ERIC Educational Resources Information Center
Siman-Tov, Ayelet; Kaniel, Shlomo
2011-01-01
The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…
McKinney, Cliff; Renk, Kimberly
2008-06-01
Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample for this study consisted of 151 male and 324 female late adolescents, who reported on their mothers' and fathers' parenting style, their family environment, their mothers' and fathers' expectations for them, the conflict that they experience with their mothers and fathers, and their own adjustment. Overall, the variables had significant relationships with one another. Further, the male-father, male-mother, and female-father structural equation models that were examined suggested that parenting style has an indirect relationship with late adolescents' adjustment through characteristics of the family environment and the conflict that is experienced in families; such findings were not evident for the female-mother model. Thus, the examination of parent-late adolescent interactions should occur in the context of the gender of parents and their late adolescents. PMID:17710537
ERIC Educational Resources Information Center
McKinney, Cliff; Renk, Kimberly
2008-01-01
Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…
Hirozawa, Anne M; Montez-Rath, Maria E; Johnson, Elizabeth C; Solnit, Stephen A; Drennan, Michael J; Katz, Mitchell H; Marx, Rani
2016-01-01
We compared prospective risk adjustment models for adjusting patient panels at the San Francisco Department of Public Health. We used 4 statistical models (linear regression, two-part model, zero-inflated Poisson, and zero-inflated negative binomial) and 4 subsets of predictor variables (age/gender categories, chronic diagnoses, homelessness, and a loss to follow-up indicator) to predict primary care visit frequency. Predicted visit frequency was then used to calculate patient weights and adjusted panel sizes. The two-part model using all predictor variables performed best (R = 0.20). This model, designed specifically for safety net patients, may prove useful for panel adjustment in other public health settings. PMID:27576054
Adjustment of geochemical background by robust multivariate statistics
Zhou, D.
1985-01-01
Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.
A "Model" Multivariable Calculus Course.
ERIC Educational Resources Information Center
Beckmann, Charlene E.; Schlicker, Steven J.
1999-01-01
Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…
Multivariate Model of Infant Competence.
ERIC Educational Resources Information Center
Kierscht, Marcia Selland; Vietze, Peter M.
This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…
Kautter, John; Pope, Gregory C.
2004-01-01
The authors document the development of the CMS frailty adjustment model, a Medicare payment approach that adjusts payments to a Medicare managed care organization (MCO) according to the functional impairment of its community-residing enrollees. Beginning in 2004, this approach is being applied to certain organizations, such as Program of All-Inclusive Care for the Elderly (PACE), that specialize in providing care to the community-residing frail elderly. In the future, frailty adjustment could be extended to more Medicare managed care organizations. PMID:25372243
Estimation of Data Uncertainty Adjustment Parameters for Multivariate Earth Rotation Series
NASA Technical Reports Server (NTRS)
Sung, Li-yu; Steppe, J. Alan
1994-01-01
We have developed a maximum likelihood method to estimate a set of data uncertainty adjustment parameters, iccluding scaling factors and additive variances and covariances, for multivariate Earth rotation series.
Multivariate pluvial flood damage models
Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom
2015-09-15
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.
ERIC Educational Resources Information Center
Loehlin, John C.; Neiderhiser, Jenae M.; Reiss, David
2005-01-01
Adolescent adjustment measures may be related to each other and to the social environment in various ways. Are these relationships similar in genetic and environmental sources of covariation, or different? A multivariate behaviorgenetic analysis was made of 6 adjustment and 3 treatment composites from the study Nonshared Environment in Adolescent…
A multivariate CAR model for mismatched lattices.
Porter, Aaron T; Oleson, Jacob J
2014-10-01
In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598
Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data
Liu, Zitao; Hauskrecht, Milos
2016-01-01
Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189
DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)
Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...
Quality Reporting of Multivariable Regression Models in Observational Studies
Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M.
2016-01-01
Abstract Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE. Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model. The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0–30.3) of the articles and 18.5% (95% CI: 14.8–22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor. A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature. PMID:27196467
Bayesian Transformation Models for Multivariate Survival Data
DE CASTRO, MÁRIO; CHEN, MING-HUI; IBRAHIM, JOSEPH G.; KLEIN, JOHN P.
2014-01-01
In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported. PMID:24904194
Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi
2015-01-01
Background. Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients. PMID:26413142
Response Surface Modeling Using Multivariate Orthogonal Functions
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; DeLoach, Richard
2001-01-01
A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.
MacNab, Ying C
2016-09-20
We present a general coregionalization framework for developing coregionalized multivariate Gaussian conditional autoregressive (cMCAR) models for Bayesian analysis of multivariate lattice data in general and multivariate disease mapping data in particular. This framework is inclusive of cMCARs that facilitate flexible modelling of spatially structured symmetric or asymmetric cross-variable local interactions, allowing a wide range of separable or non-separable covariance structures, and symmetric or asymmetric cross-covariances, to be modelled. We present a brief overview of established univariate Gaussian conditional autoregressive (CAR) models for univariate lattice data and develop coregionalized multivariate extensions. Classes of cMCARs are presented by formulating precision structures. The resulting conditional properties of the multivariate spatial models are established, which cast new light on cMCARs with richly structured covariances and cross-covariances of different spatial ranges. The related methods are illustrated via an in-depth Bayesian analysis of a Minnesota county-level cancer data set. We also bring a new dimension to the traditional enterprize of Bayesian disease mapping: estimating and mapping covariances and cross-covariances of the underlying disease risks. Maps of covariances and cross-covariances bring to light spatial characterizations of the cMCARs and inform on spatial risk associations between areas and diseases. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27091685
Multivariate Markov chain modeling for stock markets
NASA Astrophysics Data System (ADS)
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Adaptable Multivariate Calibration Models for Spectral Applications
THOMAS,EDWARD V.
