On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

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.

Should I Pack My Umbrella? Clinical versus Statistical Prediction of Mental Health Decisions

ERIC Educational Resources Information Center

Aegisdottir, Stefania; Spengler, Paul M.; White, Michael J.

2006-01-01

In this rejoinder, the authors respond to the insightful commentary of Strohmer and Arm, Chwalisz, and Hilton, Harris, and Rice about the meta-analysis on statistical versus clinical prediction techniques for mental health judgments. The authors address issues including the availability of statistical prediction techniques for real-life psychology…

The Shock and Vibration Bulletin. Part 2. Invited Papers, Structural Dynamics

DTIC Science & Technology

1974-08-01

VIKING LANDER DYNAMICS 41 Mr. Joseph C. Pohlen, Martin Marietta Aerospace, Denver, Colorado Structural Dynamics PERFORMANCE OF STATISTICAL ENERGY ANALYSIS 47...aerospace structures. Analytical prediction of these environments is beyond the current scope of classical modal techniques. Statistical energy analysis methods...have been developed that circumvent the difficulties of high-frequency nodal analysis. These statistical energy analysis methods are evaluated

Statistical model to perform error analysis of curve fits of wind tunnel test data using the techniques of analysis of variance and regression analysis

NASA Technical Reports Server (NTRS)

Alston, D. W.

1981-01-01

The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.

Primary, Secondary, and Meta-Analysis of Research

ERIC Educational Resources Information Center

Glass, Gene V.

1976-01-01

Examines data analysis at three levels: analysis of data; secondary analysis is the re-analysis of data for the purpose of answering the original research question with better statistical techniques, or answering new questions with old data; and, meta-analysis refers to the statistical analysis of many analysis results from individual studies for…

Vibration Transmission through Rolling Element Bearings in Geared Rotor Systems

DTIC Science & Technology

1990-11-01

147 4.8 Concluding Remarks ........................................................... 153 V STATISTICAL ENERGY ANALYSIS ............................................ 155...and dynamic finite element techniques are used to develop the discrete vibration models while statistical energy analysis method is used for the broad...bearing system studies, geared rotor system studies, and statistical energy analysis . Each chapter is self sufficient since it is written in a

STATISTICAL SAMPLING AND DATA ANALYSIS

EPA Science Inventory

Research is being conducted to develop approaches to improve soil and sediment sampling techniques, measurement design and geostatistics, and data analysis via chemometric, environmetric, and robust statistical methods. Improvements in sampling contaminated soil and other hetero...

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

Gilbert, Richard O.

The application of statistics to environmental pollution monitoring studies requires a knowledge of statistical analysis methods particularly well suited to pollution data. This book fills that need by providing sampling plans, statistical tests, parameter estimation procedure techniques, and references to pertinent publications. Most of the statistical techniques are relatively simple, and examples, exercises, and case studies are provided to illustrate procedures. The book is logically divided into three parts. Chapters 1, 2, and 3 are introductory chapters. Chapters 4 through 10 discuss field sampling designs and Chapters 11 through 18 deal with a broad range of statistical analysis procedures. Somemore » statistical techniques given here are not commonly seen in statistics book. For example, see methods for handling correlated data (Sections 4.5 and 11.12), for detecting hot spots (Chapter 10), and for estimating a confidence interval for the mean of a lognormal distribution (Section 13.2). Also, Appendix B lists a computer code that estimates and tests for trends over time at one or more monitoring stations using nonparametric methods (Chapters 16 and 17). Unfortunately, some important topics could not be included because of their complexity and the need to limit the length of the book. For example, only brief mention could be made of time series analysis using Box-Jenkins methods and of kriging techniques for estimating spatial and spatial-time patterns of pollution, although multiple references on these topics are provided. Also, no discussion of methods for assessing risks from environmental pollution could be included.« less

Statistical Analysis For Nucleus/Nucleus Collisions

NASA Technical Reports Server (NTRS)

Mcguire, Stephen C.

1989-01-01

Report describes use of several statistical techniques to charactertize angular distributions of secondary particles emitted in collisions of atomic nuclei in energy range of 24 to 61 GeV per nucleon. Purpose of statistical analysis to determine correlations between intensities of emitted particles and angles comfirming existence of quark/gluon plasma.

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance

NASA Astrophysics Data System (ADS)

McCray, Wilmon Wil L., Jr.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI RTM) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments

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

Bihn T. Pham; Jeffrey J. Einerson

2010-06-01

This paper describes the development and application of statistical analysis techniques to support the AGR experimental program on NGNP fuel performance. The experiments conducted in the Idaho National Laboratory’s Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel/graphite temperature) is regulated by the He-Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the SAS-based NGNP Data Management and Analysis System (NDMAS) for automatedmore » processing and qualification of the AGR measured data. The NDMAS also stores daily neutronic (power) and thermal (heat transfer) code simulation results along with the measurement data, allowing for their combined use and comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the target quantity (fuel temperature) within a given range.« less

UNCERTAINTY ON RADIATION DOSES ESTIMATED BY BIOLOGICAL AND RETROSPECTIVE PHYSICAL METHODS.

PubMed

Ainsbury, Elizabeth A; Samaga, Daniel; Della Monaca, Sara; Marrale, Maurizio; Bassinet, Celine; Burbidge, Christopher I; Correcher, Virgilio; Discher, Michael; Eakins, Jon; Fattibene, Paola; Güçlü, Inci; Higueras, Manuel; Lund, Eva; Maltar-Strmecki, Nadica; McKeever, Stephen; Rääf, Christopher L; Sholom, Sergey; Veronese, Ivan; Wieser, Albrecht; Woda, Clemens; Trompier, Francois

2018-03-01

Biological and physical retrospective dosimetry are recognised as key techniques to provide individual estimates of dose following unplanned exposures to ionising radiation. Whilst there has been a relatively large amount of recent development in the biological and physical procedures, development of statistical analysis techniques has failed to keep pace. The aim of this paper is to review the current state of the art in uncertainty analysis techniques across the 'EURADOS Working Group 10-Retrospective dosimetry' members, to give concrete examples of implementation of the techniques recommended in the international standards, and to further promote the use of Monte Carlo techniques to support characterisation of uncertainties. It is concluded that sufficient techniques are available and in use by most laboratories for acute, whole body exposures to highly penetrating radiation, but further work will be required to ensure that statistical analysis is always wholly sufficient for the more complex exposure scenarios.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.

PubMed

Xia, Li C; Steele, Joshua A; Cram, Jacob A; Cardon, Zoe G; Simmons, Sheri L; Vallino, Joseph J; Fuhrman, Jed A; Sun, Fengzhu

2011-01-01

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

PubMed Central

2011-01-01

Background The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. Results We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. Conclusions The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa. PMID:22784572

Computer program uses Monte Carlo techniques for statistical system performance analysis

NASA Technical Reports Server (NTRS)

Wohl, D. P.

1967-01-01

Computer program with Monte Carlo sampling techniques determines the effect of a component part of a unit upon the overall system performance. It utilizes the full statistics of the disturbances and misalignments of each component to provide unbiased results through simulated random sampling.

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.

Sociological Paradoxes and Graduate Statistics Classes. A Response to "The Sociology of Teaching Graduate Statistics"

ERIC Educational Resources Information Center

Hardy, Melissa

2005-01-01

This article presents a response to Timothy Patrick Moran's article "The Sociology of Teaching Graduate Statistics." In his essay, Moran argues that exciting developments in techniques of quantitative analysis are currently coupled with a much less exciting formulaic approach to teaching sociology graduate students about quantitative analysis. The…

Detecting subtle hydrochemical anomalies with multivariate statistics: an example from homogeneous groundwaters in the Great Artesian Basin, Australia

NASA Astrophysics Data System (ADS)

O'Shea, Bethany; Jankowski, Jerzy

2006-12-01

The major ion composition of Great Artesian Basin groundwater in the lower Namoi River valley is relatively homogeneous in chemical composition. Traditional graphical techniques have been combined with multivariate statistical methods to determine whether subtle differences in the chemical composition of these waters can be delineated. Hierarchical cluster analysis and principal components analysis were successful in delineating minor variations within the groundwaters of the study area that were not visually identified in the graphical techniques applied. Hydrochemical interpretation allowed geochemical processes to be identified in each statistically defined water type and illustrated how these groundwaters differ from one another. Three main geochemical processes were identified in the groundwaters: ion exchange, precipitation, and mixing between waters from different sources. Both statistical methods delineated an anomalous sample suspected of being influenced by magmatic CO2 input. The use of statistical methods to complement traditional graphical techniques for waters appearing homogeneous is emphasized for all investigations of this type. Copyright

BaTMAn: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2016-12-01

Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.

Statistical and Economic Techniques for Site-specific Nematode Management.

PubMed

Liu, Zheng; Griffin, Terry; Kirkpatrick, Terrence L

2014-03-01

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.

From fields to objects: A review of geographic boundary analysis

NASA Astrophysics Data System (ADS)

Jacquez, G. M.; Maruca, S.; Fortin, M.-J.

Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique for defining objects - geographic boundaries - on spatial fields, and for evaluating the statistical significance of characteristics of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected in the absence of boundary-generating phenomena. Close ties to the object-field dialectic eminently suit boundary analysis to GIS data. The majority of existing spatial methods are field-based in that they describe, estimate, or predict how attributes (variables defining the field) vary through geographic space. Such methods are appropriate for field representations but not object representations. As the object-field paradigm gains currency in geographic information science, appropriate techniques for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic boundary analysis is clearly a valuable addition to the spatial statistical toolbox. This paper presents the philosophy of, and motivations for geographic boundary analysis. It defines commonly used statistics for quantifying boundaries and their characteristics, as well as simulation procedures for evaluating their significance. We review applications of these techniques, with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the implementation of these methods within geographic boundary analysis software: GEM.

A Review of Meta-Analysis Packages in R

ERIC Educational Resources Information Center

Polanin, Joshua R.; Hennessy, Emily A.; Tanner-Smith, Emily E.

2017-01-01

Meta-analysis is a statistical technique that allows an analyst to synthesize effect sizes from multiple primary studies. To estimate meta-analysis models, the open-source statistical environment R is quickly becoming a popular choice. The meta-analytic community has contributed to this growth by developing numerous packages specific to…

Experimental and Computational Analysis of Modes in a Partially Constrained Plate

DTIC Science & Technology

2004-03-01

way to quantify a structure. One technique utilizing an energy method is the Statistical Energy Analysis (SEA). The SEA process involves regarding...B.R. Mace. “ Statistical Energy Analysis of Two Edge- Coupled Rectangular Plates: Ensemble Averages,” Journal of Sound and Vibration, 193(4): 793-822

An overview of data acquisition, signal coding and data analysis techniques for MST radars

NASA Technical Reports Server (NTRS)

Rastogi, P. K.

1986-01-01

An overview is given of the data acquisition, signal processing, and data analysis techniques that are currently in use with high power MST/ST (mesosphere stratosphere troposphere/stratosphere troposphere) radars. This review supplements the works of Rastogi (1983) and Farley (1984) presented at previous MAP workshops. A general description is given of data acquisition and signal processing operations and they are characterized on the basis of their disparate time scales. Then signal coding, a brief description of frequently used codes, and their limitations are discussed, and finally, several aspects of statistical data processing such as signal statistics, power spectrum and autocovariance analysis, outlier removal techniques are discussed.

A study of the feasibility of statistical analysis of airport performance simulation

NASA Technical Reports Server (NTRS)

Myers, R. H.

1982-01-01

The feasibility of conducting a statistical analysis of simulation experiments to study airport capacity is investigated. First, the form of the distribution of airport capacity is studied. Since the distribution is non-Gaussian, it is important to determine the effect of this distribution on standard analysis of variance techniques and power calculations. Next, power computations are made in order to determine how economic simulation experiments would be if they are designed to detect capacity changes from condition to condition. Many of the conclusions drawn are results of Monte-Carlo techniques.

Analysis of thrips distribution: application of spatial statistics and Kriging

Treesearch

John Aleong; Bruce L. Parker; Margaret Skinner; Diantha Howard

1991-01-01

Kriging is a statistical technique that provides predictions for spatially and temporally correlated data. Observations of thrips distribution and density in Vermont soils are made in both space and time. Traditional statistical analysis of such data assumes that the counts taken over space and time are independent, which is not necessarily true. Therefore, to analyze...

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.

The Shock and Vibration Digest. Volume 14, Number 8

DTIC Science & Technology

1982-08-01

generating interest in averaged transfer functions. Broadband transfer functions are derived using the methods of statistical energy analysis (SEA...Accelerometer, Endevco Corp., San Juan Capis- trano,CA(1982). 7. Lyon, R.H., Statistical Energy Analysis of Dy- namical Systems, MIT Press, Cambridge, MA...A fairly new technique known as statistical energy analysis , or SEA, [35-44] has been useful for many problems of noise transmission. The difficulty

Analysis of Variance in Statistical Image Processing

NASA Astrophysics Data System (ADS)

Kurz, Ludwik; Hafed Benteftifa, M.

1997-04-01

A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

Statistical analysis of RHIC beam position monitors performance

NASA Astrophysics Data System (ADS)

Calaga, R.; Tomás, R.

2004-04-01

A detailed statistical analysis of beam position monitors (BPM) performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

Single-row, double-row, and transosseous equivalent techniques for isolated supraspinatus tendon tears with minimal atrophy: A retrospective comparative outcome and radiographic analysis at minimum 2-year followup

PubMed Central

McCormick, Frank; Gupta, Anil; Bruce, Ben; Harris, Josh; Abrams, Geoff; Wilson, Hillary; Hussey, Kristen; Cole, Brian J.

2014-01-01

Purpose: The purpose of this study was to measure and compare the subjective, objective, and radiographic healing outcomes of single-row (SR), double-row (DR), and transosseous equivalent (TOE) suture techniques for arthroscopic rotator cuff repair. Materials and Methods: A retrospective comparative analysis of arthroscopic rotator cuff repairs by one surgeon from 2004 to 2010 at minimum 2-year followup was performed. Cohorts were matched for age, sex, and tear size. Subjective outcome variables included ASES, Constant, SST, UCLA, and SF-12 scores. Objective outcome variables included strength, active range of motion (ROM). Radiographic healing was assessed by magnetic resonance imaging (MRI). Statistical analysis was performed using analysis of variance (ANOVA), Mann — Whitney and Kruskal — Wallis tests with significance, and the Fisher exact probability test <0.05. Results: Sixty-three patients completed the study requirements (20 SR, 21 DR, 22 TOE). There was a clinically and statistically significant improvement in outcomes with all repair techniques (ASES mean improvement P = <0.0001). The mean final ASES scores were: SR 83; (SD 21.4); DR 87 (SD 18.2); TOE 87 (SD 13.2); (P = 0.73). There was a statistically significant improvement in strength for each repair technique (P < 0.001). There was no significant difference between techniques across all secondary outcome assessments: ASES improvement, Constant, SST, UCLA, SF-12, ROM, Strength, and MRI re-tear rates. There was a decrease in re-tear rates from single row (22%) to double-row (18%) to transosseous equivalent (11%); however, this difference was not statistically significant (P = 0.6). Conclusions: Compared to preoperatively, arthroscopic rotator cuff repair, using SR, DR, or TOE techniques, yielded a clinically and statistically significant improvement in subjective and objective outcomes at a minimum 2-year follow-up. Level of Evidence: Therapeutic level 3. PMID:24926159

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.

Experimental analysis of computer system dependability

NASA Technical Reports Server (NTRS)

Iyer, Ravishankar, K.; Tang, Dong

1993-01-01

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance.

Statistical innovations in the medical device world sparked by the FDA.

PubMed

Campbell, Gregory; Yue, Lilly Q

2016-01-01

The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.

Applying Statistics in the Undergraduate Chemistry Laboratory: Experiments with Food Dyes.

ERIC Educational Resources Information Center

Thomasson, Kathryn; Lofthus-Merschman, Sheila; Humbert, Michelle; Kulevsky, Norman

1998-01-01

Describes several experiments to teach different aspects of the statistical analysis of data using household substances and a simple analysis technique. Each experiment can be performed in three hours. Students learn about treatment of spurious data, application of a pooled variance, linear least-squares fitting, and simultaneous analysis of dyes…

A Didactic Experience of Statistical Analysis for the Determination of Glycine in a Nonaqueous Medium Using ANOVA and a Computer Program

ERIC Educational Resources Information Center

Santos-Delgado, M. J.; Larrea-Tarruella, L.

2004-01-01

The back-titration methods are compared statistically to establish glycine in a nonaqueous medium of acetic acid. Important variations in the mean values of glycine are observed due to the interaction effects between the analysis of variance (ANOVA) technique and a statistical study through a computer software.

Atmospheric statistics for aerospace vehicle operations

NASA Technical Reports Server (NTRS)

Smith, O. E.; Batts, G. W.

1993-01-01

Statistical analysis of atmospheric variables was performed for the Shuttle Transportation System (STS) design trade studies and the establishment of launch commit criteria. Atmospheric constraint statistics have been developed for the NASP test flight, the Advanced Launch System, and the National Launch System. The concepts and analysis techniques discussed in the paper are applicable to the design and operations of any future aerospace vehicle.

Sampling and Data Analysis for Environmental Microbiology

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

Murray, Christopher J.

2001-06-01

A brief review of the literature indicates the importance of statistical analysis in applied and environmental microbiology. Sampling designs are particularly important for successful studies, and it is highly recommended that researchers review their sampling design before heading to the laboratory or the field. Most statisticians have numerous stories of scientists who approached them after their study was complete only to have to tell them that the data they gathered could not be used to test the hypothesis they wanted to address. Once the data are gathered, a large and complex body of statistical techniques are available for analysis ofmore » the data. Those methods include both numerical and graphical techniques for exploratory characterization of the data. Hypothesis testing and analysis of variance (ANOVA) are techniques that can be used to compare the mean and variance of two or more groups of samples. Regression can be used to examine the relationships between sets of variables and is often used to examine the dependence of microbiological populations on microbiological parameters. Multivariate statistics provides several methods that can be used for interpretation of datasets with a large number of variables and to partition samples into similar groups, a task that is very common in taxonomy, but also has applications in other fields of microbiology. Geostatistics and other techniques have been used to examine the spatial distribution of microorganisms. The objectives of this chapter are to provide a brief survey of some of the statistical techniques that can be used for sample design and data analysis of microbiological data in environmental studies, and to provide some examples of their use from the literature.« less

Analysis of defect structure in silicon. Characterization of samples from UCP ingot 5848-13C

NASA Technical Reports Server (NTRS)

Natesh, R.; Guyer, T.; Stringfellow, G. B.

1982-01-01

Statistically significant quantitative structural imperfection measurements were made on samples from ubiquitous crystalline process (UCP) Ingot 5848 - 13 C. Important trends were noticed between the measured data, cell efficiency, and diffusion length. Grain boundary substructure appears to have an important effect on the conversion efficiency of solar cells from Semix material. Quantitative microscopy measurements give statistically significant information compared to other microanalytical techniques. A surface preparation technique to obtain proper contrast of structural defects suitable for QTM analysis was perfected.

Cluster analysis and subgrouping to investigate inter-individual variability to non-invasive brain stimulation: a systematic review.

PubMed

Pellegrini, Michael; Zoghi, Maryam; Jaberzadeh, Shapour

2018-01-12

Cluster analysis and other subgrouping techniques have risen in popularity in recent years in non-invasive brain stimulation research in the attempt to investigate the issue of inter-individual variability - the issue of why some individuals respond, as traditionally expected, to non-invasive brain stimulation protocols and others do not. Cluster analysis and subgrouping techniques have been used to categorise individuals, based on their response patterns, as responder or non-responders. There is, however, a lack of consensus and consistency on the most appropriate technique to use. This systematic review aimed to provide a systematic summary of the cluster analysis and subgrouping techniques used to date and suggest recommendations moving forward. Twenty studies were included that utilised subgrouping techniques, while seven of these additionally utilised cluster analysis techniques. The results of this systematic review appear to indicate that statistical cluster analysis techniques are effective in identifying subgroups of individuals based on response patterns to non-invasive brain stimulation. This systematic review also reports a lack of consensus amongst researchers on the most effective subgrouping technique and the criteria used to determine whether an individual is categorised as a responder or a non-responder. This systematic review provides a step-by-step guide to carrying out statistical cluster analyses and subgrouping techniques to provide a framework for analysis when developing further insights into the contributing factors of inter-individual variability in response to non-invasive brain stimulation.

Advanced statistical methods for improved data analysis of NASA astrophysics missions

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.

1992-01-01

The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis.

Comparative Analysis Between Computed and Conventional Inferior Alveolar Nerve Block Techniques.

PubMed

Araújo, Gabriela Madeira; Barbalho, Jimmy Charles Melo; Dias, Tasiana Guedes de Souza; Santos, Thiago de Santana; Vasconcellos, Ricardo José de Holanda; de Morais, Hécio Henrique Araújo

2015-11-01

The aim of this randomized, double-blind, controlled trial was to compare the computed and conventional inferior alveolar nerve block techniques in symmetrically positioned inferior third molars. Both computed and conventional anesthetic techniques were performed in 29 healthy patients (58 surgeries) aged between 18 and 40 years. The anesthetic of choice was 2% lidocaine with 1: 200,000 epinephrine. The Visual Analogue Scale assessed the pain variable after anesthetic infiltration. Patient satisfaction was evaluated using the Likert Scale. Heart and respiratory rates, mean time to perform technique, and the need for additional anesthesia were also evaluated. Pain variable means were higher for the conventional technique as compared with computed, 3.45 ± 2.73 and 2.86 ± 1.96, respectively, but no statistically significant differences were found (P > 0.05). Patient satisfaction showed no statistically significant differences. The average computed technique runtime and the conventional were 3.85 and 1.61 minutes, respectively, showing statistically significant differences (P <0.001). The computed anesthetic technique showed lower mean pain perception, but did not show statistically significant differences when contrasted to the conventional technique.

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...

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.

PubMed

MacLean, Adam L; Harrington, Heather A; Stumpf, Michael P H; Byrne, Helen M

2016-01-01

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Signal analysis techniques for incipient failure detection in turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, T.

1985-01-01

Signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery were developed, implemented and evaluated. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques were implemented on a computer and applied to dynamic signals. A laboratory evaluation of the methods with respect to signal detection capability is described. Plans for further technique evaluation and data base development to characterize turbopump incipient failure modes from Space Shuttle main engine (SSME) hot firing measurements are outlined.

Progress of statistical analysis in biomedical research through the historical review of the development of the Framingham score.

PubMed

Ignjatović, Aleksandra; Stojanović, Miodrag; Milošević, Zoran; Anđelković Apostolović, Marija

2017-12-02

The interest in developing risk models in medicine not only is appealing, but also associated with many obstacles in different aspects of predictive model development. Initially, the association of biomarkers or the association of more markers with the specific outcome was proven by statistical significance, but novel and demanding questions required the development of new and more complex statistical techniques. Progress of statistical analysis in biomedical research can be observed the best through the history of the Framingham study and development of the Framingham score. Evaluation of predictive models comes from a combination of the facts which are results of several metrics. Using logistic regression and Cox proportional hazards regression analysis, the calibration test, and the ROC curve analysis should be mandatory and eliminatory, and the central place should be taken by some new statistical techniques. In order to obtain complete information related to the new marker in the model, recently, there is a recommendation to use the reclassification tables by calculating the net reclassification index and the integrated discrimination improvement. Decision curve analysis is a novel method for evaluating the clinical usefulness of a predictive model. It may be noted that customizing and fine-tuning of the Framingham risk score initiated the development of statistical analysis. Clinically applicable predictive model should be a trade-off between all abovementioned statistical metrics, a trade-off between calibration and discrimination, accuracy and decision-making, costs and benefits, and quality and quantity of patient's life.

Statistical assessment of the learning curves of health technologies.

PubMed

Ramsay, C R; Grant, A M; Wallace, S A; Garthwaite, P H; Monk, A F; Russell, I T

2001-01-01

(1) To describe systematically studies that directly assessed the learning curve effect of health technologies. (2) Systematically to identify 'novel' statistical techniques applied to learning curve data in other fields, such as psychology and manufacturing. (3) To test these statistical techniques in data sets from studies of varying designs to assess health technologies in which learning curve effects are known to exist. METHODS - STUDY SELECTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): For a study to be included, it had to include a formal analysis of the learning curve of a health technology using a graphical, tabular or statistical technique. METHODS - STUDY SELECTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): For a study to be included, it had to include a formal assessment of a learning curve using a statistical technique that had not been identified in the previous search. METHODS - DATA SOURCES: Six clinical and 16 non-clinical biomedical databases were searched. A limited amount of handsearching and scanning of reference lists was also undertaken. METHODS - DATA EXTRACTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): A number of study characteristics were abstracted from the papers such as study design, study size, number of operators and the statistical method used. METHODS - DATA EXTRACTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): The new statistical techniques identified were categorised into four subgroups of increasing complexity: exploratory data analysis; simple series data analysis; complex data structure analysis, generic techniques. METHODS - TESTING OF STATISTICAL METHODS: Some of the statistical methods identified in the systematic searches for single (simple) operator series data and for multiple (complex) operator series data were illustrated and explored using three data sets. The first was a case series of 190 consecutive laparoscopic fundoplication procedures performed by a single surgeon; the second was a case series of consecutive laparoscopic cholecystectomy procedures performed by ten surgeons; the third was randomised trial data derived from the laparoscopic procedure arm of a multicentre trial of groin hernia repair, supplemented by data from non-randomised operations performed during the trial. RESULTS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: Of 4571 abstracts identified, 272 (6%) were later included in the study after review of the full paper. Some 51% of studies assessed a surgical minimal access technique and 95% were case series. The statistical method used most often (60%) was splitting the data into consecutive parts (such as halves or thirds), with only 14% attempting a more formal statistical analysis. The reporting of the studies was poor, with 31% giving no details of data collection methods. RESULTS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: Of 9431 abstracts assessed, 115 (1%) were deemed appropriate for further investigation and, of these, 18 were included in the study. All of the methods for complex data sets were identified in the non-clinical literature. These were discriminant analysis, two-stage estimation of learning rates, generalised estimating equations, multilevel models, latent curve models, time series models and stochastic parameter models. In addition, eight new shapes of learning curves were identified. RESULTS - TESTING OF STATISTICAL METHODS: No one particular shape of learning curve performed significantly better than another. The performance of 'operation time' as a proxy for learning differed between the three procedures. Multilevel modelling using the laparoscopic cholecystectomy data demonstrated and measured surgeon-specific and confounding effects. The inclusion of non-randomised cases, despite the possible limitations of the method, enhanced the interpretation of learning effects. CONCLUSIONS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: The statistical methods used for assessing learning effects in health technology assessment have been crude and the reporting of studies poor. CONCLUSIONS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: A number of statistical methods for assessing learning effects were identified that had not hitherto been used in health technology assessment. There was a hierarchy of methods for the identification and measurement of learning, and the more sophisticated methods for both have had little if any use in health technology assessment. This demonstrated the value of considering fields outside clinical research when addressing methodological issues in health technology assessment. CONCLUSIONS - TESTING OF STATISTICAL METHODS: It has been demonstrated that the portfolio of techniques identified can enhance investigations of learning curve effects. (ABSTRACT TRUNCATED)

The Statistical Package for the Social Sciences (SPSS) as an adjunct to pharmacokinetic analysis.

PubMed

Mather, L E; Austin, K L

1983-01-01

Computer techniques for numerical analysis are well known to pharmacokineticists. Powerful techniques for data file management have been developed by social scientists but have, in general, been ignored by pharmacokineticists because of their apparent lack of ability to interface with pharmacokinetic programs. Extensive use has been made of the Statistical Package for the Social Sciences (SPSS) for its data handling capabilities, but at the same time, techniques have been developed within SPSS to interface with pharmacokinetic programs of the users' choice and to carry out a variety of user-defined pharmacokinetic tasks within SPSS commands, apart from the expected variety of statistical tasks. Because it is based on a ubiquitous package, this methodology has all of the benefits of excellent documentation, interchangeability between different types and sizes of machines and true portability of techniques and data files. An example is given of the total management of a pharmacokinetic study previously reported in the literature by the authors.

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…

Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information.

PubMed

Kim, Seokyeon; Jeong, Seongmin; Woo, Insoo; Jang, Yun; Maciejewski, Ross; Ebert, David S

2018-03-01

Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance.

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

PubMed Central

Fernee, Christianne; Browne, Martin; Zakrzewski, Sonia

2017-01-01

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets. PMID:29216199

Statistical Analysis Techniques for Small Sample Sizes

NASA Technical Reports Server (NTRS)

Navard, S. E.

1984-01-01

The small sample sizes problem which is encountered when dealing with analysis of space-flight data is examined. Because of such a amount of data available, careful analyses are essential to extract the maximum amount of information with acceptable accuracy. Statistical analysis of small samples is described. The background material necessary for understanding statistical hypothesis testing is outlined and the various tests which can be done on small samples are explained. Emphasis is on the underlying assumptions of each test and on considerations needed to choose the most appropriate test for a given type of analysis.

Proceedings of the first ERDA statistical symposium, Los Alamos, NM, November 3--5, 1975. [Sixteen papers

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

Nicholson, W L; Harris, J L

1976-03-01

The First ERDA Statistical Symposium was organized to provide a means for communication among ERDA statisticians, and the sixteen papers presented at the meeting are given. Topics include techniques of numerical analysis used for accelerators, nuclear reactors, skewness and kurtosis statistics, radiochemical spectral analysis, quality control, and other statistics problems. Nine of the papers were previously announced in Nuclear Science Abstracts (NSA), while the remaining seven were abstracted for ERDA Energy Research Abstracts (ERA) and INIS Atomindex. (PMA)

A note on generalized Genome Scan Meta-Analysis statistics

PubMed Central

Koziol, James A; Feng, Anne C

2005-01-01

Background Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies. Results We provide an Edgeworth approximation to the null distribution of the weighted GSMA statistic, and, we examine the limiting distribution of the GSMA statistics under the order statistic formulation, and quantify the relevance of the pairwise correlations of the GSMA statistics across different bins on this limiting distribution. We also remark on aggregate criteria and multiple testing for determining significance of GSMA results. Conclusion Theoretical considerations detailed herein can lead to clarification and simplification of testing criteria for generalizations of the GSMA statistic. PMID:15717930

Probabilistic/Fracture-Mechanics Model For Service Life

NASA Technical Reports Server (NTRS)

Watkins, T., Jr.; Annis, C. G., Jr.

1991-01-01

Computer program makes probabilistic estimates of lifetime of engine and components thereof. Developed to fill need for more accurate life-assessment technique that avoids errors in estimated lives and provides for statistical assessment of levels of risk created by engineering decisions in designing system. Implements mathematical model combining techniques of statistics, fatigue, fracture mechanics, nondestructive analysis, life-cycle cost analysis, and management of engine parts. Used to investigate effects of such engine-component life-controlling parameters as return-to-service intervals, stresses, capabilities for nondestructive evaluation, and qualities of materials.

Data Analysis Techniques for Physical Scientists

NASA Astrophysics Data System (ADS)

Pruneau, Claude A.

2017-10-01

Preface; How to read this book; 1. The scientific method; Part I. Foundation in Probability and Statistics: 2. Probability; 3. Probability models; 4. Classical inference I: estimators; 5. Classical inference II: optimization; 6. Classical inference III: confidence intervals and statistical tests; 7. Bayesian inference; Part II. Measurement Techniques: 8. Basic measurements; 9. Event reconstruction; 10. Correlation functions; 11. The multiple facets of correlation functions; 12. Data correction methods; Part III. Simulation Techniques: 13. Monte Carlo methods; 14. Collision and detector modeling; List of references; Index.

NUMERICAL ANALYSIS TECHNIQUE USING THE STATISTICAL ENERGY ANALYSIS METHOD CONCERNING THE BLASTING NOISE REDUCTION BY THE SOUND INSULATION DOOR USED IN TUNNEL CONSTRUCTIONS

NASA Astrophysics Data System (ADS)

Ishida, Shigeki; Mori, Atsuo; Shinji, Masato

The main method to reduce the blasting charge noise which occurs in a tunnel under construction is to install the sound insulation door in the tunnel. However, the numerical analysis technique to predict the accurate effect of the transmission loss in the sound insulation door is not established. In this study, we measured the blasting charge noise and the vibration of the sound insulation door in the tunnel with the blasting charge, and performed analysis and modified acoustic feature. In addition, we reproduced the noise reduction effect of the sound insulation door by statistical energy analysis method and confirmed that numerical simulation is possible by this procedure.

The Malpractice of Statistical Interpretation

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

1978-01-01

Problems associated with the use of gain scores, analysis of covariance, multicollinearity, part and partial correlation, and the lack of rectilinearity in regression are discussed. Particular attention is paid to the misuse of statistical techniques. (JKS)

Network meta-analysis: a technique to gather evidence from direct and indirect comparisons

PubMed Central

2017-01-01

Systematic reviews and pairwise meta-analyses of randomized controlled trials, at the intersection of clinical medicine, epidemiology and statistics, are positioned at the top of evidence-based practice hierarchy. These are important tools to base drugs approval, clinical protocols and guidelines formulation and for decision-making. However, this traditional technique only partially yield information that clinicians, patients and policy-makers need to make informed decisions, since it usually compares only two interventions at the time. In the market, regardless the clinical condition under evaluation, usually many interventions are available and few of them have been studied in head-to-head studies. This scenario precludes conclusions to be drawn from comparisons of all interventions profile (e.g. efficacy and safety). The recent development and introduction of a new technique – usually referred as network meta-analysis, indirect meta-analysis, multiple or mixed treatment comparisons – has allowed the estimation of metrics for all possible comparisons in the same model, simultaneously gathering direct and indirect evidence. Over the last years this statistical tool has matured as technique with models available for all types of raw data, producing different pooled effect measures, using both Frequentist and Bayesian frameworks, with different software packages. However, the conduction, report and interpretation of network meta-analysis still poses multiple challenges that should be carefully considered, especially because this technique inherits all assumptions from pairwise meta-analysis but with increased complexity. Thus, we aim to provide a basic explanation of network meta-analysis conduction, highlighting its risks and benefits for evidence-based practice, including information on statistical methods evolution, assumptions and steps for performing the analysis. PMID:28503228

Statistical Analysis of Time-Series from Monitoring of Active Volcanic Vents

NASA Astrophysics Data System (ADS)

Lachowycz, S.; Cosma, I.; Pyle, D. M.; Mather, T. A.; Rodgers, M.; Varley, N. R.

2016-12-01

Despite recent advances in the collection and analysis of time-series from volcano monitoring, and the resulting insights into volcanic processes, challenges remain in forecasting and interpreting activity from near real-time analysis of monitoring data. Statistical methods have potential to characterise the underlying structure and facilitate intercomparison of these time-series, and so inform interpretation of volcanic activity. We explore the utility of multiple statistical techniques that could be widely applicable to monitoring data, including Shannon entropy and detrended fluctuation analysis, by their application to various data streams from volcanic vents during periods of temporally variable activity. Each technique reveals changes through time in the structure of some of the data that were not apparent from conventional analysis. For example, we calculate the Shannon entropy (a measure of the randomness of a signal) of time-series from the recent dome-forming eruptions of Volcán de Colima (Mexico) and Soufrière Hills (Montserrat). The entropy of real-time seismic measurements and the count rate of certain volcano-seismic event types from both volcanoes is found to be temporally variable, with these data generally having higher entropy during periods of lava effusion and/or larger explosions. In some instances, the entropy shifts prior to or coincident with changes in seismic or eruptive activity, some of which were not clearly recognised by real-time monitoring. Comparison with other statistics demonstrates the sensitivity of the entropy to the data distribution, but that it is distinct from conventional statistical measures such as coefficient of variation. We conclude that each analysis technique examined could provide valuable insights for interpretation of diverse monitoring time-series.

Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis under Conditions of Multivariate Nonnormality.

ERIC Educational Resources Information Center

Fouladi, Rachel T.

2000-01-01

Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…

On the implications of the classical ergodic theorems: analysis of developmental processes has to focus on intra-individual variation.

PubMed

Molenaar, Peter C M

2008-01-01

It is argued that general mathematical-statistical theorems imply that standard statistical analysis techniques of inter-individual variation are invalid to investigate developmental processes. Developmental processes have to be analyzed at the level of individual subjects, using time series data characterizing the patterns of intra-individual variation. It is shown that standard statistical techniques based on the analysis of inter-individual variation appear to be insensitive to the presence of arbitrary large degrees of inter-individual heterogeneity in the population. An important class of nonlinear epigenetic models of neural growth is described which can explain the occurrence of such heterogeneity in brain structures and behavior. Links with models of developmental instability are discussed. A simulation study based on a chaotic growth model illustrates the invalidity of standard analysis of inter-individual variation, whereas time series analysis of intra-individual variation is able to recover the true state of affairs. (c) 2007 Wiley Periodicals, Inc.

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

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.

