analysis, a correlated subatomic image shift of Mn-O layers with that of Y layers, exhibiting a double-arc shape of reversed curvatures, have been elucidated. The amount of image shift in Mn-O layers along the c-axis is statistically significant as small as 0.016 nm, roughly one-third of the evident image shift of 0.048 nm in Y layers. Interestingly, a careful analysis has shown that such a subatomic image shift in Mn-O layers vanishes at the tail-to-tail 180° domain boundaries. Furthermore, taking advantage of the annular bright field (ABF) imaging technique combined with MSA, the tilting of MnO5 bipyramids, the very core mechanism of multiferroicity of the material, is evaluated.

Prediction of processing tomato peeling outcomes

USDA-ARS?s Scientific Manuscript database

Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After ...

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

EXPLORING ENGINEERING CONTROL THROUGH PROCESS MANIPULATION OF RADIOACTIVE LIQUID WASTE TANK CHEMICAL CLEANING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, A.

2014-04-27

One method of remediating legacy liquid radioactive waste produced during the cold war, is aggressive in-tank chemical cleaning. Chemical cleaning has successfully reduced the curie content of residual waste heels in large underground storage tanks; however this process generates significant chemical hazards. Mercury is often the bounding hazard due to its extensive use in the separations process that produced the waste. This paper explores how variations in controllable process factors, tank level and temperature, may be manipulated to reduce the hazard potential related to mercury vapor generation. When compared using a multivariate regression analysis, findings indicated that there was amore » significant relationship between both tank level (p value of 1.65x10{sup -23}) and temperature (p value of 6.39x10{sup -6}) to the mercury vapor concentration in the tank ventilation system. Tank temperature showed the most promise as a controllable parameter for future tank cleaning endeavors. Despite statistically significant relationships, there may not be confidence in the ability to control accident scenarios to below mercury’s IDLH or PAC-III levels for future cleaning initiatives.« less

Temporal global expression data reveal known and novel salicylate-impacted processes and regulators mediating powdery mildew growth and reproduction on Arabidopsis.

PubMed

Chandran, Divya; Tai, Yu Chuan; Hather, Gregory; Dewdney, Julia; Denoux, Carine; Burgess, Diane G; Ausubel, Frederick M; Speed, Terence P; Wildermuth, Mary C

2009-03-01

Salicylic acid (SA) is a critical mediator of plant innate immunity. It plays an important role in limiting the growth and reproduction of the virulent powdery mildew (PM) Golovinomyces orontii on Arabidopsis (Arabidopsis thaliana). To investigate this later phase of the PM interaction and the role played by SA, we performed replicated global expression profiling for wild-type and SA biosynthetic mutant isochorismate synthase1 (ics1) Arabidopsis from 0 to 7 d after infection. We found that ICS1-impacted genes constitute 3.8% of profiled genes, with known molecular markers of Arabidopsis defense ranked very highly by the multivariate empirical Bayes statistic (T(2) statistic). Functional analyses of T(2)-selected genes identified statistically significant PM-impacted processes, including photosynthesis, cell wall modification, and alkaloid metabolism, that are ICS1 independent. ICS1-impacted processes include redox, vacuolar transport/secretion, and signaling. Our data also support a role for ICS1 (SA) in iron and calcium homeostasis and identify components of SA cross talk with other phytohormones. Through our analysis, 39 novel PM-impacted transcriptional regulators were identified. Insertion mutants in one of these regulators, PUX2 (for plant ubiquitin regulatory X domain-containing protein 2), results in significantly reduced reproduction of the PM in a cell death-independent manner. Although little is known about PUX2, PUX1 acts as a negative regulator of Arabidopsis CDC48, an essential AAA-ATPase chaperone that mediates diverse cellular activities, including homotypic fusion of endoplasmic reticulum and Golgi membranes, endoplasmic reticulum-associated protein degradation, cell cycle progression, and apoptosis. Future work will elucidate the functional role of the novel regulator PUX2 in PM resistance.

Characterizing Sub-Daily Flow Regimes: Implications of Hydrologic Resolution on Ecohydrology Studies

DOE PAGES

Bevelhimer, Mark S.; McManamay, Ryan A.; O'Connor, B.

2014-05-26

Natural variability in flow is a primary factor controlling geomorphic and ecological processes in riverine ecosystems. Within the hydropower industry, there is growing pressure from environmental groups and natural resource managers to change reservoir releases from daily peaking to run-of-river operations on the basis of the assumption that downstream biological communities will improve under a more natural flow regime. In this paper, we discuss the importance of assessing sub-daily flows for understanding the physical and ecological dynamics within river systems. We present a variety of metrics for characterizing sub-daily flow variation and use these metrics to evaluate general trends amongmore » streams affected by peaking hydroelectric projects, run-of-river projects and streams that are largely unaffected by flow altering activities. Univariate and multivariate techniques were used to assess similarity among different stream types on the basis of these sub-daily metrics. For comparison, similar analyses were performed using analogous metrics calculated with mean daily flow values. Our results confirm that sub-daily flow metrics reveal variation among and within streams that are not captured by daily flow statistics. Using sub-daily flow statistics, we were able to quantify the degree of difference between unaltered and peaking streams and the amount of similarity between unaltered and run-of-river streams. The sub-daily statistics were largely uncorrelated with daily statistics of similar scope. Furthermore, on short temporal scales, sub-daily statistics reveal the relatively constant nature of unaltered streamreaches and the highly variable nature of hydropower-affected streams, whereas daily statistics show just the opposite over longer temporal scales.« less