1999-12-20
Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.
Bayesian Local Contamination Models for Multivariate Outliers
Page, Garritt L.; Dunson, David B.
2013-01-01
In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465
Multivariate models of adult Pacific salmon returns.
Burke, Brian J; Peterson, William T; Beckman, Brian R; Morgan, Cheryl; Daly, Elizabeth A; Litz, Marisa
2013-01-01
Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586
Multivariate Models of Adult Pacific Salmon Returns
Burke, Brian J.; Peterson, William T.; Beckman, Brian R.; Morgan, Cheryl; Daly, Elizabeth A.; Litz, Marisa
2013-01-01
Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon. PMID:23326586
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
ERIC Educational Resources Information Center
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Comparison between Mothers and Fathers in Coping with Autistic Children: A Multivariate Model
ERIC Educational Resources Information Center
Kaniel, Shlomo; Siman-Tov, Ayelet
2011-01-01
The main purpose of this research is to compare the differences between how mothers and fathers cope with autistic children based on a multivariate model that describes the relationships between parental psychological resources, parental stress appraisal and parental adjustment. 176 parents who lived in Israel (88 mothers and 88 fathers) of…
A complete procedure for multivariate index-flood model application
NASA Astrophysics Data System (ADS)
Requena, Ana Isabel; Chebana, Fateh; Mediero, Luis
2016-04-01
Multivariate frequency analyses are needed to study floods due to dependence existing among representative variables of the flood hydrograph. Particularly, multivariate analyses are essential when flood-routing processes significantly attenuate flood peaks, such as in dams and flood management in flood-prone areas. Besides, regional analyses improve at-site quantile estimates obtained at gauged sites, especially when short flow series exist, and provide estimates at ungauged sites where flow records are unavailable. However, very few studies deal simultaneously with both multivariate and regional aspects. This study seeks to introduce a complete procedure to conduct a multivariate regional hydrological frequency analysis (HFA), providing guidelines. The methodology joins recent developments achieved in multivariate and regional HFA, such as copulas, multivariate quantiles and the multivariate index-flood model. The proposed multivariate methodology, focused on the bivariate case, is applied to a case study located in Spain by using hydrograph volume and flood peak observed series. As a result, a set of volume-peak events under a bivariate quantile curve can be obtained for a given return period at a target site, providing flexibility to practitioners to check and decide what the design event for a given purpose should be. In addition, the multivariate regional approach can also be used for obtaining the multivariate distribution of the hydrological variables when the aim is to assess the structure failure for a given return period.
Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data
ERIC Educational Resources Information Center
Poon, Wai-Yin; Wang, Hai-Bin
2010-01-01
A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To…
A unifying modeling framework for highly multivariate disease mapping.
Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S
2015-04-30
Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models. PMID:25645551
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.
A Multivariate Model of Achievement in Geometry
ERIC Educational Resources Information Center
Bailey, MarLynn; Taasoobshirazi, Gita; Carr, Martha
2014-01-01
Previous studies have shown that several key variables influence student achievement in geometry, but no research has been conducted to determine how these variables interact. A model of achievement in geometry was tested on a sample of 102 high school students. Structural equation modeling was used to test hypothesized relationships among…
A Multivariate Model of Physics Problem Solving
ERIC Educational Resources Information Center
Taasoobshirazi, Gita; Farley, John
2013-01-01
A model of expertise in physics problem solving was tested on undergraduate science, physics, and engineering majors enrolled in an introductory-level physics course. Structural equation modeling was used to test hypothesized relationships among variables linked to expertise in physics problem solving including motivation, metacognitive planning,…
Multivariate Probabilistic Analysis of an Hydrological Model
NASA Astrophysics Data System (ADS)
Franceschini, Samuela; Marani, Marco
2010-05-01
Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model
Aspects of model selection in multivariate analyses
Picard, R.
1982-01-01
Analysis of data sets that involve large numbers of variables usually entails some type of model fitting and data reduction. In regression problems, a fitted model that is obtained by a selection process can be difficult to evaluate because of optimism induced by the choice mechanism. Problems in areas such as discriminant analysis, calibration, and the like often lead to similar difficulties. The preceeding sections reviewed some of the general ideas behind assessment of regression-type predictors and illustrated how they can be easily incorporated into a standard data analysis.
MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)
We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...
Multivariate Models of Mothers' and Fathers' Aggression toward Their Children
ERIC Educational Resources Information Center
Smith Slep, Amy M.; O'Leary, Susan G.
2007-01-01
Multivariate, biopsychosocial, explanatory models of mothers' and fathers' psychological and physical aggression toward their 3- to 7-year-old children were fitted and cross-validated in 453 representatively sampled families. Models explaining mothers' and fathers' aggression were substantially similar. Surprisingly, many variables identified as…
Multivariate Bayesian Models of Extreme Rainfall
NASA Astrophysics Data System (ADS)
Rahill-Marier, B.; Devineni, N.; Lall, U.; Farnham, D.