Validating an Air Traffic Management Concept of Operation Using Statistical Modeling

NASA Technical Reports Server (NTRS)

He, Yuning; Davies, Misty Dawn

2013-01-01

Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysis

High order statistical signatures from source-driven measurements of subcritical fissile systems

NASA Astrophysics Data System (ADS)

Mattingly, John Kelly

1998-11-01

This research focuses on the development and application of high order statistical analyses applied to measurements performed with subcritical fissile systems driven by an introduced neutron source. The signatures presented are derived from counting statistics of the introduced source and radiation detectors that observe the response of the fissile system. It is demonstrated that successively higher order counting statistics possess progressively higher sensitivity to reactivity. Consequently, these signatures are more sensitive to changes in the composition, fissile mass, and configuration of the fissile assembly. Furthermore, it is shown that these techniques are capable of distinguishing the response of the fissile system to the introduced source from its response to any internal or inherent sources. This ability combined with the enhanced sensitivity of higher order signatures indicates that these techniques will be of significant utility in a variety of applications. Potential applications include enhanced radiation signature identification of weapons components for nuclear disarmament and safeguards applications and augmented nondestructive analysis of spent nuclear fuel. In general, these techniques expand present capabilities in the analysis of subcritical measurements.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

PubMed Central

Farràs-Permanyer, Laia; Guàrdia-Olmos, Joan; Peró-Cebollero, Maribel

2015-01-01

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results. PMID:26300802

ADP of multispectral scanner data for land use mapping

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1971-01-01

The advantages and disadvantages of various remote sensing instrumentation and analysis techniques are reviewed. The use of multispectral scanner data and the automatic data processing techniques are considered. A computer-aided analysis system for remote sensor data is described with emphasis on the image display, statistics processor, wavelength band selection, classification processor, and results display. Advanced techniques in using spectral and temporal data are also considered.

Statistical analysis of fNIRS data: a comprehensive review.

PubMed

Tak, Sungho; Ye, Jong Chul

2014-01-15

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure brain activities using the changes of optical absorption in the brain through the intact skull. fNIRS has many advantages over other neuroimaging modalities such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or magnetoencephalography (MEG), since it can directly measure blood oxygenation level changes related to neural activation with high temporal resolution. However, fNIRS signals are highly corrupted by measurement noises and physiology-based systemic interference. Careful statistical analyses are therefore required to extract neuronal activity-related signals from fNIRS data. In this paper, we provide an extensive review of historical developments of statistical analyses of fNIRS signal, which include motion artifact correction, short source-detector separation correction, principal component analysis (PCA)/independent component analysis (ICA), false discovery rate (FDR), serially-correlated errors, as well as inference techniques such as the standard t-test, F-test, analysis of variance (ANOVA), and statistical parameter mapping (SPM) framework. In addition, to provide a unified view of various existing inference techniques, we explain a linear mixed effect model with restricted maximum likelihood (ReML) variance estimation, and show that most of the existing inference methods for fNIRS analysis can be derived as special cases. Some of the open issues in statistical analysis are also described. Copyright © 2013 Elsevier Inc. All rights reserved.

Study of photon correlation techniques for processing of laser velocimeter signals

NASA Technical Reports Server (NTRS)

Mayo, W. T., Jr.

1977-01-01

The objective was to provide the theory and a system design for a new type of photon counting processor for low level dual scatter laser velocimeter (LV) signals which would be capable of both the first order measurements of mean flow and turbulence intensity and also the second order time statistics: cross correlation auto correlation, and related spectra. A general Poisson process model for low level LV signals and noise which is valid from the photon-resolved regime all the way to the limiting case of nonstationary Gaussian noise was used. Computer simulation algorithms and higher order statistical moment analysis of Poisson processes were derived and applied to the analysis of photon correlation techniques. A system design using a unique dual correlate and subtract frequency discriminator technique is postulated and analyzed. Expectation analysis indicates that the objective measurements are feasible.

Power flow as a complement to statistical energy analysis and finite element analysis

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1987-01-01

Present methods of analysis of the structural response and the structure-borne transmission of vibrational energy use either finite element (FE) techniques or statistical energy analysis (SEA) methods. The FE methods are a very useful tool at low frequencies where the number of resonances involved in the analysis is rather small. On the other hand SEA methods can predict with acceptable accuracy the response and energy transmission between coupled structures at relatively high frequencies where the structural modal density is high and a statistical approach is the appropriate solution. In the mid-frequency range, a relatively large number of resonances exist which make finite element method too costly. On the other hand SEA methods can only predict an average level form. In this mid-frequency range a possible alternative is to use power flow techniques, where the input and flow of vibrational energy to excited and coupled structural components can be expressed in terms of input and transfer mobilities. This power flow technique can be extended from low to high frequencies and this can be integrated with established FE models at low frequencies and SEA models at high frequencies to form a verification of the method. This method of structural analysis using power flo and mobility methods, and its integration with SEA and FE analysis is applied to the case of two thin beams joined together at right angles.

Lightweight and Statistical Techniques for Petascale PetaScale Debugging

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

Miller, Barton

2014-06-30

This project investigated novel techniques for debugging scientific applications on petascale architectures. In particular, we developed lightweight tools that narrow the problem space when bugs are encountered. We also developed techniques that either limit the number of tasks and the code regions to which a developer must apply a traditional debugger or that apply statistical techniques to provide direct suggestions of the location and type of error. We extend previous work on the Stack Trace Analysis Tool (STAT), that has already demonstrated scalability to over one hundred thousand MPI tasks. We also extended statistical techniques developed to isolate programming errorsmore » in widely used sequential or threaded applications in the Cooperative Bug Isolation (CBI) project to large scale parallel applications. Overall, our research substantially improved productivity on petascale platforms through a tool set for debugging that complements existing commercial tools. Previously, Office Of Science application developers relied either on primitive manual debugging techniques based on printf or they use tools, such as TotalView, that do not scale beyond a few thousand processors. However, bugs often arise at scale and substantial effort and computation cycles are wasted in either reproducing the problem in a smaller run that can be analyzed with the traditional tools or in repeated runs at scale that use the primitive techniques. New techniques that work at scale and automate the process of identifying the root cause of errors were needed. These techniques significantly reduced the time spent debugging petascale applications, thus leading to a greater overall amount of time for application scientists to pursue the scientific objectives for which the systems are purchased. We developed a new paradigm for debugging at scale: techniques that reduced the debugging scenario to a scale suitable for traditional debuggers, e.g., by narrowing the search for the root-cause analysis to a small set of nodes or by identifying equivalence classes of nodes and sampling our debug targets from them. We implemented these techniques as lightweight tools that efficiently work on the full scale of the target machine. We explored four lightweight debugging refinements: generic classification parameters, such as stack traces, application-specific classification parameters, such as global variables, statistical data acquisition techniques and machine learning based approaches to perform root cause analysis. Work done under this project can be divided into two categories, new algorithms and techniques for scalable debugging, and foundation infrastructure work on our MRNet multicast-reduction framework for scalability, and Dyninst binary analysis and instrumentation toolkits.« less

A constant current charge technique for low Earth orbit life testing

NASA Technical Reports Server (NTRS)

Glueck, Peter

1991-01-01

A constant current charge technique for low earth orbit testing of nickel cadmium cells is presented. The method mimics the familiar taper charge of the constant potential technique while maintaining cell independence for statistical analysis. A detailed example application is provided and the advantages and disadvantages of this technique are discussed.

Experimental toxicology: Issues of statistics, experimental design, and replication.

PubMed

Briner, Wayne; Kirwan, Jeral

2017-01-01

The difficulty of replicating experiments has drawn considerable attention. Issues with replication occur for a variety of reasons ranging from experimental design to laboratory errors to inappropriate statistical analysis. Here we review a variety of guidelines for statistical analysis, design, and execution of experiments in toxicology. In general, replication can be improved by using hypothesis driven experiments with adequate sample sizes, randomization, and blind data collection techniques. Copyright © 2016 Elsevier B.V. All rights reserved.

A PERT/CPM of the Computer Assisted Completion of The Ministry September Report. Research Report.

ERIC Educational Resources Information Center

Feeney, J. D.

Using two statistical analysis techniques (the Program Evaluation and Review Technique and the Critical Path Method), this study analyzed procedures for compiling the required yearly report of the Metropolitan Separate School Board (Catholic) of Toronto, Canada. The computer-assisted analysis organized the process of completing the report more…

Is Quality/Effectiveness An Empirically Demonstrable School Attribute? Statistical Aids for Determining Appropriate Levels of Analysis.

ERIC Educational Resources Information Center

Griffith, James

2002-01-01

Describes and demonstrates analytical techniques used in organizational psychology and contemporary multilevel analysis. Using these analytic techniques, examines the relationship between educational outcomes and the school environment. Finds that at least some indicators might be represented as school-level phenomena. Results imply that the…

Principle Component Analysis with Incomplete Data: A simulation of R pcaMethods package in Constructing an Environmental Quality Index with Missing Data

EPA Science Inventory

Missing data is a common problem in the application of statistical techniques. In principal component analysis (PCA), a technique for dimensionality reduction, incomplete data points are either discarded or imputed using interpolation methods. Such approaches are less valid when ...

Interpolative modeling of GaAs FET S-parameter data bases for use in Monte Carlo simulations

NASA Technical Reports Server (NTRS)

Campbell, L.; Purviance, J.

1992-01-01

A statistical interpolation technique is presented for modeling GaAs FET S-parameter measurements for use in the statistical analysis and design of circuits. This is accomplished by interpolating among the measurements in a GaAs FET S-parameter data base in a statistically valid manner.

Statistics for People Who (Think They) Hate Statistics. Third Edition

ERIC Educational Resources Information Center

Salkind, Neil J.

2007-01-01

This text teaches an often intimidating and difficult subject in a way that is informative, personable, and clear. The author takes students through various statistical procedures, beginning with correlation and graphical representation of data and ending with inferential techniques and analysis of variance. In addition, the text covers SPSS, and…

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography.

PubMed

Packham, B; Barnes, G; Dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-06-01

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

PubMed Central

Packham, B; Barnes, G; dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-01-01

Abstract Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity. PMID:27203477

Statistics for nuclear engineers and scientists. Part 1. Basic statistical inference

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

Beggs, W.J.

1981-02-01

This report is intended for the use of engineers and scientists working in the nuclear industry, especially at the Bettis Atomic Power Laboratory. It serves as the basis for several Bettis in-house statistics courses. The objectives of the report are to introduce the reader to the language and concepts of statistics and to provide a basic set of techniques to apply to problems of the collection and analysis of data. Part 1 covers subjects of basic inference. The subjects include: descriptive statistics; probability; simple inference for normally distributed populations, and for non-normal populations as well; comparison of two populations; themore » analysis of variance; quality control procedures; and linear regression analysis.« less

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

Noise removing in encrypted color images by statistical analysis

NASA Astrophysics Data System (ADS)

Islam, N.; Puech, W.

2012-03-01

Cryptographic techniques are used to secure confidential data from unauthorized access but these techniques are very sensitive to noise. A single bit change in encrypted data can have catastrophic impact over the decrypted data. This paper addresses the problem of removing bit error in visual data which are encrypted using AES algorithm in the CBC mode. In order to remove the noise, a method is proposed which is based on the statistical analysis of each block during the decryption. The proposed method exploits local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove the errors. Experimental results show that the proposed method can be used at the receiving end for the possible solution for noise removing in visual data in encrypted domain.

A study of two statistical methods as applied to shuttle solid rocket booster expenditures

NASA Technical Reports Server (NTRS)

Perlmutter, M.; Huang, Y.; Graves, M.

1974-01-01

The state probability technique and the Monte Carlo technique are applied to finding shuttle solid rocket booster expenditure statistics. For a given attrition rate per launch, the probable number of boosters needed for a given mission of 440 launches is calculated. Several cases are considered, including the elimination of the booster after a maximum of 20 consecutive launches. Also considered is the case where the booster is composed of replaceable components with independent attrition rates. A simple cost analysis is carried out to indicate the number of boosters to build initially, depending on booster costs. Two statistical methods were applied in the analysis: (1) state probability method which consists of defining an appropriate state space for the outcome of the random trials, and (2) model simulation method or the Monte Carlo technique. It was found that the model simulation method was easier to formulate while the state probability method required less computing time and was more accurate.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Empirical performance of interpolation techniques in risk-neutral density (RND) estimation

NASA Astrophysics Data System (ADS)

Bahaludin, H.; Abdullah, M. H.

2017-03-01

The objective of this study is to evaluate the empirical performance of interpolation techniques in risk-neutral density (RND) estimation. Firstly, the empirical performance is evaluated by using statistical analysis based on the implied mean and the implied variance of RND. Secondly, the interpolation performance is measured based on pricing error. We propose using the leave-one-out cross-validation (LOOCV) pricing error for interpolation selection purposes. The statistical analyses indicate that there are statistical differences between the interpolation techniques:second-order polynomial, fourth-order polynomial and smoothing spline. The results of LOOCV pricing error shows that interpolation by using fourth-order polynomial provides the best fitting to option prices in which it has the lowest value error.

Evaluation of the marginal fit of metal copings fabricated on three different marginal designs using conventional and accelerated casting techniques: an in vitro study.

PubMed

Vaidya, Sharad; Parkash, Hari; Bhargava, Akshay; Gupta, Sharad

2014-01-01

Abundant resources and techniques have been used for complete coverage crown fabrication. Conventional investing and casting procedures for phosphate-bonded investments require a 2- to 4-h procedure before completion. Accelerated casting techniques have been used, but may not result in castings with matching marginal accuracy. The study measured the marginal gap and determined the clinical acceptability of single cast copings invested in a phosphate-bonded investment with the use of conventional and accelerated methods. One hundred and twenty cast coping samples were fabricated using conventional and accelerated methods, with three finish lines: Chamfer, shoulder and shoulder with bevel. Sixty copings were prepared with each technique. Each coping was examined with a stereomicroscope at four predetermined sites and measurements of marginal gaps were documented for each. A master chart was prepared for all the data and was analyzed using Statistical Package for the Social Sciences version. Evidence of marginal gap was then evaluated by t-test. Analysis of variance and Post-hoc analysis were used to compare two groups as well as to make comparisons between three subgroups . Measurements recorded showed no statistically significant difference between conventional and accelerated groups. Among the three marginal designs studied, shoulder with bevel showed the best marginal fit with conventional as well as accelerated casting techniques. Accelerated casting technique could be a vital alternative to the time-consuming conventional casting technique. The marginal fit between the two casting techniques showed no statistical difference.

Analysis of defect structure in silicon. Characterization of SEMIX material. Silicon sheet growth development for the large area silicon sheet task of the low-cost solar array project

NASA Technical Reports Server (NTRS)

Natesh, R.; Stringfellow, G. B.; Virkar, A. V.; Dunn, J.; Guyer, T.

1983-01-01

Statistically significant quantitative structural imperfection measurements were made on samples from ubiquitous crystalline process (UCP) Ingot 5848 - 13C. Important correlation was obtained between defect densities, cell efficiency, and diffusion length. Grain boundary substructure displayed a strong influence on the conversion efficiency of solar cells from Semix material. Quantitative microscopy measurements gave statistically significant information compared to other microanalytical techniques. A surface preparation technique to obtain proper contrast of structural defects suitable for quantimet quantitative image analyzer (QTM) analysis was perfected and is used routinely. The relationships between hole mobility and grain boundary density was determined. Mobility was measured using the van der Pauw technique, and grain boundary density was measured using quantitative microscopy technique. Mobility was found to decrease with increasing grain boundary density.

Comparative analysis of ferroelectric domain statistics via nonlinear diffraction in random nonlinear materials.

PubMed

Wang, B; Switowski, K; Cojocaru, C; Roppo, V; Sheng, Y; Scalora, M; Kisielewski, J; Pawlak, D; Vilaseca, R; Akhouayri, H; Krolikowski, W; Trull, J

2018-01-22

We present an indirect, non-destructive optical method for domain statistic characterization in disordered nonlinear crystals having homogeneous refractive index and spatially random distribution of ferroelectric domains. This method relies on the analysis of the wave-dependent spatial distribution of the second harmonic, in the plane perpendicular to the optical axis in combination with numerical simulations. We apply this technique to the characterization of two different media, Calcium Barium Niobate and Strontium Barium Niobate, with drastically different statistical distributions of ferroelectric domains.

BATMAN: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2017-04-01

This paper describes the Bayesian Technique for Multi-image Analysis (BATMAN), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical data set containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (I.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real integral-field spectroscopic data. The output segmentations adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in regions with low signal-to-noise ratio. However, the algorithm may be sensitive to small-scale random fluctuations, and its performance in presence of spatial gradients is limited. Due to these effects, errors may be underestimated by as much as a factor of 2. Our analysis reveals that the algorithm prioritizes conservation of all the statistically significant information over noise reduction, and that the precise choice of the input data has a crucial impact on the results. Hence, the philosophy of BaTMAn is not to be used as a 'black box' to improve the signal-to-noise ratio, but as a new approach to characterize spatially resolved data prior to its analysis. The source code is publicly available at http://astro.ft.uam.es/SELGIFS/BaTMAn.

STATISTICAL ANALYSIS OF SNAP 10A THERMOELECTRIC CONVERTER ELEMENT PROCESS DEVELOPMENT VARIABLES

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

Fitch, S.H.; Morris, J.W.

1962-12-15

Statistical analysis, primarily analysis of variance, was applied to evaluate several factors involved in the development of suitable fabrication and processing techniques for the production of lead telluride thermoelectric elements for the SNAP 10A energy conversion system. The analysis methods are described as to their application for determining the effects of various processing steps, estabIishing the value of individual operations, and evaluating the significance of test results. The elimination of unnecessary or detrimental processing steps was accomplished and the number of required tests was substantially reduced by application of these statistical methods to the SNAP 10A production development effort. (auth)

An analysis of I/O efficient order-statistic-based techniques for noise power estimation in the HRMS sky survey's operational system

NASA Technical Reports Server (NTRS)

Zimmerman, G. A.; Olsen, E. T.

1992-01-01

Noise power estimation in the High-Resolution Microwave Survey (HRMS) sky survey element is considered as an example of a constant false alarm rate (CFAR) signal detection problem. Order-statistic-based noise power estimators for CFAR detection are considered in terms of required estimator accuracy and estimator dynamic range. By limiting the dynamic range of the value to be estimated, the performance of an order-statistic estimator can be achieved by simpler techniques requiring only a single pass of the data. Simple threshold-and-count techniques are examined, and it is shown how several parallel threshold-and-count estimation devices can be used to expand the dynamic range to meet HRMS system requirements with minimal hardware complexity. An input/output (I/O) efficient limited-precision order-statistic estimator with wide but limited dynamic range is also examined.

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.

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.

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.

Ozone data and mission sampling analysis

NASA Technical Reports Server (NTRS)

Robbins, J. L.

1980-01-01

A methodology was developed to analyze discrete data obtained from the global distribution of ozone. Statistical analysis techniques were applied to describe the distribution of data variance in terms of empirical orthogonal functions and components of spherical harmonic models. The effects of uneven data distribution and missing data were considered. Data fill based on the autocorrelation structure of the data is described. Computer coding of the analysis techniques is included.

Statistical process management: An essential element of quality improvement

NASA Astrophysics Data System (ADS)

Buckner, M. R.

Successful quality improvement requires a balanced program involving the three elements that control quality: organization, people and technology. The focus of the SPC/SPM User's Group is to advance the technology component of Total Quality by networking within the Group and by providing an outreach within Westinghouse to foster the appropriate use of statistic techniques to achieve Total Quality. SPM encompasses the disciplines by which a process is measured against its intrinsic design capability, in the face of measurement noise and other obscuring variability. SPM tools facilitate decisions about the process that generated the data. SPM deals typically with manufacturing processes, but with some flexibility of definition and technique it accommodates many administrative processes as well. The techniques of SPM are those of Statistical Process Control, Statistical Quality Control, Measurement Control, and Experimental Design. In addition, techniques such as job and task analysis, and concurrent engineering are important elements of systematic planning and analysis that are needed early in the design process to ensure success. The SPC/SPM User's Group is endeavoring to achieve its objectives by sharing successes that have occurred within the member's own Westinghouse department as well as within other US and foreign industry. In addition, failures are reviewed to establish lessons learned in order to improve future applications. In broader terms, the Group is interested in making SPM the accepted way of doing business within Westinghouse.

Assessing the Effectiveness of Statistical Classification Techniques in Predicting Future Employment of Participants in the Temporary Assistance for Needy Families Program

ERIC Educational Resources Information Center

Montoya, Isaac D.

2008-01-01

Three classification techniques (Chi-square Automatic Interaction Detection [CHAID], Classification and Regression Tree [CART], and discriminant analysis) were tested to determine their accuracy in predicting Temporary Assistance for Needy Families program recipients' future employment. Technique evaluation was based on proportion of correctly…

Dark matter constraints from a joint analysis of dwarf Spheroidal galaxy observations with VERITAS

DOE PAGES

Archambault, S.; Archer, A.; Benbow, W.; ...

2017-04-05

We present constraints on the annihilation cross section of weakly interacting massive particles dark matter based on the joint statistical analysis of four dwarf galaxies with VERITAS. These results are derived from an optimized photon weighting statistical technique that improves on standard imaging atmospheric Cherenkov telescope (IACT) analyses by utilizing the spectral and spatial properties of individual photon events.

The Importance of Practice in the Development of Statistics.

DTIC Science & Technology

1983-01-01

RESOLUTION TEST CHART NATIONAL BUREAU OIF STANDARDS 1963 -A NRC Technical Summary Report #2471 C THE IMORTANCE OF PRACTICE IN to THE DEVELOPMENT OF STATISTICS...component analysis, bioassay, limits for a ratio, quality control, sampling inspection, non-parametric tests , transformation theory, ARIMA time series...models, sequential tests , cumulative sum charts, data analysis plotting techniques, and a resolution of the Bayes - frequentist controversy. It appears

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.

Network Meta-Analysis Using R: A Review of Currently Available Automated Packages

PubMed Central

Neupane, Binod; Richer, Danielle; Bonner, Ashley Joel; Kibret, Taddele; Beyene, Joseph

2014-01-01

Network meta-analysis (NMA) – a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously – has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA. PMID:25541687

Network meta-analysis using R: a review of currently available automated packages.

PubMed

Neupane, Binod; Richer, Danielle; Bonner, Ashley Joel; Kibret, Taddele; Beyene, Joseph

2014-01-01

Network meta-analysis (NMA)--a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously--has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.

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 primer on the study of transitory dynamics in ecological series using the scale-dependent correlation analysis.

PubMed

Rodríguez-Arias, Miquel Angel; Rodó, Xavier

2004-03-01

Here we describe a practical, step-by-step primer to scale-dependent correlation (SDC) analysis. The analysis of transitory processes is an important but often neglected topic in ecological studies because only a few statistical techniques appear to detect temporary features accurately enough. We introduce here the SDC analysis, a statistical and graphical method to study transitory processes at any temporal or spatial scale. SDC analysis, thanks to the combination of conventional procedures and simple well-known statistical techniques, becomes an improved time-domain analogue of wavelet analysis. We use several simple synthetic series to describe the method, a more complex example, full of transitory features, to compare SDC and wavelet analysis, and finally we analyze some selected ecological series to illustrate the methodology. The SDC analysis of time series of copepod abundances in the North Sea indicates that ENSO primarily is the main climatic driver of short-term changes in population dynamics. SDC also uncovers some long-term, unexpected features in the population. Similarly, the SDC analysis of Nicholson's blowflies data locates where the proposed models fail and provides new insights about the mechanism that drives the apparent vanishing of the population cycle during the second half of the series.

Statistical characterization of short wind waves from stereo images of the sea surface

NASA Astrophysics Data System (ADS)

Mironov, Alexey; Yurovskaya, Maria; Dulov, Vladimir; Hauser, Danièle; Guérin, Charles-Antoine

2013-04-01

We propose a methodology to extract short-scale statistical characteristics of the sea surface topography by means of stereo image reconstruction. The possibilities and limitations of the technique are discussed and tested on a data set acquired from an oceanographic platform at the Black Sea. The analysis shows that reconstruction of the topography based on stereo method is an efficient way to derive non-trivial statistical properties of surface short- and intermediate-waves (say from 1 centimer to 1 meter). Most technical issues pertaining to this type of datasets (limited range of scales, lacunarity of data or irregular sampling) can be partially overcome by appropriate processing of the available points. The proposed technique also allows one to avoid linear interpolation which dramatically corrupts properties of retrieved surfaces. The processing technique imposes that the field of elevation be polynomially detrended, which has the effect of filtering out the large scales. Hence the statistical analysis can only address the small-scale components of the sea surface. The precise cut-off wavelength, which is approximatively half the patch size, can be obtained by applying a high-pass frequency filter on the reference gauge time records. The results obtained for the one- and two-points statistics of small-scale elevations are shown consistent, at least in order of magnitude, with the corresponding gauge measurements as well as other experimental measurements available in the literature. The calculation of the structure functions provides a powerful tool to investigate spectral and statistical properties of the field of elevations. Experimental parametrization of the third-order structure function, the so-called skewness function, is one of the most important and original outcomes of this study. This function is of primary importance in analytical scattering models from the sea surface and was up to now unavailable in field conditions. Due to the lack of precise reference measurements for the small-scale wave field, we could not quantify exactly the accuracy of the retrieval technique. However, it appeared clearly that the obtained accuracy is good enough for the estimation of second-order statistical quantities (such as the correlation function), acceptable for third-order quantities (such as the skwewness function) and insufficient for fourth-order quantities (such as kurtosis). Therefore, the stereo technique in the present stage should not be thought as a self-contained universal tool to characterize the surface statistics. Instead, it should be used in conjunction with other well calibrated but sparse reference measurement (such as wave gauges) for cross-validation and calibration. It then completes the statistical analysis in as much as it provides a snapshot of the three-dimensional field and allows for the evaluation of higher-order spatial statistics.

Wavelet analysis in ecology and epidemiology: impact of statistical tests

PubMed Central

Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario

2014-01-01

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the ‘beta-surrogate’ method. PMID:24284892

Wavelet analysis in ecology and epidemiology: impact of statistical tests.

PubMed

Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario

2014-02-06

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the 'beta-surrogate' method.

Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

NASA Technical Reports Server (NTRS)

Hailperin, Max

1993-01-01

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.

A comparison of sequential and spiral scanning techniques in brain CT.

PubMed

Pace, Ivana; Zarb, Francis

2015-01-01

To evaluate and compare image quality and radiation dose of sequential computed tomography (CT) examinations of the brain and spiral CT examinations of the brain imaged on a GE HiSpeed NX/I Dual Slice 2CT scanner. A random sample of 40 patients referred for CT examination of the brain was selected and divided into 2 groups. Half of the patients were scanned using the sequential technique; the other half were scanned using the spiral technique. Radiation dose data—both the computed tomography dose index (CTDI) and the dose length product (DLP)—were recorded on a checklist at the end of each examination. Using the European Guidelines on Quality Criteria for Computed Tomography, 4 radiologists conducted a visual grading analysis and rated the level of visibility of 6 anatomical structures considered necessary to produce images of high quality. The mean CTDI(vol) and DLP values were statistically significantly higher (P <.05) with the sequential scans (CTDI(vol): 22.06 mGy; DLP: 304.60 mGy • cm) than with the spiral scans (CTDI(vol): 14.94 mGy; DLP: 229.10 mGy • cm). The mean image quality rating scores for all criteria of the sequential scanning technique were statistically significantly higher (P <.05) in the visual grading analysis than those of the spiral scanning technique. In this local study, the sequential technique was preferred over the spiral technique for both overall image quality and differentiation between gray and white matter in brain CT scans. Other similar studies counter this finding. The radiation dose seen with the sequential CT scanning technique was significantly higher than that seen with the spiral CT scanning technique. However, image quality with the sequential technique was statistically significantly superior (P <.05).

Comparative evaluation of diode laser ablation and surgical stripping technique for gingival depigmentation: A clinical and immunohistochemical study.

PubMed

Bakutra, Gaurav; Shankarapillai, Rajesh; Mathur, Lalit; Manohar, Balaji

2017-01-01

There are various treatment modalities to remove the black patches of melanin pigmentation. The aim of the study is to clinically compare the diode laser ablation and surgical stripping technique for gingival depigmentation and to evaluate their effect on the histological changes in melanocyte activity. A total of 40 sites of 20 patients with bilateral melanin hyperpigmentation were treated with the surgical stripping and diode laser ablation technique. Change in Hedin index score, change in area of pigmentation using image analyzing software, pain perception, patient preference of treatment were recorded. All 40 sites were selected for immunohistochemical analysis using HMB-45 immunohistochemical marker. At 12 months post-operative visit, in all sites, repigmentation was observed with different grades of Hedin index. Paired t -test, analysis of variance, and Chi-square tests were used for statistical analysis. Repigmentation in surgical stripping is significantly lesser compared to laser ablation. Lesser numbers of melanocytes were found on immunohistological examination at 12 months postoperatively. Comparison for patient preference and pain indices give statistically significant values for diode laser techniques. Gingival hyperpigmentation is effectively managed by diode laser ablation technique and surgical stripping method. In this study, surgical stripping technique found to be better compared to diode laser ablation.

Comparative evaluation of diode laser ablation and surgical stripping technique for gingival depigmentation: A clinical and immunohistochemical study

PubMed Central

Bakutra, Gaurav; Shankarapillai, Rajesh; Mathur, Lalit; Manohar, Balaji

2017-01-01

Introduction: There are various treatment modalities to remove the black patches of melanin pigmentation. The aim of the study is to clinically compare the diode laser ablation and surgical stripping technique for gingival depigmentation and to evaluate their effect on the histological changes in melanocyte activity. Materials and Methods: A total of 40 sites of 20 patients with bilateral melanin hyperpigmentation were treated with the surgical stripping and diode laser ablation technique. Change in Hedin index score, change in area of pigmentation using image analyzing software, pain perception, patient preference of treatment were recorded. All 40 sites were selected for immunohistochemical analysis using HMB-45 immunohistochemical marker. Results: At 12 months post-operative visit, in all sites, repigmentation was observed with different grades of Hedin index. Paired t-test, analysis of variance, and Chi-square tests were used for statistical analysis. Repigmentation in surgical stripping is significantly lesser compared to laser ablation. Lesser numbers of melanocytes were found on immunohistological examination at 12 months postoperatively. Comparison for patient preference and pain indices give statistically significant values for diode laser techniques. Conclusion: Gingival hyperpigmentation is effectively managed by diode laser ablation technique and surgical stripping method. In this study, surgical stripping technique found to be better compared to diode laser ablation. PMID:28539864

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

The use and misuse of statistical analyses. [in geophysics and space physics

NASA Technical Reports Server (NTRS)

Reiff, P. H.

1983-01-01

The statistical techniques most often used in space physics include Fourier analysis, linear correlation, auto- and cross-correlation, power spectral density, and superposed epoch analysis. Tests are presented which can evaluate the significance of the results obtained through each of these. Data presented without some form of error analysis are frequently useless, since they offer no way of assessing whether a bump on a spectrum or on a superposed epoch analysis is real or merely a statistical fluctuation. Among many of the published linear correlations, for instance, the uncertainty in the intercept and slope is not given, so that the significance of the fitted parameters cannot be assessed.

Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

NASA Technical Reports Server (NTRS)

Hailperin, M.

1993-01-01

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.

Gene Identification Algorithms Using Exploratory Statistical Analysis of Periodicity

NASA Astrophysics Data System (ADS)

Mukherjee, Shashi Bajaj; Sen, Pradip Kumar

2010-10-01

Studying periodic pattern is expected as a standard line of attack for recognizing DNA sequence in identification of gene and similar problems. But peculiarly very little significant work is done in this direction. This paper studies statistical properties of DNA sequences of complete genome using a new technique. A DNA sequence is converted to a numeric sequence using various types of mappings and standard Fourier technique is applied to study the periodicity. Distinct statistical behaviour of periodicity parameters is found in coding and non-coding sequences, which can be used to distinguish between these parts. Here DNA sequences of Drosophila melanogaster were analyzed with significant accuracy.

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Are conventional statistical techniques exhaustive for defining metal background concentrations in harbour sediments? A case study: The Coastal Area of Bari (Southeast Italy).

PubMed

Mali, Matilda; Dell'Anna, Maria Michela; Mastrorilli, Piero; Damiani, Leonardo; Ungaro, Nicola; Belviso, Claudia; Fiore, Saverio

2015-11-01

Sediment contamination by metals poses significant risks to coastal ecosystems and is considered to be problematic for dredging operations. The determination of the background values of metal and metalloid distribution based on site-specific variability is fundamental in assessing pollution levels in harbour sediments. The novelty of the present work consists of addressing the scope and limitation of analysing port sediments through the use of conventional statistical techniques (such as: linear regression analysis, construction of cumulative frequency curves and the iterative 2σ technique), that are commonly employed for assessing Regional Geochemical Background (RGB) values in coastal sediments. This study ascertained that although the tout court use of such techniques in determining the RGB values in harbour sediments seems appropriate (the chemical-physical parameters of port sediments fit well with statistical equations), it should nevertheless be avoided because it may be misleading and can mask key aspects of the study area that can only be revealed by further investigations, such as mineralogical and multivariate statistical analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using Multilevel Modeling in Language Assessment Research: A Conceptual Introduction

ERIC Educational Resources Information Center

Barkaoui, Khaled

2013-01-01

This article critiques traditional single-level statistical approaches (e.g., multiple regression analysis) to examining relationships between language test scores and variables in the assessment setting. It highlights the conceptual, methodological, and statistical problems associated with these techniques in dealing with multilevel or nested…

Powerlaw: a Python package for analysis of heavy-tailed distributions.

PubMed

Alstott, Jeff; Bullmore, Ed; Plenz, Dietmar

2014-01-01

Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1990-01-01

Descriptive and analytical techniques for NASA trend analysis applications are presented in this standard. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. This document should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend analysis is neither a precise term nor a circumscribed methodology: it generally connotes quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this document. The basic ideas needed for qualitative and quantitative assessment of trends along with relevant examples are presented.

Statistical approach for selection of biologically informative genes.

PubMed

Das, Samarendra; Rai, Anil; Mishra, D C; Rai, Shesh N

2018-05-20

Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes from high dimensional expression data for breeding and system biology studies. Published by Elsevier B.V.

Variation in reaction norms: Statistical considerations and biological interpretation.

PubMed

Morrissey, Michael B; Liefting, Maartje

2016-09-01

Analysis of reaction norms, the functions by which the phenotype produced by a given genotype depends on the environment, is critical to studying many aspects of phenotypic evolution. Different techniques are available for quantifying different aspects of reaction norm variation. We examine what biological inferences can be drawn from some of the more readily applicable analyses for studying reaction norms. We adopt a strongly biologically motivated view, but draw on statistical theory to highlight strengths and drawbacks of different techniques. In particular, consideration of some formal statistical theory leads to revision of some recently, and forcefully, advocated opinions on reaction norm analysis. We clarify what simple analysis of the slope between mean phenotype in two environments can tell us about reaction norms, explore the conditions under which polynomial regression can provide robust inferences about reaction norm shape, and explore how different existing approaches may be used to draw inferences about variation in reaction norm shape. We show how mixed model-based approaches can provide more robust inferences than more commonly used multistep statistical approaches, and derive new metrics of the relative importance of variation in reaction norm intercepts, slopes, and curvatures. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Statistical analysis of secondary particle distributions in relativistic nucleus-nucleus collisions

NASA Technical Reports Server (NTRS)

Mcguire, Stephen C.

1987-01-01

The use is described of several statistical techniques to characterize structure in the angular distributions of secondary particles from nucleus-nucleus collisions in the energy range 24 to 61 GeV/nucleon. The objective of this work was to determine whether there are correlations between emitted particle intensity and angle that may be used to support the existence of the quark gluon plasma. The techniques include chi-square null hypothesis tests, the method of discrete Fourier transform analysis, and fluctuation analysis. We have also used the method of composite unit vectors to test for azimuthal asymmetry in a data set of 63 JACEE-3 events. Each method is presented in a manner that provides the reader with some practical detail regarding its application. Of those events with relatively high statistics, Fe approaches 0 at 55 GeV/nucleon was found to possess an azimuthal distribution with a highly non-random structure. No evidence of non-statistical fluctuations was found in the pseudo-rapidity distributions of the events studied. It is seen that the most effective application of these methods relies upon the availability of many events or single events that possess very high multiplicities.

FT. Sam 91 Whiskey Combat Medic Medical Simulation Training Quantitative Integration Enhancement Program

DTIC Science & Technology

2011-07-01

joined the project team in the statistical and research coordination role. Dr. Collin is an employee at the University of Pittsburgh. A successful...3. Submit to Ft. Detrick Completed Milestone: Statistical analysis planning 1. Review planned data metrics and data gathering tools...approach to performance assessment for continuous quality improvement.  Analyzing data with modern statistical techniques to determine the

Common pitfalls in statistical analysis: Measures of agreement.

PubMed

Ranganathan, Priya; Pramesh, C S; Aggarwal, Rakesh

2017-01-01

Agreement between measurements refers to the degree of concordance between two (or more) sets of measurements. Statistical methods to test agreement are used to assess inter-rater variability or to decide whether one technique for measuring a variable can substitute another. In this article, we look at statistical measures of agreement for different types of data and discuss the differences between these and those for assessing correlation.