Identifying environmental features for land management decisions

NASA Technical Reports Server (NTRS)

1984-01-01

Multivariate statistical analysis and imaging processing techniques are being applied to the study of arid/semiarid environments, with emphasis on desertification. Field level indicators of land-soil biota degradation are being sifted out with staging up to the low aircraft reconnaissance level, to LANDSAT TM & MSS, and even to the AVHRR level. Three completed projects are reviewed: riparian habitat on the Humboldt River floodplain, Salt Lake County Urban expansion detection, and salinization/desertification detection in the delta area. Beginning projects summarized include: comparative condition of rangeland in Rush Valley; modeling a GIS/remote sensing data base for Cache County; universal soil loss equation applied to Pinyon-Juniper; relating MSS to ground radiometry near Battle Mountain; and riparian habitat mapping on Mary's River, Nevada.

Double-Layer Mediated Electromechanical Response of Amyloid Fibrils in Liquid Environment

PubMed Central

Nikiforov, M.P.; Thompson, G.L.; Reukov, V.V.; Jesse, S.; Guo, S.; Rodriguez, B.J.; Seal, K.; Vertegel, A.A.; Kalinin, S.V.

2010-01-01

Harnessing electrical bias-induced mechanical motion on the nanometer and molecular scale is a critical step towards understanding the fundamental mechanisms of redox processes and implementation of molecular electromechanical machines. Probing these phenomena in biomolecular systems requires electromechanical measurements be performed in liquid environments. Here we demonstrate the use of band excitation piezoresponse force microscopy for probing electromechanical coupling in amyloid fibrils. The approaches for separating the elastic and electromechanical contributions based on functional fits and multivariate statistical analysis are presented. We demonstrate that in the bulk of the fibril the electromechanical response is dominated by double-layer effects (consistent with shear piezoelectricity of biomolecules), while a number of electromechanically active hot spots possibly related to structural defects are observed. PMID:20088597

Latent structure of the Wisconsin Card Sorting Test: a confirmatory factor analytic study.

PubMed

Greve, Kevin W; Stickle, Timothy R; Love, Jeffrey M; Bianchini, Kevin J; Stanford, Matthew S

2005-05-01

The present study represents the first large scale confirmatory factor analysis of the Wisconsin Card Sorting Test (WCST). The results generally support the three factor solutions reported in the exploratory factor analysis literature. However, only the first factor, which reflects general executive functioning, is statistically sound. The secondary factors, while likely reflecting meaningful cognitive abilities, are less stable except when all subjects complete all 128 cards. It is likely that having two discontinuation rules for the WCST has contributed to the varied factor analytic solutions reported in the literature and early discontinuation may result in some loss of useful information. Continued multivariate research will be necessary to better clarify the processes underlying WCST performance and their relationships to one another.

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

1995-01-01

Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prolonged instability prior to a regime shift

USGS Publications Warehouse

Spanbauer, Trisha; Allen, Craig R.; Angeler, David G.; Eason, Tarsha; Fritz, Sherilyn C.; Garmestani, Ahjond S.; Nash, Kirsty L.; Stone, Jeffery R.

2014-01-01

Regime shifts are generally defined as the point of ‘abrupt’ change in the state of a system. However, a seemingly abrupt transition can be the product of a system reorganization that has been ongoing much longer than is evident in statistical analysis of a single component of the system. Using both univariate and multivariate statistical methods, we tested a long-term high-resolution paleoecological dataset with a known change in species assemblage for a regime shift. Analysis of this dataset with Fisher Information and multivariate time series modeling showed that there was a∼2000 year period of instability prior to the regime shift. This period of instability and the subsequent regime shift coincide with regional climate change, indicating that the system is undergoing extrinsic forcing. Paleoecological records offer a unique opportunity to test tools for the detection of thresholds and stable-states, and thus to examine the long-term stability of ecosystems over periods of multiple millennia.

Dissolution comparisons using a Multivariate Statistical Distance (MSD) test and a comparison of various approaches for calculating the measurements of dissolution profile comparison.

PubMed

Cardot, J-M; Roudier, B; Schütz, H

2017-07-01

The f 2 test is generally used for comparing dissolution profiles. In cases of high variability, the f 2 test is not applicable, and the Multivariate Statistical Distance (MSD) test is frequently proposed as an alternative by the FDA and EMA. The guidelines provide only general recommendations. MSD tests can be performed either on raw data with or without time as a variable or on parameters of models. In addition, data can be limited-as in the case of the f 2 test-to dissolutions of up to 85% or to all available data. In the context of the present paper, the recommended calculation included all raw dissolution data up to the first point greater than 85% as a variable-without the various times as parameters. The proposed MSD overcomes several drawbacks found in other methods.