2013-12-01
Accounting for spatial heterogeneity in extreme rainfall has important ramifications in hydrological design and climate models alike. Traditional methods, including areal reduction factors and kriging, are sensitive to catchment shape assumptions and return periods, and do not explicitly model spatial dependence between between data points. More recent spatially dense rainfall simulators depend on newer data sources such as radar and may struggle to reproduce extremes because of physical assumptions in the model and short historical records. Rain gauges offer the longest historical record, key when considering rainfall extremes and changes over time, and particularly relevant in today's environment of designing for climate change. In this paper we propose a probabilistic approach of accounting for spatial dependence using the lengthy but spatially disparate hourly rainfall network in the greater New York City area. We build a hierarchical Bayesian model allowing extremes at one station to co-vary with concurrent rainfall fields occurring at other stations. Subsequently we pool across the extreme rainfall fields of all stations, and demonstrate that the expected catchment-wide events are significantly lower when considering spatial fields instead of maxima-only fields. We additionally demonstrate the importance of using concurrent spatial fields, rather than annual maxima, in producing covariance matrices that describe true storm dynamics. This approach is also unique in that it considers short duration storms - from one hour to twenty-four hours - rather than the daily values typically derived from rainfall gauges. The same methodology can be extended to include the radar fields available in the past decade. The hierarchical multilevel approach lends itself easily to integration of long-record parameters and short-record parameters at a station or regional level. In addition climate covariates can be introduced to support the relationship of spatial covariance with
Inclusion of Dominance Effects in the Multivariate GBLUP Model
Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Von Pinho, Renzo Garcia
2016-01-01
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056
Inclusion of Dominance Effects in the Multivariate GBLUP Model.
Dos Santos, Jhonathan Pedroso Rigal; Vasconcellos, Renato Coelho de Castro; Pires, Luiz Paulo Miranda; Balestre, Marcio; Von Pinho, Renzo Garcia
2016-01-01
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components. PMID:27074056
FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING
This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...
Validation of multivariate model of leaf ionome is fundamentally confounded.
Technology Transfer Automated Retrieval System (TEKTRAN)
The multivariable signature model reported by Baxter et al. (1) to predict Fe and P homeostasis in Arabidopsis is fundamentally flawed for two reasons: 1) The initial experiments identified a correlation between trace metal (Mn, Co, Zn, Mo, Cd) signature and “Fe-deficiency,” which was used to train ...
A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.
ERIC Educational Resources Information Center
Hackett, Paul M. W.; And Others
1993-01-01
Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…
Studying Resist Stochastics with the Multivariate Poisson Propagation Model
Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; Bhattarai, Suchit; Neureuther, Andrew
2014-01-01
Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.
Tvedebrink, Torben; Eriksen, Poul Svante; Morling, Niels
2015-11-01
In this paper, we discuss the construction of a multivariate generalisation of the Dirichlet-multinomial distribution. An example from forensic genetics in the statistical analysis of DNA mixtures motivates the study of this multivariate extension. In forensic genetics, adjustment of the match probabilities due to remote ancestry in the population is often done using the so-called θ-correction. This correction increases the probability of observing multiple copies of rare alleles in a subpopulation and thereby reduces the weight of the evidence for rare genotypes. A recent publication by Cowell et al. (2015) showed elegantly how to use Bayesian networks for efficient computations of likelihood ratios in a forensic genetic context. However, their underlying population genetic model assumed independence of alleles, which is not realistic in real populations. We demonstrate how the so-called θ-correction can be incorporated in Bayesian networks to make efficient computations by modifying the Markov structure of Cowell et al. (2015). By numerical examples, we show how the θ-correction incorporated in the multivariate Dirichlet-multinomial distribution affects the weight of evidence. PMID:26344785
Overpaying morbidity adjusters in risk equalization models.
van Kleef, R C; van Vliet, R C J A; van de Ven, W P M M
2016-09-01
Most competitive social health insurance markets include risk equalization to compensate insurers for predictable variation in healthcare expenses. Empirical literature shows that even the most sophisticated risk equalization models-with advanced morbidity adjusters-substantially undercompensate insurers for selected groups of high-risk individuals. In the presence of premium regulation, these undercompensations confront consumers and insurers with incentives for risk selection. An important reason for the undercompensations is that not all information with predictive value regarding healthcare expenses is appropriate for use as a morbidity adjuster. To reduce incentives for selection regarding specific groups we propose overpaying morbidity adjusters that are already included in the risk equalization model. This paper illustrates the idea of overpaying by merging data on morbidity adjusters and healthcare expenses with health survey information, and derives three preconditions for meaningful application. Given these preconditions, we think overpaying may be particularly useful for pharmacy-based cost groups. PMID:26420555
Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG
Kim, Sun-Hee; Faloutsos, Christos; Yang, Hyung-Jeong
2013-01-01
Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with −1 and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately. PMID:23710252
Modeling rainfall-runoff relationship using multivariate GARCH model
NASA Astrophysics Data System (ADS)
Modarres, R.; Ouarda, T. B. M. J.
2013-08-01
The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.
Collision prediction models using multivariate Poisson-lognormal regression.