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Proceedings of the second annual Forest Inventory and Analysis symposium; Salt Lake City, UT. October 17-18, 2000

Treesearch

Gregory A. Reams; Ronald E. McRoberts; Paul C. van Deusen; [Editors

2001-01-01

Documents progress in developing techniques in remote sensing, statistics, information management, and analysis required for full implementation of the national Forest Inventory and Analysis program’s annual forest inventory system.

Secondary Analysis of National Longitudinal Transition Study 2 Data

ERIC Educational Resources Information Center

Hicks, Tyler A.; Knollman, Greg A.

2015-01-01

This review examines published secondary analyses of National Longitudinal Transition Study 2 (NLTS2) data, with a primary focus upon statistical objectives, paradigms, inferences, and methods. Its primary purpose was to determine which statistical techniques have been common in secondary analyses of NLTS2 data. The review begins with an…

Genetic structure of populations and differentiation in forest trees

Treesearch

Raymond P. Guries; F. Thomas Ledig

1981-01-01

Electrophoretic techniques permit population biologists to analyze genetic structure of natural populations by using large numbers of allozyme loci. Several methods of analysis have been applied to allozyme data, including chi-square contingency tests, F-statistics, and genetic distance. This paper compares such statistics for pitch pine (Pinus rigida...

STATISTICAL TECHNIQUES FOR DETERMINATION AND PREDICTION OF FUNDAMENTAL FISH ASSEMBLAGES OF THE MID-ATLANTIC HIGHLANDS

EPA Science Inventory

A statistical software tool, Stream Fish Community Predictor (SFCP), based on EMAP stream sampling in the mid-Atlantic Highlands, was developed to predict stream fish communities using stream and watershed characteristics. Step one in the tool development was a cluster analysis t...

Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

It is now feasible to examine the composition and diversity of microbial communities (i.e., “microbiomes”) that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology “Metastats” across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained on a small to moderate number of samples. PMID:26734061

Use of density equalizing map projections (DEMP) in the analysis of childhood cancer in four California counties

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

Merrill, D.W.; Selvin, S.; Close, E.R.

In studying geographic disease distributions, one normally compares rates of arbitrarily defined geographic subareas (e.g. census tracts), thereby sacrificing the geographic detail of the original data. The sparser the data, the larger the subareas must be in order to calculate stable rates. This dilemma is avoided with the technique of Density Equalizing Map Projections (DEMP). Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease-rates are constant. On the transformed map, the statistical analysis of the observed distribution ismore » greatly simplified. Even for sparse distributions, the statistical significance of a supposed disease cluster can be reliably calculated. The present report describes the first successful application of the DEMP technique to a sizeable ``real-world`` data set of epidemiologic interest. An improved DEMP algorithm [GUSE93, CLOS94] was applied to a data set previously analyzed with conventional techniques [SATA90, REYN91]. The results from the DEMP analysis and a conventional analysis are compared.« less

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.

Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis.

PubMed

Hoch, Jeffrey S; Briggs, Andrew H; Willan, Andrew R

2002-07-01

Economic evaluation is often seen as a branch of health economics divorced from mainstream econometric techniques. Instead, it is perceived as relying on statistical methods for clinical trials. Furthermore, the statistic of interest in cost-effectiveness analysis, the incremental cost-effectiveness ratio is not amenable to regression-based methods, hence the traditional reliance on comparing aggregate measures across the arms of a clinical trial. In this paper, we explore the potential for health economists undertaking cost-effectiveness analysis to exploit the plethora of established econometric techniques through the use of the net-benefit framework - a recently suggested reformulation of the cost-effectiveness problem that avoids the reliance on cost-effectiveness ratios and their associated statistical problems. This allows the formulation of the cost-effectiveness problem within a standard regression type framework. We provide an example with empirical data to illustrate how a regression type framework can enhance the net-benefit method. We go on to suggest that practical advantages of the net-benefit regression approach include being able to use established econometric techniques, adjust for imperfect randomisation, and identify important subgroups in order to estimate the marginal cost-effectiveness of an intervention. Copyright 2002 John Wiley & Sons, Ltd.

The composite sequential clustering technique for analysis of multispectral scanner data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

Detection of Anomalies in Hydrometric Data Using Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Lauzon, N.; Lence, B. J.

2002-12-01

This work focuses on the detection of anomalies in hydrometric data sequences, such as 1) outliers, which are individual data having statistical properties that differ from those of the overall population; 2) shifts, which are sudden changes over time in the statistical properties of the historical records of data; and 3) trends, which are systematic changes over time in the statistical properties. For the purpose of the design and management of water resources systems, it is important to be aware of these anomalies in hydrometric data, for they can induce a bias in the estimation of water quantity and quality parameters. These anomalies may be viewed as specific patterns affecting the data, and therefore pattern recognition techniques can be used for identifying them. However, the number of possible patterns is very large for each type of anomaly and consequently large computing capacities are required to account for all possibilities using the standard statistical techniques, such as cluster analysis. Artificial intelligence techniques, such as the Kohonen neural network and fuzzy c-means, are clustering techniques commonly used for pattern recognition in several areas of engineering and have recently begun to be used for the analysis of natural systems. They require much less computing capacity than the standard statistical techniques, and therefore are well suited for the identification of outliers, shifts and trends in hydrometric data. This work constitutes a preliminary study, using synthetic data representing hydrometric data that can be found in Canada. The analysis of the results obtained shows that the Kohonen neural network and fuzzy c-means are reasonably successful in identifying anomalies. This work also addresses the problem of uncertainties inherent to the calibration procedures that fit the clusters to the possible patterns for both the Kohonen neural network and fuzzy c-means. Indeed, for the same database, different sets of clusters can be established with these calibration procedures. A simple method for analyzing uncertainties associated with the Kohonen neural network and fuzzy c-means is developed here. The method combines the results from several sets of clusters, either from the Kohonen neural network or fuzzy c-means, so as to provide an overall diagnosis as to the identification of outliers, shifts and trends. The results indicate an improvement in the performance for identifying anomalies when the method of combining cluster sets is used, compared with when only one cluster set is used.

Independent component analysis for automatic note extraction from musical trills

NASA Astrophysics Data System (ADS)

Brown, Judith C.; Smaragdis, Paris

2004-05-01

The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

Kinematic and kinetic analysis of overhand, sidearm and underhand lacrosse shot techniques.

PubMed

Macaulay, Charles A J; Katz, Larry; Stergiou, Pro; Stefanyshyn, Darren; Tomaghelli, Luciano

2017-12-01

Lacrosse requires the coordinated performance of many complex skills. One of these skills is shooting on the opponents' net using one of three techniques: overhand, sidearm or underhand. The purpose of this study was to (i) determine which technique generated the highest ball velocity and greatest shot accuracy and (ii) identify kinematic and kinetic variables that contribute to a high velocity and high accuracy shot. Twelve elite male lacrosse players participated in this study. Kinematic data were sampled at 250 Hz, while two-dimensional force plates collected ground reaction force data (1000 Hz). Statistical analysis showed significantly greater ball velocity for the sidearm technique than overhand (P < 0.001) and underhand (P < 0.001) techniques. No statistical difference was found for shot accuracy (P > 0.05). Kinematic and kinetic variables were not significantly correlated to shot accuracy or velocity across all shot types; however, when analysed independently, the lead foot horizontal impulse showed a negative correlation with underhand ball velocity (P = 0.042). This study identifies the technique with the highest ball velocity, defines kinematic and kinetic predictors related to ball velocity and provides information to coaches and athletes concerned with improving lacrosse shot performance.

Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks

NASA Astrophysics Data System (ADS)

Fernandez, Carlos; Platero, Carlos; Campoy, Pascual; Aracil, Rafael

1994-11-01

This paper describes some texture-based techniques that can be applied to quality assessment of flat products continuously produced (metal strips, wooden surfaces, cork, textile products, ...). Since the most difficult task is that of inspecting for product appearance, human-like inspection ability is required. A common feature to all these products is the presence of non- deterministic texture on their surfaces. Two main subjects are discussed: statistical techniques for both surface finishing determination and surface defect analysis as well as real-time implementation for on-line inspection in high-speed applications. For surface finishing determination a Gray Level Difference technique is presented to perform over low resolution images, that is, no-zoomed images. Defect analysis is performed by means of statistical texture analysis over defective portions of the surface. On-line implementation is accomplished by means of neural networks. When a defect arises, textural analysis is applied which result in a data-vector, acting as input of a neural net, previously trained in a supervised way. This approach tries to reach on-line performance in automated visual inspection applications when texture is presented in flat product surfaces.

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

A comparative analysis of conventional cytopreparatory and liquid based cytological techniques (Sure Path) in evaluation of serous effusion fluids.

PubMed

Dadhich, Hrishikesh; Toi, Pampa Ch; Siddaraju, Neelaiah; Sevvanthi, Kalidas

2016-11-01

Clinically, detection of malignant cells in serous body fluids is critical, as their presence implies the upstaging of the disease. Cytology of body cavity fluids serves as an important tool when other diagnostic tests cannot be performed. In most laboratories, currently, the effusion fluid samples are analysed chiefly by the conventional cytopreparatory (CCP) technique. Although, there are several studies comparing the liquid-based cytology (LBC), with CCP technique in the field of cervicovaginal cytology; the literature on such comparison with respect to serous body fluid examination is sparse. One hundred samples of serous body fluids were processed by both CCP and LBC techniques. Slides prepared by these techniques were studied using six parameters. A comparative analysis of the advantages and disadvantages of the techniques in detection of malignant cells was carried out with appropriate statistical tests. The samples comprised 52 pleural, 44 peritoneal and four pericardial fluids. No statistically significant difference was noted with respect to cellularity (P values = 0.22), cell distribution (P values = 0.39) and diagnosis of malignancy (P values = 0.20). As for the remaining parameters, LBC provided statistically significant clearer smear background (P values < 0.0001) and shorter screening time (P values < 0.0001), while CPP technique provided a significantly better staining quality (P values 0.01) and sharper cytomorphologic features (P values 0.05). Although, a reduced screening time and clearer smear background are the two major advantages of LBC; the CCP technique provides the better staining quality with sharper cytomorphologic features which is more critical from the cytologic interpretation point of view. Diagn. Cytopathol. 2016;44:874-879. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1988-01-01

This Standard presents descriptive and analytical techniques for NASA trend analysis applications. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. Use of this Standard is not mandatory; however, it should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend Analysis is neither a precise term nor a circumscribed methodology, but rather connotes, generally, quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this Standard. The document presents the basic ideas needed for qualitative and quantitative assessment of trends, together with relevant examples. A list of references provides additional sources of information.

Rescaled earthquake recurrence time statistics: application to microrepeaters

NASA Astrophysics Data System (ADS)

Goltz, Christian; Turcotte, Donald L.; Abaimov, Sergey G.; Nadeau, Robert M.; Uchida, Naoki; Matsuzawa, Toru

2009-01-01

Slip on major faults primarily occurs during `characteristic' earthquakes. The recurrence statistics of characteristic earthquakes play an important role in seismic hazard assessment. A major problem in determining applicable statistics is the short sequences of characteristic earthquakes that are available worldwide. In this paper, we introduce a rescaling technique in which sequences can be superimposed to establish larger numbers of data points. We consider the Weibull and log-normal distributions, in both cases we rescale the data using means and standard deviations. We test our approach utilizing sequences of microrepeaters, micro-earthquakes which recur in the same location on a fault. It seems plausible to regard these earthquakes as a miniature version of the classic characteristic earthquakes. Microrepeaters are much more frequent than major earthquakes, leading to longer sequences for analysis. In this paper, we present results for the analysis of recurrence times for several microrepeater sequences from Parkfield, CA as well as NE Japan. We find that, once the respective sequence can be considered to be of sufficient stationarity, the statistics can be well fitted by either a Weibull or a log-normal distribution. We clearly demonstrate this fact by our technique of rescaled combination. We conclude that the recurrence statistics of the microrepeater sequences we consider are similar to the recurrence statistics of characteristic earthquakes on major faults.

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

Kogalovskii, M.R.

This paper presents a review of problems related to statistical database systems, which are wide-spread in various fields of activity. Statistical databases (SDB) are referred to as databases that consist of data and are used for statistical analysis. Topics under consideration are: SDB peculiarities, properties of data models adequate for SDB requirements, metadata functions, null-value problems, SDB compromise protection problems, stored data compression techniques, and statistical data representation means. Also examined is whether the present Database Management Systems (DBMS) satisfy the SDB requirements. Some actual research directions in SDB systems are considered.

21 CFR 820.250 - Statistical techniques.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Statistical techniques. 820.250 Section 820.250...) MEDICAL DEVICES QUALITY SYSTEM REGULATION Statistical Techniques § 820.250 Statistical techniques. (a... statistical techniques required for establishing, controlling, and verifying the acceptability of process...

21 CFR 820.250 - Statistical techniques.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Statistical techniques. 820.250 Section 820.250...) MEDICAL DEVICES QUALITY SYSTEM REGULATION Statistical Techniques § 820.250 Statistical techniques. (a... statistical techniques required for establishing, controlling, and verifying the acceptability of process...

Examination of two methods for statistical analysis of data with magnitude and direction emphasizing vestibular research applications

NASA Technical Reports Server (NTRS)

Calkins, D. S.

1998-01-01

When the dependent (or response) variable response variable in an experiment has direction and magnitude, one approach that has been used for statistical analysis involves splitting magnitude and direction and applying univariate statistical techniques to the components. However, such treatment of quantities with direction and magnitude is not justifiable mathematically and can lead to incorrect conclusions about relationships among variables and, as a result, to flawed interpretations. This note discusses a problem with that practice and recommends mathematically correct procedures to be used with dependent variables that have direction and magnitude for 1) computation of mean values, 2) statistical contrasts of and confidence intervals for means, and 3) correlation methods.

Statistical Analysis of speckle noise reduction techniques for echocardiographic Images

NASA Astrophysics Data System (ADS)

Saini, Kalpana; Dewal, M. L.; Rohit, Manojkumar

2011-12-01

Echocardiography is the safe, easy and fast technology for diagnosing the cardiac diseases. As in other ultrasound images these images also contain speckle noise. In some cases this speckle noise is useful such as in motion detection. But in general noise removal is required for better analysis of the image and proper diagnosis. Different Adaptive and anisotropic filters are included for statistical analysis. Statistical parameters such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) calculated for performance measurement. One more important aspect that there may be blurring during speckle noise removal. So it is prefered that filter should be able to enhance edges during noise removal.

Factors That Attenuate the Correlation Coefficient and Its Analogs.

ERIC Educational Resources Information Center

Dolenz, Beverly

The correlation coefficient is an integral part of many other statistical techniques (analysis of variance, t-tests, etc.), since all analytic methods are actually correlational (G. V. Glass and K. D. Hopkins, 1984). The correlation coefficient is a statistical summary that represents the degree and direction of relationship between two variables.…

Theory and analysis of statistical discriminant techniques as applied to remote sensing data

NASA Technical Reports Server (NTRS)

Odell, P. L.

1973-01-01

Classification of remote earth resources sensing data according to normed exponential density statistics is reported. The use of density models appropriate for several physical situations provides an exact solution for the probabilities of classifications associated with the Bayes discriminant procedure even when the covariance matrices are unequal.

Improved analyses using function datasets and statistical modeling

Treesearch

John S. Hogland; Nathaniel M. Anderson

2014-01-01

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework...

Sexual Abuse, Family Environment, and Psychological Symptoms: On the Validity of Statistical Control.

ERIC Educational Resources Information Center

Briere, John; Elliott, Diana M.

1993-01-01

Responds to article in which Nash et al. reported on effects of controlling for family environment when studying sexual abuse sequelae. Considers findings in terms of theoretical and statistical constraints placed on analysis of covariance and other partializing procedures. Questions use of covariate techniques to test hypotheses about causal role…

Statistical description of tectonic motions

NASA Technical Reports Server (NTRS)

Agnew, Duncan Carr

1993-01-01

This report summarizes investigations regarding tectonic motions. The topics discussed include statistics of crustal deformation, Earth rotation studies, using multitaper spectrum analysis techniques applied to both space-geodetic data and conventional astrometric estimates of the Earth's polar motion, and the development, design, and installation of high-stability geodetic monuments for use with the global positioning system.

Statistical performance and information content of time lag analysis and redundancy analysis in time series modeling.

PubMed

Angeler, David G; Viedma, Olga; Moreno, José M

2009-11-01

Time lag analysis (TLA) is a distance-based approach used to study temporal dynamics of ecological communities by measuring community dissimilarity over increasing time lags. Despite its increased use in recent years, its performance in comparison with other more direct methods (i.e., canonical ordination) has not been evaluated. This study fills this gap using extensive simulations and real data sets from experimental temporary ponds (true zooplankton communities) and landscape studies (landscape categories as pseudo-communities) that differ in community structure and anthropogenic stress history. Modeling time with a principal coordinate of neighborhood matrices (PCNM) approach, the canonical ordination technique (redundancy analysis; RDA) consistently outperformed the other statistical tests (i.e., TLAs, Mantel test, and RDA based on linear time trends) using all real data. In addition, the RDA-PCNM revealed different patterns of temporal change, and the strength of each individual time pattern, in terms of adjusted variance explained, could be evaluated, It also identified species contributions to these patterns of temporal change. This additional information is not provided by distance-based methods. The simulation study revealed better Type I error properties of the canonical ordination techniques compared with the distance-based approaches when no deterministic component of change was imposed on the communities. The simulation also revealed that strong emphasis on uniform deterministic change and low variability at other temporal scales is needed to result in decreased statistical power of the RDA-PCNM approach relative to the other methods. Based on the statistical performance of and information content provided by RDA-PCNM models, this technique serves ecologists as a powerful tool for modeling temporal change of ecological (pseudo-) communities.

Analysis and Interpretation of Findings Using Multiple Regression Techniques

ERIC Educational Resources Information Center

Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

2006-01-01

Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

Silt fences: An economical technique for measuring hillslope soil erosion

Treesearch

Peter R. Robichaud; Robert E. Brown

2002-01-01

Measuring hillslope erosion has historically been a costly, time-consuming practice. An easy to install low-cost technique using silt fences (geotextile fabric) and tipping bucket rain gauges to measure onsite hillslope erosion was developed and tested. Equipment requirements, installation procedures, statistical design, and analysis methods for measuring hillslope...

Estimating sunspot number

NASA Technical Reports Server (NTRS)

Wilson, R. M.; Reichmann, E. J.; Teuber, D. L.

1984-01-01

An empirical method is developed to predict certain parameters of future solar activity cycles. Sunspot cycle statistics are examined, and curve fitting and linear regression analysis techniques are utilized.

[Comparative study of the repair of full thickness tear of the supraspinatus by means of "single row" or "suture bridge" techniques].

PubMed

Arroyo-Hernández, M; Mellado-Romero, M A; Páramo-Díaz, P; Martín-López, C M; Cano-Egea, J M; Vilá Y Rico, J

2015-01-01

The purpose of this study is to analyze if there is any difference between the arthroscopic reparation of full-thickness supraspinatus tears with simple row technique versus suture bridge technique. We accomplished a retrospective study of 123 patients with full-thickness supraspinatus tears between January 2009 and January 2013 in our hospital. There were 60 simple row reparations, and 63 suture bridge ones. The mean age in the simple row group was 62.9, and in the suture bridge group was 63.3 years old. There were more women than men in both groups (67%). All patients were studied using the Constant test. The mean Constant test in the suture bridge group was 76.7, and in the simple row group was 72.4. We have also accomplished a statistical analysis of each Constant item. Strength was higher in the suture bridge group, with a significant statistical difference (p 0.04). The range of movement was also greater in the suture bridge group, but was not statistically significant. Suture bridge technique has better clinical results than single row reparations, but the difference is not statistically significant (p = 0.298).

A Monte Carlo study of Weibull reliability analysis for space shuttle main engine components

NASA Technical Reports Server (NTRS)

Abernethy, K.

1986-01-01

The incorporation of a number of additional capabilities into an existing Weibull analysis computer program and the results of Monte Carlo computer simulation study to evaluate the usefulness of the Weibull methods using samples with a very small number of failures and extensive censoring are discussed. Since the censoring mechanism inherent in the Space Shuttle Main Engine (SSME) data is hard to analyze, it was decided to use a random censoring model, generating censoring times from a uniform probability distribution. Some of the statistical techniques and computer programs that are used in the SSME Weibull analysis are described. The methods documented in were supplemented by adding computer calculations of approximate (using iteractive methods) confidence intervals for several parameters of interest. These calculations are based on a likelihood ratio statistic which is asymptotically a chisquared statistic with one degree of freedom. The assumptions built into the computer simulations are described. The simulation program and the techniques used in it are described there also. Simulation results are tabulated for various combinations of Weibull shape parameters and the numbers of failures in the samples.

Point pattern analysis of FIA data

Treesearch

Chris Woodall

2002-01-01

Point pattern analysis is a branch of spatial statistics that quantifies the spatial distribution of points in two-dimensional space. Point pattern analysis was conducted on stand stem-maps from FIA fixed-radius plots to explore point pattern analysis techniques and to determine the ability of pattern descriptions to describe stand attributes. Results indicate that the...

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.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

2015-05-01

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Image analysis library software development

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.; Bryant, J.

1977-01-01

The Image Analysis Library consists of a collection of general purpose mathematical/statistical routines and special purpose data analysis/pattern recognition routines basic to the development of image analysis techniques for support of current and future Earth Resources Programs. Work was done to provide a collection of computer routines and associated documentation which form a part of the Image Analysis Library.

MetaGenyo: a web tool for meta-analysis of genetic association studies.

PubMed

Martorell-Marugan, Jordi; Toro-Dominguez, Daniel; Alarcon-Riquelme, Marta E; Carmona-Saez, Pedro

2017-12-16

Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .

Statistics 101 for Radiologists.

PubMed

Anvari, Arash; Halpern, Elkan F; Samir, Anthony E

2015-10-01

Diagnostic tests have wide clinical applications, including screening, diagnosis, measuring treatment effect, and determining prognosis. Interpreting diagnostic test results requires an understanding of key statistical concepts used to evaluate test efficacy. This review explains descriptive statistics and discusses probability, including mutually exclusive and independent events and conditional probability. In the inferential statistics section, a statistical perspective on study design is provided, together with an explanation of how to select appropriate statistical tests. Key concepts in recruiting study samples are discussed, including representativeness and random sampling. Variable types are defined, including predictor, outcome, and covariate variables, and the relationship of these variables to one another. In the hypothesis testing section, we explain how to determine if observed differences between groups are likely to be due to chance. We explain type I and II errors, statistical significance, and study power, followed by an explanation of effect sizes and how confidence intervals can be used to generalize observed effect sizes to the larger population. Statistical tests are explained in four categories: t tests and analysis of variance, proportion analysis tests, nonparametric tests, and regression techniques. We discuss sensitivity, specificity, accuracy, receiver operating characteristic analysis, and likelihood ratios. Measures of reliability and agreement, including κ statistics, intraclass correlation coefficients, and Bland-Altman graphs and analysis, are introduced. © RSNA, 2015.

A simple white noise analysis of neuronal light responses.

PubMed

Chichilnisky, E J

2001-05-01

A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

Differential Item Functioning Analysis Using Rasch Item Information Functions

ERIC Educational Resources Information Center

Wyse, Adam E.; Mapuranga, Raymond

2009-01-01

Differential item functioning (DIF) analysis is a statistical technique used for ensuring the equity and fairness of educational assessments. This study formulates a new DIF analysis method using the information similarity index (ISI). ISI compares item information functions when data fits the Rasch model. Through simulations and an international…

Molecular Modeling in Drug Design for the Development of Organophosphorus Antidotes/Prophylactics.

DTIC Science & Technology

1986-06-01

multidimensional statistical QSAR analysis techniques to suggest new structures for synthesis and evaluation. C. Application of quantum chemical techniques to...compounds for synthesis and testing for antidotal potency. E. Use of computer-assisted methods to determine the steric constraints at the active site...modeling techniques to model the enzyme acetylcholinester-se. H. Suggestion of some novel compounds for synthesis and testing for reactivating

A Review of the Study Designs and Statistical Methods Used in the Determination of Predictors of All-Cause Mortality in HIV-Infected Cohorts: 2002–2011

PubMed Central

Otwombe, Kennedy N.; Petzold, Max; Martinson, Neil; Chirwa, Tobias

2014-01-01

Background Research in the predictors of all-cause mortality in HIV-infected people has widely been reported in literature. Making an informed decision requires understanding the methods used. Objectives We present a review on study designs, statistical methods and their appropriateness in original articles reporting on predictors of all-cause mortality in HIV-infected people between January 2002 and December 2011. Statistical methods were compared between 2002–2006 and 2007–2011. Time-to-event analysis techniques were considered appropriate. Data Sources Pubmed/Medline. Study Eligibility Criteria Original English-language articles were abstracted. Letters to the editor, editorials, reviews, systematic reviews, meta-analysis, case reports and any other ineligible articles were excluded. Results A total of 189 studies were identified (n = 91 in 2002–2006 and n = 98 in 2007–2011) out of which 130 (69%) were prospective and 56 (30%) were retrospective. One hundred and eighty-two (96%) studies described their sample using descriptive statistics while 32 (17%) made comparisons using t-tests. Kaplan-Meier methods for time-to-event analysis were commonly used in the earlier period (n = 69, 76% vs. n = 53, 54%, p = 0.002). Predictors of mortality in the two periods were commonly determined using Cox regression analysis (n = 67, 75% vs. n = 63, 64%, p = 0.12). Only 7 (4%) used advanced survival analysis methods of Cox regression analysis with frailty in which 6 (3%) were used in the later period. Thirty-two (17%) used logistic regression while 8 (4%) used other methods. There were significantly more articles from the first period using appropriate methods compared to the second (n = 80, 88% vs. n = 69, 70%, p-value = 0.003). Conclusion Descriptive statistics and survival analysis techniques remain the most common methods of analysis in publications on predictors of all-cause mortality in HIV-infected cohorts while prospective research designs are favoured. Sophisticated techniques of time-dependent Cox regression and Cox regression with frailty are scarce. This motivates for more training in the use of advanced time-to-event methods. PMID:24498313

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).

The Use of Satellite Observed Cloud Patterns in Northern Hemisphere 300 mb and 1000/300 mb Numerical Analysis.

DTIC Science & Technology

1984-02-01

prediction Extratropical cyclones Objective analysis Bogus techniques 20. ABSTRACT (Continue on reverse aide If necooearn mid Identify by block number) Jh A...quasi-objective statistical method for deriving 300 mb geopotential heights and 1000/300 mb thicknesses in the vicinity of extratropical cyclones 0I...with the aid of satellite imagery is presented. The technique utilizes satellite observed extratropical spiral cloud pattern parameters in conjunction

Tackling missing radiographic progression data: multiple imputation technique compared with inverse probability weights and complete case analysis.

PubMed

Descalzo, Miguel Á; Garcia, Virginia Villaverde; González-Alvaro, Isidoro; Carbonell, Jordi; Balsa, Alejandro; Sanmartí, Raimon; Lisbona, Pilar; Hernandez-Barrera, Valentín; Jiménez-Garcia, Rodrigo; Carmona, Loreto

2013-02-01

To describe the results of different statistical ways of addressing radiographic outcome affected by missing data--multiple imputation technique, inverse probability weights and complete case analysis--using data from an observational study. A random sample of 96 RA patients was selected for a follow-up study in which radiographs of hands and feet were scored. Radiographic progression was tested by comparing the change in the total Sharp-van der Heijde radiographic score (TSS) and the joint erosion score (JES) from baseline to the end of the second year of follow-up. MI technique, inverse probability weights in weighted estimating equation (WEE) and CC analysis were used to fit a negative binomial regression. Major predictors of radiographic progression were JES and joint space narrowing (JSN) at baseline, together with baseline disease activity measured by DAS28 for TSS and MTX use for JES. Results from CC analysis show larger coefficients and s.e.s compared with MI and weighted techniques. The results from the WEE model were quite in line with those of MI. If it seems plausible that CC or MI analysis may be valid, then MI should be preferred because of its greater efficiency. CC analysis resulted in inefficient estimates or, translated into non-statistical terminology, could guide us into inaccurate results and unwise conclusions. The methods discussed here will contribute to the use of alternative approaches for tackling missing data in observational studies.

Comparative evaluation of marginal fit and axial wall adaptability of copings fabricated by metal laser sintering and lost-wax technique: An in vitro study.

PubMed

Gaikwad, Bhushan Satish; Nazirkar, Girish; Dable, Rajani; Singh, Shailendra

2018-01-01

The present study aims to compare and evaluate the marginal fit and axial wall adaptability of Co-Cr copings fabricated by metal laser sintering (MLS) and lost-wax (LW) techniques using a stereomicroscope. A stainless steel master die assembly was fabricated simulating a prepared crown; 40 replicas of master die were fabricated in gypsum type IV and randomly divided in two equal groups. Group A coping was fabrication by LW technique and the Group B coping fabrication by MLS technique. The copings were seated on their respective gypsum dies and marginal fit was measured using stereomicroscope and image analysis software. For evaluation of axial wall adaptability, the coping and die assembly were embedded in autopolymerizing acrylic resin and sectioned vertically. The discrepancies between the dies and copings were measured along the axial wall on each halves. The data were subjected to statistical analysis using unpaired t -test. The mean values of marginal fit for copings in Group B (MLS) were lower (24.6 μm) than the copings in Group A (LW) (39.53 μm), and the difference was statistically significant ( P < 0.05). The mean axial wall discrepancy value was lower for Group B (31.03 μm) as compared with Group A (54.49 μm) and the difference was statistically significant ( P < 0.05). The copings fabricated by MLS technique had better marginal fit and axial wall adaptability in comparison with copings fabricated by the LW technique. However, the values of marginal fit of copings fabricated that the two techniques were within the clinically acceptable limit (<50 μm).

SMALL COLOUR VISION VARIATIONS AND THEIR EFFECT IN VISUAL COLORIMETRY,

DTIC Science & Technology

COLOR VISION, PERFORMANCE(HUMAN), TEST EQUIPMENT, PERFORMANCE(HUMAN), CORRELATION TECHNIQUES, STATISTICAL PROCESSES, COLORS, ANALYSIS OF VARIANCE, AGING(MATERIALS), COLORIMETRY , BRIGHTNESS, ANOMALIES, PLASTICS, UNITED KINGDOM.

Wave Propagation Measurements on Two-Dimensional Lattice.

DTIC Science & Technology

1985-09-15

of boundaries, lattice member connectivities, and structural defects on these parameters. Perhaps, statistical energy analysis or pattern recognition techniques would also be of benefit in such efforts.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

Demonstration of Wavelet Techniques in the Spectral Analysis of Bypass Transition Data

NASA Technical Reports Server (NTRS)

Lewalle, Jacques; Ashpis, David E.; Sohn, Ki-Hyeon

1997-01-01

A number of wavelet-based techniques for the analysis of experimental data are developed and illustrated. A multiscale analysis based on the Mexican hat wavelet is demonstrated as a tool for acquiring physical and quantitative information not obtainable by standard signal analysis methods. Experimental data for the analysis came from simultaneous hot-wire velocity traces in a bypass transition of the boundary layer on a heated flat plate. A pair of traces (two components of velocity) at one location was excerpted. A number of ensemble and conditional statistics related to dominant time scales for energy and momentum transport were calculated. The analysis revealed a lack of energy-dominant time scales inside turbulent spots but identified transport-dominant scales inside spots that account for the largest part of the Reynolds stress. Momentum transport was much more intermittent than were energetic fluctuations. This work is the first step in a continuing study of the spatial evolution of these scale-related statistics, the goal being to apply the multiscale analysis results to improve the modeling of transitional and turbulent industrial flows.

An unsupervised classification technique for multispectral remote sensing data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Cummings, R. E.

1973-01-01

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

Principal component analysis of the cytokine and chemokine response to human traumatic brain injury.

PubMed

Helmy, Adel; Antoniades, Chrystalina A; Guilfoyle, Mathew R; Carpenter, Keri L H; Hutchinson, Peter J

2012-01-01

There is a growing realisation that neuro-inflammation plays a fundamental role in the pathology of Traumatic Brain Injury (TBI). This has led to the search for biomarkers that reflect these underlying inflammatory processes using techniques such as cerebral microdialysis. The interpretation of such biomarker data has been limited by the statistical methods used. When analysing data of this sort the multiple putative interactions between mediators need to be considered as well as the timing of production and high degree of statistical co-variance in levels of these mediators. Here we present a cytokine and chemokine dataset from human brain following human traumatic brain injury and use principal component analysis and partial least squares discriminant analysis to demonstrate the pattern of production following TBI, distinct phases of the humoral inflammatory response and the differing patterns of response in brain and in peripheral blood. This technique has the added advantage of making no assumptions about the Relative Recovery (RR) of microdialysis derived parameters. Taken together these techniques can be used in complex microdialysis datasets to summarise the data succinctly and generate hypotheses for future study.

Pyrotechnic Shock Analysis Using Statistical Energy Analysis

DTIC Science & Technology

2015-10-23

SEA subsystems. A couple of validation examples are provided to demonstrate the new approach. KEY WORDS : Peak Ratio, phase perturbation...Ballistic Shock Prediction Models and Techniques for Use in the Crusader Combat Vehicle Program,” 11th Annual US Army Ground Vehicle Survivability

Methods of analysis and resources available for genetic trait mapping.

PubMed

Ott, J

1999-01-01

Methods of genetic linkage analysis are reviewed and put in context with other mapping techniques. Sources of information are outlined (books, web sites, computer programs). Special consideration is given to statistical problems in canine genetic mapping (heterozygosity, inbreeding, marker maps).

Treatment of control data in lunar phototriangulation. [application of statistical procedures and development of mathematical and computer techniques

NASA Technical Reports Server (NTRS)

Wong, K. W.

1974-01-01

In lunar phototriangulation, there is a complete lack of accurate ground control points. The accuracy analysis of the results of lunar phototriangulation must, therefore, be completely dependent on statistical procedure. It was the objective of this investigation to examine the validity of the commonly used statistical procedures, and to develop both mathematical techniques and computer softwares for evaluating (1) the accuracy of lunar phototriangulation; (2) the contribution of the different types of photo support data on the accuracy of lunar phototriangulation; (3) accuracy of absolute orientation as a function of the accuracy and distribution of both the ground and model points; and (4) the relative slope accuracy between any triangulated pass points.

Digital communications: Microwave applications

NASA Astrophysics Data System (ADS)

Feher, K.

Transmission concepts and techniques of digital systems are presented; and practical state-of-the-art implementation of digital communications systems by line-of-sight microwaves is described. Particular consideration is given to statistical methods in digital transmission systems analysis, digital modulation methods, microwave amplifiers, system gain, m-ary and QAM microwave systems, correlative techniques and applications to digital radio systems, hybrid systems, digital microwave systems design, diversity and protection switching techniques, measurement techniques, and research and development trends and unsolved problems.

Biomechanical evaluation of oversized drilling technique on primary implant stability measured by insertion torque and resonance frequency analysis

PubMed Central

Santamaría-Arrieta, Gorka; Brizuela-Velasco, Aritza; Fernández-González, Felipe J.; Chávarri-Prado, David; Chento-Valiente, Yelko; Solaberrieta, Eneko; Diéguez-Pereira, Markel; Yurrebaso-Asúa, Jaime

2016-01-01

Background This study evaluated the influence of implant site preparation depth on primary stability measured by insertion torque and resonance frequency analysis (RFA). Material and Methods Thirty-two implant sites were prepared in eight veal rib blocks. Sixteen sites were prepared using the conventional drilling sequence recommended by the manufacturer to a working depth of 10mm. The remaining 16 sites were prepared using an oversize drilling technique (overpreparation) to a working depth of 12mm. Bone density was determined using cone beam computerized tomography (CBCT). The implants were placed and primary stability was measured by two methods: insertion torque (Ncm), and RFA (implant stability quotient [ISQ]). Results The highest torque values were achieved by the conventional drilling technique (10mm). The ANOVA test confirmed that there was a significant correlation between torque and drilling depth (p<0.05). However, no statistically significant differences were obtained between ISQ values at 10 or 12 mm drilling depths (p>0.05) at either measurement direction (cortical and medullar). No statistical relation between torque and ISQ values was identified, or between bone density and primary stability (p >0.05). Conclusions Vertical overpreparation of the implant bed will obtain lower insertion torque values, but does not produce statistically significant differences in ISQ values. Key words:Implant stability quotient, overdrilling, primary stability, resonance frequency analysis, torque. PMID:27398182

Impact of Damping Uncertainty on SEA Model Response Variance

NASA Technical Reports Server (NTRS)

Schiller, Noah; Cabell, Randolph; Grosveld, Ferdinand

2010-01-01

Statistical Energy Analysis (SEA) is commonly used to predict high-frequency vibroacoustic levels. This statistical approach provides the mean response over an ensemble of random subsystems that share the same gross system properties such as density, size, and damping. Recently, techniques have been developed to predict the ensemble variance as well as the mean response. However these techniques do not account for uncertainties in the system properties. In the present paper uncertainty in the damping loss factor is propagated through SEA to obtain more realistic prediction bounds that account for both ensemble and damping variance. The analysis is performed on a floor-equipped cylindrical test article that resembles an aircraft fuselage. Realistic bounds on the damping loss factor are determined from measurements acquired on the sidewall of the test article. The analysis demonstrates that uncertainties in damping have the potential to significantly impact the mean and variance of the predicted response.

GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran)

NASA Astrophysics Data System (ADS)

Haghizadeh, Ali; Moghaddam, Davoud Davoudi; Pourghasemi, Hamid Reza

2017-12-01

Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster-Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.

A Comparative Evaluation of the Efficacy of Different Caries Excavation Techniques in reducing the Cariogenic Flora: An in vivo Study.

PubMed

Hassan, Afrah Fatima; Yadav, Gunjan; Tripathi, Abhay Mani; Mehrotra, Mridul; Saha, Sonali; Garg, Nishita

2016-01-01

Caries excavation is a noninvasive technique of caries removal with maximum preservation of healthy tooth structure. To compare the efficacy of three different caries excavation techniques in reducing the count of cariogenic flora. Sixty healthy primary molars were selected from 26 healthy children with occlusal carious lesions without pulpal involvement and divided into three groups in which caries excavation was done with the help of (1) carbide bur; (2) polymer bur using slow-speed handpiece; and (3) ultrasonic tip with ultrasonic machine. Samples were collected before and after caries excavation for microbiological analysis with the help of sterile sharp spoon excavator. Samples were inoculated on blood agar plate and incubated at 37°C for 48 hours. After bacterial cultivation, the bacterial count of Streptococcus mutans was obtained. All statistical analysis was performed using SPSS 13 statistical software version. Kruskal-Wallis analysis of variance, Wilcoxon matched pairs test, and Z test were performed to reveal the statistical significance. The decrease in bacterial count of S. mutans before and after caries excavation was significant (p < 0.001) in all the three groups. Carbide bur showed most efficient reduction in cariogenic flora, while ultrasonic tip showed almost comparable results, while polymer bur showed least reduction in cariogenic flora after caries excavation. Hassan AF, Yadav G, Tripathi AM, Mehrotra M, Saha S, Garg N. A Comparative Evaluation of the Efficacy of Different Caries Excavation Techniques in reducing the Cariogenic Flora: An in vivo Study. Int J Clin Pediatr Dent 2016;9(3):214-217.

An overview of groundwater chemistry studies in Malaysia.

PubMed

Kura, Nura Umar; Ramli, Mohammad Firuz; Sulaiman, Wan Nor Azmin; Ibrahim, Shaharin; Aris, Ahmad Zaharin

2018-03-01

In this paper, numerous studies on groundwater in Malaysia were reviewed with the aim of evaluating past trends and the current status for discerning the sustainability of the water resources in the country. It was found that most of the previous groundwater studies (44 %) focused on the islands and mostly concentrated on qualitative assessment with more emphasis being placed on seawater intrusion studies. This was then followed by inland-based studies, with Selangor state leading the studies which reflected the current water challenges facing the state. From a methodological perspective, geophysics, graphical methods, and statistical analysis are the dominant techniques (38, 25, and 25 %) respectively. The geophysical methods especially the 2D resistivity method cut across many subjects such as seawater intrusion studies, quantitative assessment, and hydraulic parameters estimation. The statistical techniques used include multivariate statistical analysis techniques and ANOVA among others, most of which are quality related studies using major ions, in situ parameters, and heavy metals. Conversely, numerical techniques like MODFLOW were somewhat less admired which is likely due to their complexity in nature and high data demand. This work will facilitate researchers in identifying the specific areas which need improvement and focus, while, at the same time, provide policymakers and managers with an executive summary and knowledge of the current situation in groundwater studies and where more work needs to be done for sustainable development.

A guide to understanding meta-analysis.

PubMed

Israel, Heidi; Richter, Randy R

2011-07-01

With the focus on evidence-based practice in healthcare, a well-conducted systematic review that includes a meta-analysis where indicated represents a high level of evidence for treatment effectiveness. The purpose of this commentary is to assist clinicians in understanding meta-analysis as a statistical tool using both published articles and explanations of components of the technique. We describe what meta-analysis is, what heterogeneity is, and how it affects meta-analysis, effect size, the modeling techniques of meta-analysis, and strengths and weaknesses of meta-analysis. Common components like forest plot interpretation, software that may be used, special cases for meta-analysis, such as subgroup analysis, individual patient data, and meta-regression, and a discussion of criticisms, are included.

Scaling up to address data science challenges

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

Wendelberger, Joanne R.

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Scaling up to address data science challenges

DOE PAGES

Wendelberger, Joanne R.

2017-04-27

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Statistical analysis of flight times for space shuttle ferry flights

NASA Technical Reports Server (NTRS)

Graves, M. E.; Perlmutter, M.

1974-01-01

Markov chain and Monte Carlo analysis techniques are applied to the simulated Space Shuttle Orbiter Ferry flights to obtain statistical distributions of flight time duration between Edwards Air Force Base and Kennedy Space Center. The two methods are compared, and are found to be in excellent agreement. The flights are subjected to certain operational and meteorological requirements, or constraints, which cause eastbound and westbound trips to yield different results. Persistence of events theory is applied to the occurrence of inclement conditions to find their effect upon the statistical flight time distribution. In a sensitivity test, some of the constraints are varied to observe the corresponding changes in the results.

Signal Detection Techniques for Diagnostic Monitoring of Space Shuttle Main Engine Turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Jong, Jen-Yi

1986-01-01

An investigation to develop, implement, and evaluate signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery is reviewed. A brief description of the Space Shuttle Main Engine (SSME) test/measurement program is presented. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques have been implemented on a computer and applied to dynamc signals. A laboratory evaluation of the methods with respect to signal detection capability is described. A unique coherence function (the hyper-coherence) was developed through the course of this investigation, which appears promising as a diagnostic tool. This technique and several other non-linear methods of signal analysis are presented and illustrated by application. Software for application of these techniques has been installed on the signal processing system at the NASA/MSFC Systems Dynamics Laboratory.

Performance analysis of clustering techniques over microarray data: A case study

NASA Astrophysics Data System (ADS)

Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

Statistical Measurements of Contact Conditions of 478 Transport-airplane Landings During Routine Daytime Operations

NASA Technical Reports Server (NTRS)

Silsby, Norman S

1955-01-01

Statistical measurements of contact conditions have been obtained, by means of a special photographic technique, of 478 landings of present-day transport airplanes made during routine daylight operations in clear air at the Washington National Airport. From the measurements, sinking speeds, rolling velocities, bank angles, and horizontal speeds at the instant before contact have been evaluated and a limited statistical analysis of the results has been made and is reported in this report.

Bladder radiotherapy treatment: A retrospective comparison of 3-dimensional conformal radiotherapy, intensity-modulated radiation therapy, and volumetric-modulated arc therapy plans

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

Pasciuti, Katia, E-mail: k.pasciuti@virgilio.it; Kuthpady, Shrinivas; Anderson, Anne

To examine tumor's and organ's response when different radiotherapy plan techniques are used. Ten patients with confirmed bladder tumors were first treated using 3-dimensional conformal radiotherapy (3DCRT) and subsequently the original plans were re-optimized using the intensity-modulated radiation treatment (IMRT) and volumetric-modulated arc therapy (VMAT)-techniques. Targets coverage in terms of conformity and homogeneity index, TCP, and organs' dose limits, including integral dose analysis were evaluated. In addition, MUs and treatment delivery times were compared. Better minimum target coverage (1.3%) was observed in VMAT plans when compared to 3DCRT and IMRT ones confirmed by a statistically significant conformity index (CI) results.more » Large differences were observed among techniques in integral dose results of the femoral heads. Even if no statistically significant differences were reported in rectum and tissue, a large amount of energy deposition was observed in 3DCRT plans. In any case, VMAT plans provided better organs and tissue sparing confirmed also by the normal tissue complication probability (NTCP) analysis as well as a better tumor control probability (TCP) result. Our analysis showed better overall results in planning using VMAT techniques. Furthermore, a total time reduction in treatment observed among techniques including gantry and collimator rotation could encourage using the more recent one, reducing target movements and patient discomfort.« less

Configural Frequency Analysis as a Statistical Tool for Developmental Research.

ERIC Educational Resources Information Center

Lienert, Gustav A.; Oeveste, Hans Zur

1985-01-01

Configural frequency analysis (CFA) is suggested as a technique for longitudinal research in developmental psychology. Stability and change in answers to multiple choice and yes-no item patterns obtained with repeated measurements are identified by CFA and illustrated by developmental analysis of an item from Gorham's Proverb Test. (Author/DWH)

AIDS Education for Tanzanian Youth: A Mediation Analysis

ERIC Educational Resources Information Center

Stigler, Melissa H.; Kugler, K. C.; Komro, K. A.; Leshabari, M. T.; Klepp, K. I.

2006-01-01

Mediation analysis is a statistical technique that can be used to identify mechanisms by which intervention programs achieve their effects. This paper presents the results of a mediation analysis of Ngao, an acquired immunodeficiency syndrome (AIDS) education program that was implemented with school children in Grades 6 and 7 in Tanzania in the…

Visualization and Image Analysis of Yeast Cells.

PubMed

Bagley, Steve

2016-01-01

When converting real-life data via visualization to numbers and then onto statistics the whole system needs to be considered so that conversion from the analogue to the digital is accurate and repeatable. Here we describe the points to consider when approaching yeast cell analysis visualization, processing, and analysis of a population by screening techniques.

Analyzing Immunoglobulin Repertoires

PubMed Central

Chaudhary, Neha; Wesemann, Duane R.

2018-01-01

Somatic assembly of T cell receptor and B cell receptor (BCR) genes produces a vast diversity of lymphocyte antigen recognition capacity. The advent of efficient high-throughput sequencing of lymphocyte antigen receptor genes has recently generated unprecedented opportunities for exploration of adaptive immune responses. With these opportunities have come significant challenges in understanding the analysis techniques that most accurately reflect underlying biological phenomena. In this regard, sample preparation and sequence analysis techniques, which have largely been borrowed and adapted from other fields, continue to evolve. Here, we review current methods and challenges of library preparation, sequencing and statistical analysis of lymphocyte receptor repertoire studies. We discuss the general steps in the process of immune repertoire generation including sample preparation, platforms available for sequencing, processing of sequencing data, measurable features of the immune repertoire, and the statistical tools that can be used for analysis and interpretation of the data. Because BCR analysis harbors additional complexities, such as immunoglobulin (Ig) (i.e., antibody) gene somatic hypermutation and class switch recombination, the emphasis of this review is on Ig/BCR sequence analysis. PMID:29593723

[The evaluation of costs: standards of medical care and clinical statistic groups].

PubMed

Semenov, V Iu; Samorodskaia, I V

2014-01-01

The article presents the comparative analysis of techniques of evaluation of costs of hospital treatment using medical economic standards of medical care and clinical statistical groups. The technique of evaluation of costs on the basis of clinical statistical groups was developed almost fifty years ago and is largely applied in a number of countries. Nowadays, in Russia the payment for completed case of treatment on the basis of medical economic standards is the main mode of payment for medical care in hospital. It is very conditionally a Russian analogue of world-wide prevalent system of diagnostic related groups. The tariffs for these cases of treatment as opposed to clinical statistical groups are counted on basis of standards of provision of medical care approved by Minzdrav of Russia. The information derived from generalization of cases of treatment of real patients is not applied.

Statistical Techniques for Assessing water‐quality effects of BMPs

USGS Publications Warehouse

Walker, John F.

1994-01-01

Little has been published on the effectiveness of various management practices in small rural lakes and streams at the watershed scale. In this study, statistical techniques were used to test for changes in water‐quality data from watersheds where best management practices (BMPs) were implemented. Reductions in data variability due to climate and seasonality were accomplished through the use of regression methods. This study discusses the merits of using storm‐mass‐transport data as a means of improving the ability to detect BMP effects on stream‐water quality. Statistical techniques were applied to suspended‐sediment records from three rural watersheds in Illinois for the period 1981–84. None of the techniques identified changes in suspended sediment, primarily because of the small degree of BMP implementation and because of potential errors introduced through the estimation of storm‐mass transport. A Monte Carlo sensitivity analysis was used to determine the level of discrete change that could be detected for each watershed. In all cases, the use of regressions improved the ability to detect trends.Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9437(1994)120:2(334)

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.

Regional Morphology Analysis Package (RMAP): Empirical Orthogonal Function Analysis, Background and Examples

DTIC Science & Technology

2007-10-01

1984. Complex principal component analysis : Theory and examples. Journal of Climate and Applied Meteorology 23: 1660-1673. Hotelling, H. 1933...Sediments 99. ASCE: 2,566-2,581. Von Storch, H., and A. Navarra. 1995. Analysis of climate variability. Applications of statistical techniques. Berlin...ERDC TN-SWWRP-07-9 October 2007 Regional Morphology Empirical Analysis Package (RMAP): Orthogonal Function Analysis , Background and Examples by

Hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis method for mid-frequency analysis of built-up systems with epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yin, Shengwen; Yu, Dejie; Yin, Hui; Lü, Hui; Xia, Baizhan

2017-09-01

Considering the epistemic uncertainties within the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model when it is used for the response analysis of built-up systems in the mid-frequency range, the hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis (ETFE/SEA) model is established by introducing the evidence theory. Based on the hybrid ETFE/SEA model and the sub-interval perturbation technique, the hybrid Sub-interval Perturbation and Evidence Theory-based Finite Element/Statistical Energy Analysis (SIP-ETFE/SEA) approach is proposed. In the hybrid ETFE/SEA model, the uncertainty in the SEA subsystem is modeled by a non-parametric ensemble, while the uncertainty in the FE subsystem is described by the focal element and basic probability assignment (BPA), and dealt with evidence theory. Within the hybrid SIP-ETFE/SEA approach, the mid-frequency response of interest, such as the ensemble average of the energy response and the cross-spectrum response, is calculated analytically by using the conventional hybrid FE/SEA method. Inspired by the probability theory, the intervals of the mean value, variance and cumulative distribution are used to describe the distribution characteristics of mid-frequency responses of built-up systems with epistemic uncertainties. In order to alleviate the computational burdens for the extreme value analysis, the sub-interval perturbation technique based on the first-order Taylor series expansion is used in ETFE/SEA model to acquire the lower and upper bounds of the mid-frequency responses over each focal element. Three numerical examples are given to illustrate the feasibility and effectiveness of the proposed method.

DEIVA: a web application for interactive visual analysis of differential gene expression profiles.

PubMed

Harshbarger, Jayson; Kratz, Anton; Carninci, Piero

2017-01-07

Differential gene expression (DGE) analysis is a technique to identify statistically significant differences in RNA abundance for genes or arbitrary features between different biological states. The result of a DGE test is typically further analyzed using statistical software, spreadsheets or custom ad hoc algorithms. We identified a need for a web-based system to share DGE statistical test results, and locate and identify genes in DGE statistical test results with a very low barrier of entry. We have developed DEIVA, a free and open source, browser-based single page application (SPA) with a strong emphasis on being user friendly that enables locating and identifying single or multiple genes in an immediate, interactive, and intuitive manner. By design, DEIVA scales with very large numbers of users and datasets. Compared to existing software, DEIVA offers a unique combination of design decisions that enable inspection and analysis of DGE statistical test results with an emphasis on ease of use.

Dentascan – Is the Investment Worth the Hype ???

PubMed Central

Shah, Monali A; Shah, Sneha S; Dave, Deepak

2013-01-01

Background: Open Bone Measurement (OBM) and Bone Sounding (BS) are most reliable but invasive clinical methods for Alveolar Bone Level (ABL) assessment, causing discomfort to the patient. Routinely, IOPAs & OPGs are the commonest radiographic techniques used, which tend to underestimate bone loss and obscure buccal/lingual defects. Novel technique like dentascan (CBCT) eliminates this limitation by giving images in 3 planes – sagittal, coronal and axial. Aim: To compare & correlate non-invasive 3D radiographic technique of Dentascan with BS & OBM, and IOPA and OPG, in assessing the ABL. Settings and Design: Cross-sectional diagnostic study. Material and Methods: Two hundred and five sites were subjected to clinical and radiographic diagnostic techniques. Relative distance between the alveolar bone crest and reference wire was measured. All the measurements were compared and tested against the OBM. Statistical Analysis: Student’s t-test, ANOVA, Pearson correlation coefficient. Results: There is statistically significant difference between dentascan and OBM, only BS showed agreement with OBM (p < 0.05). Dentascan weakly correlated with OBM & BS lingually.Rest all techniques showed statistically significant difference between them (p= 0.00). Conclusion: Within the limitations of this study, only BS seems to be comparable with OBM with no superior result of Dentascan over the conventional techniques, except for lingual measurements. PMID:24551722

Review and classification of variability analysis techniques with clinical applications.

PubMed

Bravi, Andrea; Longtin, André; Seely, Andrew J E

2011-10-10

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

Evaluation of analysis techniques for low frequency interior noise and vibration of commercial aircraft

NASA Technical Reports Server (NTRS)

Landmann, A. E.; Tillema, H. F.; Marshall, S. E.

1989-01-01

The application of selected analysis techniques to low frequency cabin noise associated with advanced propeller engine installations is evaluated. Three design analysis techniques were chosen for evaluation including finite element analysis, statistical energy analysis (SEA), and a power flow method using element of SEA (computer program Propeller Aircraft Interior Noise). An overview of the three procedures is provided. Data from tests of a 727 airplane (modified to accept a propeller engine) were used to compare with predictions. Comparisons of predicted and measured levels at the end of the first year's effort showed reasonable agreement leading to the conclusion that each technique had value for propeller engine noise predictions on large commercial transports. However, variations in agreement were large enough to remain cautious and to lead to recommendations for further work with each technique. Assessment of the second year's results leads to the conclusion that the selected techniques can accurately predict trends and can be useful to a designer, but that absolute level predictions remain unreliable due to complexity of the aircraft structure and low modal densities.

Review and classification of variability analysis techniques with clinical applications

PubMed Central

2011-01-01

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis. PMID:21985357

Data mining and statistical inference in selective laser melting

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

Kamath, Chandrika

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

Data mining and statistical inference in selective laser melting

DOE PAGES

Kamath, Chandrika

2016-01-11

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

Unsupervised classification of earth resources data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Jayroe, R. R., Jr.; Cummings, R. E.

1972-01-01

A new clustering technique is presented. It consists of two parts: (a) a sequential statistical clustering which is essentially a sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by existing supervised maximum liklihood classification technique.

Single-case research design in pediatric psychology: considerations regarding data analysis.

PubMed

Cohen, Lindsey L; Feinstein, Amanda; Masuda, Akihiko; Vowles, Kevin E

2014-03-01

Single-case research allows for an examination of behavior and can demonstrate the functional relation between intervention and outcome in pediatric psychology. This review highlights key assumptions, methodological and design considerations, and options for data analysis. Single-case methodology and guidelines are reviewed with an in-depth focus on visual and statistical analyses. Guidelines allow for the careful evaluation of design quality and visual analysis. A number of statistical techniques have been introduced to supplement visual analysis, but to date, there is no consensus on their recommended use in single-case research design. Single-case methodology is invaluable for advancing pediatric psychology science and practice, and guidelines have been introduced to enhance the consistency, validity, and reliability of these studies. Experts generally agree that visual inspection is the optimal method of analysis in single-case design; however, statistical approaches are becoming increasingly evaluated and used to augment data interpretation.

Event coincidence analysis for quantifying statistical interrelationships between event time series. On the role of flood events as triggers of epidemic outbreaks

NASA Astrophysics Data System (ADS)

Donges, J. F.; Schleussner, C.-F.; Siegmund, J. F.; Donner, R. V.

2016-05-01

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

A Robust New Method for Analzing Community Change and an Example using 83 years of Avian Response to Forest Succession

EPA Science Inventory

This manuscript describes a novel statistical analysis technique developed by the authors for use in combining survey data carried out under different field protocols. We apply the technique to 83 years of survey data on avian songbird populations in northern lower Michigan to de...

Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications

ERIC Educational Resources Information Center

Pabon, Peter; Ternstrom, Sten; Lamarche, Anick

2011-01-01

Purpose: To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. Method: A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the…

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

2017-01-01

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

Frontiers of Two-Dimensional Correlation Spectroscopy. Part 1. New concepts and noteworthy developments

NASA Astrophysics Data System (ADS)

Noda, Isao

2014-07-01

A comprehensive survey review of new and noteworthy developments, which are advancing forward the frontiers in the field of 2D correlation spectroscopy during the last four years, is compiled. This review covers books, proceedings, and review articles published on 2D correlation spectroscopy, a number of significant conceptual developments in the field, data pretreatment methods and other pertinent topics, as well as patent and publication trends and citation activities. Developments discussed include projection 2D correlation analysis, concatenated 2D correlation, and correlation under multiple perturbation effects, as well as orthogonal sample design, predicting 2D correlation spectra, manipulating and comparing 2D spectra, correlation strategy based on segmented data blocks, such as moving-window analysis, features like determination of sequential order and enhanced spectral resolution, statistical 2D spectroscopy using covariance and other statistical metrics, hetero-correlation analysis, and sample-sample correlation technique. Data pretreatment operations prior to 2D correlation analysis are discussed, including the correction for physical effects, background and baseline subtraction, selection of reference spectrum, normalization and scaling of data, derivatives spectra and deconvolution technique, and smoothing and noise reduction. Other pertinent topics include chemometrics and statistical considerations, peak position shift phenomena, variable sampling increments, computation and software, display schemes, such as color coded format, slice and power spectra, tabulation, and other schemes.

Arsenic health risk assessment in drinking water and source apportionment using multivariate statistical techniques in Kohistan region, northern Pakistan.

PubMed

Muhammad, Said; Tahir Shah, M; Khan, Sardar

2010-10-01

The present study was conducted in Kohistan region, where mafic and ultramafic rocks (Kohistan island arc and Indus suture zone) and metasedimentary rocks (Indian plate) are exposed. Water samples were collected from the springs, streams and Indus river and analyzed for physical parameters, anions, cations and arsenic (As(3+), As(5+) and arsenic total). The water quality in Kohistan region was evaluated by comparing the physio-chemical parameters with permissible limits set by Pakistan environmental protection agency and world health organization. Most of the studied parameters were found within their respective permissible limits. However in some samples, the iron and arsenic concentrations exceeded their permissible limits. For health risk assessment of arsenic, the average daily dose, hazards quotient (HQ) and cancer risk were calculated by using statistical formulas. The values of HQ were found >1 in the samples collected from Jabba, Dubair, while HQ values were <1 in rest of the samples. This level of contamination should have low chronic risk and medium cancer risk when compared with US EPA guidelines. Furthermore, the inter-dependence of physio-chemical parameters and pollution load was also calculated by using multivariate statistical techniques like one-way ANOVA, correlation analysis, regression analysis, cluster analysis and principle component analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Maxwell, Katherine A.; Glass, David E.; Vaughn, Wallace L.; Barger, Weston; Cook, Mylan

2016-01-01

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.

Nonlinear histogram binning for quantitative analysis of lung tissue fibrosis in high-resolution CT data

NASA Astrophysics Data System (ADS)

Zavaletta, Vanessa A.; Bartholmai, Brian J.; Robb, Richard A.

2007-03-01

Diffuse lung diseases, such as idiopathic pulmonary fibrosis (IPF), can be characterized and quantified by analysis of volumetric high resolution CT scans of the lungs. These data sets typically have dimensions of 512 x 512 x 400. It is too subjective and labor intensive for a radiologist to analyze each slice and quantify regional abnormalities manually. Thus, computer aided techniques are necessary, particularly texture analysis techniques which classify various lung tissue types. Second and higher order statistics which relate the spatial variation of the intensity values are good discriminatory features for various textures. The intensity values in lung CT scans range between [-1024, 1024]. Calculation of second order statistics on this range is too computationally intensive so the data is typically binned between 16 or 32 gray levels. There are more effective ways of binning the gray level range to improve classification. An optimal and very efficient way to nonlinearly bin the histogram is to use a dynamic programming algorithm. The objective of this paper is to show that nonlinear binning using dynamic programming is computationally efficient and improves the discriminatory power of the second and higher order statistics for more accurate quantification of diffuse lung disease.

Logistic regression applied to natural hazards: rare event logistic regression with replications

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets

NASA Astrophysics Data System (ADS)

Goel, Amit; Montgomery, Michele

2015-08-01

Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.

Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method.

PubMed

Karain, Wael I

2017-11-28

Proteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the protein's function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines bootstrapping and recurrence quantification analysis. We start from the assumption that a protein has a "baseline" recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein. We apply this technique to a 132 ns long molecular dynamics simulation of the β-Lactamase Inhibitory Protein BLIP. We are able to detect conformational transitions in the nanosecond range in the recurrence dynamics of the BLIP protein during the simulation. The results compare favorably to those extracted using the principal component analysis technique. The recurrence quantification analysis based bootstrap technique is able to detect transitions between different dynamics states for a protein over different time scales. It is not limited to linear dynamics regimes, and can be generalized to any time scale. It also has the potential to be used to cluster frames in molecular dynamics trajectories according to the nature of their recurrence dynamics. One shortcoming for this method is the need to have large enough time windows to insure good statistical quality for the recurrence complexity measures needed to detect the transitions.

AGARD Flight Test Techniques Series. Volume 14. Introduction to Flight Test Engineering (Introduction a la Technique d’essais en vol)

DTIC Science & Technology

1995-09-01

path and aircraft attitude and other flight or aircraft parameters • Calculations in the frequency domain ( Fast Fourier Transform) • Data analysis...Signal filtering Image processing of video and radar data Parameter identification Statistical analysis Power spectral density Fast Fourier Transform...airspeeds both fast and slow, altitude, load factor both above and below 1g, centers of gravity (fore and aft), and with system/subsystem failures. Whether

Accuracy of trace element determinations in alternate fuels

NASA Technical Reports Server (NTRS)

Greenbauer-Seng, L. A.

1980-01-01

A review of the techniques used at Lewis Research Center (LeRC) in trace metals analysis is presented, including the results of Atomic Absorption Spectrometry and DC Arc Emission Spectrometry of blank levels and recovery experiments for several metals. The design of an Interlaboratory Study conducted by LeRC is presented. Several factors were investigated, including: laboratory, analytical technique, fuel type, concentration, and ashing additive. Conclusions drawn from the statistical analysis will help direct research efforts toward those areas most responsible for the poor interlaboratory analytical results.

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

Compression technique for large statistical data bases

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

Eggers, S.J.; Olken, F.; Shoshani, A.

1981-03-01

The compression of large statistical databases is explored and are proposed for organizing the compressed data, such that the time required to access the data is logarithmic. The techniques exploit special characteristics of statistical databases, namely, variation in the space required for the natural encoding of integer attributes, a prevalence of a few repeating values or constants, and the clustering of both data of the same length and constants in long, separate series. The techniques are variations of run-length encoding, in which modified run-lengths for the series are extracted from the data stream and stored in a header, which ismore » used to form the base level of a B-tree index into the database. The run-lengths are cumulative, and therefore the access time of the data is logarithmic in the size of the header. The details of the compression scheme and its implementation are discussed, several special cases are presented, and an analysis is given of the relative performance of the various versions.« less

Uncertainty Quantification and Statistical Convergence Guidelines for PIV Data

NASA Astrophysics Data System (ADS)

Stegmeir, Matthew; Kassen, Dan

2016-11-01

As Particle Image Velocimetry has continued to mature, it has developed into a robust and flexible technique for velocimetry used by expert and non-expert users. While historical estimates of PIV accuracy have typically relied heavily on "rules of thumb" and analysis of idealized synthetic images, recently increased emphasis has been placed on better quantifying real-world PIV measurement uncertainty. Multiple techniques have been developed to provide per-vector instantaneous uncertainty estimates for PIV measurements. Often real-world experimental conditions introduce complications in collecting "optimal" data, and the effect of these conditions is important to consider when planning an experimental campaign. The current work utilizes the results of PIV Uncertainty Quantification techniques to develop a framework for PIV users to utilize estimated PIV confidence intervals to compute reliable data convergence criteria for optimal sampling of flow statistics. Results are compared using experimental and synthetic data, and recommended guidelines and procedures leveraging estimated PIV confidence intervals for efficient sampling for converged statistics are provided.

Statistical summaries of fatigue data for design purposes

NASA Technical Reports Server (NTRS)

Wirsching, P. H.

1983-01-01

Two methods are discussed for constructing a design curve on the safe side of fatigue data. Both the tolerance interval and equivalent prediction interval (EPI) concepts provide such a curve while accounting for both the distribution of the estimators in small samples and the data scatter. The EPI is also useful as a mechanism for providing necessary statistics on S-N data for a full reliability analysis which includes uncertainty in all fatigue design factors. Examples of statistical analyses of the general strain life relationship are presented. The tolerance limit and EPI techniques for defining a design curve are demonstrated. Examples usng WASPALOY B and RQC-100 data demonstrate that a reliability model could be constructed by considering the fatigue strength and fatigue ductility coefficients as two independent random variables. A technique given for establishing the fatigue strength for high cycle lives relies on an extrapolation technique and also accounts for "runners." A reliability model or design value can be specified.

Volumetric analysis of hand, reciprocating and rotary instrumentation techniques in primary molars using spiral computed tomography: An in vitro comparative study.

PubMed

Jeevanandan, Ganesh; Thomas, Eapen

2018-01-01

This present study was conducted to analyze the volumetric change in the root canal space and instrumentation time between hand files, hand files in reciprocating motion, and three rotary files in primary molars. One hundred primary mandibular molars were randomly allotted to one of the five groups. Instrumentation was done using Group I; nickel-titanium (Ni-Ti) hand file, Group II; Ni-Ti hand files in reciprocating motion, Group III; Race rotary files, Group IV; prodesign pediatric rotary files, and Group V; ProTaper rotary files. The mean volumetric changes were assessed using pre- and post-operative spiral computed tomography scans. Instrumentation time was recorded. Statistical analysis to access intergroup comparison for mean canal volume and instrumentation time was done using Bonferroni-adjusted Mann-Whitney test and Mann-Whitney test, respectively. Intergroup comparison of mean canal volume showed statistically significant difference between Groups II versus IV, Groups III versus V, and Groups IV versus V. Intergroup comparison of mean instrumentation time showed statistically significant difference among all the groups except Groups IV versus V. Among the various instrumentation techniques available, rotary instrumentation is the considered to be the better instrumentation technique for canal preparation in primary teeth.

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

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.

Performance of dental impression materials: Benchmarking of materials and techniques by three-dimensional analysis.

PubMed

Rudolph, Heike; Graf, Michael R S; Kuhn, Katharina; Rupf-Köhler, Stephanie; Eirich, Alfred; Edelmann, Cornelia; Quaas, Sebastian; Luthardt, Ralph G

2015-01-01

Among other factors, the precision of dental impressions is an important and determining factor for the fit of dental restorations. The aim of this study was to examine the three-dimensional (3D) precision of gypsum dies made using a range of impression techniques and materials. Ten impressions of a steel canine were fabricated for each of the 24 material-method-combinations and poured with type 4 die stone. The dies were optically digitized, aligned to the CAD model of the steel canine, and 3D differences were calculated. The results were statistically analyzed using one-way analysis of variance. Depending on material and impression technique, the mean values had a range between +10.9/-10.0 µm (SD 2.8/2.3) and +16.5/-23.5 µm (SD 11.8/18.8). Qualitative analysis using colorcoded graphs showed a characteristic location of deviations for different impression techniques. Three-dimensional analysis provided a comprehensive picture of the achievable precision. Processing aspects and impression technique were of significant influence.

Utilization of an Enhanced Canonical Correlation Analysis (ECCA) to Predict Daily Precipitation and Temperature in a Semi-Arid Environment

NASA Astrophysics Data System (ADS)

Lopez, S. R.; Hogue, T. S.

2011-12-01

Global climate models (GCMs) are primarily used to generate historical and future large-scale circulation patterns at a coarse resolution (typical order of 50,000 km2) and fail to capture climate variability at the ground level due to localized surface influences (i.e topography, marine, layer, land cover, etc). Their inability to accurately resolve these processes has led to the development of numerous 'downscaling' techniques. The goal of this study is to enhance statistical downscaling of daily precipitation and temperature for regions with heterogeneous land cover and topography. Our analysis was divided into two periods, historical (1961-2000) and contemporary (1980-2000), and tested using sixteen predictand combinations from four GCMs (GFDL CM2.0, GFDL CM2.1, CNRM-CM3 and MRI-CGCM2 3.2a. The Southern California area was separated into five county regions: Santa Barbara, Ventura, Los Angeles, Orange and San Diego. Principle component analysis (PCA) was performed on ground-based observations in order to (1) reduce the number of redundant gauges and minimize dimensionality and (2) cluster gauges that behave statistically similarly for post-analysis. Post-PCA analysis included extensive testing of predictor-predictand relationships using an enhanced canonical correlation analysis (ECCA). The ECCA includes obtaining the optimal predictand sets for all models within each spatial domain (county) as governed by daily and monthly overall statistics. Results show all models maintain mean annual and monthly behavior within each county and daily statistics are improved. The level of improvement highly depends on the vegetation extent within each county and the land-to-ocean ratio within the GCM spatial grid. The utilization of the entire historical period also leads to better statistical representation of observed daily precipitation. The validated ECCA technique is being applied to future climate scenarios distributed by the IPCC in order to provide forcing data for regional hydrologic models and assess future water resources in the Southern California region.

Improved processes for meeting the data requirements for implementing the Highway Safety Manual (HSM) and Safety Analyst in Florida.

DOT National Transportation Integrated Search

2014-03-01

Recent research in highway safety has focused on the more advanced and statistically proven techniques of highway : safety analysis. This project focuses on the two most recent safety analysis tools, the Highway Safety Manual (HSM) : and SafetyAnalys...

Effective Analysis of Reaction Time Data

ERIC Educational Resources Information Center

Whelan, Robert

2008-01-01

Most analyses of reaction time (RT) data are conducted by using the statistical techniques with which psychologists are most familiar, such as analysis of variance on the sample mean. Unfortunately, these methods are usually inappropriate for RT data, because they have little power to detect genuine differences in RT between conditions. In…

Prediction of Recidivism in Juvenile Offenders Based on Discriminant Analysis.

ERIC Educational Resources Information Center

Proefrock, David W.

The recent development of strong statistical techniques has made accurate predictions of recidivism possible. To investigate the utility of discriminant analysis methodology in making predictions of recidivism in juvenile offenders, the court records of 271 male and female juvenile offenders, aged 12-16, were reviewed. A cross validation group…

A New Femtosecond Laser-Based Three-Dimensional Tomography Technique

NASA Astrophysics Data System (ADS)

Echlin, McLean P.

2011-12-01

Tomographic imaging has dramatically changed science, most notably in the fields of medicine and biology, by producing 3D views of structures which are too complex to understand in any other way. Current tomographic techniques require extensive time both for post-processing and data collection. Femtosecond laser based tomographic techniques have been developed in both standard atmosphere (femtosecond laser-based serial sectioning technique - FSLSS) and in vacuum (Tri-Beam System) for the fast collection (10 5mum3/s) of mm3 sized 3D datasets. Both techniques use femtosecond laser pulses to selectively remove layer-by-layer areas of material with low collateral damage and a negligible heat affected zone. To the authors knowledge, femtosecond lasers have never been used to serial section and these techniques have been entirely and uniquely developed by the author and his collaborators at the University of Michigan and University of California Santa Barbara. The FSLSS was applied to measure the 3D distribution of TiN particles in a 4330 steel. Single pulse ablation morphologies and rates were measured and collected from literature. Simultaneous two-phase ablation of TiN and steel matrix was shown to occur at fluences of 0.9-2 J/cm2. Laser scanning protocols were developed minimizing surface roughness to 0.1-0.4 mum for laser-based sectioning. The FSLSS technique was used to section and 3D reconstruct titanium nitride (TiN) containing 4330 steel. Statistical analysis of 3D TiN particle sizes, distribution parameters, and particle density were measured. A methodology was developed to use the 3D datasets to produce statistical volume elements (SVEs) for toughness modeling. Six FSLSS TiN datasets were sub-sampled into 48 SVEs for statistical analysis and toughness modeling using the Rice-Tracey and Garrison-Moody models. A two-parameter Weibull analysis was performed and variability in the toughness data agreed well with Ruggieri et al. bulk toughness measurements. The Tri-Beam system combines the benefits of laser based material removal (speed, low-damage, automated) with detectors that collect chemical, structural, and topological information. Multi-modal sectioning information was collected after many laser scanning passes demonstrating the capability of the Tri-Beam system.

Phospholipid Fatty Acid Analysis: Past, Present and Future

NASA Astrophysics Data System (ADS)

Findlay, R. H.