Multivariate analysis and geochemical approach for assessment of metal pollution state in sediment cores.

PubMed

Jamshidi-Zanjani, Ahmad; Saeedi, Mohsen

2017-07-01

Vertical distribution of metals (Cu, Zn, Cr, Fe, Mn, Pb, Ni, Cd, and Li) in four sediment core samples (C 1 , C 2 , C 3 , and C 4 ) from Anzali international wetland located southwest of the Caspian Sea was examined. Background concentration of each metal was calculated according to different statistical approaches. The results of multivariate statistical analysis showed that Fe and Mn might have significant role in the fate of Ni and Zn in sediment core samples. Different sediment quality indexes were utilized to assess metal pollution in sediment cores. Moreover, a new sediment quality index named aggregative toxicity index (ATI) based on sediment quality guidelines (SQGs) was developed to assess the degree of metal toxicity in an aggregative manner. The increasing pattern of metal pollution and their toxicity degree in upper layers of core samples indicated increasing effects of anthropogenic sources in the study area.

Geographically Sourcing Cocaine's Origin - Delineation of the Nineteen Major Coca Growing Regions in South America.

PubMed

Mallette, Jennifer R; Casale, John F; Jordan, James; Morello, David R; Beyer, Paul M

2016-03-23

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses ((2)H and (18)O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Geographically Sourcing Cocaine’s Origin - Delineation of the Nineteen Major Coca Growing Regions in South America

NASA Astrophysics Data System (ADS)

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-03-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Multivariate space - time analysis of PRE-STORM precipitation

NASA Technical Reports Server (NTRS)

Polyak, Ilya; North, Gerald R.; Valdes, Juan B.

1994-01-01

This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

A first application of independent component analysis to extracting structure from stock returns.

PubMed

Back, A D; Weigend, A S

1997-08-01

This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

Learning multivariate distributions by competitive assembly of marginals.

PubMed

Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

2013-02-01

We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

A novel statistical approach shows evidence for multi-system physiological dysregulation during aging.

PubMed

Cohen, Alan A; Milot, Emmanuel; Yong, Jian; Seplaki, Christopher L; Fülöp, Tamàs; Bandeen-Roche, Karen; Fried, Linda P

2013-03-01

Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Multivariate inference of pathway activity in host immunity and response to therapeutics

PubMed Central

Goel, Gautam; Conway, Kara L.; Jaeger, Martin; Netea, Mihai G.; Xavier, Ramnik J.

2014-01-01

Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method. PMID:25147207

Agro-forest landscape and the 'fringe' city: a multivariate assessment of land-use changes in a sprawling region and implications for planning.

PubMed

Salvati, Luca

2014-08-15

The present study evaluates the impact of urban expansion on landscape transformations in Rome's metropolitan area (1500 km(2)) during the last sixty years. Landscape composition, structure and dynamics were assessed for 1949 and 2008 by analyzing the distribution of 26 metrics for nine land-use classes. Changes in landscape structure are analysed by way of a multivariate statistical approach providing a summary measure of rapidity-to-change for each metric and class. Land fragmentation increased during the study period due to urban expansion. Poorly protected or medium-low value added classes (vineyards, arable land, olive groves and pastures) experienced fragmentation processes compared with protected or high-value added classes (e.g. forests, olive groves) showing larger 'core' areas and lower fragmentation. The relationship observed between class area and mean patch size indicates increased fragmentation for all uses of land (both expanding and declining) except for urban areas and forests. Reducing the impact of urban expansion for specific land-use classes is an effective planning strategy to contrast the simplification of Mediterranean landscape in peri-urban areas. Copyright © 2014 Elsevier B.V. All rights reserved.

Evolution of the Max and Mlx networks in animals.

PubMed

McFerrin, Lisa G; Atchley, William R

2011-01-01

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

NASA Astrophysics Data System (ADS)

Mancini, F.; Ceppi, C.; Ritrovato, G.

2010-09-01

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

Arm structure in normal spiral galaxies, 1: Multivariate data for 492 galaxies

NASA Technical Reports Server (NTRS)

Magri, Christopher

1994-01-01

Multivariate data have been collected as part of an effort to develop a new classification system for spiral galaxies, one which is not necessarily based on subjective morphological properties. A sample of 492 moderately bright northern Sa and Sc spirals was chosen for future statistical analysis. New observations were made at 20 and 21 cm; the latter data are described in detail here. Infrared Astronomy Satellite (IRAS) fluxes were obtained from archival data. Finally, new estimates of arm pattern radomness and of local environmental harshness were compiled for most sample objects.