El-Basyouny, Karim; Sayed, Tarek
2009-07-01
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models. PMID:19540972
A pairwise interaction model for multivariate functional and longitudinal data
Chiou, Jeng-Min; Müller, Hans-Georg
2016-01-01
Functional data vectors consisting of samples of multivariate data where each component is a random function are encountered increasingly often but have not yet been comprehensively investigated. We introduce a simple pairwise interaction model that leads to an interpretable and straightforward decomposition of multivariate functional data and of their variation into component-specific processes and pairwise interaction processes. The latter quantify the degree of pairwise interactions between the components of the functional data vectors, while the component-specific processes reflect the functional variation of a particular functional vector component that cannot be explained by the other components. Thus the proposed model provides an extension of the usual notion of a covariance or correlation matrix for multivariate vector data to functional data vectors and generates an interpretable functional interaction map. The decomposition provided by the model can also serve as a basis for subsequent analysis, such as study of the network structure of functional data vectors. The decomposition of the total variance into componentwise and interaction contributions can be quantified by an \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$R^2$\\end{document}-like decomposition. We provide consistency results for the proposed methods and illustrate the model by applying it to sparsely sampled longitudinal data from the Baltimore Longitudinal Study of Aging, examining the relationships between body mass index and blood fats. PMID:27279664
Analysis of Forest Foliage Using a Multivariate Mixture Model
NASA Technical Reports Server (NTRS)
Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.
1997-01-01
Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.
Various forms of indexing HDMR for modelling multivariate classification problems
Aksu, Çağrı; Tunga, M. Alper
2014-12-10
The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.
Various forms of indexing HDMR for modelling multivariate classification problems
NASA Astrophysics Data System (ADS)
Aksu, ćaǧrı; Tunga, M. Alper
2014-12-01
The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.
Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia
2004-01-01
A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.
Multivariable Parametric Cost Model for Ground Optical Telescope Assembly
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia
2005-01-01
A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.
The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.
Enhancing scientific reasoning by refining students' models of multivariable causality
NASA Astrophysics Data System (ADS)
Keselman, Alla
Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained
Multivariate Statistical Modelling of Drought and Heat Wave Events
NASA Astrophysics Data System (ADS)
Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele
2016-04-01
Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A
Multivariate 3D modelling of Scottish soil properties
NASA Astrophysics Data System (ADS)
Poggio, Laura; Gimona, Alessandro
2015-04-01
Information regarding soil properties across landscapes at national or continental scales is critical for better soil and environmental management and for climate regulation and adaptation policy. The prediction of soil properties variation in space and time and their uncertainty is an important part of environmental modelling. Soil properties, and in particular the 3 fractions of soil texture, exhibit strong co-variation among themselves and therefore taking into account this correlation leads to spatially more accurate results. In this study the continuous vertical and lateral distributions of relevant soil properties in Scottish soils were modelled with a multivariate 3D-GAM+GS approach. The approach used involves 1) modelling the multivariate trend with full 3D spatial correlation, i.e., exploiting the values of the neighbouring pixels in 3D-space, and 2) 3D kriging to interpolate the residuals. The values at each cell for each of the considered depth layers were defined using a hybrid GAM-geostatistical 3D model, combining the fitting of a GAM (generalised Additive Models) to estimate multivariate trend of the variables, using a 3D smoother with related covariates. Gaussian simulations of the model residuals were used as spatial component to account for local details. A dataset of about 26,000 horizons (7,800 profiles) was used for this study. A validation set was randomly selected as 25% of the full dataset. Numerous covariates derived from globally available data, such as MODIS and SRTM, are considered. The results of the 3D-GAM+kriging showed low RMSE values, good R squared and an accurate reproduction of the spatial structure of the data for a range of soil properties. The results have an out-of-sample RMSE between 10 to 15% of the observed range when taking into account the whole profile. The approach followed allows the assessment of the uncertainty of both the trend and the residuals.
A Cyber-Attack Detection Model Based on Multivariate Analyses
NASA Astrophysics Data System (ADS)
Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi
In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.
Multivariate moment closure techniques for stochastic kinetic models
Lakatos, Eszter Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.
2015-09-07
Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.
Multivariate moment closure techniques for stochastic kinetic models
NASA Astrophysics Data System (ADS)
Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.
2015-09-01
Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.
Multivariate moment closure techniques for stochastic kinetic models.
Lakatos, Eszter; Ale, Angelique; Kirk, Paul D W; Stumpf, Michael P H
2015-09-01
Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs. PMID:26342359
Multivariate longitudinal data analysis with mixed effects hidden Markov models.
Raffa, Jesse D; Dubin, Joel A
2015-09-01
Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. PMID:25761965
A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.
ERIC Educational Resources Information Center
Rohner, Ronald P.; Rohner, Evelyn C.
This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…
Multivariate models of mothers' and fathers' aggression toward their children.
Smith Slep, Amy M; O'Leary, Susan G
2007-10-01
Multivariate, biopsychosocial, explanatory models of mothers' and fathers' psychological and physical aggression toward their 3- to 7-year-old children were fitted and cross-validated in 453 representatively sampled families. Models explaining mothers' and fathers' aggression were substantially similar. Surprisingly, many variables identified as risk factors in the parental aggression and physical child abuse literatures, such as income, unrealistic expectations, and alcohol problems, although correlated with aggression bivariately, did not contribute uniquely to the models. In contrast, a small number of variables (i.e., child responsible attributions, overreactive discipline style, anger expression, and attitudes approving of aggression) appeared to be important pathways to parent aggression, mediating the effects of more distal risk factors. Models accounted for a moderate proportion of the variance in aggression. PMID:17907856
Optimal model-free prediction from multivariate time series.
Runge, Jakob; Donner, Reik V; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation. PMID:26066231
Modeling pharmacokinetic data using heavy-tailed multivariate distributions.