2008-12-01

With their 1980 publication, Bobbie and White initiated the use of phospholipid fatty acids for the study of microbial communities. This method, integrated with a previously published biomass assay based on the colorimetric detection of orthophosphate liberated from phospholipids, provided the first quantitative method for determining microbial community structure. The method is based on a quantitative extraction of lipids from the sample matrix, isolation of the phospholipids, conversion of the phospholipid fatty acids to their corresponding fatty acid methyl esters (known by the acronym FAME) and the separation, identification and quantification of the FAME by gas chromatography. Early laboratory and field samples focused on correlating individual fatty acids to particular groups of microorganisms. Subsequent improvements to the methodology include reduced solvent volumes for extractions, improved sensitivity in the detection of orthophosphate and the use of solid phase extraction technology. Improvements in the field of gas chromatography also increased accessibility of the technique and it has been widely applied to water, sediment, soil and aerosol samples. Whole cell fatty acid analysis, a related but not equal technique, is currently used for phenotypic characterization in bacterial species descriptions and is the basis for a commercial, rapid bacterial identification system. In the early 1990ês application of multivariate statistical analysis, first cluster analysis and then principal component analysis, further improved the usefulness of the technique and allowed the development of a functional group approach to interpretation of phospholipid fatty acid profiles. Statistical techniques currently applied to the analysis of phospholipid fatty acid profiles include constrained ordinations and neutral networks. Using redundancy analysis, a form of constrained ordination, we have recently shown that both cation concentration and dissolved organic matter (DOM) quality are determinates of microbial community structure in forested headwater streams. One of the most exciting recent developments in phospholipid fatty acid analysis is the application of compound specific stable isotope analysis. We are currently applying this technique to stream sediments to help determine which microorganisms are involved in the initial processing of DOM and the technique promises to be a useful tool for assigning ecological function to microbial populations.

The impact of varicocelectomy on sperm parameters: a meta-analysis.

PubMed

Schauer, Ingrid; Madersbacher, Stephan; Jost, Romy; Hübner, Wilhelm Alexander; Imhof, Martin

2012-05-01

We determined the impact of 3 surgical techniques (high ligation, inguinal varicocelectomy and the subinguinal approach) for varicocelectomy on sperm parameters (count and motility) and pregnancy rates. By searching the literature using MEDLINE and the Cochrane Library with the last search performed in February 2011, focusing on the last 20 years, a total of 94 articles published between 1975 and 2011 reporting on sperm parameters before and after varicocelectomy were identified. Inclusion criteria for this meta-analysis were at least 2 semen analyses (before and 3 or more months after the procedure), patient age older than 19 years, clinical subfertility and/or abnormal semen parameters, and a clinically palpable varicocele. To rule out skewing factors a bias analysis was performed, and statistical analysis was done with RevMan5(®) and SPSS 15.0(®). A total of 14 articles were included in the statistical analysis. All 3 surgical approaches led to significant or highly significant postoperative improvement of both parameters with only slight numeric differences among the techniques. This difference did not reach statistical significance for sperm count (p = 0.973) or sperm motility (p = 0.372). After high ligation surgery sperm count increased by 10.85 million per ml (p = 0.006) and motility by 6.80% (p <0.00001) on the average. Inguinal varicocelectomy led to an improvement in sperm count of 7.17 million per ml (p <0.0001) while motility changed by 9.44% (p = 0.001). Subinguinal varicocelectomy provided an increase in sperm count of 9.75 million per ml (p = 0.002) and sperm motility by 12.25% (p = 0.001). Inguinal varicocelectomy showed the highest pregnancy rate of 41.48% compared to 26.90% and 26.56% after high ligation and subinguinal varicocelectomy, respectively, and the difference was statistically significant (p = 0.035). This meta-analysis suggests that varicocelectomy leads to significant improvements in sperm count and motility regardless of surgical technique, with the inguinal approach offering the highest pregnancy rate. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Increase of lower esophageal sphincter pressure after osteopathic intervention on the diaphragm in patients with gastroesophageal reflux.

PubMed

da Silva, R C V; de Sá, C C; Pascual-Vaca, Á O; de Souza Fontes, L H; Herbella Fernandes, F A M; Dib, R A; Blanco, C R; Queiroz, R A; Navarro-Rodriguez, T

2013-07-01

The treatment of gastroesophageal reflux disease may be clinical or surgical. The clinical consists basically of the use of drugs; however, there are new techniques to complement this treatment, osteopathic intervention in the diaphragmatic muscle is one these. The objective of the study is to compare pressure values in the examination of esophageal manometry of the lower esophageal sphincter (LES) before and immediately after osteopathic intervention in the diaphragm muscle. Thirty-eight patients with gastroesophageal reflux disease - 16 submitted to sham technique and 22 submitted osteopathic technique - were randomly selected. The average respiratory pressure (ARP) and the maximum expiratory pressure (MEP) of the LES were measured by manometry before and after osteopathic technique at the point of highest pressure. Statistical analysis was performed using the Student's t-test and Mann-Whitney, and magnitude of the technique proposed was measured using the Cohen's index. Statistically significant difference in the osteopathic technique was found in three out of four in relation to the group of patients who performed the sham technique for the following measures of LES pressure: ARP with P= 0.027. The MEP had no statistical difference (P= 0.146). The values of Cohen d for the same measures were: ARP with d= 0.80 and MEP d= 0.52. Osteopathic manipulative technique produces a positive increment in the LES region soon after its performance. © 2012 Copyright the Authors. Journal compilation © 2012, Wiley Periodicals, Inc. and the International Society for Diseases of the Esophagus.

Multi-scale statistical analysis of coronal solar activity

DOE PAGES

Gamborino, Diana; del-Castillo-Negrete, Diego; Martinell, Julio J.

2016-07-08

Multi-filter images from the solar corona are used to obtain temperature maps that are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions, we show that the multi-scale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also to be extracted from the analysis.

Statistical Approaches to Interpretation of Local, Regional, and National Highway-Runoff and Urban-Stormwater Data

USGS Publications Warehouse

Tasker, Gary D.; Granato, Gregory E.

2000-01-01

Decision makers need viable methods for the interpretation of local, regional, and national-highway runoff and urban-stormwater data including flows, concentrations and loads of chemical constituents and sediment, potential effects on receiving waters, and the potential effectiveness of various best management practices (BMPs). Valid (useful for intended purposes), current, and technically defensible stormwater-runoff models are needed to interpret data collected in field studies, to support existing highway and urban-runoffplanning processes, to meet National Pollutant Discharge Elimination System (NPDES) requirements, and to provide methods for computation of Total Maximum Daily Loads (TMDLs) systematically and economically. Historically, conceptual, simulation, empirical, and statistical models of varying levels of detail, complexity, and uncertainty have been used to meet various data-quality objectives in the decision-making processes necessary for the planning, design, construction, and maintenance of highways and for other land-use applications. Water-quality simulation models attempt a detailed representation of the physical processes and mechanisms at a given site. Empirical and statistical regional water-quality assessment models provide a more general picture of water quality or changes in water quality over a region. All these modeling techniques share one common aspect-their predictive ability is poor without suitable site-specific data for calibration. To properly apply the correct model, one must understand the classification of variables, the unique characteristics of water-resources data, and the concept of population structure and analysis. Classifying variables being used to analyze data may determine which statistical methods are appropriate for data analysis. An understanding of the characteristics of water-resources data is necessary to evaluate the applicability of different statistical methods, to interpret the results of these techniques, and to use tools and techniques that account for the unique nature of water-resources data sets. Populations of data on stormwater-runoff quantity and quality are often best modeled as logarithmic transformations. Therefore, these factors need to be considered to form valid, current, and technically defensible stormwater-runoff models. Regression analysis is an accepted method for interpretation of water-resources data and for prediction of current or future conditions at sites that fit the input data model. Regression analysis is designed to provide an estimate of the average response of a system as it relates to variation in one or more known variables. To produce valid models, however, regression analysis should include visual analysis of scatterplots, an examination of the regression equation, evaluation of the method design assumptions, and regression diagnostics. A number of statistical techniques are described in the text and in the appendixes to provide information necessary to interpret data by use of appropriate methods. Uncertainty is an important part of any decisionmaking process. In order to deal with uncertainty problems, the analyst needs to know the severity of the statistical uncertainty of the methods used to predict water quality. Statistical models need to be based on information that is meaningful, representative, complete, precise, accurate, and comparable to be deemed valid, up to date, and technically supportable. To assess uncertainty in the analytical tools, the modeling methods, and the underlying data set, all of these components need be documented and communicated in an accessible format within project publications.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

PubMed

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

A FORTRAN technique for correlating a circular environmental variable with a linear physiological variable in the sugar maple.

PubMed

Pease, J M; Morselli, M F

1987-01-01

This paper deals with a computer program adapted to a statistical method for analyzing an unlimited quantity of binary recorded data of an independent circular variable (e.g. wind direction), and a linear variable (e.g. maple sap flow volume). Circular variables cannot be statistically analyzed with linear methods, unless they have been transformed. The program calculates a critical quantity, the acrophase angle (PHI, phi o). The technique is adapted from original mathematics [1] and is written in Fortran 77 for easier conversion between computer networks. Correlation analysis can be performed following the program or regression which, because of the circular nature of the independent variable, becomes periodic regression. The technique was tested on a file of approximately 4050 data pairs.

Fourier descriptor analysis and unification of voice range profile contours: method and applications.

PubMed

Pabon, Peter; Ternström, Sten; Lamarche, Anick

2011-06-01

To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the contour, is assessed and also is compared to density-based VRP averaging methods that use the overlap count. VRP contours can be usefully described and compared using FDs. The method also permits the visualization of the local covariation along the contour average. For example, the FD-based analysis shows that the population variance for ensembles of VRP contours is usually smallest at the upper left part of the VRP. To illustrate the method's advantages and possible further application, graphs are given that compare the averaged contours from different authors and recording devices--for normal, trained, and untrained male and female voices as well as for child voices. The proposed technique allows any VRP shape to be brought to the same uniform base. On this uniform base, VRP contours or contour elements coming from a variety of sources may be placed within the same graph for comparison and for statistical analysis.

Statistical, Graphical, and Learning Methods for Sensing, Surveillance, and Navigation Systems

DTIC Science & Technology

2016-06-28

harsh propagation environments. Conventional filtering techniques fail to provide satisfactory performance in many important nonlinear or non...Gaussian scenarios. In addition, there is a lack of a unified methodology for the design and analysis of different filtering techniques. To address...these problems, we have proposed a new filtering methodology called belief condensation (BC) DISTRIBUTION A: Distribution approved for public release

Studies of EGRET sources with a novel image restoration technique

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

Tajima, Hiroyasu; Cohen-Tanugi, Johann; Kamae, Tuneyoshi

2007-07-12

We have developed an image restoration technique based on the Richardson-Lucy algorithm optimized for GLAST-LAT image analysis. Our algorithm is original since it utilizes the PSF (point spread function) that is calculated for each event. This is critical for EGRET and GLAST-LAT image analysis since the PSF depends on the energy and angle of incident gamma-rays and varies by more than one order of magnitude. EGRET and GLAST-LAT image analysis also faces Poisson noise due to low photon statistics. Our technique incorporates wavelet filtering to minimize noise effects. We present studies of EGRET sources using this novel image restoration techniquemore » for possible identification of extended gamma-ray sources.« less

Center of Excellence for Applied Mathematical and Statistical Research in support of development of multicrop production monitoring capability

NASA Technical Reports Server (NTRS)

Woodward, W. A.; Gray, H. L.

1983-01-01

Efforts in support of the development of multicrop production monitoring capability are reported. In particular, segment level proportion estimation techniques based upon a mixture model were investigated. Efforts have dealt primarily with evaluation of current techniques and development of alternative ones. A comparison of techniques is provided on both simulated and LANDSAT data along with an analysis of the quality of profile variables obtained from LANDSAT data.

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.

Software for Data Analysis with Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Roy, H. Scott

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: problem decomposition and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Analysis techniques for residual acceleration data

NASA Technical Reports Server (NTRS)

Rogers, Melissa J. B.; Alexander, J. Iwan D.; Snyder, Robert S.

1990-01-01

Various aspects of residual acceleration data are of interest to low-gravity experimenters. Maximum and mean values and various other statistics can be obtained from data as collected in the time domain. Additional information may be obtained through manipulation of the data. Fourier analysis is discussed as a means of obtaining information about dominant frequency components of a given data window. Transformation of data into different coordinate axes is useful in the analysis of experiments with different orientations and can be achieved by the use of a transformation matrix. Application of such analysis techniques to residual acceleration data provides additional information than what is provided in a time history and increases the effectiveness of post-flight analysis of low-gravity experiments.

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.

Authorship Attribution.

ERIC Educational Resources Information Center

Holmes, David I.

1994-01-01

Considers problems of quantifying literary style. Examines several variables that may be used as stylistic "fingerprints" of a writer. Reviews work done on statistical analysis of change over time in literary style and applies this technique to the Bible. (CFR)

Measuring Circulation Desk Activities Using a Random Alarm Mechanism.

ERIC Educational Resources Information Center

Mosborg, Stella Frank

1980-01-01

Reports a job analysis methodology to gather meaningful data related to circulation desk activity. The technique is designed to give librarians statistical data on actual time expenditures for complex and varying activities. (Author/RAA)

Analysis of Landsat-4 Thematic Mapper data for classification of forest stands in Baldwin County, Alabama

NASA Technical Reports Server (NTRS)

Hill, C. L.

1984-01-01

A computer-implemented classification has been derived from Landsat-4 Thematic Mapper data acquired over Baldwin County, Alabama on January 15, 1983. One set of spectral signatures was developed from the data by utilizing a 3x3 pixel sliding window approach. An analysis of the classification produced from this technique identified forested areas. Additional information regarding only the forested areas. Additional information regarding only the forested areas was extracted by employing a pixel-by-pixel signature development program which derived spectral statistics only for pixels within the forested land covers. The spectral statistics from both approaches were integrated and the data classified. This classification was evaluated by comparing the spectral classes produced from the data against corresponding ground verification polygons. This iterative data analysis technique resulted in an overall classification accuracy of 88.4 percent correct for slash pine, young pine, loblolly pine, natural pine, and mixed hardwood-pine. An accuracy assessment matrix has been produced for the classification.

Assessing the Effectiveness of Public Research Universities: Using NSF/NCES Data and Data Envelopment Analysis Technique. AIR 2000 Annual Forum Paper.

ERIC Educational Resources Information Center

Zheng, Henry Y.; Stewart, Alice A.

This study explores data envelopment analysis (DEA) as a tool for assessing and benchmarking the performance of public research universities. Using of national databases such as those conducted by the National Science Foundation and the National Center for Education Statistics, DEA analysis was conducted of the research and instructional outcomes…

An Example of the Informative Potential of Polar Coordinate Analysis: Sprint Tactics in Elite 1,500-m Track Events

ERIC Educational Resources Information Center

Aragón, Sonia; Lapresa, Daniel; Arana, Javier; Anguera, M. Teresa; Garzón, Belén

2017-01-01

Polar coordinate analysis is a powerful data reduction technique based on the Zsum statistic, which is calculated from adjusted residuals obtained by lag sequential analysis. Its use has been greatly simplified since the addition of a module in the free software program HOISAN for performing the necessary computations and producing…

Visual and Statistical Analysis of Digital Elevation Models Generated Using Idw Interpolator with Varying Powers

NASA Astrophysics Data System (ADS)

Asal, F. F.

2012-07-01

Digital elevation data obtained from different Engineering Surveying techniques is utilized in generating Digital Elevation Model (DEM), which is employed in many Engineering and Environmental applications. This data is usually in discrete point format making it necessary to utilize an interpolation approach for the creation of DEM. Quality assessment of the DEM is a vital issue controlling its use in different applications; however this assessment relies heavily on statistical methods with neglecting the visual methods. The research applies visual analysis investigation on DEMs generated using IDW interpolator of varying powers in order to examine their potential in the assessment of the effects of the variation of the IDW power on the quality of the DEMs. Real elevation data has been collected from field using total station instrument in a corrugated terrain. DEMs have been generated from the data at a unified cell size using IDW interpolator with power values ranging from one to ten. Visual analysis has been undertaken using 2D and 3D views of the DEM; in addition, statistical analysis has been performed for assessment of the validity of the visual techniques in doing such analysis. Visual analysis has shown that smoothing of the DEM decreases with the increase in the power value till the power of four; however, increasing the power more than four does not leave noticeable changes on 2D and 3D views of the DEM. The statistical analysis has supported these results where the value of the Standard Deviation (SD) of the DEM has increased with increasing the power. More specifically, changing the power from one to two has produced 36% of the total increase (the increase in SD due to changing the power from one to ten) in SD and changing to the powers of three and four has given 60% and 75% respectively. This refers to decrease in DEM smoothing with the increase in the power of the IDW. The study also has shown that applying visual methods supported by statistical analysis has proven good potential in the DEM quality assessment.

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis

PubMed Central

Steele, Joe; Bastola, Dhundy

2014-01-01

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base–base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel–Ziv techniques from data compression. PMID:23904502

In Situ Distribution Guided Analysis and Visualization of Transonic Jet Engine Simulations.

PubMed

Dutta, Soumya; Chen, Chun-Ming; Heinlein, Gregory; Shen, Han-Wei; Chen, Jen-Ping

2017-01-01

Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.

Proficiency Testing for Determination of Water Content in Toluene of Chemical Reagents by iteration robust statistic technique

NASA Astrophysics Data System (ADS)

Wang, Hao; Wang, Qunwei; He, Ming

2018-05-01

In order to investigate and improve the level of detection technology of water content in liquid chemical reagents of domestic laboratories, proficiency testing provider PT0031 (CNAS) has organized proficiency testing program of water content in toluene, 48 laboratories from 18 provinces/cities/municipals took part in the PT. This paper introduces the implementation process of proficiency testing for determination of water content in toluene, including sample preparation, homogeneity and stability test, the results of statistics of iteration robust statistic technique and analysis, summarized and analyzed those of the different test standards which are widely used in the laboratories, put forward the technological suggestions for the improvement of the test quality of water content. Satisfactory results were obtained by 43 laboratories, amounting to 89.6% of the total participating laboratories.

A retrospective analysis of mathieu and tip urethroplasty techniques for distal hypospadias repair; A 20 year experience.

PubMed

Oztorun, Kenan; Bagbanci, Sahin; Dadali, Mumtaz; Emir, Levent; Karabulut, Ayhan

2017-09-01

We aimed to identify the changes in the application rate of two surgical techniques in distal hypospadias repair in years and compare the most popular two surgical repair techniques for distal hypospadias in terms of surgical outcomes, the factors that affect the outcomes, which were performed over a 20 year period. In this study, the records of 492 consecutive patients that had undergone an operation for distal hypospadias in the urology clinic of Ankara between May 1990 and December 2010 using either Mathieu or TIPU surgical techniques were reviewed retrospectively. The patients who had glanular, coronal, and subcoronal meatus, were accepted as distal hypospadias cases. Among the 492 examined medical records, it was revealed that 331 and 161 surgical interventions were performed by using the Mathieu urethroplasty technique (Group-1) and TIP urethroplasty technique (Group-2), respectively. Group-1 was divided into two subgroups; namely Group-1a (patients with primary hypospadias) and Group-1b (patients with previous hypospadias operation). Likewise, Group-2 was divided into two subgroups; namely group-2a and group-2b. The patients' ages, number of previously urethroplasty operations, localization of the external urethral meatus prior to the operation, chordee state, length of the newly formed urethra, whether urinary diversion was done or not, post-operative complications and data regarding the follow-up period were evaluated, and the effects of these variables on the surgical outcome were investigated via statistical analyses. The primary objective of this study is to identify the changes in the application rate of two surgical techniques in distal hypospadias repair over the a 20 year period, and the secondary objectives are to compare the most popular two surgical repair techniques for distal hypospadias in terms of surgical outcomes, and the factors affecting the outcomes. Independent samples t test and Pearson's Chisquare test was used for statistical analysis. p<0.05 was considered as statistically significant. There were no statistically significant differences between the subgroups in terms of age, length of the neo-urethra, number of previously performed urethroplasty operations, surgical success rates, or complications (p>0.05). The concurrent utilization of the cystostomy and urethral stent was significantly more frequent in group-1 (p<0.05; Pearson's Chi-square test). It was determined that over time, TIP urethroplasty has become a more preferred technique for the repair of distal hypospadias. Both surgical techniques have similar success rates in distal hypospadias cases. TIP urethroplasty has become the method of choice over time.

Clinical validation of robot simulation of toothbrushing - comparative plaque removal efficacy

PubMed Central

2014-01-01

Background Clinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal. Methods Clinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33–47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33–47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis. Results The individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites. Conclusions The robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing. This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning. PMID:24996973

Clinical validation of robot simulation of toothbrushing--comparative plaque removal efficacy.

PubMed

Lang, Tomas; Staufer, Sebastian; Jennes, Barbara; Gaengler, Peter

2014-07-04

Clinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal. Clinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33-47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33-47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis. The individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites. The robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing.This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning.

Generic Techniques for the Calibration of Robots with Application of the 3-D Fixtures and Statistical Technique on the PUMA 500 and ARID Robots

NASA Technical Reports Server (NTRS)

Tawfik, Hazem

1991-01-01

A relatively simple, inexpensive, and generic technique that could be used in both laboratories and some operation site environments is introduced at the Robotics Applications and Development Laboratory (RADL) at Kennedy Space Center (KSC). In addition, this report gives a detailed explanation of the set up procedure, data collection, and analysis using this new technique that was developed at the State University of New York at Farmingdale. The technique was used to evaluate the repeatability, accuracy, and overshoot of the Unimate Industrial Robot, PUMA 500. The data were statistically analyzed to provide an insight into the performance of the systems and components of the robot. Also, the same technique was used to check the forward kinematics against the inverse kinematics of RADL's PUMA robot. Recommendations were made for RADL to use this technique for laboratory calibration of the currently existing robots such as the ASEA, high speed controller, Automated Radiator Inspection Device (ARID) etc. Also, recommendations were made to develop and establish other calibration techniques that will be more suitable for site calibration environment and robot certification.

Directional spectra of ocean waves from microwave backscatter: A physical optics solution with application to the short-pulse and two-frequency measurement techniques

NASA Technical Reports Server (NTRS)

Jackson, F. C.

1979-01-01

Two simple microwave radar techniques that are potentially capable of providing routine satellite measurements of the directional spectrum of ocean waves were developed. One technique, the short pulse technique, makes use of very short pulses to resolve ocean surface wave contrast features in the range direction; the other technique, the two frequency correlation technique makes use of coherency in the transmitted waveform to detect the large ocean wave contrast modulation as a beat or mixing frequency in the power backscattered at two closely separated microwave frequencies. A frequency domain analysis of the short pulse and two frequency systems shows that the two measurement systems are essentially duals; they each operate on the generalized (three frequency) fourth-order statistical moment of the surface transfer function in different, but symmetrical ways, and they both measure the same directional contrast modulation spectrum. A three dimensional physical optics solution for the fourth-order moment was obtained for backscatter in the near vertical, specular regime, assuming Gaussian surface statistics.

Reliability of horizontal and vertical tube shift techniques in the localisation of supernumerary teeth.

PubMed

Mallineni, S K; Anthonappa, R P; King, N M

2016-12-01

To assess the reliability of the vertical tube shift technique (VTST) and horizontal tube shift technique (HTST) for the localisation of unerupted supernumerary teeth (ST) in the anterior region of the maxilla. A convenience sample of 83 patients who attended a major teaching hospital because of unerupted ST was selected. Only non-syndromic patients with ST and who had complete clinical and radiographic and surgical records were included in the study. Ten examiners independently rated the paired set of radiographs for each technique. Chi-square test, paired t test and kappa statistics were employed to assess the intra- and inter-examiner reliability. Paired sets of 1660 radiographs (830 pairs for each technique) were available for the analysis. The overall sensitivity for VTST and HTST was 80.6 and 72.1% respectively, with slight inter-examiner and good intra-examiner reliability. Statistically significant differences were evident between the two localisation techniques (p < 0.05). Localisation of unerupted ST using VTST was more successful than HTST in the anterior region of the maxilla.

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

An Analysis of Variance Framework for Matrix Sampling.

ERIC Educational Resources Information Center

Sirotnik, Kenneth

Significant cost savings can be achieved with the use of matrix sampling in estimating population parameters from psychometric data. The statistical design is intuitively simple, using the framework of the two-way classification analysis of variance technique. For example, the mean and variance are derived from the performance of a certain grade…

Using Rasch Analysis to Identify Uncharacteristic Responses to Undergraduate Assessments

ERIC Educational Resources Information Center

Edwards, Antony; Alcock, Lara

2010-01-01

Rasch Analysis is a statistical technique that is commonly used to analyse both test data and Likert survey data, to construct and evaluate question item banks, and to evaluate change in longitudinal studies. In this article, we introduce the dichotomous Rasch model, briefly discussing its assumptions. Then, using data collected in an…

Teaching for Art Criticism: Incorporating Feldman's Critical Analysis Learning Model in Students' Studio Practice

ERIC Educational Resources Information Center

Subramaniam, Maithreyi; Hanafi, Jaffri; Putih, Abu Talib

2016-01-01

This study adopted 30 first year graphic design students' artwork, with critical analysis using Feldman's model of art criticism. Data were analyzed quantitatively; descriptive statistical techniques were employed. The scores were viewed in the form of mean score and frequencies to determine students' performances in their critical ability.…

Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

PubMed Central

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level. PMID:25830807

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

PubMed

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

The new statistics: why and how.

PubMed

Cumming, Geoff

2014-01-01

We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

PubMed

Sojoudi, Alireza; Goodyear, Bradley G

2016-12-01

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Analyzing thematic maps and mapping for accuracy

USGS Publications Warehouse

Rosenfield, G.H.

1982-01-01

Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by either the row totals or the column totals from the original classification error matrices. In hypothesis testing, when the results of tests of multiple sample cases prove to be significant, some form of statistical test must be used to separate any results that differ significantly from the others. In the past, many analyses of the data in this error matrix were made by comparing the relative magnitudes of the percentage of correct classifications, for either individual categories, the entire map or both. More rigorous analyses have used data transformations and (or) two-way classification analysis of variance. A more sophisticated step of data analysis techniques would be to use the entire classification error matrices using the methods of discrete multivariate analysis or of multiviariate analysis of variance.

Spectral analysis of groove spacing on Ganymede

NASA Technical Reports Server (NTRS)

Grimm, R. E.

1984-01-01

The technique used to analyze groove spacing on Ganymede is presented. Data from Voyager images are used determine the surface topography and position of the grooves. Power spectal estimates are statistically analyzed and sample data is included.

Optimizing construction quality management of pavements using mechanistic performance analysis.

DOT National Transportation Integrated Search

2004-08-01

This report presents a statistical-based algorithm that was developed to reconcile the results from several pavement performance models used in the state of practice with systematic process control techniques. These algorithms identify project-specif...

Statistical strategy for anisotropic adventitia modelling in IVUS.

PubMed

Gil, Debora; Hernández, Aura; Rodriguez, Oriol; Mauri, Josepa; Radeva, Petia

2006-06-01

Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.

Automated Identification and Shape Analysis of Chorus Elements in the Van Allen Radiation Belts

NASA Astrophysics Data System (ADS)

Sen Gupta, Ananya; Kletzing, Craig; Howk, Robin; Kurth, William; Matheny, Morgan

2017-12-01

An important goal of the Van Allen Probes mission is to understand wave-particle interaction by chorus emissions in terrestrial Van Allen radiation belts. To test models, statistical characterization of chorus properties, such as amplitude variation and sweep rates, is an important scientific goal. The Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite provides measurements of wave electric and magnetic fields as well as DC magnetic fields for the Van Allen Probes mission. However, manual inspection across terabytes of EMFISIS data is not feasible and as such introduces human confirmation bias. We present signal processing techniques for automated identification, shape analysis, and sweep rate characterization of high-amplitude whistler-mode chorus elements in the Van Allen radiation belts. Specifically, we develop signal processing techniques based on the radon transform that disambiguate chorus elements with a dominant sweep rate against hiss-like chorus. We present representative results validating our techniques and also provide statistical characterization of detected chorus elements across a case study of a 6 s epoch.

Fitting and Modeling in the ASC Data Analysis Environment

NASA Astrophysics Data System (ADS)

Doe, S.; Siemiginowska, A.; Joye, W.; McDowell, J.

As part of the AXAF Science Center (ASC) Data Analysis Environment, we will provide to the astronomical community a Fitting Application. We present a design of the application in this paper. Our design goal is to give the user the flexibility to use a variety of optimization techniques (Levenberg-Marquardt, maximum entropy, Monte Carlo, Powell, downhill simplex, CERN-Minuit, and simulated annealing) and fit statistics (chi (2) , Cash, variance, and maximum likelihood); our modular design allows the user easily to add their own optimization techniques and/or fit statistics. We also present a comparison of the optimization techniques to be provided by the Application. The high spatial and spectral resolutions that will be obtained with AXAF instruments require a sophisticated data modeling capability. We will provide not only a suite of astronomical spatial and spectral source models, but also the capability of combining these models into source models of up to four data dimensions (i.e., into source functions f(E,x,y,t)). We will also provide tools to create instrument response models appropriate for each observation.

Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search.

PubMed

Mena, Carlos; Sepúlveda, Cesar; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

2018-05-07

Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

Japanese migration in contemporary Japan: economic segmentation and interprefectural migration.

PubMed

Fukurai, H

1991-01-01

This paper examines the economic segmentation model in explaining 1985-86 Japanese interregional migration. The analysis takes advantage of statistical graphic techniques to illustrate the following substantive issues of interregional migration: (1) to examine whether economic segmentation significantly influences Japanese regional migration and (2) to explain socioeconomic characteristics of prefectures for both in- and out-migration. Analytic techniques include a latent structural equation (LISREL) methodology and statistical residual mapping. The residual dispersion patterns, for instance, suggest the extent to which socioeconomic and geopolitical variables explain migration differences by showing unique clusters of unexplained residuals. The analysis further points out that extraneous factors such as high residential land values, significant commuting populations, and regional-specific cultures and traditions need to be incorporated in the economic segmentation model in order to assess the extent of the model's reliability in explaining the pattern of interprefectural migration.

Adaptive distributed source coding.

PubMed

Varodayan, David; Lin, Yao-Chung; Girod, Bernd

2012-05-01

We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.

Statistical intensity variation analysis for rapid volumetric imaging of capillary network flux

PubMed Central

Lee, Jonghwan; Jiang, James Y.; Wu, Weicheng; Lesage, Frederic; Boas, David A.

2014-01-01

We present a novel optical coherence tomography (OCT)-based technique for rapid volumetric imaging of red blood cell (RBC) flux in capillary networks. Previously we reported that OCT can capture individual RBC passage within a capillary, where the OCT intensity signal at a voxel fluctuates when an RBC passes the voxel. Based on this finding, we defined a metric of statistical intensity variation (SIV) and validated that the mean SIV is proportional to the RBC flux [RBC/s] through simulations and measurements. From rapidly scanned volume data, we used Hessian matrix analysis to vectorize a segment path of each capillary and estimate its flux from the mean of the SIVs gathered along the path. Repeating this process led to a 3D flux map of the capillary network. The present technique enabled us to trace the RBC flux changes over hundreds of capillaries with a temporal resolution of ~1 s during functional activation. PMID:24761298

A Meta-Analysis of Hypnotherapeutic Techniques in the Treatment of PTSD Symptoms.

PubMed

O'Toole, Siobhan K; Solomon, Shelby L; Bergdahl, Stephen A

2016-02-01

The efficacy of hypnotherapeutic techniques as treatment for symptoms of posttraumatic stress disorder (PTSD) was explored through meta-analytic methods. Studies were selected through a search of 29 databases. Altogether, 81 studies discussing hypnotherapy and PTSD were reviewed for inclusion criteria. The outcomes of 6 studies representing 391 participants were analyzed using meta-analysis. Evaluation of effect sizes related to avoidance and intrusion, in addition to overall PTSD symptoms after hypnotherapy treatment, revealed that all studies showed that hypnotherapy had a positive effect on PTSD symptoms. The overall Cohen's d was large (-1.18) and statistically significant (p < .001). Effect sizes varied based on study quality; however, they were large and statistically significant. Using the classic fail-safe N to assess for publication bias, it was determined it would take 290 nonsignificant studies to nullify these findings. Copyright © 2016 International Society for Traumatic Stress Studies.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

PubMed

Baldi, P; Sadowski, P; Whiteson, D

2015-03-20

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

Index of Selected Publications Through December 1983,

DTIC Science & Technology

1984-03-01

substantiating methodology , and is designed mainly for * readers with a professional interest in the subject but do * not have a primary responsibility in that...Navy in postwar American security policy -- computer subroutines - CRC 20 H 1052 experimental design techniques, computer North Atlantic-Norwegian...statistical tion and Congestion, With an Example from Southern experimental design technique aids the analysis California, 27 pp., Jan 1971, AD 719 906 of

Using statistical text classification to identify health information technology incidents

PubMed Central

Chai, Kevin E K; Anthony, Stephen; Coiera, Enrico; Magrabi, Farah

2013-01-01

Objective To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. Design We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both ‘balanced’ (50% HIT) and ‘stratified’ (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined. Measurements κ statistic, F1 score, precision and recall. Results Classification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165). Conclusions Statistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation. PMID:23666777

Mathematical problem solving ability of sport students in the statistical study

NASA Astrophysics Data System (ADS)

Sari, E. F. P.; Zulkardi; Putri, R. I. I.

2017-12-01

This study aims to determine the problem-solving ability of sport students of PGRI Palembang semester V in the statistics course. Subjects in this study were sport students of PGRI Palembang semester V which amounted to 31 people. The research method used is quasi experiment type one case shoot study. Data collection techniques in this study use the test and data analysis used is quantitative descriptive statistics. The conclusion of this study shown that the mathematical problem solving ability of PGRI Palembang sport students of V semester in the statistical course is categorized well with the average of the final test score of 80.3.

Impact of posterior rhabdosphincter reconstruction during robot-assisted radical prostatectomy: retrospective analysis of time to continence.

PubMed

Woo, Jason R; Shikanov, Sergey; Zorn, Kevin C; Shalhav, Arieh L; Zagaja, Gregory P

2009-12-01

Posterior rhabdosphincter (PR) reconstruction during robot-assisted radical prostatectomy (RARP) was introduced in an attempt to improve postoperative continence. In the present study, we evaluate time to achieve continence in patients who are undergoing RARP with and without PR reconstruction. A prospective RARP database was searched for most recent cases that were accomplished with PR reconstruction (group 1, n = 69) or with standard technique (group 2, n = 63). We performed the analysis applying two definitions of continence: 0 pads per day or 0-1 security pad per day. Patients were evaluated by telephone interview. Statistical analysis was carried out using the Kaplan-Meier method and log-rank test. With PR reconstruction, continence was improved when defined as 0-1 security pad per day (median time of 90 vs 150 days; P = 0.01). This difference did not achieve statistical significance when continence was defined as 0 pads per day (P = 0.12). A statistically significant improvement in continence rate and time to achieve continence is seen in patients who are undergoing PR reconstruction during RARP, with continence defined as 0-1 security/safety pad per day. A larger, prospective and randomized study is needed to better understand the impact of this technique on postoperative continence.

DICON: interactive visual analysis of multidimensional clusters.

PubMed

Cao, Nan; Gotz, David; Sun, Jimeng; Qu, Huamin

2011-12-01

Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis. © 2011 IEEE

Three-dimensional accuracy of different correction methods for cast implant bars

PubMed Central

Kwon, Ji-Yung; Kim, Chang-Whe; Lim, Young-Jun; Kwon, Ho-Beom

2014-01-01

PURPOSE The aim of the present study was to evaluate the accuracy of three techniques for correction of cast implant bars. MATERIALS AND METHODS Thirty cast implant bars were fabricated on a metal master model. All cast implant bars were sectioned at 5 mm from the left gold cylinder using a disk of 0.3 mm thickness, and then each group of ten specimens was corrected by gas-air torch soldering, laser welding, and additional casting technique. Three dimensional evaluation including horizontal, vertical, and twisting measurements was based on measurement and comparison of (1) gap distances of the right abutment replica-gold cylinder interface at buccal, distal, lingual side, (2) changes of bar length, and (3) axis angle changes of the right gold cylinders at the step of the post-correction measurements on the three groups with a contact and non-contact coordinate measuring machine. One-way analysis of variance (ANOVA) and paired t-test were performed at the significance level of 5%. RESULTS Gap distances of the cast implant bars after correction procedure showed no statistically significant difference among groups. Changes in bar length between pre-casting and post-correction measurement were statistically significance among groups. Axis angle changes of the right gold cylinders were not statistically significance among groups. CONCLUSION There was no statistical significance among three techniques in horizontal, vertical and axial errors. But, gas-air torch soldering technique showed the most consistent and accurate trend in the correction of implant bar error. However, Laser welding technique, showed a large mean and standard deviation in vertical and twisting measurement and might be technique-sensitive method. PMID:24605205

Classifiers utilized to enhance acoustic based sensors to identify round types of artillery/mortar

NASA Astrophysics Data System (ADS)

Grasing, David; Desai, Sachi; Morcos, Amir

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar type classification based on detected acoustic transients

NASA Astrophysics Data System (ADS)

Morcos, Amir; Grasing, David; Desai, Sachi

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar round type classification to increase system situational awareness

NASA Astrophysics Data System (ADS)

Desai, Sachi; Grasing, David; Morcos, Amir; Hohil, Myron

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice.