Measurements of multi-scalar mixing in a turbulent coaxial jet

NASA Astrophysics Data System (ADS)

Hewes, Alais; Mydlarski, Laurent

2017-11-01

There are relatively few studies of turbulent multi-scalar mixing, despite the occurrence of this phenomenon in common processes (e.g. chemically reacting flows, oceanic mixing). In the present work, we simultaneously measure the evolution of two passive scalars (temperature and helium concentration) and velocity in a coaxial jet. Such a flow is particularly relevant, as coaxial jets are regularly employed in applications of turbulent non-premixed combustion, which relies on multi-scalar mixing. The coaxial jet used in the current experiment is based on the work of Cai et al. (J. Fluid Mech., 2011), and consists of a vertically oriented central jet of helium and air, surrounded by an annular flow of (unheated) pure air, emanating into a slow co-flow of (pure) heated air. The simultaneous two-scalar and velocity measurements are made using a 3-wire hot-wire anemometry probe. The first two wires of this probe form an interference (or Way-Libby) probe, and measure velocity and concentration. The third wire, a hot-wire operating at a low overheat ratio, measures temperature. The 3-wire probe is used to obtain concurrent velocity, concentration, and temperature statistics to characterize the mixing process by way of single and multivariable/joint statistics. Supported by the Natural Sciences and Engineering Research Council of Canada (Grant 217184).

The European Southern Observatory-MIDAS table file system

NASA Technical Reports Server (NTRS)

Peron, M.; Grosbol, P.

1992-01-01

The new and substantially upgraded version of the Table File System in MIDAS is presented as a scientific database system. MIDAS applications for performing database operations on tables are discussed, for instance, the exchange of the data to and from the TFS, the selection of objects, the uncertainty joins across tables, and the graphical representation of data. This upgraded version of the TFS is a full implementation of the binary table extension of the FITS format; in addition, it also supports arrays of strings. Different storage strategies for optimal access of very large data sets are implemented and are addressed in detail. As a simple relational database, the TFS may be used for the management of personal data files. This opens the way to intelligent pipeline processing of large amounts of data. One of the key features of the Table File System is to provide also an extensive set of tools for the analysis of the final results of a reduction process. Column operations using standard and special mathematical functions as well as statistical distributions can be carried out; commands for linear regression and model fitting using nonlinear least square methods and user-defined functions are available. Finally, statistical tests of hypothesis and multivariate methods can also operate on tables.

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

A comparison of likelihood ratio tests and Rao's score test for three separable covariance matrix structures.

PubMed

Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha

2017-01-01

The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Monte Carlo simulations are then used to study the comparative behavior of the null distribution of the RST statistic, as well as that of the LRT statistic, in terms of sample size considerations, and for the estimation of the empirical percentiles. Our findings are compared with existing results where the first component of the separable structure is a compound symmetry (CS) correlation matrix. It is also shown by simulations that the empirical null distribution of the RST statistic converges faster than the empirical null distribution of the LRT statistic to the limiting χ 2 distribution. The tests are implemented on a real dataset from medical studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

PubMed

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Time management in acute vertebrobasilar occlusion.

PubMed

Kamper, Lars; Rybacki, Konrad; Mansour, Michael; Winkler, Sven B; Kempkes, Udo; Haage, Patrick

2009-03-01

Acute vertebrobasilar occlusion (VBO) is associated with a high risk of stroke and death. Although local thrombolysis may achieve recanalization and improve outcome, mortality is still between 35% and 75%. However, without recanalization the chance of a good outcome is extremely poor, with mortality rates of 80-90%. Early treatment is a fundamental factor, but detailed studies of the exact time management of the diagnostic and interventional workflow are still lacking. Data on 18 patients were retrospectively evaluated. Time periods between symptom onset, admission to hospital, time of diagnosis, and beginning of intervention were correlated with postinterventional neurological status. The Glasgow Coma Scale and National Institute of Health Stroke Scale (NIHSS) were used to examine patients before and after local thrombolysis. Additionally, multivariate statistics were applied to reveal similarities between patients with neurological improvement. Primary recanalization was achieved in 77% of patients. The overall mortality was 55%. Major complications were intracranial hemorrhage and peripheral embolism. The time period from symptom onset to intervention showed a strong correlation with the postinterventional NIHSS as well as the patient's age, with the best results in a 4-h interval. Multivariate statistics revealed similarities among the patients. Evaluation of time management in acute VBO by multivariate statistics is a helpful tool for definition of similarities in this patient group. Similarly to the door-to-balloon time for acute coronary interventions, the chances for a good outcome depend on a short time interval between symptom onset and intervention. While the only manipulable time period starts with hospital admission, our results emphasize the necessity of efficient intrahospital workflow.

Comparison of Dissolution Similarity Assessment Methods for Products with Large Variations: f2 Statistics and Model-Independent Multivariate Confidence Region Procedure for Dissolution Profiles of Multiple Oral Products.

PubMed

Yoshida, Hiroyuki; Shibata, Hiroko; Izutsu, Ken-Ichi; Goda, Yukihiro

2017-01-01

The current Japanese Ministry of Health Labour and Welfare (MHLW)'s Guideline for Bioequivalence Studies of Generic Products uses averaged dissolution rates for the assessment of dissolution similarity between test and reference formulations. This study clarifies how the application of model-independent multivariate confidence region procedure (Method B), described in the European Medical Agency and U.S. Food and Drug Administration guidelines, affects similarity outcomes obtained empirically from dissolution profiles with large variations in individual dissolution rates. Sixty-one datasets of dissolution profiles for immediate release, oral generic, and corresponding innovator products that showed large variation in individual dissolution rates in generic products were assessed on their similarity by using the f 2 statistics defined in the MHLW guidelines (MHLW f 2 method) and two different Method B procedures, including a bootstrap method applied with f 2 statistics (BS method) and a multivariate analysis method using the Mahalanobis distance (MV method). The MHLW f 2 and BS methods provided similar dissolution similarities between reference and generic products. Although a small difference in the similarity assessment may be due to the decrease in the lower confidence interval for expected f 2 values derived from the large variation in individual dissolution rates, the MV method provided results different from those obtained through MHLW f 2 and BS methods. Analysis of actual dissolution data for products with large individual variations would provide valuable information towards an enhanced understanding of these methods and their possible incorporation in the MHLW guidelines.