Lindsey, J K; Jones, B
2000-08-01
Pharmacokinetic studies of drug and metabolite concentrations in the blood are usually conducted as crossover trials, especially in Phases I and II. A longitudinal series of measurements is collected on each subject within each period. Dependence among such observations, within and between periods, will generally be fairly complex, requiring two levels of variance components, for the subjects and for the periods within subjects, and an autocorrelation within periods as well as a time-varying variance. Until now, the standard way in which this has been modeled is using a multivariate normal distribution. Here, we introduce procedures for simultaneously handling these various types of dependence in a wider class of distributions called the multivariate power exponential and Student t families. They can have the heavy tails required for handling the extreme observations that may occur in such contexts. We also consider various forms of serial dependence among the observations and find that they provide more improvement to our models than do the variance components. An integrated Ornstein-Uhlenbeck (IOU) stochastic process fits much better to our data set than the conventional continuous first-order autoregression, CAR(1). We apply these models to a Phase I study of the drug, flosequinan, and its metabolite. PMID:10959917
Multivariate Models for Normal and Binary Responses in Intervention Studies
ERIC Educational Resources Information Center
Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen
2016-01-01
Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
Recency of Pap smear screening: a multivariate model.
Howe, H L; Bzduch, H
1987-01-01
Most descriptive reports of women who have not received recent Pap smear screening have been limited to bivariate descriptions. The purpose of this study was to develop a multivariate model to predict the recency of Pap smear screening. A systematic sample of women residents, aged 25 to 74 years, in upstate New York was selected. The women were asked to report use of Pap smear screening during several time periods, their congruence with recommended medical practice, general use of medical services, and a variety of sociodemographic indicators. A log linear weighted least squares regression model was developed, and it explained 30 percent of the variance in recency of Pap smear screening behavior. While the sociodemographic variables were important predictors in the model, the medical care variables were the strongest predictors of recent Pap smear use. A significant relationship between race and recency of Pap smear testing was not supported by these data. PMID:3108946
ASSESSING PHENOTYPIC CORRELATION THROUGH THE MULTIVARIATE PHYLOGENETIC LATENT LIABILITY MODEL
Cybis, Gabriela B.; Sinsheimer, Janet S.; Bedford, Trevor; Mather, Alison E.; Lemey, Philippe; Suchard, Marc A.
2016-01-01
Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits, and discrete traits with multiple ordered and unordered states. Previous approaches have entertained a single data type generally along a fixed history, precluding estimation of correlation between traits and ignoring uncertainty in the history. We implement our model in a Bayesian phylogenetic framework, and discuss inference techniques for hypothesis testing. Finally, we showcase the method through applications to columbine flower morphology, antibiotic resistance in Salmonella, and epitope evolution in influenza. PMID:27053974
Transfer of multivariate calibration models between spectrometers: A progress report
Haaland, D.; Jones, H.; Rohrback, B.
1994-12-31
Multivariate calibration methods are extremely powerful for quantitative spectral analyses and have myriad uses in quality control and process monitoring. However, when analyses are to be completed at multiple sites or when spectrometers drift, recalibration is required. Often a full recalibration of an instrument can be impractical: the problem is particularly acute when the number of calibration standards is large or the standards chemically unstable. Furthermore, simply using Instrument A`s calibration model to predict unknowns on Instrument B can lead to enormous errors. Therefore, a mathematical procedure that would allow for the efficient transfer of a multivariate calibration model from one instrument to others using a small number of transfer standards is highly desirable. In this study, near-infrared spectral data have been collected from two sets of statistically designed round-robin samples on multiple FT-IR and grating spectrometers. One set of samples encompasses a series of dilute aqueous solutions of urea, creatinine, and NaCl while the second set is derived from mixtures of heptane, monochlorobenzene, and toluene. A systematic approach has been used to compare the results from four published transfer algorithms in order to determine parameters that affect the quality of the transfer for each class of sample and each type of spectrometer.
Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling
ERIC Educational Resources Information Center
Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao
2013-01-01
Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…
Multivariable frequency weighted model order reduction for control synthesis
NASA Technical Reports Server (NTRS)
Schmidt, David K.
1989-01-01
Quantitative criteria are presented for model simplification, or order reduction, such that the reduced order model may be used to synthesize and evaluate a control law, and the stability robustness obtained using the reduced order model will be preserved when controlling the full-order system. The error introduced due to model simplification is treated as modeling uncertainty, and some of the results from multivariate robustness theory are brought to bear on the model simplification problem. A numerical procedure developed previously is shown to lead to results that meet the necessary criteria. The procedure is applied to reduce the model of a flexible aircraft. Also, the importance of the control law itself, in meeting the modeling criteria, is underscored. An example is included that demonstrates that an apparently robust control law actually amplifies modest modeling errors in the critical frequency region, and leads to undesirable results. The cause of this problem is associated with the canceling of lightly damped transmission zeroes in the plant. An attempt is made to expand on some of the earlier results and to further clarify the theoretical basis behind the proposed methodology.
Simplex Factor Models for Multivariate Unordered Categorical Data
Bhattacharya, Anirban; Dunson, David B.