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta-analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta-analysis and a tailored meta-regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within-study variance, between-study variance, study sample size, and the number of studies in the meta-analysis. Finally, we apply Vn to two published meta-analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta-analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Arthrodesis following failed total knee arthroplasty: comprehensive review and meta-analysis of recent literature.

PubMed

Damron, T A; McBeath, A A

1995-04-01

With the increasing duration of follow up on total knee arthroplasties, more revision arthroplasties are being performed. When revision is not advisable, a salvage procedure such as arthrodesis or resection arthroplasty is indicated. This article provides a comprehensive review of the literature regarding arthrodesis following failed total knee arthroplasty. In addition, a statistical meta-analysis of five studies using modern arthrodesis techniques is presented. A statistically significant greater fusion rate with intramedullary nail arthrodesis compared to external fixation is documented. Gram negative and mixed infections are found to be significant risk factors for failure of arthrodesis.

Specialized data analysis of SSME and advanced propulsion system vibration measurements

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Swanson, Wayne L.; Jong, Yen-Yi

1993-01-01

The basic objectives of this contract were to perform detailed analysis and evaluation of dynamic data obtained during Space Shuttle Main Engine (SSME) test and flight operations, including analytical/statistical assessment of component dynamic performance, and to continue the development and implementation of analytical/statistical models to effectively define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational conditions. This study was to provide timely assessment of engine component operational status, identify probable causes of malfunction, and define feasible engineering solutions. The work was performed under three broad tasks: (1) Analysis, Evaluation, and Documentation of SSME Dynamic Test Results; (2) Data Base and Analytical Model Development and Application; and (3) Development and Application of Vibration Signature Analysis Techniques.

Tipping point analysis of atmospheric oxygen concentration

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

Livina, V. N.; Forbes, A. B.; Vaz Martins, T. M.

2015-03-15

We apply tipping point analysis to nine observational oxygen concentration records around the globe, analyse their dynamics and perform projections under possible future scenarios, leading to oxygen deficiency in the atmosphere. The analysis is based on statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the observed data using Bayesian and wavelet techniques.

Comparative Analysis of Academic Grades in Compulsory Secondary Education in Spain Using Statistical Techniques

ERIC Educational Resources Information Center

Veas, Alejandro; Gilar, Raquel; Miñano, Pablo; Castejón, Juan Luis

2017-01-01

The present study, based on the construct comparability approach, performs a comparative analysis of general points average for seven courses, using exploratory factor analysis (EFA) and the Partial Credit model (PCM) with a sample of 1398 student subjects (M = 12.5, SD = 0.67) from 8 schools in the province of Alicante (Spain). EFA confirmed a…

Assessing statistical differences between parameters estimates in Partial Least Squares path modeling.

PubMed

Rodríguez-Entrena, Macario; Schuberth, Florian; Gelhard, Carsten

2018-01-01

Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.

Urban environmental health applications of remote sensing, summary report

NASA Technical Reports Server (NTRS)

Rush, M.; Goldstein, J.; Hsi, B. P.; Olsen, C. B.

1975-01-01

Health and its association with the physical environment was studied based on the hypothesis that there is a relationship between the man-made physical environment and health status of a population. The statistical technique of regression analysis was employed to show the degree of association and aspects of physical environment which accounted for the greater variation in health status. Mortality, venereal disease, tuberculosis, hepatitis, meningitis, shigella/salmonella, hypertension and cardiac arrest/myocardial infarction were examined. The statistical techniques were used to measure association and variation, not necessarily cause and effect. Conclusions drawn show that the association still exists in the decade of the 1970's and that it can be successfully monitored with the methodology of remote sensing.

Functional data analysis on ground reaction force of military load carriage increment

NASA Astrophysics Data System (ADS)

Din, Wan Rozita Wan; Rambely, Azmin Sham

2014-06-01

Analysis of ground reaction force on military load carriage is done through functional data analysis (FDA) statistical technique. The main objective of the research is to investigate the effect of 10% load increment and to find the maximum suitable load for the Malaysian military. Ten military soldiers age 31 ± 6.2 years, weigh 71.6 ± 10.4 kg and height of 166.3 ± 5.9 cm carrying different military load range from 0% body weight (BW) up to 40% BW participated in an experiment to gather the GRF and kinematic data using Vicon Motion Analysis System, Kirstler force plates and thirty nine body markers. The analysis is conducted in sagittal, medial lateral and anterior posterior planes. The results show that 10% BW load increment has an effect when heel strike and toe-off for all the three planes analyzed with P-value less than 0.001 at 0.05 significant levels. FDA proves to be one of the best statistical techniques in analyzing the functional data. It has the ability to handle filtering, smoothing and curve aligning according to curve features and points of interest.

Advantage of the modified Lunn-McNeil technique over Kalbfleisch-Prentice technique in competing risks

NASA Astrophysics Data System (ADS)

Lukman, Iing; Ibrahim, Noor A.; Daud, Isa B.; Maarof, Fauziah; Hassan, Mohd N.

2002-03-01

Survival analysis algorithm is often applied in the data mining process. Cox regression is one of the survival analysis tools that has been used in many areas, and it can be used to analyze the failure times of aircraft crashed. Another survival analysis tool is the competing risks where we have more than one cause of failure acting simultaneously. Lunn-McNeil analyzed the competing risks in the survival model using Cox regression with censored data. The modified Lunn-McNeil technique is a simplify of the Lunn-McNeil technique. The Kalbfleisch-Prentice technique is involving fitting models separately from each type of failure, treating other failure types as censored. To compare the two techniques, (the modified Lunn-McNeil and Kalbfleisch-Prentice) a simulation study was performed. Samples with various sizes and censoring percentages were generated and fitted using both techniques. The study was conducted by comparing the inference of models, using Root Mean Square Error (RMSE), the power tests, and the Schoenfeld residual analysis. The power tests in this study were likelihood ratio test, Rao-score test, and Wald statistics. The Schoenfeld residual analysis was conducted to check the proportionality of the model through its covariates. The estimated parameters were computed for the cause-specific hazard situation. Results showed that the modified Lunn-McNeil technique was better than the Kalbfleisch-Prentice technique based on the RMSE measurement and Schoenfeld residual analysis. However, the Kalbfleisch-Prentice technique was better than the modified Lunn-McNeil technique based on power tests measurement.

Extension of vibrational power flow techniques to two-dimensional structures

NASA Technical Reports Server (NTRS)

Cuschieri, Joseph M.

1988-01-01

In the analysis of the vibration response and structure-borne vibration transmission between elements of a complex structure, statistical energy analysis (SEA) or finite element analysis (FEA) are generally used. However, an alternative method is using vibrational power flow techniques which can be especially useful in the mid frequencies between the optimum frequency regimes for SEA and FEA. Power flow analysis has in general been used on 1-D beam-like structures or between structures with point joints. In this paper, the power flow technique is extended to 2-D plate-like structures joined along a common edge without frequency or spatial averaging the results, such that the resonant response of the structure is determined. The power flow results are compared to results obtained using FEA results at low frequencies and SEA at high frequencies. The agreement with FEA results is good but the power flow technique has an improved computational efficiency. Compared to the SEA results the power flow results show a closer representation of the actual response of the structure.

Extension of vibrational power flow techniques to two-dimensional structures

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1987-01-01

In the analysis of the vibration response and structure-borne vibration transmission between elements of a complex structure, statistical energy analysis (SEA) or Finite Element Analysis (FEA) are generally used. However, an alternative method is using vibrational power flow techniques which can be especially useful in the mid- frequencies between the optimum frequency regimes for FEA and SEA. Power flow analysis has in general been used on one-dimensional beam-like structures or between structures with point joints. In this paper, the power flow technique is extended to two-dimensional plate like structures joined along a common edge without frequency or spatial averaging the results, such that the resonant response of the structure is determined. The power flow results are compared to results obtained using FEA at low frequencies and SEA at high frequencies. The agreement with FEA results is good but the power flow technique has an improved computational efficiency. Compared to the SEA results the power flow results show a closer representation of the actual response of the structure.

Comparing the Efficiency of Two Different Extraction Techniques in Removal of Maxillary Third Molars: A Randomized Controlled Trial.

PubMed

Edward, Joseph; Aziz, Mubarak A; Madhu Usha, Arjun; Narayanan, Jyothi K

2017-12-01

Extractions are routine procedures in dental surgery. Traditional extraction techniques use a combination of severing the periodontal attachment, luxation with an elevator, and removal with forceps. A new technique of extraction of maxillary third molar is introduced in this study-Joedds technique, which is compared with the conventional technique. One hundred people were included in the study, the people were divided into two groups by means of simple random sampling. In one group conventional technique of maxillary third molar extraction was used and on second Joedds technique was used. Statistical analysis was carried out with student's t test. Analysis of 100 patients based on parameters showed that the novel joedds technique had minimal trauma to surrounding tissues, less tuberosity and root fractures and the time taken for extraction was <2 min while compared to other group of patients. This novel technique has proved to be better than conventional third molar extraction technique, with minimal complications. If Proper selection of cases and right technique are used.

Proceedings of the NASA Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1983-01-01

The application of mathematical and statistical analyses techniques to imagery obtained by remote sensors is described by Principal Investigators. Scene-to-map registration, geometric rectification, and image matching are among the pattern recognition aspects discussed.

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

Clustering analysis for muon tomography data elaboration in the Muon Portal project

NASA Astrophysics Data System (ADS)

Bandieramonte, M.; Antonuccio-Delogu, V.; Becciani, U.; Costa, A.; La Rocca, P.; Massimino, P.; Petta, C.; Pistagna, C.; Riggi, F.; Riggi, S.; Sciacca, E.; Vitello, F.

2015-05-01

Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

Quantitative Analysis of the Interdisciplinarity of Applied Mathematics.

PubMed

Xie, Zheng; Duan, Xiaojun; Ouyang, Zhenzheng; Zhang, Pengyuan

2015-01-01

The increasing use of mathematical techniques in scientific research leads to the interdisciplinarity of applied mathematics. This viewpoint is validated quantitatively here by statistical and network analysis on the corpus PNAS 1999-2013. A network describing the interdisciplinary relationships between disciplines in a panoramic view is built based on the corpus. Specific network indicators show the hub role of applied mathematics in interdisciplinary research. The statistical analysis on the corpus content finds that algorithms, a primary topic of applied mathematics, positively correlates, increasingly co-occurs, and has an equilibrium relationship in the long-run with certain typical research paradigms and methodologies. The finding can be understood as an intrinsic cause of the interdisciplinarity of applied mathematics.

Finite Element Analysis of Reverberation Chambers

NASA Technical Reports Server (NTRS)

Bunting, Charles F.; Nguyen, Duc T.

2000-01-01

The primary motivating factor behind the initiation of this work was to provide a deterministic means of establishing the validity of the statistical methods that are recommended for the determination of fields that interact in -an avionics system. The application of finite element analysis to reverberation chambers is the initial step required to establish a reasonable course of inquiry in this particularly data-intensive study. The use of computational electromagnetics provides a high degree of control of the "experimental" parameters that can be utilized in a simulation of reverberating structures. As the work evolved there were four primary focus areas they are: 1. The eigenvalue problem for the source free problem. 2. The development of a complex efficient eigensolver. 3. The application of a source for the TE and TM fields for statistical characterization. 4. The examination of shielding effectiveness in a reverberating environment. One early purpose of this work was to establish the utility of finite element techniques in the development of an extended low frequency statistical model for reverberation phenomena. By employing finite element techniques, structures of arbitrary complexity can be analyzed due to the use of triangular shape functions in the spatial discretization. The effects of both frequency stirring and mechanical stirring are presented. It is suggested that for the low frequency operation the typical tuner size is inadequate to provide a sufficiently random field and that frequency stirring should be used. The results of the finite element analysis of the reverberation chamber illustrate io-W the potential utility of a 2D representation for enhancing the basic statistical characteristics of the chamber when operating in a low frequency regime. The basic field statistics are verified for frequency stirring over a wide range of frequencies. Mechanical stirring is shown to provide an effective frequency deviation.

Load balancing for massively-parallel soft-real-time systems

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

Hailperin, M.

1988-09-01

Global load balancing, if practical, would allow the effective use of massively-parallel ensemble architectures for large soft-real-problems. The challenge is to replace quick global communications, which is impractical in a massively-parallel system, with statistical techniques. In this vein, the author proposes a novel approach to decentralized load balancing based on statistical time-series analysis. Each site estimates the system-wide average load using information about past loads of individual sites and attempts to equal that average. This estimation process is practical because the soft-real-time systems of interest naturally exhibit loads that are periodic, in a statistical sense akin to seasonality in econometrics.more » It is shown how this load-characterization technique can be the foundation for a load-balancing system in an architecture employing cut-through routing and an efficient multicast protocol.« less

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Evaluating the decision accuracy and speed of clinical data visualizations.

PubMed

Pieczkiewicz, David S; Finkelstein, Stanley M

2010-01-01

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

Residual stress in glass: indentation crack and fractography approaches.

PubMed

Anunmana, Chuchai; Anusavice, Kenneth J; Mecholsky, John J

2009-11-01

To test the hypothesis that the indentation crack technique can determine surface residual stresses that are not statistically significantly different from those determined from the analytical procedure using surface cracks, the four-point flexure test, and fracture surface analysis. Soda-lime-silica glass bar specimens (4 mm x 2.3 mm x 28 mm) were prepared and annealed at 650 degrees C for 30 min before testing. The fracture toughness values of the glass bars were determined from 12 specimens based on induced surface cracks, four-point flexure, and fractographic analysis. To determine the residual stress from the indentation technique, 18 specimens were indented under 19.6N load using a Vickers microhardness indenter. Crack lengths were measured within 1 min and 24h after indentation, and the measured crack lengths were compared with the mean crack lengths of annealed specimens. Residual stress was calculated from an equation developed for the indentation technique. All specimens were fractured in a four-point flexure fixture and the residual stress was calculated from the strength and measured crack sizes on the fracture surfaces. The results show that there was no significant difference between the residual stresses calculated from the two techniques. However, the differences in mean residual stresses calculated within 1 min compared with those calculated after 24h were statistically significant (p=0.003). This study compared the indentation technique with the fractographic analysis method for determining the residual stress in the surface of soda-lime-silica glass. The indentation method may be useful for estimating residual stress in glass.

Residual stress in glass: indentation crack and fractography approaches

PubMed Central

Anunmana, Chuchai; Anusavice, Kenneth J.; Mecholsky, John J.

2009-01-01

Objective To test the hypothesis that the indentation crack technique can determine surface residual stresses that are not statistically significantly different from those determined from the analytical procedure using surface cracks, the four-point flexure test, and fracture surface analysis. Methods Soda-lime-silica glass bar specimens (4 mm × 2.3 mm × 28 mm) were prepared and annealed at 650 °C for 30 min before testing. The fracture toughness values of the glass bars were determined from 12 specimens based on induced surface cracks, four-point flexure, and fractographic analysis. To determine the residual stress from the indentation technique, 18 specimens were indented under 19.6 N load using a Vickers microhardness indenter. Crack lengths were measured within 1 min and 24 h after indentation, and the measured crack lengths were compared with the mean crack lengths of annealed specimens. Residual stress was calculated from an equation developed for the indentation technique. All specimens were fractured in a four-point flexure fixture and the residual stress was calculated from the strength and measured crack sizes on the fracture surfaces. Results The results show that there was no significant difference between the residual stresses calculated from the two techniques. However, the differences in mean residual stresses calculated within 1 min compared with those calculated after 24 h were statistically significant (p=0.003). Significance This study compared the indentation technique with the fractographic analysis method for determining the residual stress in the surface of soda-lime silica glass. The indentation method may be useful for estimating residual stress in glass. PMID:19671475

Using Shakespeare's Sotto Voce to Determine True Identity From Text

PubMed Central

Kernot, David; Bossomaier, Terry; Bradbury, Roger

2018-01-01

Little is known of the private life of William Shakespeare, but he is famous for his collection of plays and poems, even though many of the works attributed to him were published anonymously. Determining the identity of Shakespeare has fascinated scholars for 400 years, and four significant figures in English literary history have been suggested as likely alternatives to Shakespeare for some disputed works: Bacon, de Vere, Stanley, and Marlowe. A myriad of computational and statistical tools and techniques have been used to determine the true authorship of his works. Many of these techniques rely on basic statistical correlations, word counts, collocated word groups, or keyword density, but no one method has been decided on. We suggest that an alternative technique that uses word semantics to draw on personality can provide an accurate profile of a person. To test this claim, we analyse the works of Shakespeare, Christopher Marlowe, and Elizabeth Cary. We use Word Accumulation Curves, Hierarchical Clustering overlays, Principal Component Analysis, and Linear Discriminant Analysis techniques in combination with RPAS, a multi-faceted text analysis approach that draws on a writer's personality, or self to identify subtle characteristics within a person's writing style. Here we find that RPAS can separate the known authored works of Shakespeare from Marlowe and Cary. Further, it separates their contested works, works suspected of being written by others. While few authorship identification techniques identify self from the way a person writes, we demonstrate that these stylistic characteristics are as applicable 400 years ago as they are today and have the potential to be used within cyberspace for law enforcement purposes. PMID:29599734

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Components of spatial information management in wildlife ecology: Software for statistical and modeling analysis [Chapter 14

Treesearch

Hawthorne L. Beyer; Jeff Jenness; Samuel A. Cushman

2010-01-01

Spatial information systems (SIS) is a term that describes a wide diversity of concepts, techniques, and technologies related to the capture, management, display and analysis of spatial information. It encompasses technologies such as geographic information systems (GIS), global positioning systems (GPS), remote sensing, and relational database management systems (...

Exploring the Micro-Social Geography of Children's Interactions in Preschool: A Long-Term Observational Study and Analysis Using Geographic Information Technologies

ERIC Educational Resources Information Center

Torrens, Paul M.; Griffin, William A.

2013-01-01

The authors describe an observational and analytic methodology for recording and interpreting dynamic microprocesses that occur during social interaction, making use of space--time data collection techniques, spatial-statistical analysis, and visualization. The scheme has three investigative foci: Structure, Activity Composition, and Clustering.…

An exploratory investigation of weight estimation techniques for hypersonic flight vehicles

NASA Technical Reports Server (NTRS)

Cook, E. L.

1981-01-01

The three basic methods of weight prediction (fixed-fraction, statistical correlation, and point stress analysis) and some of the computer programs that have been developed to implement them are discussed. A modified version of the WAATS (Weights Analysis of Advanced Transportation Systems) program is presented, along with input data forms and an example problem.

Prerequisites for Systems Analysts: Analytic and Management Demands of a New Approach to Educational Administration.

ERIC Educational Resources Information Center

Ammentorp, William

There is much to be gained by using systems analysis in educational administration. Most administrators, presently relying on classical statistical techniques restricted to problems having few variables, should be trained to use more sophisticated tools such as systems analysis. The systems analyst, interested in the basic processes of a group or…

Passing the Test: Ecological Regression Analysis in the Los Angeles County Case and Beyond.

ERIC Educational Resources Information Center

Lichtman, Allan J.

1991-01-01

Statistical analysis of racially polarized voting prepared for the Garza v County of Los Angeles (California) (1990) voting rights case is reviewed to demonstrate that ecological regression is a flexible, robust technique that illuminates the reality of ethnic voting, and superior to the neighborhood model supported by the defendants. (SLD)

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

Bennett, Janine Camille; Thompson, David; Pebay, Philippe Pierre

Statistical analysis is typically used to reduce the dimensionality of and infer meaning from data. A key challenge of any statistical analysis package aimed at large-scale, distributed data is to address the orthogonal issues of parallel scalability and numerical stability. Many statistical techniques, e.g., descriptive statistics or principal component analysis, are based on moments and co-moments and, using robust online update formulas, can be computed in an embarrassingly parallel manner, amenable to a map-reduce style implementation. In this paper we focus on contingency tables, through which numerous derived statistics such as joint and marginal probability, point-wise mutual information, information entropy,more » and {chi}{sup 2} independence statistics can be directly obtained. However, contingency tables can become large as data size increases, requiring a correspondingly large amount of communication between processors. This potential increase in communication prevents optimal parallel speedup and is the main difference with moment-based statistics (which we discussed in [1]) where the amount of inter-processor communication is independent of data size. Here we present the design trade-offs which we made to implement the computation of contingency tables in parallel. We also study the parallel speedup and scalability properties of our open source implementation. In particular, we observe optimal speed-up and scalability when the contingency statistics are used in their appropriate context, namely, when the data input is not quasi-diffuse.« less

Variational Bayesian Parameter Estimation Techniques for the General Linear Model

PubMed Central

Starke, Ludger; Ostwald, Dirk

2017-01-01

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation. PMID:28966572

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Statistical ultrasonics: the influence of Robert F. Wagner

NASA Astrophysics Data System (ADS)

Insana, Michael F.

2009-02-01

An important ongoing question for higher education is how to successfully mentor the next generation of scientists and engineers. It has been my privilege to have been mentored by one of the best, Dr Robert F. Wagner and his colleagues at the CDRH/FDA during the mid 1980s. Bob introduced many of us in medical ultrasonics to statistical imaging techniques. These ideas continue to broadly influence studies on adaptive aperture management (beamforming, speckle suppression, compounding), tissue characterization (texture features, Rayleigh/Rician statistics, scatterer size and number density estimators), and fundamental questions about how limitations of the human eye-brain system for extracting information from textured images can motivate image processing. He adapted the classical techniques of signal detection theory to coherent imaging systems that, for the first time in ultrasonics, related common engineering metrics for image quality to task-based clinical performance. This talk summarizes my wonderfully-exciting three years with Bob as I watched him explore topics in statistical image analysis that formed a rational basis for many of the signal processing techniques used in commercial systems today. It is a story of an exciting time in medical ultrasonics, and of how a sparkling personality guided and motivated the development of junior scientists who flocked around him in admiration and amazement.

The multiple imputation method: a case study involving secondary data analysis.

PubMed

Walani, Salimah R; Cleland, Charles M

2015-05-01

To illustrate with the example of a secondary data analysis study the use of the multiple imputation method to replace missing data. Most large public datasets have missing data, which need to be handled by researchers conducting secondary data analysis studies. Multiple imputation is a technique widely used to replace missing values while preserving the sample size and sampling variability of the data. The 2004 National Sample Survey of Registered Nurses. The authors created a model to impute missing values using the chained equation method. They used imputation diagnostics procedures and conducted regression analysis of imputed data to determine the differences between the log hourly wages of internationally educated and US-educated registered nurses. The authors used multiple imputation procedures to replace missing values in a large dataset with 29,059 observations. Five multiple imputed datasets were created. Imputation diagnostics using time series and density plots showed that imputation was successful. The authors also present an example of the use of multiple imputed datasets to conduct regression analysis to answer a substantive research question. Multiple imputation is a powerful technique for imputing missing values in large datasets while preserving the sample size and variance of the data. Even though the chained equation method involves complex statistical computations, recent innovations in software and computation have made it possible for researchers to conduct this technique on large datasets. The authors recommend nurse researchers use multiple imputation methods for handling missing data to improve the statistical power and external validity of their studies.

Logistic regression for risk factor modelling in stuttering research.

PubMed

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Combined magnetic and gravity analysis

NASA Technical Reports Server (NTRS)

Hinze, W. J.; Braile, L. W.; Chandler, V. W.; Mazella, F. E.

1975-01-01

Efforts are made to identify methods of decreasing magnetic interpretation ambiguity by combined gravity and magnetic analysis, to evaluate these techniques in a preliminary manner, to consider the geologic and geophysical implications of correlation, and to recommend a course of action to evaluate methods of correlating gravity and magnetic anomalies. The major thrust of the study was a search and review of the literature. The literature of geophysics, geology, geography, and statistics was searched for articles dealing with spatial correlation of independent variables. An annotated bibliography referencing the Germane articles and books is presented. The methods of combined gravity and magnetic analysis techniques are identified and reviewed. A more comprehensive evaluation of two types of techniques is presented. Internal correspondence of anomaly amplitudes is examined and a combined analysis is done utilizing Poisson's theorem. The geologic and geophysical implications of gravity and magnetic correlation based on both theoretical and empirical relationships are discussed.

Statistical power analysis of cardiovascular safety pharmacology studies in conscious rats.

PubMed

Bhatt, Siddhartha; Li, Dingzhou; Flynn, Declan; Wisialowski, Todd; Hemkens, Michelle; Steidl-Nichols, Jill

2016-01-01

Cardiovascular (CV) toxicity and related attrition are a major challenge for novel therapeutic entities and identifying CV liability early is critical for effective derisking. CV safety pharmacology studies in rats are a valuable tool for early investigation of CV risk. Thorough understanding of data analysis techniques and statistical power of these studies is currently lacking and is imperative for enabling sound decision-making. Data from 24 crossover and 12 parallel design CV telemetry rat studies were used for statistical power calculations. Average values of telemetry parameters (heart rate, blood pressure, body temperature, and activity) were logged every 60s (from 1h predose to 24h post-dose) and reduced to 15min mean values. These data were subsequently binned into super intervals for statistical analysis. A repeated measure analysis of variance was used for statistical analysis of crossover studies and a repeated measure analysis of covariance was used for parallel studies. Statistical power analysis was performed to generate power curves and establish relationships between detectable CV (blood pressure and heart rate) changes and statistical power. Additionally, data from a crossover CV study with phentolamine at 4, 20 and 100mg/kg are reported as a representative example of data analysis methods. Phentolamine produced a CV profile characteristic of alpha adrenergic receptor antagonism, evidenced by a dose-dependent decrease in blood pressure and reflex tachycardia. Detectable blood pressure changes at 80% statistical power for crossover studies (n=8) were 4-5mmHg. For parallel studies (n=8), detectable changes at 80% power were 6-7mmHg. Detectable heart rate changes for both study designs were 20-22bpm. Based on our results, the conscious rat CV model is a sensitive tool to detect and mitigate CV risk in early safety studies. Furthermore, these results will enable informed selection of appropriate models and study design for early stage CV studies. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

A scanning electron microscope study and statistical analysis of adipocyte morphology in lipofilling: comparing the effects of harvesting and purification procedures with 2 different techniques.

PubMed

Rubino, Corrado; Mazzarello, Vittorio; Faenza, Mario; Montella, Andrea; Santanelli, Fabio; Farace, Francesco

2015-06-01

The aim of this study was to evaluate the effects on adipocyte morphology of 2 techniques of fat harvesting and of fat purification in lipofilling, considering that the number of viable healthy adipocytes is important in fat survival in recipient areas of lipofilling. Fat harvesting was performed in 10 female patients from flanks, on one side with a 2-mm Coleman cannula and on the other side with a 3-mm Mercedes cannula. Thirty milliliter of fat tissue from each side was collected and divided into three 10 mL syringes: A, B, and C. The fat inside syringe A was left untreated, the fat in syringe B underwent simple sedimentation, and the fat inside syringe C underwent centrifugation at 3000 rpm for 3 minutes. Each fat graft specimen was processed for examination under low-vacuum scanning electron microscope. Diameter (μ) and number of adipocytes per square millimeter and number of altered adipocytes per square millimeter were evaluated. Untreated specimens harvested with the 2 different techniques were first compared, then sedimented versus centrifuged specimens harvested with the same technique were compared. Statistical analysis was performed using Wilcoxon signed rank test. The number of adipocytes per square millimeter was statistically higher in specimens harvested with the 3-mm Mercedes cannula (P = 0.0310). The number of altered cells was statistically higher in centrifuged specimens than in sedimented ones using both methods of fat harvesting (P = 0.0080) with a 2-mm Coleman cannula and (P = 0.0050) with a 3-mm Mercedes cannula. Alterations in adipocyte morphology consisted in wrinkling of the membrane, opening of pore with leakage of oily material, reduction of cellular diameter, and total collapse of the cellular membrane. Fat harvesting by a 3-mm cannula results in a higher number of adipocytes and centrifugation of the harvested fat results in a higher number of morphologic altered cells than sedimentation.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

An investigation of the feasibility of improving oculometer data analysis through application of advanced statistical techniques

NASA Technical Reports Server (NTRS)

Rana, D. S.

1980-01-01

The data reduction capabilities of the current data reduction programs were assessed and a search for a more comprehensive system with higher data analytic capabilities was made. Results of the investigation are presented.

Incorporating principal component analysis into air quality model evaluation

EPA Science Inventory

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Princi...

Digital image analysis techniques for fiber and soil mixtures.

DOT National Transportation Integrated Search

1999-05-01

The objective of image processing is to visually enhance, quantify, and/or statistically evaluate some aspect of an image not readily apparent in its original form. Processed digital image data can be analyzed in numerous ways. In order to summarize ...

Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform

NASA Astrophysics Data System (ADS)

Abd-el-Malek, Mina; Abdelsalam, Ahmed K.; Hassan, Ola E.

2017-09-01

Robustness, low running cost and reduced maintenance lead Induction Motors (IMs) to pioneerly penetrate the industrial drive system fields. Broken rotor bars (BRBs) can be considered as an important fault that needs to be early assessed to minimize the maintenance cost and labor time. The majority of recent BRBs' fault diagnostic techniques focus on differentiating between healthy and faulty rotor cage. In this paper, a new technique is proposed for detecting the location of the broken bar in the rotor. The proposed technique relies on monitoring certain statistical parameters estimated from the analysis of the start-up stator current envelope. The envelope of the signal is obtained using Hilbert Transformation (HT). The proposed technique offers non-invasive, fast computational and accurate location diagnostic process. Various simulation scenarios are presented that validate the effectiveness of the proposed technique.

Statistical Analysis of Human Body Movement and Group Interactions in Response to Music

NASA Astrophysics Data System (ADS)

Desmet, Frank; Leman, Marc; Lesaffre, Micheline; de Bruyn, Leen

Quantification of time series that relate to physiological data is challenging for empirical music research. Up to now, most studies have focused on time-dependent responses of individual subjects in controlled environments. However, little is known about time-dependent responses of between-subject interactions in an ecological context. This paper provides new findings on the statistical analysis of group synchronicity in response to musical stimuli. Different statistical techniques were applied to time-dependent data obtained from an experiment on embodied listening in individual and group settings. Analysis of inter group synchronicity are described. Dynamic Time Warping (DTW) and Cross Correlation Function (CCF) were found to be valid methods to estimate group coherence of the resulting movements. It was found that synchronicity of movements between individuals (human-human interactions) increases significantly in the social context. Moreover, Analysis of Variance (ANOVA) revealed that the type of music is the predominant factor in both the individual and the social context.

Descriptive and inferential statistical methods used in burns research.

PubMed

Al-Benna, Sammy; Al-Ajam, Yazan; Way, Benjamin; Steinstraesser, Lars

2010-05-01

Burns research articles utilise a variety of descriptive and inferential methods to present and analyse data. The aim of this study was to determine the descriptive methods (e.g. mean, median, SD, range, etc.) and survey the use of inferential methods (statistical tests) used in articles in the journal Burns. This study defined its population as all original articles published in the journal Burns in 2007. Letters to the editor, brief reports, reviews, and case reports were excluded. Study characteristics, use of descriptive statistics and the number and types of statistical methods employed were evaluated. Of the 51 articles analysed, 11(22%) were randomised controlled trials, 18(35%) were cohort studies, 11(22%) were case control studies and 11(22%) were case series. The study design and objectives were defined in all articles. All articles made use of continuous and descriptive data. Inferential statistics were used in 49(96%) articles. Data dispersion was calculated by standard deviation in 30(59%). Standard error of the mean was quoted in 19(37%). The statistical software product was named in 33(65%). Of the 49 articles that used inferential statistics, the tests were named in 47(96%). The 6 most common tests used (Student's t-test (53%), analysis of variance/co-variance (33%), chi(2) test (27%), Wilcoxon & Mann-Whitney tests (22%), Fisher's exact test (12%)) accounted for the majority (72%) of statistical methods employed. A specified significance level was named in 43(88%) and the exact significance levels were reported in 28(57%). Descriptive analysis and basic statistical techniques account for most of the statistical tests reported. This information should prove useful in deciding which tests should be emphasised in educating burn care professionals. These results highlight the need for burn care professionals to have a sound understanding of basic statistics, which is crucial in interpreting and reporting data. Advice should be sought from professionals in the fields of biostatistics and epidemiology when using more advanced statistical techniques. Copyright 2009 Elsevier Ltd and ISBI. All rights reserved.

Assessment of statistical methods used in library-based approaches to microbial source tracking.

PubMed

Ritter, Kerry J; Carruthers, Ethan; Carson, C Andrew; Ellender, R D; Harwood, Valerie J; Kingsley, Kyle; Nakatsu, Cindy; Sadowsky, Michael; Shear, Brian; West, Brian; Whitlock, John E; Wiggins, Bruce A; Wilbur, Jayson D

2003-12-01

Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

New efficient optimizing techniques for Kalman filters and numerical weather prediction models

NASA Astrophysics Data System (ADS)

Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

2016-06-01

The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

New Optical Transforms For Statistical Image Recognition

NASA Astrophysics Data System (ADS)

Lee, Sing H.

1983-12-01

In optical implementation of statistical image recognition, new optical transforms on large images for real-time recognition are of special interest. Several important linear transformations frequently used in statistical pattern recognition have now been optically implemented, including the Karhunen-Loeve transform (KLT), the Fukunaga-Koontz transform (FKT) and the least-squares linear mapping technique (LSLMT).1-3 The KLT performs principle components analysis on one class of patterns for feature extraction. The FKT performs feature extraction for separating two classes of patterns. The LSLMT separates multiple classes of patterns by maximizing the interclass differences and minimizing the intraclass variations.

Estimation of elastic moduli of graphene monolayer in lattice statics approach at nonzero temperature

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

Zubko, I. Yu., E-mail: zoubko@list.ru; Kochurov, V. I.

2015-10-27

For the aim of the crystal temperature control the computational-statistical approach to studying thermo-mechanical properties for finite sized crystals is presented. The approach is based on the combination of the high-performance computational techniques and statistical analysis of the crystal response on external thermo-mechanical actions for specimens with the statistically small amount of atoms (for instance, nanoparticles). The heat motion of atoms is imitated in the statics approach by including the independent degrees of freedom for atoms connected with their oscillations. We obtained that under heating, graphene material response is nonsymmetric.

[A comparison of convenience sampling and purposive sampling].

PubMed

Suen, Lee-Jen Wu; Huang, Hui-Man; Lee, Hao-Hsien

2014-06-01

Convenience sampling and purposive sampling are two different sampling methods. This article first explains sampling terms such as target population, accessible population, simple random sampling, intended sample, actual sample, and statistical power analysis. These terms are then used to explain the difference between "convenience sampling" and purposive sampling." Convenience sampling is a non-probabilistic sampling technique applicable to qualitative or quantitative studies, although it is most frequently used in quantitative studies. In convenience samples, subjects more readily accessible to the researcher are more likely to be included. Thus, in quantitative studies, opportunity to participate is not equal for all qualified individuals in the target population and study results are not necessarily generalizable to this population. As in all quantitative studies, increasing the sample size increases the statistical power of the convenience sample. In contrast, purposive sampling is typically used in qualitative studies. Researchers who use this technique carefully select subjects based on study purpose with the expectation that each participant will provide unique and rich information of value to the study. As a result, members of the accessible population are not interchangeable and sample size is determined by data saturation not by statistical power analysis.

A Monte Carlo investigation of thrust imbalance of solid rocket motor pairs

NASA Technical Reports Server (NTRS)

Sforzini, R. H.; Foster, W. A., Jr.; Johnson, J. S., Jr.

1974-01-01

A technique is described for theoretical, statistical evaluation of the thrust imbalance of pairs of solid-propellant rocket motors (SRMs) firing in parallel. Sets of the significant variables, determined as a part of the research, are selected using a random sampling technique and the imbalance calculated for a large number of motor pairs. The performance model is upgraded to include the effects of statistical variations in the ovality and alignment of the motor case and mandrel. Effects of cross-correlations of variables are minimized by selecting for the most part completely independent input variables, over forty in number. The imbalance is evaluated in terms of six time - varying parameters as well as eleven single valued ones which themselves are subject to statistical analysis. A sample study of the thrust imbalance of 50 pairs of 146 in. dia. SRMs of the type to be used on the space shuttle is presented. The FORTRAN IV computer program of the analysis and complete instructions for its use are included. Performance computation time for one pair of SRMs is approximately 35 seconds on the IBM 370/155 using the FORTRAN H compiler.