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

PubMed

Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

2015-01-01

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

Estimating the decomposition of predictive information in multivariate systems

NASA Astrophysics Data System (ADS)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

Robustness of Multiple Objective Decision Analysis Preference Functions

DTIC Science & Technology

2002-06-01

p p′ : The probability of some event. ,i ip q : The probability of event . i Π : An aggregation of proportional data used in calculating a test ...statistical tests of the significance of the term and also is conducted in a multivariate framework rather than the ROSA univariate approach. A...residual error is ˆ−e = y y (45) The coefficient provides a ready indicator of the contribution for the associated variable and statistical tests

Multivariate flood risk assessment: reinsurance perspective

NASA Astrophysics Data System (ADS)

Ghizzoni, Tatiana; Ellenrieder, Tobias

2013-04-01

For insurance and re-insurance purposes the knowledge of the spatial characteristics of fluvial flooding is fundamental. The probability of simultaneous flooding at different locations during one event and the associated severity and losses have to be estimated in order to assess premiums and for accumulation control (Probable Maximum Losses calculation). Therefore, the identification of a statistical model able to describe the multivariate joint distribution of flood events in multiple location is necessary. In this context, copulas can be viewed as alternative tools for dealing with multivariate simulations as they allow to formalize dependence structures of random vectors. An application of copula function for flood scenario generation is presented for Australia (Queensland, New South Wales and Victoria) where 100.000 possible flood scenarios covering approximately 15.000 years were simulated.

DEFINITION OF MULTIVARIATE GEOCHEMICAL ASSOCIATIONS WITH POLYMETALLIC MINERAL OCCURRENCES USING A SPATIALLY DEPENDENT CLUSTERING TECHNIQUE AND RASTERIZED STREAM SEDIMENT DATA - AN ALASKAN EXAMPLE.

USGS Publications Warehouse

Jenson, Susan K.; Trautwein, C.M.

1984-01-01

The application of an unsupervised, spatially dependent clustering technique (AMOEBA) to interpolated raster arrays of stream sediment data has been found to provide useful multivariate geochemical associations for modeling regional polymetallic resource potential. The technique is based on three assumptions regarding the compositional and spatial relationships of stream sediment data and their regional significance. These assumptions are: (1) compositionally separable classes exist and can be statistically distinguished; (2) the classification of multivariate data should minimize the pair probability of misclustering to establish useful compositional associations; and (3) a compositionally defined class represented by three or more contiguous cells within an array is a more important descriptor of a terrane than a class represented by spatial outliers.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Study of the questionnaire of the Polytechnic University of Valencia (UPV) teaching staff, using students opinion survey. Statistical treatment

NASA Astrophysics Data System (ADS)

Martinez Gomez, Monica

Quality improvement of university institutions represents the most important challenge in the next years, and the potential tool to achieve it is based on the institutional evaluation in general, and specially the evaluation of the teaching performance. The opinion questionnaire from the students is the most generalised tool used to evaluate the teaching performance at Spanish universities. The general objective of this thesis is to develop a statistical methodology suitable to extract, analyse and interpret the information contained in the Questionnaire of Teaching Evaluation from Student Opinion (CEDA) of the UPV, aimed at optimising its practical use. The study is centred in the application of different multivariate techniques and has been structured in three parts: (1) Evaluation of the reliability, validity and dimensionality of the tool. The multivariate method used for this purpose is the Factorial Analysis. (2) Determination of the capacity of the questionnaire to identify different profiles of lecturers based on the quality perceived by students. This target is conducted with different multivariate classification techniques: hierarchical cluster analysis, non-hierarchical and two-stage analysis. Moreover, those items that best discriminate among the teaching typologies obtained are identified in the questionnaire. (3) Identification of the teaching typologies according to different descriptive characteristics referent to the subject and lecturer, with the use of decision trees. Once identified these typologies, a new discriminant analysis is conducted aimed at identifying those items that best characterise each typology. Finally, a study is carried out with the classification method SIMCA (Soft Independent Modelling of Class Analogy) in order to determine the discriminant loading of every item among the identified teaching typologies, allowing the identification of those that best distinguish the different classes obtained. With the combined use of the proposed techniques, it is expected to optimise the use of CEDA as a measuring tool and an indicator of the teaching quality at the university, that would allow the introduction of actions for the continuous improvement in the teaching processes of the UPV.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

Multivariate frequency domain analysis (MFDA) is proposed to characterize collective vibrational dynamics of protein obtained by a molecular dynamics (MD) simulation. MFDA performs principal component analysis (PCA) for a bandpass filtered multivariate time series using the multitaper method of spectral estimation. By applying MFDA to MD trajectories of bovine pancreatic trypsin inhibitor, we determined the collective vibrational modes in the frequency domain, which were identified by their vibrational frequencies and eigenvectors. At near zero temperature, the vibrational modes determined by MFDA agreed well with those calculated by normal mode analysis. At 300 K, the vibrational modes exhibited characteristic features that were considerably different from the principal modes of the static distribution given by the standard PCA. The influences of aqueous environments were discussed based on two different sets of vibrational modes, one derived from a MD simulation in water and the other from a simulation in vacuum. Using the varimax rotation, an algorithm of the multivariate statistical analysis, the representative orthogonal set of eigenmodes was determined at each vibrational frequency.

PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Brain shaving: adaptive detection for brain PET data

NASA Astrophysics Data System (ADS)

Grecchi, Elisabetta; Doyle, Orla M.; Bertoldo, Alessandra; Pavese, Nicola; Turkheimer, Federico E.

2014-05-01

The intricacy of brain biology is such that the variation of imaging end-points in health and disease exhibits an unpredictable range of spatial distributions from the extremely localized to the very diffuse. This represents a challenge for the two standard approaches to analysis, the mass univariate and the multivariate that exhibit either strong specificity but not as good sensitivity (the former) or poor specificity and comparatively better sensitivity (the latter). In this work, we develop an analytical methodology for positron emission tomography that operates an extraction (‘shaving’) of coherent patterns of signal variation while maintaining control of the type I error. The methodology operates two rotations on the image data, one local using the wavelet transform and one global using the singular value decomposition. The control of specificity is obtained by using the gap statistic that selects, within each eigenvector, a subset of significantly coherent elements. Face-validity of the algorithm is demonstrated using a paradigmatic data-set with two radiotracers, [11C]-raclopride and [11C]-(R)-PK11195, measured on the same Huntington's disease patients, a disorder with a genetic based diagnosis. The algorithm is able to detect the two well-known separate but connected processes of dopamine neuronal loss (localized in the basal ganglia) and neuroinflammation (diffusive around the whole brain). These processes are at the two extremes of the distributional envelope, one being very sparse and the latter being perfectly Gaussian and they are not adequately detected by the univariate and the multivariate approaches.

Contribution au developpement d'une methode de controle des procedes dans une usine de bouletage

NASA Astrophysics Data System (ADS)

Gosselin, Claude

This thesis, a collaborative effort between Ecole de technologie superieure and ArcelorMittal Company, presents the development of a methodology for monitoring and quality control of multivariable industrial production processes. This innovation research mandate was developed at ArcelorMittal Exploitation Miniere (AMEM) pellet plant in Port-Cartier (Quebec, Canada). With this undertaking, ArcelorMittal is striving to maintain its world class level of excellence and continues to pursue initiatives that can augment its competitive advantage worldwide. The plant's gravimetric classification process was retained as a prototype and development laboratory due to its effect on the company's competitiveness and its impact on subsequent steps leading to final production of iron oxide pellets. Concretely, the development of this expertise in process control and in situ monitoring will establish a firm basic knowledge in the fields of complex system physical modeling, data reconciliation, statistical observers, multivariate command and quality control using real-time monitoring of the desirability function. The hydraulic classifier is mathematically modeled. Using planned disturbances on the production line, an identification procedure was established to provide empirical estimations of the model's structural parameters. A new sampling campaign and a previously unpublished data collection and consolidation policy were implemented plant-wide. Access to these invaluable data sources has enabled the establishment of new thresholds that govern the production process and its control. Finally, as a substitute for the traditional quality control process, we have implemented a new strategy based on the use of the desirability function. Our innovation is not in using this Finally, as a substitute for the traditional quality control process, we have implemented a new strategy based on the use of the desirability function. Our innovation is not in using this function as an indicator of overall (economic) satisfaction in the production process, but rather in proposing it as an "observer" of the system's state. The first implementation steps have already demonstrated the method's feasibility as well as other numerous industrial impacts on production processes within the company. Namely, the emergence of the economical aspect as a strategic variable that assures better governance of production processes where quality variables present strategic issues.

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.

Spatial Elucidation of Spinal Cord Lipid- and Metabolite- Regulations in Amyotrophic Lateral Sclerosis

NASA Astrophysics Data System (ADS)

Hanrieder, Jörg; Ewing, Andrew G.

2014-06-01

Amyotrophic lateral sclerosis (ALS) is a devastating, rapidly progressing disease of the central nervous system that is characterized by motor neuron degeneration in the brain stem and the spinal cord. We employed time of flight secondary ion mass spectrometry (ToF-SIMS) to profile spatial lipid- and metabolite- regulations in post mortem human spinal cord tissue from ALS patients to investigate chemical markers of ALS pathogenesis. ToF-SIMS scans and multivariate analysis of image and spectral data were performed on thoracic human spinal cord sections. Multivariate statistics of the image data allowed delineation of anatomical regions of interest based on their chemical identity. Spectral data extracted from these regions were compared using two different approaches for multivariate statistics, for investigating ALS related lipid and metabolite changes. The results show a significant decrease for cholesterol, triglycerides, and vitamin E in the ventral horn of ALS samples, which is presumably a consequence of motor neuron degeneration. Conversely, the biogenic mediator lipid lysophosphatidylcholine and its fragments were increased in ALS ventral spinal cord, pointing towards neuroinflammatory mechanisms associated with neuronal cell death. ToF-SIMS imaging is a promising approach for chemical histology and pathology for investigating the subcellular mechanisms underlying motor neuron degeneration in amyotrophic lateral sclerosis.