2013-01-01
Gaussian latent factor models are routinely used for modeling of dependence in continuous, binary, and ordered categorical data. For unordered categorical variables, Gaussian latent factor models lead to challenging computation and complex modeling structures. As an alternative, we propose a novel class of simplex factor models. In the single-factor case, the model treats the different categorical outcomes as independent with unknown marginals. The model can characterize flexible dependence structures parsimoniously with few factors, and as factors are added, any multivariate categorical data distribution can be accurately approximated. Using a Bayesian approach for computation and inferences, a Markov chain Monte Carlo (MCMC) algorithm is proposed that scales well with increasing dimension, with the number of factors treated as unknown. We develop an efficient proposal for updating the base probability vector in hierarchical Dirichlet models. Theoretical properties are described, and we evaluate the approach through simulation examples. Applications are described for modeling dependence in nucleotide sequences and prediction from high-dimensional categorical features. PMID:23908561
NASA Technical Reports Server (NTRS)
Achtemeier, Gary L.; Ochs, Harry T., III
1988-01-01
The variational method of undetermined multipliers is used to derive a multivariate model for objective analysis. The model is intended for the assimilation of 3-D fields of rawinsonde height, temperature and wind, and mean level temperature observed by satellite into a dynamically consistent data set. Relative measurement errors are taken into account. The dynamic equations are the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation. The model Euler-Lagrange equations are eleven linear and/or nonlinear partial differential and/or algebraic equations. A cyclical solution sequence is described. Other model features include a nonlinear terrain-following vertical coordinate that eliminates truncation error in the pressure gradient terms of the horizontal momentum equations and easily accommodates satellite observed mean layer temperatures in the middle and upper troposphere. A projection of the pressure gradient onto equivalent pressure surfaces removes most of the adverse impacts of the lower coordinate surface on the variational adjustment.
Multivariate screening in food adulteration: untargeted versus targeted modelling.
López, M Isabel; Trullols, Esther; Callao, M Pilar; Ruisánchez, Itziar
2014-03-15
Two multivariate screening strategies (untargeted and targeted modelling) have been developed to compare their ability to detect food fraud. As a case study, possible adulteration of hazelnut paste is considered. Two different adulterants were studied, almond paste and chickpea flour. The models were developed from near-infrared (NIR) data coupled with soft independent modelling of class analogy (SIMCA) as a classification technique. Regarding the untargeted strategy, only unadulterated samples were modelled, obtaining 96.3% of correct classification. The prediction of adulterated samples gave errors between 5.5% and 2%. Regarding targeted modelling, two classes were modelled: Class 1 (unadulterated samples) and Class 2 (almond adulterated samples). Samples adulterated with chickpea were predicted to prove its ability to deal with non-modelled adulterants. The results show that samples adulterated with almond were mainly classified in their own class (90.9%) and samples with chickpea were classified in Class 2 (67.3%) or not in any class (30.9%), but no one only as unadulterated. PMID:24206702
Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification
Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen
2014-01-01
Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922
Multivariate models of inter-subject anatomical variability
Ashburner, John; Klöppel, Stefan
2011-01-01
This paper presents a very selective review of some of the approaches for multivariate modelling of inter-subject variability among brain images. It focusses on applying probabilistic kernel-based pattern recognition approaches to pre-processed anatomical MRI, with the aim of most accurately modelling the difference between populations of subjects. Some of the principles underlying the pattern recognition approaches of Gaussian process classification and regression are briefly described, although the reader is advised to look elsewhere for full implementational details. Kernel pattern recognition methods require matrices that encode the degree of similarity between the images of each pair of subjects. This review focusses on similarity measures derived from the relative shapes of the subjects' brains. Pre-processing is viewed as generative modelling of anatomical variability, and there is a special emphasis on the diffeomorphic image registration framework, which provides a very parsimonious representation of relative shapes. Although the review is largely methodological, excessive mathematical notation is avoided as far as possible, as the paper attempts to convey a more intuitive understanding of various concepts. The paper should be of interest to readers wishing to apply pattern recognition methods to MRI data, with the aim of clinical diagnosis or biomarker development. It also tries to explain that the best models are those that most accurately predict, so similar approaches should also be relevant to basic science. Knowledge of some basic linear algebra and probability theory should make the review easier to follow, although it may still have something to offer to those readers whose mathematics may be more limited. PMID:20347998
Harinath, Eranda; Mann, George K I
2008-06-01
This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system. PMID:18558531
Linear multivariate evaluation models for spatial perception of soundscape.
Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu
2015-11-01
Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed. PMID:26627762
Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M
2016-05-01
Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature. PMID:27196467
Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection.
Ueki, Masao; Tamiya, Gen
2016-04-01
We develop a new genetic prediction method, smooth-threshold multivariate genetic prediction, using single nucleotide polymorphisms (SNPs) data in genome-wide association studies (GWASs). Our method consists of two stages. At the first stage, unlike the usual discontinuous SNP screening as used in the gene score method, our method continuously screens SNPs based on the output from standard univariate analysis for marginal association of each SNP. At the second stage, the predictive model is built by a generalized ridge regression simultaneously using the screened SNPs with SNP weight determined by the strength of marginal association. Continuous SNP screening by the smooth thresholding not only makes prediction stable but also leads to a closed form expression of generalized degrees of freedom (GDF). The GDF leads to the Stein's unbiased risk estimation (SURE), which enables data-dependent choice of optimal SNP screening cutoff without using cross-validation. Our method is very rapid because computationally expensive genome-wide scan is required only once in contrast to the penalized regression methods including lasso and elastic net. Simulation studies that mimic real GWAS data with quantitative and binary traits demonstrate that the proposed method outperforms the gene score method and genomic best linear unbiased prediction (GBLUP), and also shows comparable or sometimes improved performance with the lasso and elastic net being known to have good predictive ability but with heavy computational cost. Application to whole-genome sequencing (WGS) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) exhibits that the proposed method shows higher predictive power than the gene score and GBLUP methods. PMID:26947266
Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.
Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong
2015-05-01
In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955
Jonathan, Aguero-Valverde; Wu, Kun-Feng Ken; Donnell, Eric T
2016-02-01
Many studies have proposed the use of a systemic approach to identify sites with promise (SWiPs). Proponents of the systemic approach to road safety management suggest that it is more effective in reducing crash frequency than the traditional hot spot approach. The systemic approach aims to identify SWiPs by crash type(s) and, therefore, effectively connects crashes to their corresponding countermeasures. Nevertheless, a major challenge to implementing this approach is the low precision of crash frequency models, which results from the systemic approach considering subsets (crash types) of total crashes leading to higher variability in modeling outcomes. This study responds to the need for more precise statistical output and proposes a multivariate spatial model for simultaneously modeling crash frequencies for different crash types. The multivariate spatial model not only induces a multivariate correlation structure between crash types at the same site, but also spatial correlation among adjacent sites to enhance model precision. This study utilized crash, traffic, and roadway inventory data on rural two-lane highways in Pennsylvania to construct and test the multivariate spatial model. Four models with and without the multivariate and spatial correlations were tested and compared. The results show that the model that considers both multivariate and spatial correlation has the best fit. Moreover, it was found that the multivariate correlation plays a stronger role than the spatial correlation when modeling crash frequencies in terms of different crash types. PMID:26615494
Bailit, Jennifer L.; Grobman, William A.; Rice, Madeline Murguia; Spong, Catherine Y.; Wapner, Ronald J.; Varner, Michael W.; Thorp, John M.; Leveno, Kenneth J.; Caritis, Steve N.; Shubert, Phillip J.; Tita, Alan T. N.; Saade, George; Sorokin, Yoram; Rouse, Dwight J.; Blackwell, Sean C.; Tolosa, Jorge E.; Van Dorsten, J. Peter
2014-01-01
Objective Regulatory bodies and insurers evaluate hospital quality using obstetrical outcomes, however meaningful comparisons should take pre-existing patient characteristics into account. Furthermore, if risk-adjusted outcomes are consistent within a hospital, fewer measures and resources would be needed to assess obstetrical quality. Our objective was to establish risk-adjusted models for five obstetric outcomes and assess hospital performance across these outcomes. Study Design A cohort study of 115,502 women and their neonates born in 25 hospitals in the United States between March 2008 and February 2011. Hospitals were ranked according to their unadjusted and risk-adjusted frequency of venous thromboembolism, postpartum hemorrhage, peripartum infection, severe perineal laceration, and a composite neonatal adverse outcome. Correlations between hospital risk-adjusted outcome frequencies were assessed. Results Venous thromboembolism occurred too infrequently (0.03%, 95% CI 0.02% – 0.04%) for meaningful assessment. Other outcomes occurred frequently enough for assessment (postpartum hemorrhage 2.29% (95% CI 2.20–2.38), peripartum infection 5.06% (95% CI 4.93–5.19), severe perineal laceration at spontaneous vaginal delivery 2.16% (95% CI 2.06–2.27), neonatal composite 2.73% (95% CI 2.63–2.84)). Although there was high concordance between unadjusted and adjusted hospital rankings, several individual hospitals had an adjusted rank that was substantially different (as much as 12 rank tiers) than their unadjusted rank. None of the correlations between hospital adjusted outcome frequencies was significant. For example, the hospital with the lowest adjusted frequency of peripartum infection had the highest adjusted frequency of severe perineal laceration. Conclusions Evaluations based on a single risk-adjusted outcome cannot be generalized to overall hospital obstetric performance. PMID:23891630
Multivariate Effect Size Estimation: Confidence Interval Construction via Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2010-01-01
A latent variable modeling method is outlined for constructing a confidence interval (CI) of a popular multivariate effect size measure. The procedure uses the conventional multivariate analysis of variance (MANOVA) setup and is applicable with large samples. The approach provides a population range of plausible values for the proportion of…
Multi-Variable Model-Based Parameter Estimation Model for Antenna Radiation Pattern Prediction
NASA Technical Reports Server (NTRS)
Deshpande, Manohar D.; Cravey, Robin L.
2002-01-01
A new procedure is presented to develop multi-variable model-based parameter estimation (MBPE) model to predict far field intensity of antenna. By performing MBPE model development procedure on a single variable at a time, the present method requires solution of smaller size matrices. The utility of the present method is demonstrated by determining far field intensity due to a dipole antenna over a frequency range of 100-1000 MHz and elevation angle range of 0-90 degrees.
Multivariate Search of the Standard Model Higgs Boson at LHC
Mjahed, Mostafa
2007-01-12
resent an attempt to identify the SM Higgs boson at LHC in the channel (pp-bar {yields} HX {yields} W+ W-X {yields} l+ vl- v X). We use a multivariate processing of data as a tool for a better discrimination between signal and background (via Principal Components Analysis, Genetic Algorithms and Neural Network). Events were produced at LHC energies (MH = 140 - 200 GeV), using the Lund Monte Carlo generator PYTHIA 6.1. Higgs boson events (pp-bar {yields} HX {yields} W+W-X {yields} l+ vl- v X) and the most relevant background are considered.