Comparison of apical debris extrusion during root canal preparation using instrumentation techniques with two operating principles: An in vitro study

PubMed Central

Verma, Mudita; Meena, N.; Kumari, R. Anitha; Mallandur, Sudhanva; Vikram, R.; Gowda, Vishwas

2017-01-01

Aims: The aim of this study was to quantify the debris extruded apically from teeth using rotary and reciprocation instrumentation systems. Subjects and Methods: Eighty extracted human mandibular premolars with single canals and similar lengths were instrumented using ProTaper Universal (40, 06; Dentsply Maillefer, Ballaigues, Switzerland), ProTaper Next (40, 06; Dentsply Maillefer, Ballaigues, Switzerland), WaveOne (40, 06; Dentsply Maillefer, Ballaigues, Switzerland), and Reciproc (R40; VDW GmbH, Munich, Germany). Debris extruded during instrumentation was collected into preweighed Eppendorf tubes, which were then stored in an incubator at 70°C for 5 days. The final weight of the Eppendorf tubes with the extruded debris was calculated after obtaining the mean of three consecutive weights obtained for each tube. Statistical Analysis Used: Statistical analysis was performed using SPSS version 16.0 software. The groups were compared using the Kruskal–Wallis test for all variables. Results: There was no statistically significant difference between the groups (P = 0.1114). However, the ProTaper Universal group produced more extrusion and ProTaper Next produced least debris extrusion among the instrument groups (P > 0.05). Conclusions: All instrumentation techniques were associated with extruded debris. PMID:28855755

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis.

PubMed

Bonham-Carter, Oliver; Steele, Joe; Bastola, Dhundy

2014-11-01

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base-base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

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.

Testing statistical isotropy in cosmic microwave background polarization maps

NASA Astrophysics Data System (ADS)

Rath, Pranati K.; Samal, Pramoda Kumar; Panda, Srikanta; Mishra, Debesh D.; Aluri, Pavan K.

2018-04-01

We apply our symmetry based Power tensor technique to test conformity of PLANCK Polarization maps with statistical isotropy. On a wide range of angular scales (l = 40 - 150), our preliminary analysis detects many statistically anisotropic multipoles in foreground cleaned full sky PLANCK polarization maps viz., COMMANDER and NILC. We also study the effect of residual foregrounds that may still be present in the Galactic plane using both common UPB77 polarization mask, as well as the individual component separation method specific polarization masks. However, some of the statistically anisotropic modes still persist, albeit significantly in NILC map. We further probed the data for any coherent alignments across multipoles in several bins from the chosen multipole range.

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.

PubMed

Teo, Guoshou; Kim, Sinae; Tsou, Chih-Chiang; Collins, Ben; Gingras, Anne-Claude; Nesvizhskii, Alexey I; Choi, Hyungwon

2015-11-03

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome. The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

PubMed Central

Riley, Richard D.

2017-01-01

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28620945

Advancing statistical analysis of ambulatory assessment data in the study of addictive behavior: A primer on three person-oriented techniques.

PubMed

Foster, Katherine T; Beltz, Adriene M

2018-08-01

Ambulatory assessment (AA) methodologies have the potential to increase understanding and treatment of addictive behavior in seemingly unprecedented ways, due in part, to their emphasis on intensive repeated assessments of an individual's addictive behavior in context. But, many analytic techniques traditionally applied to AA data - techniques that average across people and time - do not fully leverage this potential. In an effort to take advantage of the individualized, temporal nature of AA data on addictive behavior, the current paper considers three underutilized person-oriented analytic techniques: multilevel modeling, p-technique, and group iterative multiple model estimation. After reviewing prevailing analytic techniques, each person-oriented technique is presented, AA data specifications are mentioned, an example analysis using generated data is provided, and advantages and limitations are discussed; the paper closes with a brief comparison across techniques. Increasing use of person-oriented techniques will substantially enhance inferences that can be drawn from AA data on addictive behavior and has implications for the development of individualized interventions. Copyright © 2017. Published by Elsevier Ltd.

Evaluation of Primary Immunization Coverage of Infants Under Universal Immunization Programme in an Urban Area of Bangalore City Using Cluster Sampling and Lot Quality Assurance Sampling Techniques

PubMed Central

K, Punith; K, Lalitha; G, Suman; BS, Pradeep; Kumar K, Jayanth

2008-01-01

Research Question: Is LQAS technique better than cluster sampling technique in terms of resources to evaluate the immunization coverage in an urban area? Objective: To assess and compare the lot quality assurance sampling against cluster sampling in the evaluation of primary immunization coverage. Study Design: Population-based cross-sectional study. Study Setting: Areas under Mathikere Urban Health Center. Study Subjects: Children aged 12 months to 23 months. Sample Size: 220 in cluster sampling, 76 in lot quality assurance sampling. Statistical Analysis: Percentages and Proportions, Chi square Test. Results: (1) Using cluster sampling, the percentage of completely immunized, partially immunized and unimmunized children were 84.09%, 14.09% and 1.82%, respectively. With lot quality assurance sampling, it was 92.11%, 6.58% and 1.31%, respectively. (2) Immunization coverage levels as evaluated by cluster sampling technique were not statistically different from the coverage value as obtained by lot quality assurance sampling techniques. Considering the time and resources required, it was found that lot quality assurance sampling is a better technique in evaluating the primary immunization coverage in urban area. PMID:19876474

Multi objective climate change impact assessment using multi downscaled climate scenarios

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid

2016-04-01

Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional and global scale. In the present study, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from a set of statistically downscaled GCM projections for Columbia River Basin (CRB). Analysis is performed using 2 different statistically downscaled climate projections namely the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. Analysis is performed on spatial, temporal and frequency based parameters in the future period at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice-versa for temperature. Frequency analysis provided insights into possible explanation to changes in precipitation.

Stochastic dynamic analysis of marine risers considering Gaussian system uncertainties

NASA Astrophysics Data System (ADS)

Ni, Pinghe; Li, Jun; Hao, Hong; Xia, Yong

2018-03-01

This paper performs the stochastic dynamic response analysis of marine risers with material uncertainties, i.e. in the mass density and elastic modulus, by using Stochastic Finite Element Method (SFEM) and model reduction technique. These uncertainties are assumed having Gaussian distributions. The random mass density and elastic modulus are represented by using the Karhunen-Loève (KL) expansion. The Polynomial Chaos (PC) expansion is adopted to represent the vibration response because the covariance of the output is unknown. Model reduction based on the Iterated Improved Reduced System (IIRS) technique is applied to eliminate the PC coefficients of the slave degrees of freedom to reduce the dimension of the stochastic system. Monte Carlo Simulation (MCS) is conducted to obtain the reference response statistics. Two numerical examples are studied in this paper. The response statistics from the proposed approach are compared with those from MCS. It is noted that the computational time is significantly reduced while the accuracy is kept. The results demonstrate the efficiency of the proposed approach for stochastic dynamic response analysis of marine risers.

Precise measurement of the half-life of the Fermi {beta} decay of {sup 26}Al{sup m}

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

Scott, Rebecca J.; Thompson, Maxwell N.; Rassool, Roger P.

2011-08-15

State-of-the-art signal digitization and analysis techniques have been used to measure the half-life of the Fermi {beta} decay of {sup 26}Al{sup m}. The half-life was determined to be 6347.8 {+-} 2.5 ms. This new datum contributes to the experimental testing of the conserved-vector-current hypothesis and the required unitarity of the Cabibbo-Kobayashi-Maskawa matrix: two essential components of the standard model. Detailed discussion of the experimental techniques and data analysis and a thorough investigation of the statistical and systematic uncertainties are presented.

HYPOTHESIS SETTING AND ORDER STATISTIC FOR ROBUST GENOMIC META-ANALYSIS.

PubMed

Song, Chi; Tseng, George C

2014-01-01

Meta-analysis techniques have been widely developed and applied in genomic applications, especially for combining multiple transcriptomic studies. In this paper, we propose an order statistic of p-values ( r th ordered p-value, rOP) across combined studies as the test statistic. We illustrate different hypothesis settings that detect gene markers differentially expressed (DE) "in all studies", "in the majority of studies", or "in one or more studies", and specify rOP as a suitable method for detecting DE genes "in the majority of studies". We develop methods to estimate the parameter r in rOP for real applications. Statistical properties such as its asymptotic behavior and a one-sided testing correction for detecting markers of concordant expression changes are explored. Power calculation and simulation show better performance of rOP compared to classical Fisher's method, Stouffer's method, minimum p-value method and maximum p-value method under the focused hypothesis setting. Theoretically, rOP is found connected to the naïve vote counting method and can be viewed as a generalized form of vote counting with better statistical properties. The method is applied to three microarray meta-analysis examples including major depressive disorder, brain cancer and diabetes. The results demonstrate rOP as a more generalizable, robust and sensitive statistical framework to detect disease-related markers.

Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

DTIC Science & Technology

2008-07-07

analyzing multivariate data sets. The system was developed using the Java Development Kit (JDK) version 1.5; and it yields interactive performance on a... script and captures output from the MATLAB’s “regress” and “stepwisefit” utilities that perform simple and stepwise regression, respectively. The MATLAB...Statistical Association, vol. 85, no. 411, pp. 664–675, 1990. [9] H. Hauser, F. Ledermann, and H. Doleisch, “ Angular brushing of extended parallel coordinates

Noise characteristics of the Skylab S-193 altimeter altitude measurements

NASA Technical Reports Server (NTRS)

Hatch, W. E.

1975-01-01

The statistical characteristics of the SKYLAB S-193 altimeter altitude noise are considered. These results are reported in a concise format for use and analysis by the scientific community. In most instances the results have been grouped according to satellite pointing so that the effects of pointing on the statistical characteristics can be readily seen. The altimeter measurements and the processing techniques are described. The mathematical descriptions of the computer programs used for these results are included.

Evaluation of the ecological relevance of mysid toxicity tests using population modeling techniques

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

Kuhn-Hines, A.; Munns, W.R. Jr.; Lussier, S.

1995-12-31

A number of acute and chronic bioassay statistics are used to evaluate the toxicity and risks of chemical stressors to the mysid shrimp, Mysidopsis bahia. These include LC{sub 50}S from acute tests, NOECs from 7-day and life-cycle tests, and the US EPA Water Quality Criteria Criterion Continuous Concentrations (CCC). Because these statistics are generated from endpoints which focus upon the responses of individual organisms, their relationships to significant effects at higher levels of ecological organization are unknown. This study was conducted to evaluate the quantitative relationships between toxicity test statistics and a concentration-based statistic derived from exposure-response models describing populationmore » growth rate ({lambda}) to stressor concentration. This statistic, C{sup {sm_bullet}} (concentration where {lambda} = I, zero population growth) describes the concentration above which mysid populations are projected to decline in abundance as determined using population modeling techniques. An analysis of M. bahia responses to 9 metals and 9 organic contaminants indicated the NOEC from life-cycle tests to be the best predictor of C{sup {sm_bullet}}, although the acute LC{sub 50} predicted population-level response surprisingly well. These analyses provide useful information regarding uncertainties of extrapolation among test statistics in assessments of ecological risk.« less

Comparative Evaluation of Conventional and Accelerated Castings on Marginal Fit and Surface Roughness

PubMed Central

Jadhav, Vivek Dattatray; Motwani, Bhagwan K.; Shinde, Jitendra; Adhapure, Prasad

2017-01-01

Aims: The aim of this study was to evaluate the marginal fit and surface roughness of complete cast crowns made by a conventional and an accelerated casting technique. Settings and Design: This study was divided into three parts. In Part I, the marginal fit of full metal crowns made by both casting techniques in the vertical direction was checked, in Part II, the fit of sectional metal crowns in the horizontal direction made by both casting techniques was checked, and in Part III, the surface roughness of disc-shaped metal plate specimens made by both casting techniques was checked. Materials and Methods: A conventional technique was compared with an accelerated technique. In Part I of the study, the marginal fit of the full metal crowns as well as in Part II, the horizontal fit of sectional metal crowns made by both casting techniques was determined, and in Part III, the surface roughness of castings made with the same techniques was compared. Statistical Analysis Used: The results of the t-test and independent sample test do not indicate statistically significant differences in the marginal discrepancy detected between the two casting techniques. Results: For the marginal discrepancy and surface roughness, crowns fabricated with the accelerated technique were significantly different from those fabricated with the conventional technique. Conclusions: Accelerated casting technique showed quite satisfactory results, but the conventional technique was superior in terms of marginal fit and surface roughness. PMID:29042726

Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains.

PubMed

Cejnar, Pavel; Kuckova, Stepanka; Prochazka, Ales; Karamonova, Ludmila; Svobodova, Barbora

2018-06-15

Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available free of charge from http://uprt.vscht.cz/ms. Copyright © 2018 John Wiley & Sons, Ltd.

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

ERIC Educational Resources Information Center

Fagginger Auer, Marije F.; Hickendorff, Marian; Van Putten, Cornelis M.; Béguin, Anton A.; Heiser, Willem J.

2016-01-01

A first application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint influence of teacher and student variables.…

Statistical Analysis for the Solomon Four-Group Design. Research Report 99-06.

ERIC Educational Resources Information Center

van Engelenburg, Gijsbert

The Solomon four-group design (R. Solomon, 1949) is a very useful experimental design to investigate the main effect of a pretest and the interaction of pretest and treatment. Although the design was proposed half a century ago, no proper data analysis techniques have been available. This paper describes how data from the Solomon four-group design…

The Effect of Unequal Samples, Heterogeneity of Covariance Matrices, and Number of Variables on Discriminant Analysis Classification Tables and Related Statistics.

ERIC Educational Resources Information Center

Spearing, Debra; Woehlke, Paula

To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…

Two-Year versus One-Year Head Start Program Impact: Addressing Selection Bias by Comparing Regression Modeling with Propensity Score Analysis

ERIC Educational Resources Information Center

Leow, Christine; Wen, Xiaoli; Korfmacher, Jon

2015-01-01

This article compares regression modeling and propensity score analysis as different types of statistical techniques used in addressing selection bias when estimating the impact of two-year versus one-year Head Start on children's school readiness. The analyses were based on the national Head Start secondary dataset. After controlling for…

Using "Excel" for White's Test--An Important Technique for Evaluating the Equality of Variance Assumption and Model Specification in a Regression Analysis

ERIC Educational Resources Information Center

Berenson, Mark L.

2013-01-01

There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient…

Consequences of Assumption Violations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance "F" Test.

ERIC Educational Resources Information Center

Lix, Lisa M.; And Others

1996-01-01

Meta-analytic techniques were used to summarize the statistical robustness literature on Type I error properties of alternatives to the one-way analysis of variance "F" test. The James (1951) and Welch (1951) tests performed best under violations of the variance homogeneity assumption, although their use is not always appropriate. (SLD)

Forest Fire History... A Computer Method of Data Analysis

Treesearch

Romain M. Meese

1973-01-01

A series of computer programs is available to extract information from the individual Fire Reports (U.S. Forest Service Form 5100-29). The programs use a statistical technique to fit a continuous distribution to a set of sampled data. The goodness-of-fit program is applicable to data other than the fire history. Data summaries illustrate analysis of fire occurrence,...

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Laser speckle imaging of rat retinal blood flow with hybrid temporal and spatial analysis method

NASA Astrophysics Data System (ADS)

Cheng, Haiying; Yan, Yumei; Duong, Timothy Q.

2009-02-01

Noninvasive monitoring of blood flow in retinal circulation will reveal the progression and treatment of ocular disorders, such as diabetic retinopathy, age-related macular degeneration and glaucoma. A non-invasive and direct BF measurement technique with high spatial-temporal resolution is needed for retinal imaging. Laser speckle imaging (LSI) is such a method. Currently, there are two analysis methods for LSI: spatial statistics LSI (SS-LSI) and temporal statistical LSI (TS-LSI). Comparing these two analysis methods, SS-LSI has higher signal to noise ratio (SNR) and TSLSI is less susceptible to artifacts from stationary speckle. We proposed a hybrid temporal and spatial analysis method (HTS-LSI) to measure the retinal blood flow. Gas challenge experiment was performed and images were analyzed by HTS-LSI. Results showed that HTS-LSI can not only remove the stationary speckle but also increase the SNR. Under 100% O2, retinal BF decreased by 20-30%. This was consistent with the results observed with laser Doppler technique. As retinal blood flow is a critical physiological parameter and its perturbation has been implicated in the early stages of many retinal diseases, HTS-LSI will be an efficient method in early detection of retina diseases.

Geospatial methods and data analysis for assessing distribution of grazing livestock

USDA-ARS?s Scientific Manuscript database

Free-ranging livestock research must begin with a well conceived problem statement and employ appropriate data acquisition tools and analytical techniques to accomplish the research objective. These requirements are especially critical in addressing animal distribution. Tools and statistics used t...

Electromagnetic Induction E-Sensor for Underwater UXO Detection

DTIC Science & Technology

2011-12-01

EMF Electromotive force FET Field Effect Transitor Hz Hertz ms millisecond nV nanoVolt QFS QUASAR Federal...processing. Statistical discrimination techniques based on model analysis, such as the Time-Domain Three Dipole (TD3D) model, can separate UXO-like objects

Data handling and analysis for the 1971 corn blight watch experiment.

NASA Technical Reports Server (NTRS)

Anuta, P. E.; Phillips, T. L.; Landgrebe, D. A.

1972-01-01

Review of the data handling and analysis methods used in the near-operational test of remote sensing systems provided by the 1971 corn blight watch experiment. The general data analysis techniques and, particularly, the statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data are described. Some of the results obtained are examined, and the implications of the experiment for future data communication requirements of earth resource survey systems are discussed.

Laser ektacytometry and evaluation of statistical characteristics of inhomogeneous ensembles of red blood cells

NASA Astrophysics Data System (ADS)

Nikitin, S. Yu.; Priezzhev, A. V.; Lugovtsov, A. E.; Ustinov, V. D.; Razgulin, A. V.

2014-10-01

The paper is devoted to development of the laser ektacytometry technique for evaluation of the statistical characteristics of inhomogeneous ensembles of red blood cells (RBCs). We have analyzed theoretically laser beam scattering by the inhomogeneous ensembles of elliptical discs, modeling red blood cells in the ektacytometer. The analysis shows that the laser ektacytometry technique allows for quantitative evaluation of such population characteristics of RBCs as the cells mean shape, the cells deformability variance and asymmetry of the cells distribution in the deformability. Moreover, we show that the deformability distribution itself can be retrieved by solving a specific Fredholm integral equation of the first kind. At this stage we do not take into account the scatter in the RBC sizes.

Results of a joint NOAA/NASA sounder simulation study

NASA Technical Reports Server (NTRS)

Phillips, N.; Susskind, Joel; Mcmillin, L.

1988-01-01

This paper presents the results of a joint NOAA and NASA sounder simulation study in which the accuracies of atmospheric temperature profiles and surface skin temperature measuremnents retrieved from two sounders were compared: (1) the currently used IR temperature sounder HIRS2 (High-resolution Infrared Radiation Sounder 2); and (2) the recently proposed high-spectral-resolution IR sounder AMTS (Advanced Moisture and Temperature Sounder). Simulations were conducted for both clear and partial cloud conditions. Data were analyzed at NASA using a physical inversion technique and at NOAA using a statistical technique. Results show significant improvement of AMTS compared to HIRS2 for both clear and cloudy conditions. The improvements are indicated by both methods of data analysis, but the physical retrievals outperform the statistical retrievals.

Desensitized Optimal Filtering and Sensor Fusion Toolkit

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.

2015-01-01

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Application of the statistical process control method for prospective patient safety monitoring during the learning phase: robotic kidney transplantation with regional hypothermia (IDEAL phase 2a-b).

PubMed

Sood, Akshay; Ghani, Khurshid R; Ahlawat, Rajesh; Modi, Pranjal; Abaza, Ronney; Jeong, Wooju; Sammon, Jesse D; Diaz, Mireya; Kher, Vijay; Menon, Mani; Bhandari, Mahendra

2014-08-01

Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption phase. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Smart Sampling and HPC-based Probabilistic Look-ahead Contingency Analysis Implementation and its Evaluation with Real-world Data

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

Chen, Yousu; Etingov, Pavel V.; Ren, Huiying

This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested withmore » real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.« less

An analysis of pilot error-related aircraft accidents

NASA Technical Reports Server (NTRS)

Kowalsky, N. B.; Masters, R. L.; Stone, R. B.; Babcock, G. L.; Rypka, E. W.

1974-01-01

A multidisciplinary team approach to pilot error-related U.S. air carrier jet aircraft accident investigation records successfully reclaimed hidden human error information not shown in statistical studies. New analytic techniques were developed and applied to the data to discover and identify multiple elements of commonality and shared characteristics within this group of accidents. Three techniques of analysis were used: Critical element analysis, which demonstrated the importance of a subjective qualitative approach to raw accident data and surfaced information heretofore unavailable. Cluster analysis, which was an exploratory research tool that will lead to increased understanding and improved organization of facts, the discovery of new meaning in large data sets, and the generation of explanatory hypotheses. Pattern recognition, by which accidents can be categorized by pattern conformity after critical element identification by cluster analysis.

Epipolis-laser in situ keratomileusis versus photorefractive keratectomy for the correction of myopia: a meta-analysis.

PubMed

Wu, Wenjing; Wang, Yan; Xu, Lulu

2015-10-01

It is unclear whether epipolis-laser in situ keratomileusis (Epi-LASIK) has any significant advantage over photorefractive keratectomy (PRK) for correcting myopia. We undertook this meta-analysis of randomized controlled trials and cohort studies to examine possible differences in efficacy, predictability, and side effects between Epi-LASIK and PRK for correcting myopia. A system literature review was conducted in the PubMed, Cochrane Library EMBASE. The statistical analysis was performed by RevMan 5.0 software. The results included efficacy outcomes (percentage of eyes with 20/20 uncorrected visual acuity post-treatment), predictability (proportion of eyes within ±0.5 D of the target refraction), epithelial healing time, and the incidence of significant haze and pain scores after surgery. There are seven articles with total 987 eyes suitable for the meta-analysis. There is no statistical significance in the predictability between Epi-LASIK and PRK, the risk ratio (RR) is 1.03, 95% confidence interval (CI) [0.92, 1.16], p = 0.18; with respect to efficacy, the odds ratio is 1.43, 95% CI = [0.85, 2.40], p = 0.56 between Epi-LASIK and PRK, there is no statistical significance either. The epithelial cell layer healing time and the pain scores and the incidence of significant haze showed no significance between these two techniques although more pains can be found in Epi-LASIK than PRK at the early-stage post-operation. According to the above analysis, Epi-LASIK has good efficacy and predictability as PRK. In addition, both techniques have low pain scores and low incidence of significant haze.

Detector noise statistics in the non-linear regime

NASA Technical Reports Server (NTRS)

Shopbell, P. L.; Bland-Hawthorn, J.

1992-01-01

The statistical behavior of an idealized linear detector in the presence of threshold and saturation levels is examined. It is assumed that the noise is governed by the statistical fluctuations in the number of photons emitted by the source during an exposure. Since physical detectors cannot have infinite dynamic range, our model illustrates that all devices have non-linear regimes, particularly at high count rates. The primary effect is a decrease in the statistical variance about the mean signal due to a portion of the expected noise distribution being removed via clipping. Higher order statistical moments are also examined, in particular, skewness and kurtosis. In principle, the expected distortion in the detector noise characteristics can be calibrated using flatfield observations with count rates matched to the observations. For this purpose, some basic statistical methods that utilize Fourier analysis techniques are described.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Characterization and classification of oral tissues using excitation and emission matrix: a statistical modeling approach

NASA Astrophysics Data System (ADS)

Kanniyappan, Udayakumar; Gnanatheepaminstein, Einstein; Prakasarao, Aruna; Dornadula, Koteeswaran; Singaravelu, Ganesan

2017-02-01

Cancer is one of the most common human threats around the world and diagnosis based on optical spectroscopy especially fluorescence technique has been established as the standard approach among scientist to explore the biochemical and morphological changes in tissues. In this regard, the present work aims to extract spectral signatures of the various fluorophores present in oral tissues using parallel factor analysis (PARAFAC). Subsequently, the statistical analysis also to be performed to show its diagnostic potential in distinguishing malignant, premalignant from normal oral tissues. Hence, the present study may lead to the possible and/or alternative tool for oral cancer diagnosis.

Methods of Single Station and Limited Data Analysis and Forecasting

DTIC Science & Technology

1985-08-15

example using real data. Discusses modifications of SSA technique in certain climatological regimes and describes some statistical tech- niques for SSA of... caster has access to radar or satellite observations, or any computer products during the period of his isolation. Where calculations are involved, it is...chapters of the text will deal with special topics such as modifications of the SSA technique that must be considered for certain clima- tological regimes

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments.

PubMed

Krami, Loghman Khoda; Amiri, Fazel; Sefiyanian, Alireza; Shariff, Abdul Rashid B Mohamed; Tabatabaie, Tayebeh; Pradhan, Biswajeet

2013-12-01

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF(G)) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF(G) techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF(G) technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.

Design and statistical problems in prevention.

PubMed

Gullberg, B

1996-01-01

Clinical and epidemiological research in osteoporosis can benefit from using the methods and techniques established in the area of chronic disease epidemiology. However, attention has to be given to the special characteristics such as the multifactorial nature and the fact that the subjects usually are of high ages. In order to evaluate prevention it is of course first necessary to detect and confirm reversible risk factors. The advantage and disadvantage of different design (cross-sectional, cohort and case-control) are well known. The effects of avoidable biases, e.g. selection, observation and confounding have to be balanced against practical conveniences like time, expenses, recruitment etc. The translation of relative risks into population attributable risks (etiologic fractions, prevented fractions) are complex and are usually performed under unrealistic, simplified assumptions. The consequences of interactions (synergy) between risk factors are often neglected. The multifactorial structure requires application of more advanced multi-level statistical techniques. The common strategy in prevention to target a cluster of risk factors in order to avoid the multifactorial nature implies that in the end it is impossible to separate each unique factor. Experimental designs for evaluating prevention like clinical trials and intervention have to take into account the distinction between explanatory and pragmatic studies. An explanatory approach is similar to an idealized laboratory trial while the pragmatic design is more realistic, practical and has a general public health perspective. The statistical techniques to be used in osteoporosis research are implemented in easy available computer-packages like SAS, SPSS, BMDP and GLIM. In addition to the traditional logistic regression methods like Cox analysis and Poisson regression also analysis of repeated measurement and cluster analysis are relevant.

Taking a statistical approach

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

Wild, M.; Rouhani, S.

1995-02-01

A typical site investigation entails extensive sampling and monitoring. In the past, sampling plans have been designed on purely ad hoc bases, leading to significant expenditures and, in some cases, collection of redundant information. In many instances, sampling costs exceed the true worth of the collected data. The US Environmental Protection Agency (EPA) therefore has advocated the use of geostatistics to provide a logical framework for sampling and analysis of environmental data. Geostatistical methodology uses statistical techniques for the spatial analysis of a variety of earth-related data. The use of geostatistics was developed by the mining industry to estimate oremore » concentrations. The same procedure is effective in quantifying environmental contaminants in soils for risk assessments. Unlike classical statistical techniques, geostatistics offers procedures to incorporate the underlying spatial structure of the investigated field. Sample points spaced close together tend to be more similar than samples spaced further apart. This can guide sampling strategies and determine complex contaminant distributions. Geostatistic techniques can be used to evaluate site conditions on the basis of regular, irregular, random and even spatially biased samples. In most environmental investigations, it is desirable to concentrate sampling in areas of known or suspected contamination. The rigorous mathematical procedures of geostatistics allow for accurate estimates at unsampled locations, potentially reducing sampling requirements. The use of geostatistics serves as a decision-aiding and planning tool and can significantly reduce short-term site assessment costs, long-term sampling and monitoring needs, as well as lead to more accurate and realistic remedial design criteria.« less

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

Investigation of machinability characteristics on EN47 steel for cutting force and tool wear using optimization technique

NASA Astrophysics Data System (ADS)

M, Vasu; Shivananda Nayaka, H.

2018-06-01

In this experimental work dry turning process carried out on EN47 spring steel with coated tungsten carbide tool insert with 0.8 mm nose radius are optimized by using statistical technique. Experiments were conducted at three different cutting speeds (625, 796 and 1250 rpm) with three different feed rates (0.046, 0.062 and 0.093 mm/rev) and depth of cuts (0.2, 0.3 and 0.4 mm). Experiments are conducted based on full factorial design (FFD) 33 three factors and three levels. Analysis of variance is used to identify significant factor for each output response. The result reveals that feed rate is the most significant factor influencing on cutting force followed by depth of cut and cutting speed having less significance. Optimum machining condition for cutting force obtained from the statistical technique. Tool wear measurements are performed with optimum condition of Vc = 796 rpm, ap = 0.2 mm, f = 0.046 mm/rev. The minimum tool wear observed as 0.086 mm with 5 min machining. Analysis of tool wear was done by confocal microscope it was observed that tool wear increases with increasing cutting time.

The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

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

Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my

2016-02-01

Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rockmore » properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.« less

Column ratio mapping: a processing technique for atomic resolution high-angle annular dark-field (HAADF) images.

PubMed

Robb, Paul D; Craven, Alan J

2008-12-01

An image processing technique is presented for atomic resolution high-angle annular dark-field (HAADF) images that have been acquired using scanning transmission electron microscopy (STEM). This technique is termed column ratio mapping and involves the automated process of measuring atomic column intensity ratios in high-resolution HAADF images. This technique was developed to provide a fuller analysis of HAADF images than the usual method of drawing single intensity line profiles across a few areas of interest. For instance, column ratio mapping reveals the compositional distribution across the whole HAADF image and allows a statistical analysis and an estimation of errors. This has proven to be a very valuable technique as it can provide a more detailed assessment of the sharpness of interfacial structures from HAADF images. The technique of column ratio mapping is described in terms of a [110]-oriented zinc-blende structured AlAs/GaAs superlattice using the 1 angstroms-scale resolution capability of the aberration-corrected SuperSTEM 1 instrument.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729

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.

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

A Categorization of Dynamic Analyzers

NASA Technical Reports Server (NTRS)

Lujan, Michelle R.

1997-01-01

Program analysis techniques and tools are essential to the development process because of the support they provide in detecting errors and deficiencies at different phases of development. The types of information rendered through analysis includes the following: statistical measurements of code, type checks, dataflow analysis, consistency checks, test data,verification of code, and debugging information. Analyzers can be broken into two major categories: dynamic and static. Static analyzers examine programs with respect to syntax errors and structural properties., This includes gathering statistical information on program content, such as the number of lines of executable code, source lines. and cyclomatic complexity. In addition, static analyzers provide the ability to check for the consistency of programs with respect to variables. Dynamic analyzers in contrast are dependent on input and the execution of a program providing the ability to find errors that cannot be detected through the use of static analysis alone. Dynamic analysis provides information on the behavior of a program rather than on the syntax. Both types of analysis detect errors in a program, but dynamic analyzers accomplish this through run-time behavior. This paper focuses on the following broad classification of dynamic analyzers: 1) Metrics; 2) Models; and 3) Monitors. Metrics are those analyzers that provide measurement. The next category, models, captures those analyzers that present the state of the program to the user at specified points in time. The last category, monitors, checks specified code based on some criteria. The paper discusses each classification and the techniques that are included under them. In addition, the role of each technique in the software life cycle is discussed. Familiarization with the tools that measure, model and monitor programs provides a framework for understanding the program's dynamic behavior from different, perspectives through analysis of the input/output data.

Passive fit and accuracy of three dental implant impression techniques.

PubMed

Al Quran, Firas A; Rashdan, Bashar A; Zomar, AbdelRahman A Abu; Weiner, Saul

2012-02-01

To reassess the accuracy of three impression techniques relative to the passive fit of the prosthesis. An edentulous maxillary cast was fabricated in epoxy resin with four dental implants embedded and secured with heat-cured acrylic resin. Three techniques were tested: closed tray, open tray nonsplinted, and open tray splinted. One light-cured custom acrylic tray was fabricated for each impression technique, and transfer copings were attached to the implants. Fifteen impressions for each technique were prepared with medium-bodied consistency polyether. Subsequently, the impressions were poured in type IV die stone. The distances between the implants were measured using a digital micrometer. The statistical analysis of the data was performed with ANOVA and a one-sample t test at a 95% confidence interval. The lowest mean difference in dimensional accuracy was found within the direct (open tray) splinted technique. Also, the one-sample t test showed that the direct splinted technique has the least statistical significant difference from direct nonsplinted and indirect (closed tray) techniques. All discrepancies were less than 100 Μm. Within the limitations of this study, the best accuracy of the definitive prosthesis was achieved when the impression copings were splinted with autopolymerized acrylic resin, sectioned, and rejoined. However, the errors associated with all of these techniques were less than 100 Μm, and based on the current definitions of passive fit, they all would be clinically acceptable.

Does the piezoelectric surgical technique produce fewer postoperative sequelae after lower third molar surgery than conventional rotary instruments? A systematic review and meta analysis.

PubMed

Al-Moraissi, E A; Elmansi, Y A; Al-Sharaee, Y A; Alrmali, A E; Alkhutari, A S

2016-03-01

A systematic review and meta-analysis was conducted to answer the clinical question "Does the piezoelectric surgical technique produce fewer postoperative sequelae after lower third molar surgery than conventional rotary instruments?" A systematic and electronic search of several databases with specific key words, a reference search, and a manual search were performed from respective dates of inception through November 2014. The inclusion criteria were clinical human studies, including randomized controlled trials (RCTs), controlled clinical trials (CCTs), and retrospective studies, with the aim of comparing the piezoelectric surgical osteotomy technique to the standard rotary instrument technique in lower third molar surgery. Postoperative sequelae (oedema, trismus, and pain), the total number of analgesics taken, and the duration of surgery were analyzed. A total of nine articles were included, six RCTs, two CCTs, and one retrospective study. Six studies had a low risk of bias and three had a moderate risk of bias. A statistically significant difference was found between piezoelectric surgery and conventional rotary instrument surgery for lower third molar extraction with regard to postoperative sequelae (oedema, trismus, and pain) and the total number of analgesics taken (P=0.0001, P=0.0001, P<0.00001, and P<0.0001, respectively). However, a statistically significant increased surgery time was required in the piezoelectric osteotomy group (P<0.00001). The results of the meta-analysis showed that piezoelectric surgery significantly reduced the occurrence of postoperative sequelae (oedema, trismus, and pain) and the total number of analgesics taken compared to the conventional rotary instrument technique in lower third molar surgery, but required a longer surgery time. Copyright © 2015 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Data handling and analysis for the 1971 corn blight watch experiment

NASA Technical Reports Server (NTRS)

Anuta, P. E.; Phillips, T. L.

1973-01-01

The overall corn blight watch experiment data flow is described and the organization of the LARS/Purdue data center is discussed. Data analysis techniques are discussed in general and the use of statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data is described. Some of the results obtained are discussed and the implications of the experiment on future data communication requirements for earth resource survey systems is discussed.

Analysis of Information Content in High-Spectral Resolution Sounders using Subset Selection Analysis

NASA Technical Reports Server (NTRS)

Velez-Reyes, Miguel; Joiner, Joanna

1998-01-01

In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.

Comparison of two headspace sampling techniques for the analysis of off-flavour volatiles from oat based products.

PubMed

Cognat, Claudine; Shepherd, Tom; Verrall, Susan R; Stewart, Derek

2012-10-01

Two different headspace sampling techniques were compared for analysis of aroma volatiles from freshly produced and aged plain oatcakes. Solid phase microextraction (SPME) using a Carboxen-Polydimethylsiloxane (PDMS) fibre and entrainment on Tenax TA within an adsorbent tube were used for collection of volatiles. The effects of variation in the sampling method were also considered using SPME. The data obtained using both techniques were processed by multivariate statistical analysis (PCA). Both techniques showed similar capacities to discriminate between the samples at different ages. Discrimination between fresh and rancid samples could be made on the basis of changes in the relative abundances of 14-15 of the constituents in the volatile profiles. A significant effect on the detection level of volatile compounds was observed when samples were crushed and analysed by SPME-GC-MS, in comparison to undisturbed product. The applicability and cost effectiveness of both methods were considered. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluation of adding item-response theory analysis for evaluation of the European Board of Ophthalmology Diploma examination.

PubMed

Mathysen, Danny G P; Aclimandos, Wagih; Roelant, Ella; Wouters, Kristien; Creuzot-Garcher, Catherine; Ringens, Peter J; Hawlina, Marko; Tassignon, Marie-José

2013-11-01

To investigate whether introduction of item-response theory (IRT) analysis, in parallel to the 'traditional' statistical analysis methods available for performance evaluation of multiple T/F items as used in the European Board of Ophthalmology Diploma (EBOD) examination, has proved beneficial, and secondly, to study whether the overall assessment performance of the current written part of EBOD is sufficiently high (KR-20≥ 0.90) to be kept as examination format in future EBOD editions. 'Traditional' analysis methods for individual MCQ item performance comprise P-statistics, Rit-statistics and item discrimination, while overall reliability is evaluated through KR-20 for multiple T/F items. The additional set of statistical analysis methods for the evaluation of EBOD comprises mainly IRT analysis. These analysis techniques are used to monitor whether the introduction of negative marking for incorrect answers (since EBOD 2010) has a positive influence on the statistical performance of EBOD as a whole and its individual test items in particular. Item-response theory analysis demonstrated that item performance parameters should not be evaluated individually, but should be related to one another. Before the introduction of negative marking, the overall EBOD reliability (KR-20) was good though with room for improvement (EBOD 2008: 0.81; EBOD 2009: 0.78). After the introduction of negative marking, the overall reliability of EBOD improved significantly (EBOD 2010: 0.92; EBOD 2011:0.91; EBOD 2012: 0.91). Although many statistical performance parameters are available to evaluate individual items, our study demonstrates that the overall reliability assessment remains the only crucial parameter to be evaluated allowing comparison. While individual item performance analysis is worthwhile to undertake as secondary analysis, drawing final conclusions seems to be more difficult. Performance parameters need to be related, as shown by IRT analysis. Therefore, IRT analysis has proved beneficial for the statistical analysis of EBOD. Introduction of negative marking has led to a significant increase in the reliability (KR-20 > 0.90), indicating that the current examination format can be kept for future EBOD examinations. © 2013 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Analyzing Mixed-Dyadic Data Using Structural Equation Models

ERIC Educational Resources Information Center

Peugh, James L.; DiLillo, David; Panuzio, Jillian

2013-01-01

Mixed-dyadic data, collected from distinguishable (nonexchangeable) or indistinguishable (exchangeable) dyads, require statistical analysis techniques that model the variation within dyads and between dyads appropriately. The purpose of this article is to provide a tutorial for performing structural equation modeling analyses of cross-sectional…

Wrestling with Philosophy: Improving Scholarship in Higher Education

ERIC Educational Resources Information Center

Kezar, Adrianna

2004-01-01

Method is usually viewed as completely separate from philosophy or theory, focusing instead on techniques and procedures of interviewing, focus groups, observation, or statistical analysis. Several texts on methodology published recently have added significant sections on philosophy, such as Creswell's (1998) Qualitative inquiry and research…

Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique

NASA Astrophysics Data System (ADS)

Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.