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Characterization of Chinese liquor aroma components during aging process and liquor age discrimination using gas chromatography combined with multivariable statistics

NASA Astrophysics Data System (ADS)

Xu, M. L.; Yu, Y.; Ramaswamy, H. S.; Zhu, S. M.

2017-01-01

Chinese liquor aroma components were characterized during the aging process using gas chromatography (GC). Principal component and cluster analysis (PCA, CA) were used to discriminate the Chinese liquor age which has a great economic value. Of a total of 21 major aroma components identified and quantified, 13 components which included several acids, alcohols, esters, aldehydes and furans decreased significantly in the first year of aging, maintained the same levels (p > 0.05) for next three years and decreased again (p < 0.05) in the fifth year. On the contrary, a significant increase was observed in propionic acid, furfural and phenylethanol. Ethyl lactate was found to be the most stable aroma component during aging process. Results of PCA and CA demonstrated that young liquor (fresh) and aged liquors were well separated from each other, which is in consistent with the evolution of aroma components along with the aging process. These findings provide a quantitative basis for discriminating the Chinese liquor age and a scientific basis for further research on elucidating the liquor aging process, and a possible tool to guard against counterfeit and defective products.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of factors' classes. Depending on these results be able to help planning managers localize the high risk areas of landslide or safely areas by building and human activities.

Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil.

PubMed

Alves, Darlan Daniel; Riegel, Roberta Plangg; de Quevedo, Daniela Müller; Osório, Daniela Montanari Migliavacca; da Costa, Gustavo Marques; do Nascimento, Carlos Augusto; Telöken, Franko

2018-06-08

Assessment of surface water quality is an issue of currently high importance, especially in polluted rivers which provide water for treatment and distribution as drinking water, as is the case of the Sinos River, southern Brazil. Multivariate statistical techniques allow a better understanding of the seasonal variations in water quality, as well as the source identification and source apportionment of water pollution. In this study, the multivariate statistical techniques of cluster analysis (CA), principal component analysis (PCA), and positive matrix factorization (PMF) were used, along with the Kruskal-Wallis test and Spearman's correlation analysis in order to interpret a water quality data set resulting from a monitoring program conducted over a period of almost two years (May 2013 to April 2015). The water samples were collected from the raw water inlet of the municipal water treatment plant (WTP) operated by the Water and Sewage Services of Novo Hamburgo (COMUSA). CA allowed the data to be grouped into three periods (autumn and summer (AUT-SUM); winter (WIN); spring (SPR)). Through the PCA, it was possible to identify that the most important parameters in contribution to water quality variations are total coliforms (TCOLI) in SUM-AUT, water level (WL), water temperature (WT), and electrical conductivity (EC) in WIN and color (COLOR) and turbidity (TURB) in SPR. PMF was applied to the complete data set and enabled the source apportionment water pollution through three factors, which are related to anthropogenic sources, such as the discharge of domestic sewage (mostly represented by Escherichia coli (ECOLI)), industrial wastewaters, and agriculture runoff. The results provided by this study demonstrate the contribution provided by the use of integrated statistical techniques in the interpretation and understanding of large data sets of water quality, showing also that this approach can be used as an efficient methodology to optimize indicators for water quality assessment.

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

Using Fisher information to track stability in multivariate systems

EPA Science Inventory

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI...

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach.

PubMed

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach

PubMed Central

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

Objectives This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. Methodology A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. Results The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. Conclusion This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff. PMID:23559904

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Multivariate normality

NASA Technical Reports Server (NTRS)

Crutcher, H. L.; Falls, L. W.

1976-01-01

Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

Variation of Water Quality Parameters with Siltation Depth for River Ichamati Along International Border with Bangladesh Using Multivariate Statistical Techniques

NASA Astrophysics Data System (ADS)

Roy, P. K.; Pal, S.; Banerjee, G.; Biswas Roy, M.; Ray, D.; Majumder, A.

2014-12-01

River is considered as one of the main sources of freshwater all over the world. Hence analysis and maintenance of this water resource is globally considered a matter of major concern. This paper deals with the assessment of surface water quality of the Ichamati river using multivariate statistical techniques. Eight distinct surface water quality observation stations were located and samples were collected. For the samples collected statistical techniques were applied to the physico-chemical parameters and depth of siltation. In this paper cluster analysis is done to determine the relations between surface water quality and siltation depth of river Ichamati. Multiple regressions and mathematical equation modeling have been done to characterize surface water quality of Ichamati river on the basis of physico-chemical parameters. It was found that surface water quality of the downstream river was different from the water quality of the upstream. The analysis of the water quality parameters of the Ichamati river clearly indicate high pollution load on the river water which can be accounted to agricultural discharge, tidal effect and soil erosion. The results further reveal that with the increase in depth of siltation, water quality degraded.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

PubMed Central

Broadaway, K. Alaine; Cutler, David J.; Duncan, Richard; Moore, Jacob L.; Ware, Erin B.; Jhun, Min A.; Bielak, Lawrence F.; Zhao, Wei; Smith, Jennifer A.; Peyser, Patricia A.; Kardia, Sharon L.R.; Ghosh, Debashis; Epstein, Michael P.

2016-01-01

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. PMID:26942286

The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

NASA Astrophysics Data System (ADS)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.

Application of multivariate analysis and mass transfer principles for refinement of a 3-L bioreactor scale-down model--when shake flasks mimic 15,000-L bioreactors better.

PubMed

Ahuja, Sanjeev; Jain, Shilpa; Ram, Kripa

2015-01-01

Characterization of manufacturing processes is key to understanding the effects of process parameters on process performance and product quality. These studies are generally conducted using small-scale model systems. Because of the importance of the results derived from these studies, the small-scale model should be predictive of large scale. Typically, small-scale bioreactors, which are considered superior to shake flasks in simulating large-scale bioreactors, are used as the scale-down models for characterizing mammalian cell culture processes. In this article, we describe a case study where a cell culture unit operation in bioreactors using one-sided pH control and their satellites (small-scale runs conducted using the same post-inoculation cultures and nutrient feeds) in 3-L bioreactors and shake flasks indicated that shake flasks mimicked the large-scale performance better than 3-L bioreactors. We detail here how multivariate analysis was used to make the pertinent assessment and to generate the hypothesis for refining the existing 3-L scale-down model. Relevant statistical techniques such as principal component analysis, partial least square, orthogonal partial least square, and discriminant analysis were used to identify the outliers and to determine the discriminatory variables responsible for performance differences at different scales. The resulting analysis, in combination with mass transfer principles, led to the hypothesis that observed similarities between 15,000-L and shake flask runs, and differences between 15,000-L and 3-L runs, were due to pCO2 and pH values. This hypothesis was confirmed by changing the aeration strategy at 3-L scale. By reducing the initial sparge rate in 3-L bioreactor, process performance and product quality data moved closer to that of large scale. © 2015 American Institute of Chemical Engineers.

Documentation of pain care processes does not accurately reflect pain management delivered in primary care.

PubMed

Krebs, Erin E; Bair, Matthew J; Carey, Timothy S; Weinberger, Morris

2010-03-01

Researchers and quality improvement advocates sometimes use review of chart-documented pain care processes to assess the quality of pain management. Studies have found that primary care providers frequently fail to document pain assessment and management. To assess documentation of pain care processes in an academic primary care clinic and evaluate the validity of this documentation as a measure of pain care delivered. Prospective observational study. 237 adult patients at a university-affiliated internal medicine clinic who reported any pain in the last week. Immediately after a visit, we asked patients to report the pain treatment they received. Patients completed the Brief Pain Inventory (BPI) to assess pain severity at baseline and 1 month later. We extracted documentation of pain care processes from the medical record and used kappa statistics to assess agreement between documentation and patient report of pain treatment. Using multivariable linear regression, we modeled whether documented or patient-reported pain care predicted change in pain at 1 month. Participants' mean age was 53.7 years, 66% were female, and 74% had chronic pain. Physicians documented pain assessment for 83% of visits. Patients reported receiving pain treatment more often (67%) than was documented by physicians (54%). Agreement between documentation and patient report was moderate for receiving a new pain medication (k = 0.50) and slight for receiving pain management advice (k = 0.13). In multivariable models, documentation of new pain treatment was not associated with change in pain (p = 0.134). In contrast, patient-reported receipt of new pain treatment predicted pain improvement (p = 0.005). Chart documentation underestimated pain care delivered, compared with patient report. Documented pain care processes had no relationship with pain outcomes at 1 month, but patient report of receiving care predicted clinically significant improvement. Chart review measures may not accurately reflect the pain management patients receive in primary care.

Data-Enabled Quantification of Aluminum Microstructural Damage Under Tensile Loading

NASA Astrophysics Data System (ADS)

Wayne, Steven F.; Qi, G.; Zhang, L.

2016-08-01

The study of material failure with digital analytics is in its infancy and offers a new perspective to advance our understanding of damage initiation and evolution in metals. In this article, we study the failure of aluminum using data-enabled methods, statistics and data mining. Through the use of tension tests, we establish a multivariate acoustic-data matrix of random damage events, which typically are not visible and are very difficult to measure due to their variability, diversity and interactivity during damage processes. Aluminium alloy 6061-T651 and single crystal aluminium with a (111) orientation were evaluated by comparing the collection of acoustic signals from damage events caused primarily by slip in the single crystal and multimode fracture of the alloy. We found the resulting acoustic damage-event data to be large semi-structured volumes of Big Data with the potential to be mined for information that describes the materials damage state under strain. Our data-enabled analyses has allowed us to determine statistical distributions of multiscale random damage that provide a means to quantify the material damage state.