2018-05-01

The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

Vibroacoustic Response of the NASA ACTS Spacecraft Antenna to Launch Acoustic Excitation

NASA Technical Reports Server (NTRS)

Larko, Jeffrey M.; Cotoni, Vincent

2008-01-01

The Advanced Communications Technology Satellite was an experimental NASA satellite launched from the Space Shuttle Discovery. As part of the ground test program, the satellite s large, parabolic reflector antennas were exposed to a reverberant acoustic loading to simulate the launch acoustics in the Shuttle payload bay. This paper describes the modelling and analysis of the dynamic response of these large, composite spacecraft antenna structure subjected to a diffuse acoustic field excitation. Due to the broad frequency range of the excitation, different models were created to make predictions in the various frequency regimes of interest: a statistical energy analysis (SEA) model to capture the high frequency response and a hybrid finite element-statistical energy (hybrid FE-SEA) model for the low to mid-frequency responses. The strengths and limitations of each of the analytical techniques are discussed. The predictions are then compared to the measured acoustic test data and to a boundary element (BEM) model to evaluate the performance of the hybrid techniques.

Statistical methodology: II. Reliability and validity assessment in study design, Part B.

PubMed

Karras, D J

1997-02-01

Validity measures the correspondence between a test and other purported measures of the same or similar qualities. When a reference standard exists, a criterion-based validity coefficient can be calculated. If no such standard is available, the concepts of content and construct validity may be used, but quantitative analysis may not be possible. The Pearson and Spearman tests of correlation are often used to assess the correspondence between tests, but do not account for measurement biases and may yield misleading results. Techniques that measure interest differences may be more meaningful in validity assessment, and the kappa statistic is useful for analyzing categorical variables. Questionnaires often can be designed to allow quantitative assessment of reliability and validity, although this may be difficult. Inclusion of homogeneous questions is necessary to assess reliability. Analysis is enhanced by using Likert scales or similar techniques that yield ordinal data. Validity assessment of questionnaires requires careful definition of the scope of the test and comparison with previously validated tools.

Some Interesting Applications of Probabilistic Techiques in Structural Dynamic Analysis of Rocket Engines

NASA Technical Reports Server (NTRS)

Brown, Andrew M.

2014-01-01

Numerical and Analytical methods developed to determine damage accumulation in specific engine components when speed variation included. Dither Life Ratio shown to be well over factor of 2 for specific example. Steady-State assumption shown to be accurate for most turbopump cases, allowing rapid calculation of DLR. If hot-fire speed data unknown, Monte Carlo method developed that uses speed statistics for similar engines. Application of techniques allow analyst to reduce both uncertainty and excess conservatism. High values of DLR could allow previously unacceptable part to pass HCF criteria without redesign. Given benefit and ease of implementation, recommend that any finite life turbomachine component analysis adopt these techniques. Probability Values calculated, compared, and evaluated for several industry-proposed methods for combining random and harmonic loads. Two new excel macros written to calculate combined load for any specific probability level. Closed form Curve fits generated for widely used 3(sigma) and 2(sigma) probability levels. For design of lightweight aerospace components, obtaining accurate, reproducible, statistically meaningful answer critical.

Recognition of speaker-dependent continuous speech with KEAL

NASA Astrophysics Data System (ADS)

Mercier, G.; Bigorgne, D.; Miclet, L.; Le Guennec, L.; Querre, M.

1989-04-01

A description of the speaker-dependent continuous speech recognition system KEAL is given. An unknown utterance, is recognized by means of the followng procedures: acoustic analysis, phonetic segmentation and identification, word and sentence analysis. The combination of feature-based, speaker-independent coarse phonetic segmentation with speaker-dependent statistical classification techniques is one of the main design features of the acoustic-phonetic decoder. The lexical access component is essentially based on a statistical dynamic programming technique which aims at matching a phonemic lexical entry containing various phonological forms, against a phonetic lattice. Sentence recognition is achieved by use of a context-free grammar and a parsing algorithm derived from Earley's parser. A speaker adaptation module allows some of the system parameters to be adjusted by matching known utterances with their acoustical representation. The task to be performed, described by its vocabulary and its grammar, is given as a parameter of the system. Continuously spoken sentences extracted from a 'pseudo-Logo' language are analyzed and results are presented.

Using data mining techniques to predict the severity of bicycle crashes.

PubMed

Prati, Gabriele; Pietrantoni, Luca; Fraboni, Federico

2017-04-01

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates

PubMed Central

Malone, Brian J.

2017-01-01

Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis. PMID:28877194

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.

Mediation analysis in nursing research: a methodological review.

PubMed

Liu, Jianghong; Ulrich, Connie

2016-12-01

Mediation statistical models help clarify the relationship between independent predictor variables and dependent outcomes of interest by assessing the impact of third variables. This type of statistical analysis is applicable for many clinical nursing research questions, yet its use within nursing remains low. Indeed, mediational analyses may help nurse researchers develop more effective and accurate prevention and treatment programs as well as help bridge the gap between scientific knowledge and clinical practice. In addition, this statistical approach allows nurse researchers to ask - and answer - more meaningful and nuanced questions that extend beyond merely determining whether an outcome occurs. Therefore, the goal of this paper is to provide a brief tutorial on the use of mediational analyses in clinical nursing research by briefly introducing the technique and, through selected empirical examples from the nursing literature, demonstrating its applicability in advancing nursing science.

Statistical Analysis of a Round-Robin Measurement Survey of Two Candidate Materials for a Seebeck Coefficient Standard Reference Material

PubMed Central

Lu, Z. Q. J.; Lowhorn, N. D.; Wong-Ng, W.; Zhang, W.; Thomas, E. L.; Otani, M.; Green, M. L.; Tran, T. N.; Caylor, C.; Dilley, N. R.; Downey, A.; Edwards, B.; Elsner, N.; Ghamaty, S.; Hogan, T.; Jie, Q.; Li, Q.; Martin, J.; Nolas, G.; Obara, H.; Sharp, J.; Venkatasubramanian, R.; Willigan, R.; Yang, J.; Tritt, T.

2009-01-01

In an effort to develop a Standard Reference Material (SRM™) for Seebeck coefficient, we have conducted a round-robin measurement survey of two candidate materials—undoped Bi2Te3 and Constantan (55 % Cu and 45 % Ni alloy). Measurements were performed in two rounds by twelve laboratories involved in active thermoelectric research using a number of different commercial and custom-built measurement systems and techniques. In this paper we report the detailed statistical analyses on the interlaboratory measurement results and the statistical methodology for analysis of irregularly sampled measurement curves in the interlaboratory study setting. Based on these results, we have selected Bi2Te3 as the prototype standard material. Once available, this SRM will be useful for future interlaboratory data comparison and instrument calibrations. PMID:27504212

Archival Legacy Investigations of Circumstellar Environments (ALICE): Statistical assessment of point source detections

NASA Astrophysics Data System (ADS)

Choquet, Élodie; Pueyo, Laurent; Soummer, Rémi; Perrin, Marshall D.; Hagan, J. Brendan; Gofas-Salas, Elena; Rajan, Abhijith; Aguilar, Jonathan

2015-09-01

The ALICE program, for Archival Legacy Investigation of Circumstellar Environment, is currently conducting a virtual survey of about 400 stars, by re-analyzing the HST-NICMOS coronagraphic archive with advanced post-processing techniques. We present here the strategy that we adopted to identify detections and potential candidates for follow-up observations, and we give a preliminary overview of our detections. We present a statistical analysis conducted to evaluate the confidence level on these detection and the completeness of our candidate search.

Evaluation of the color stability of two techniques for reproducing artificial irides after microwave polymerization

PubMed Central

GOIATO, Marcelo Coelho; dos SANTOS, Daniela Micheline; MORENO, Amália; GENNARI-FILHO, Humberto; PELLIZZER, Eduardo Piza

2011-01-01

The use of ocular prostheses for ophthalmic patients aims to rebuild facial aesthetics and provide an artificial substitute to the visual organ. Natural intemperate conditions promote discoloration of artificial irides and many studies have attempted to produce irides with greater chromatic paint durability using different paint materials. Objectives The present study evaluated the color stability of artificial irides obtained with two techniques (oil painting and digital image) and submitted to microwave polymerization. Material and Methods Forty samples were fabricated simulating ocular prostheses. Each sample was constituted by one disc of acrylic resin N1 and one disc of colorless acrylic resin with the iris interposed between the discs. The irides in brown and blue color were obtained by oil painting or digital image. The color stability was determined by a reflection spectrophotometer and measurements were taken before and after microwave polymerization. Statistical analysis of the techniques for reproducing artificial irides was performed by applying the normal data distribution test followed by 2-way ANOVA and Tukey HSD test (α=.05). Results Chromatic alterations occurred in all specimens and statistically significant differences were observed between the oil-painted samples and those obtained by digital imaging. There was no statistical difference between the brown and blue colors. Independently of technique, all samples suffered color alterations after microwave polymerization. Conclusion The digital imaging technique for reproducing irides presented better color stability after microwave polymerization. PMID:21625733

Comparison of Two Surface Contamination Sampling Techniques Conducted for the Characterization of Two Pajarito Site Manhattan Project National Historic Park Properties

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

Lopez, Tammy Ann

Technical Area-18 (TA-18), also known as Pajarito Site, is located on Los Alamos National Laboratory property and has historic buildings that will be included in the Manhattan Project National Historic Park. Characterization studies of metal contamination were needed in two of the four buildings that are on the historic registry in this area, a “battleship” bunker building (TA-18-0002) and the Pond cabin (TA-18-0029). However, these two buildings have been exposed to the elements, are decades old, and have porous and rough surfaces (wood and concrete). Due to these conditions, it was questioned whether standard wipe sampling would be adequate tomore » detect surface dust metal contamination in these buildings. Thus, micro-vacuum and surface wet wipe sampling techniques were performed side-by-side at both buildings and results were compared statistically. A two-tail paired t-test revealed that the micro-vacuum and wet wipe techniques were statistically different for both buildings. Further mathematical analysis revealed that the wet wipe technique picked up more metals from the surface than the microvacuum technique. Wet wipes revealed concentrations of beryllium and lead above internal housekeeping limits; however, using an yttrium normalization method with linear regression analysis between beryllium and yttrium revealed a correlation indicating that the beryllium levels were likely due to background and not operational contamination. PPE and administrative controls were implemented for National Park Service (NPS) and Department of Energy (DOE) tours as a result of this study. Overall, this study indicates that the micro-vacuum technique may not be an efficient technique to sample for metal dust contamination.« less

A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

NASA Astrophysics Data System (ADS)

Hu, Yijia; Zhong, Zhong; Zhu, Yimin; Ha, Yao

2018-04-01

In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV.

Computed tomography assessment of the efficiency of different techniques for removal of root canal filling material.

PubMed

Dall'Agnol, Cristina; Hartmann, Mateus Silveira Martins; Barletta, Fernando Branco

2008-01-01

This study evaluated the efficiency of different techniques for removal of filling material from root canals, using computed tomography (CT). Sixty mesial roots from extracted human mandibular molars were used. Root canals were filled and, after 6 months, the teeth were randomly assigned to 3 groups, according to the root-filling removal technique: Group A - hand instrumentation with K-type files; Group B - reciprocating instrumentation with engine-driven K-type files; and Group C rotary instrumentation with engine-driven ProTaper system. CT scans were used to assess the volume of filling material inside the root canals before and after the removal procedure. In both moments, the area of filling material was outlined by an experienced radiologist and the volume of filling material was automatically calculated by the CT software program. Based on the volume of initial and residual filling material of each specimen, the percentage of filling material removed from the root canals by the different techniques was calculated. Data were analyzed statistically by ANOVA and chi-square test for linear trend (?=0.05). No statistically significant difference (p=0.36) was found among the groups regarding the percent means of removed filling material. The analysis of the association between the percentage of filling material removal (high or low) and the proposed techniques by chi-square test showed statistically significant difference (p=0.015), as most cases in group B (reciprocating technique) presented less than 50% of filling material removed (low percent removal). In conclusion, none of the techniques evaluated in this study was effective in providing complete removal of filling material from the root canals.

Adverse effects of metallic artifacts on voxel-wise analysis and tract-based spatial statistics in diffusion tensor imaging.

PubMed

Goto, Masami; Abe, Osamu; Hata, Junichi; Fukunaga, Issei; Shimoji, Keigo; Kunimatsu, Akira; Gomi, Tsutomu

2017-02-01

Background Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that reflects the Brownian motion of water molecules constrained within brain tissue. Fractional anisotropy (FA) is one of the most commonly measured DTI parameters, and can be applied to quantitative analysis of white matter as tract-based spatial statistics (TBSS) and voxel-wise analysis. Purpose To show an association between metallic implants and the results of statistical analysis (voxel-wise group comparison and TBSS) for fractional anisotropy (FA) mapping, in DTI of healthy adults. Material and Methods Sixteen healthy volunteers were scanned with 3-Tesla MRI. A magnetic keeper type of dental implant was used as the metallic implant. DTI was acquired three times in each participant: (i) without a magnetic keeper (FAnon1); (ii) with a magnetic keeper (FAimp); and (iii) without a magnetic keeper (FAnon2) as reproducibility of FAnon1. Group comparisons with paired t-test were performed as FAnon1 vs. FAnon2, and as FAnon1 vs. FAimp. Results Regions of significantly reduced and increased local FA values were revealed by voxel-wise group comparison analysis (a P value of less than 0.05, corrected with family-wise error), but not by TBSS. Conclusion Metallic implants existing outside the field of view produce artifacts that affect the statistical analysis (voxel-wise group comparisons) for FA mapping. When statistical analysis for FA mapping is conducted by researchers, it is important to pay attention to any dental implants present in the mouths of the participants.

Survey of statistical techniques used in validation studies of air pollution prediction models

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

Bornstein, R D; Anderson, S F

1979-03-01

Statistical techniques used by meteorologists to validate predictions made by air pollution models are surveyed. Techniques are divided into the following three groups: graphical, tabular, and summary statistics. Some of the practical problems associated with verification are also discussed. Characteristics desired in any validation program are listed and a suggested combination of techniques that possesses many of these characteristics is presented.

A method of using cluster analysis to study statistical dependence in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

Comparison analysis for classification algorithm in data mining and the study of model use

NASA Astrophysics Data System (ADS)

Chen, Junde; Zhang, Defu

2018-04-01

As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

The evaluation of alternate methodologies for land cover classification in an urbanizing area

NASA Technical Reports Server (NTRS)

Smekofski, R. M.

1981-01-01

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

Photographic and photometric enhancement of Lunar Orbiter products, projects A, B and C

NASA Technical Reports Server (NTRS)

1972-01-01

A detailed discussion is presented of the framelet joining, photometric data improvement, and statistical error analysis. The Lunar Orbiter film handling system, readout system, and the digitization are described, along with the technique of joining adjacent framelets by a using a digital computer. Time and cost estimates are given. The problems and techniques involved in improving the digitized data are discussed. It was found that spectacular improvements are possible. Program documentations are included.

Detection of Erroneous Payments Utilizing Supervised And Unsupervised Data Mining Techniques

DTIC Science & Technology

2004-09-01

will look at which statistical analysis technique will work best in developing and enhancing existing erroneous payment models . Chapter I and II... payment models that are used for selection of records to be audited. The models are set up such that if two or more records have the same payment...Identification Number, Invoice Number and Delivery Order Number are not compared. The DM0102 Duplicate Payment Model will be analyzed in this thesis

Linear retrieval and global measurements of wind speed from the Seasat SMMR

NASA Technical Reports Server (NTRS)

Pandey, P. C.

1983-01-01

Retrievals of wind speed (WS) from Seasat Scanning Multichannel Microwave Radiometer (SMMR) were performed using a two-step statistical technique. Nine subsets of two to five SMMR channels were examined for wind speed retrieval. These subsets were derived by using a leaps and bound procedure based on the coefficient of determination selection criteria to a statistical data base of brightness temperatures and geophysical parameters. Analysis of Monsoon Experiment and ocean station PAPA data showed a strong correlation between sea surface temperature and water vapor. This relation was used in generating the statistical data base. Global maps of WS were produced for one and three month periods.

Statistical crystallography of surface micelle spacing

NASA Technical Reports Server (NTRS)

Noever, David A.

1992-01-01

The aggregation of the recently reported surface micelles of block polyelectrolytes is analyzed using techniques of statistical crystallography. A polygonal lattice (Voronoi mosaic) connects center-to-center points, yielding statistical agreement with crystallographic predictions; Aboav-Weaire's law and Lewis's law are verified. This protocol supplements the standard analysis of surface micelles leading to aggregation number determination and, when compared to numerical simulations, allows further insight into the random partitioning of surface films. In particular, agreement with Lewis's law has been linked to the geometric packing requirements of filling two-dimensional space which compete with (or balance) physical forces such as interfacial tension, electrostatic repulsion, and van der Waals attraction.

Automatic identification of bacterial types using statistical imaging methods

NASA Astrophysics Data System (ADS)

Trattner, Sigal; Greenspan, Hayit; Tepper, Gapi; Abboud, Shimon

2003-05-01

The objective of the current study is to develop an automatic tool to identify bacterial types using computer-vision and statistical modeling techniques. Bacteriophage (phage)-typing methods are used to identify and extract representative profiles of bacterial types, such as the Staphylococcus Aureus. Current systems rely on the subjective reading of plaque profiles by human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes.

Verification of a computer-aided replica technique for evaluating prosthesis adaptation using statistical agreement analysis.

PubMed

Mai, Hang-Nga; Lee, Kyeong Eun; Lee, Kyu-Bok; Jeong, Seung-Mi; Lee, Seok-Jae; Lee, Cheong-Hee; An, Seo-Young; Lee, Du-Hyeong

2017-10-01

The purpose of this study was to evaluate the reliability of computer-aided replica technique (CART) by calculating its agreement with the replica technique (RT), using statistical agreement analysis. A prepared metal die and a metal crown were fabricated. The gap between the restoration and abutment was replicated using silicone indicator paste (n = 25). Gap measurements differed in the control (RT) and experimental (CART) groups. In the RT group, the silicone replica was manually sectioned, and the marginal and occlusal gaps were measured using a microscope. In the CART group, the gap was digitized using optical scanning and image superimposition, and the gaps were measured using a software program. The agreement between the measurement techniques was evaluated by using the 95% Bland-Altman limits of agreement and concordance correlation coefficients (CCC). The least acceptable CCC was 0.90. The RT and CART groups showed linear association, with a strong positive correlation in gap measurements, but without significant differences. The 95% limits of agreement between the paired gap measurements were 3.84% and 7.08% of the mean. The lower 95% confidence limits of CCC were 0.9676 and 0.9188 for the marginal and occlusal gap measurements, respectively, and the values were greater than the allowed limit. The CART is a reliable digital approach for evaluating the fit accuracy of fixed dental prostheses.

An ANOVA approach for statistical comparisons of brain networks.

PubMed

Fraiman, Daniel; Fraiman, Ricardo

2018-03-16

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

A Course Model for Teaching Research Evaluation in Colleges of Pharmacy.

ERIC Educational Resources Information Center

Draugalis, JoLaine R.; Slack, Marion K.

1992-01-01

A University of Arizona undergraduate pharmacy course designed to develop student skills in evaluation of research has five parts: introduction to the scientific method; statistical techniques/data analysis review; research design; fundamentals of clinical studies; and practical applications. Prerequisites include biostatistics and drug…

Thinking big

NASA Astrophysics Data System (ADS)

Collins, Harry

2008-02-01

Physicists are often quick to discount social research based on qualitative techniques such as ethnography and "deep case studies" - where a researcher draws conclusions about a community based on immersion in the field - thinking that only quantitative research backed up by statistical analysis is sound. The balance is not so clear, however.

Project Sell, Title VII: Final Evaluation 1970-1971.

ERIC Educational Resources Information Center

Condon, Elaine C.; And Others

This evaluative report consists of two parts. The first is a narrative report which represents a summary by the evaluation team and recommendations regarding project activities; the second part provides a statistical analysis of project achievements. Details are provided on evaluation techniques, staff, management, instructional materials,…

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.

A new method to reduce the statistical and systematic uncertainty of chance coincidence backgrounds measured with waveform digitizers

DOE PAGES

O'Donnell, John M.

2015-06-30

We present a new method for measuring chance-coincidence backgrounds during the collection of coincidence data. The method relies on acquiring data with near-zero dead time, which is now realistic due to the increasing deployment of flash electronic-digitizer (waveform digitizer) techniques. An experiment designed to use this new method is capable of acquiring more coincidence data, and a much reduced statistical fluctuation of the measured background. A statistical analysis is presented, and us ed to derive a figure of merit for the new method. Factors of four improvement over other analyses are realistic. The technique is illustrated with preliminary data takenmore » as part of a program to make new measurements of the prompt fission neutron spectra at Los Alamo s Neutron Science Center. In conclusion, it is expected that the these measurements will occur in a regime where the maximum figure of merit will be exploited« less

Fragment size distribution statistics in dynamic fragmentation of laser shock-loaded tin

NASA Astrophysics Data System (ADS)

He, Weihua; Xin, Jianting; Zhao, Yongqiang; Chu, Genbai; Xi, Tao; Shui, Min; Lu, Feng; Gu, Yuqiu

2017-06-01

This work investigates the geometric statistics method to characterize the size distribution of tin fragments produced in the laser shock-loaded dynamic fragmentation process. In the shock experiments, the ejection of the tin sample with etched V-shape groove in the free surface are collected by the soft recovery technique. Subsequently, the produced fragments are automatically detected with the fine post-shot analysis techniques including the X-ray micro-tomography and the improved watershed method. To characterize the size distributions of the fragments, a theoretical random geometric statistics model based on Poisson mixtures is derived for dynamic heterogeneous fragmentation problem, which reveals linear combinational exponential distribution. The experimental data related to fragment size distributions of the laser shock-loaded tin sample are examined with the proposed theoretical model, and its fitting performance is compared with that of other state-of-the-art fragment size distribution models. The comparison results prove that our proposed model can provide far more reasonable fitting result for the laser shock-loaded tin.

On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements

NASA Astrophysics Data System (ADS)

Yi, Yong; Chen, Zhengying; Wang, Liming

2018-05-01

Corona-originated discharge of DC transmission lines is the main reason for the radiated electromagnetic interference (EMI) field in the vicinity of transmission lines. A joint time-frequency analysis technique was proposed to extract the radiated EMI current (excitation current) of DC corona based on corona current statistical measurements. A reduced-scale experimental platform was setup to measure the statistical distributions of current waveform parameters of aluminum conductor steel reinforced. Based on the measured results, the peak value, root-mean-square value and average value with 9 kHz and 200 Hz band-with of 0.5 MHz radiated EMI current were calculated by the technique proposed and validated with conventional excitation function method. Radio interference (RI) was calculated based on the radiated EMI current and a wire-to-plate platform was built for the validity of the RI computation results. The reason for the certain deviation between the computations and measurements was detailed analyzed.

Applications of Bayesian Statistics to Problems in Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Meegan, Charles A.

1997-01-01

This presentation will describe two applications of Bayesian statistics to Gamma Ray Bursts (GRBS). The first attempts to quantify the evidence for a cosmological versus galactic origin of GRBs using only the observations of the dipole and quadrupole moments of the angular distribution of bursts. The cosmological hypothesis predicts isotropy, while the galactic hypothesis is assumed to produce a uniform probability distribution over positive values for these moments. The observed isotropic distribution indicates that the Bayes factor for the cosmological hypothesis over the galactic hypothesis is about 300. Another application of Bayesian statistics is in the estimation of chance associations of optical counterparts with galaxies. The Bayesian approach is preferred to frequentist techniques here because the Bayesian approach easily accounts for galaxy mass distributions and because one can incorporate three disjoint hypotheses: (1) bursts come from galactic centers, (2) bursts come from galaxies in proportion to luminosity, and (3) bursts do not come from external galaxies. This technique was used in the analysis of the optical counterpart to GRB970228.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Toward improved analysis of concentration data: Embracing nondetects.

PubMed

Shoari, Niloofar; Dubé, Jean-Sébastien

2018-03-01

Various statistical tests on concentration data serve to support decision-making regarding characterization and monitoring of contaminated media, assessing exposure to a chemical, and quantifying the associated risks. However, the routine statistical protocols cannot be directly applied because of challenges arising from nondetects or left-censored observations, which are concentration measurements below the detection limit of measuring instruments. Despite the existence of techniques based on survival analysis that can adjust for nondetects, these are seldom taken into account properly. A comprehensive review of the literature showed that managing policies regarding analysis of censored data do not always agree and that guidance from regulatory agencies may be outdated. Therefore, researchers and practitioners commonly resort to the most convenient way of tackling the censored data problem by substituting nondetects with arbitrary constants prior to data analysis, although this is generally regarded as a bias-prone approach. Hoping to improve the interpretation of concentration data, the present article aims to familiarize researchers in different disciplines with the significance of left-censored observations and provides theoretical and computational recommendations (under both frequentist and Bayesian frameworks) for adequate analysis of censored data. In particular, the present article synthesizes key findings from previous research with respect to 3 noteworthy aspects of inferential statistics: estimation of descriptive statistics, hypothesis testing, and regression analysis. Environ Toxicol Chem 2018;37:643-656. © 2017 SETAC. © 2017 SETAC.

Using SERVQUAL and Kano research techniques in a patient service quality survey.

PubMed

Christoglou, Konstantinos; Vassiliadis, Chris; Sigalas, Ioakim

2006-01-01

This article presents the results of a service quality study. After an introduction to the SERVQUAL and the Kano research techniques, a Kano analysis of 75 patients from the General Hospital of Katerini in Greece is presented. The service quality criterion used satisfaction and dissatisfaction indices. The Kano statistical analysis process results strengthened the hypothesis of previous research regarding the importance of personal knowledge, the courtesy of the hospital employees and their ability to convey trust and confidence (assurance dimension). Managerial suggestions are made regarding the best way of acting and approaching hospital patients based on the basic SERVQUAL model.

A method for the measurement and analysis of ride vibrations of transportation systems

NASA Technical Reports Server (NTRS)

Catherines, J. J.; Clevenson, S. A.; Scholl, H. F.

1972-01-01

The measurement and recording of ride vibrations which affect passenger comfort in transportation systems and the subsequent data-reduction methods necessary for interpreting the data present exceptional instrumentation requirements and necessitate the use of computers for specialized analysis techniques. A method is presented for both measuring and analyzing ride vibrations of the type encountered in ground and air transportation systems. A portable system for measuring and recording low-frequency, low-amplitude accelerations and specialized data-reduction procedures are described. Sample vibration measurements in the form of statistical parameters representative of typical transportation systems are also presented to demonstrate the utility of the techniques.

Analysis of teaching materials of civic education is characterized by the value of character in building an anti-corruption culture

NASA Astrophysics Data System (ADS)

Sarmini; Suyanto, Totok; Nadiroh, Ulin

2018-01-01

In general, corruption is very harmful to society. One of the efforts in preventing corruption is by the culture of Anti-Corruption Education in the young generation through teaching materials in schools. The research method used is qualitative description. The sample in this research is 60 junior high school teachers of Citizenship Education in Surabaya. Data analysis technique used in this research is descriptive statistic with percentage technique. The result of this research is that it is very important that the value of the character of anti-corruption education in teaching materials to grow in the young generation.

Using foreground/background analysis to determine leaf and canopy chemistry

NASA Technical Reports Server (NTRS)

Pinzon, J. E.; Ustin, S. L.; Hart, Q. J.; Jacquemoud, S.; Smith, M. O.

1995-01-01

Spectral Mixture Analysis (SMA) has become a well established procedure for analyzing imaging spectrometry data, however, the technique is relatively insensitive to minor sources of spectral variation (e.g., discriminating stressed from unstressed vegetation and variations in canopy chemistry). Other statistical approaches have been tried e.g., stepwise multiple linear regression analysis to predict canopy chemistry. Grossman et al. reported that SMLR is sensitive to measurement error and that the prediction of minor chemical components are not independent of patterns observed in more dominant spectral components like water. Further, they observed that the relationships were strongly dependent on the mode of expressing reflectance (R, -log R) and whether chemistry was expressed on a weight (g/g) or are basis (g/sq m). Thus, alternative multivariate techniques need to be examined. Smith et al. reported a revised SMA that they termed Foreground/Background Analysis (FBA) that permits directing the analysis along any axis of variance by identifying vectors through the n-dimensional spectral volume orthonormal to each other. Here, we report an application of the FBA technique for the detection of canopy chemistry using a modified form of the analysis.

Molecular Analysis of Date Palm Genetic Diversity Using Random Amplified Polymorphic DNA (RAPD) and Inter-Simple Sequence Repeats (ISSRs).

PubMed

El Sharabasy, Sherif F; Soliman, Khaled A

2017-01-01

The date palm is an ancient domesticated plant with great diversity and has been cultivated in the Middle East and North Africa for at last 5000 years. Date palm cultivars are classified based on the fruit moisture content, as dry, semidry, and soft dates. There are a number of biochemical and molecular techniques available for characterization of the date palm variation. This chapter focuses on the DNA-based markers random amplified polymorphic DNA (RAPD) and inter-simple sequence repeats (ISSR) techniques, in addition to biochemical markers based on isozyme analysis. These techniques coupled with appropriate statistical tools proved useful for determining phylogenetic relationships among date palm cultivars and provide information resources for date palm gene banks.

Planning representation for automated exploratory data analysis

NASA Astrophysics Data System (ADS)

St. Amant, Robert; Cohen, Paul R.

1994-03-01

Igor is a knowledge-based system for exploratory statistical analysis of complex systems and environments. Igor has two related goals: to help automate the search for interesting patterns in data sets, and to help develop models that capture significant relationships in the data. We outline a language for Igor, based on techniques of opportunistic planning, which balances control and opportunism. We describe the application of Igor to the analysis of the behavior of Phoenix, an artificial intelligence planning system.

Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

PubMed

Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom

2015-01-01

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

Land use/land cover mapping (1:25000) of Taiwan, Republic of China by automated multispectral interpretation of LANDSAT imagery

NASA Technical Reports Server (NTRS)

Sung, Q. C.; Miller, L. D.

1977-01-01

Three methods were tested for collection of the training sets needed to establish the spectral signatures of the land uses/land covers sought due to the difficulties of retrospective collection of representative ground control data. Computer preprocessing techniques applied to the digital images to improve the final classification results were geometric corrections, spectral band or image ratioing and statistical cleaning of the representative training sets. A minimal level of statistical verification was made based upon the comparisons between the airphoto estimates and the classification results. The verifications provided a further support to the selection of MSS band 5 and 7. It also indicated that the maximum likelihood ratioing technique can achieve more agreeable classification results with the airphoto estimates than the stepwise discriminant analysis.

A rheumatoid arthritis study by Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Carvalho, Carolina S.; Silva, Ana Carla A.; Santos, Tatiano J. P. S.; Martin, Airton A.; dos Santos Fernandes, Ana Célia; Andrade, Luís E.; Raniero, Leandro

2012-01-01

Rheumatoid arthritis is a systemic inflammatory disease of unknown causes and a new methods to identify it in early stages are needed. The main purpose of this work is the biochemical differentiation of sera between normal and RA patients, through the establishment of a statistical method that can be appropriately used for serological analysis. The human sera from 39 healthy donors and 39 rheumatics donors were collected and analyzed by Fourier Transform Infrared Spectroscopy. The results show significant spectral variations with p<0.05 in regions corresponding to protein, lipids and immunoglobulins. The technique of latex particles, coated with human IgG and monoclonal anti-CRP by indirect agglutination known as FR and CRP, was performed to confirm possible false-negative results within the groups, facilitating the statistical interpretation and validation of the technique.

Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.

PubMed

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J; Grau, Raúl; Barat, José M

2016-10-19

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis

PubMed Central

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J.; Grau, Raúl; Barat, José M.

2016-01-01

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0–6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead’s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R2 of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness. PMID:27775556

Immediate Feedback Assessment Technique in a Chemistry Classroom

NASA Astrophysics Data System (ADS)

Taylor, Kate R.

The Immediate Feedback Assessment Technique, or IFAT, is a new testing system that turns a student's traditional multiple-choice testing into a chance for hands-on learning; and provides teachers with an opportunity to obtain more information about a student's knowledge during testing. In the current study we wanted to know if: When students are given the second-chance afforded by the IFAT system, are they guessing or using prior knowledge when making their second chance choice. Additionally, while there has been some adaptation of this testing system in non-science disciplines, we wanted to study if the IFAT-system would be well- received among faculty in the sciences, more specifically chemistry faculty. By comparing the students rate of success on second-chance afforded by the IFAT-system versus the statistical likelihood of guessing correctly, statistical analysis was used to determine if we observed enough students earning the second-chance points to reject the likelihood that students were randomly guessing. Our data analysis revealed that is statistically highly unlikely that students were only guessing when the IFAT system was utilized. (It is important to note that while we can find that students are getting the answer correct at a much higher rate than random guessing we can never truly know if every student is using thought or not.).

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand.

PubMed

Nakapan, Supachai; Tripathi, Nitin Kumar; Tipdecho, Taravudh; Souris, Marc

2012-10-24

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

Bayesian Orbit Computation Tools for Objects on Geocentric Orbits

NASA Astrophysics Data System (ADS)

Virtanen, J.; Granvik, M.; Muinonen, K.; Oszkiewicz, D.

2013-08-01

We consider the space-debris orbital inversion problem via the concept of Bayesian inference. The methodology has been put forward for the orbital analysis of solar system small bodies in early 1990's [7] and results in a full solution of the statistical inverse problem given in terms of a posteriori probability density function (PDF) for the orbital parameters. We demonstrate the applicability of our statistical orbital analysis software to Earth orbiting objects, both using well-established Monte Carlo (MC) techniques (for a review, see e.g. [13] as well as recently developed Markov-chain MC (MCMC) techniques (e.g., [9]). In particular, we exploit the novel virtual observation MCMC method [8], which is based on the characterization of the phase-space volume of orbital solutions before the actual MCMC sampling. Our statistical methods and the resulting PDFs immediately enable probabilistic impact predictions to be carried out. Furthermore, this can be readily done also for very sparse data sets and data sets of poor quality - providing that some a priori information on the observational uncertainty is available. For asteroids, impact probabilities with the Earth from the discovery night onwards have been provided, e.g., by [11] and [10], the latter study includes the sampling of the observational-error standard deviation as a random variable.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Detecting most influencing courses on students grades using block PCA

NASA Astrophysics Data System (ADS)

Othman, Osama H.; Gebril, Rami Salah

2014-12-01

One of the modern solutions adopted in dealing with the problem of large number of variables in statistical analyses is the Block Principal Component Analysis (Block PCA). This modified technique can be used to reduce the vertical dimension (variables) of the data matrix Xn×p by selecting a smaller number of variables, (say m) containing most of the statistical information. These selected variables can then be employed in further investigations and analyses. Block PCA is an adapted multistage technique of the original PCA. It involves the application of Cluster Analysis (CA) and variable selection throughout sub principal components scores (PC's). The application of Block PCA in this paper is a modified version of the original work of Liu et al (2002). The main objective was to apply PCA on each group of variables, (established using cluster analysis), instead of involving the whole large pack of variables which was proved to be unreliable. In this work, the Block PCA is used to reduce the size of a huge data matrix ((n = 41) × (p = 251)) consisting of Grade Point Average (GPA) of the students in 251 courses (variables) in the faculty of science in Benghazi University. In other words, we are constructing a smaller analytical data matrix of the GPA's of the students with less variables containing most variation (statistical information) in the original database. By applying the Block PCA, (12) courses were found to `absorb' most of the variation or influence from the original data matrix, and hence worth to be keep for future statistical exploring and analytical studies. In addition, the course Independent Study (Math.) was found to be the most influencing course on students GPA among the 12 selected courses.

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.