The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models
Padayachee, Trishanta; Khamiakova, Tatsiana; Shkedy, Ziv; Perola, Markus; Salo, Perttu; Burzykowski, Tomasz
2016-01-01
Investigating whether metabolites regulate the co-expression of a predefined gene module is one of the relevant questions posed in the integrative analysis of metabolomic and transcriptomic data. This article concerns the integrative analysis of the two high-dimensional datasets by means of multivariate models and statistical tests for the dependence between metabolites and the co-expression of a gene module. The general linear model (GLM) for correlated data that we propose models the dependence between adjusted gene expression values through a block-diagonal variance-covariance structure formed by metabolic-subset specific general variance-covariance blocks. Performance of statistical tests for the inference of conditional co-expression are evaluated through a simulation study. The proposed methodology is applied to the gene expression data of the previously characterized lipid-leukocyte module. Our results show that the GLM approach improves on a previous approach by being less prone to the detection of spurious conditional co-expression. PMID:26918614
Technology Transfer Automated Retrieval System (TEKTRAN)
Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in heat production, or energy expenditure (EE). Multivariate adaptive regression splines (MARS), is a nonparametric method that estimates complex nonlinear relationships by a seri...
Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model
NASA Astrophysics Data System (ADS)
Ping, Pung Yean; Ahmad, Maizah Hura Binti
2014-12-01
World gold price is a popular investment commodity. The series have often been modeled using univariate models. The objective of this paper is to show that there is a co-movement between gold price and USD foreign exchange rate. Using the effect of the USD foreign exchange rate on the gold price, a model that can be used to forecast future gold prices is developed. For this purpose, the current paper proposes a multivariate GARCH (Bivariate GARCH) model. Using daily prices of both series from 01.01.2000 to 05.05.2014, a causal relation between the two series understudied are found and a bivariate GARCH model is produced.
When univariate model-free time series prediction is better than multivariate
NASA Astrophysics Data System (ADS)
Chayama, Masayoshi; Hirata, Yoshito
2016-07-01
The delay coordinate method is known to be a practically useful technique for reconstructing the states of an observed system. While this method is theoretically supported by Takens' embedding theorem concerning observations of a scalar time series, we can extend the method to include a multivariate time series. It is often assumed that a better prediction can be obtained using a multivariate time series than by using a scalar time series. However, multivariate time series contains various types of information, and it may be difficult to extract information that is useful for predicting the states. Thus, univariate prediction may sometimes be superior to multivariate prediction. Here, we compare univariate model-free time series predictions with multivariate ones, and demonstrate that univariate model-free prediction is better than multivariate one when the prediction steps are small, while multivariate prediction performs better when the prediction steps become larger. We show the validity of the former finding by using artificial datasets generated from the Lorenz 96 models and a real solar irradiance dataset. The results indicate that it is possible to determine which method is the best choice by considering how far into the future we want to predict.
MacNab, Ying C
2016-08-01
This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. PMID:27566769
NASA Technical Reports Server (NTRS)
Waszak, Martin R.
1997-01-01
The Benchmark Active Controls Technology (BACT) project is part of NASA Langley Research Center s Benchmark Models Program for studying transonic aeroelastic phenomena. In January of 1996 the BACT wind-tunnel model was used to successfully demonstrate the application of robust multivariable control design methods (H and -synthesis) to flutter suppression. This paper addresses the design and experimental evaluation of robust multivariable flutter suppression control laws with particular attention paid to the degree to which stability and performance robustness was achieved.
Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H
2016-07-30
Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26603500
Tang, An-Min; Tang, Nian-Sheng
2015-02-28
We propose a semiparametric multivariate skew-normal joint model for multivariate longitudinal and multivariate survival data. One main feature of the posited model is that we relax the commonly used normality assumption for random effects and within-subject error by using a centered Dirichlet process prior to specify the random effects distribution and using a multivariate skew-normal distribution to specify the within-subject error distribution and model trajectory functions of longitudinal responses semiparametrically. A Bayesian approach is proposed to simultaneously obtain Bayesian estimates of unknown parameters, random effects and nonparametric functions by combining the Gibbs sampler and the Metropolis-Hastings algorithm. Particularly, a Bayesian local influence approach is developed to assess the effect of minor perturbations to within-subject measurement error and random effects. Several simulation studies and an example are presented to illustrate the proposed methodologies. PMID:25404574
Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.
Aguero-Valverde, Jonathan
2013-10-01
Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. PMID:23872657
Multivariate Regression Models for Estimating Journal Usefulness in Physics.
ERIC Educational Resources Information Center
Bennion, Bruce C.; Karschamroon, Sunee
1984-01-01
This study examines possibility of ranking journals in physics by means of bibliometric regression models that estimate usefulness as it is reported by 167 physicists in United States and Canada. Development of four models, patterns of deviation from models, and validity and application are discussed. Twenty-six references are cited. (EJS)
A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series
ERIC Educational Resources Information Center
Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.
2011-01-01
Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…
Preliminary Multi-Variable Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. This paper reviews the methodology used to develop space telescope cost models; summarizes recently published single variable models; and presents preliminary results for two and three variable cost models. Some of the findings are that increasing mass reduces cost; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and technology development as a function of time reduces cost at the rate of 50% per 17 years.
A Multivariate Model for Coastal Water Quality Mapping Using Satellite Remote Sensing Images
Su, Yuan-Fong; Liou, Jun-Jih; Hou, Ju-Chen; Hung, Wei-Chun; Hsu, Shu-Mei; Lien, Yi-Ting; Su, Ming-Daw; Cheng, Ke-Sheng; Wang, Yeng-Fung
2008-01-01
This study demonstrates the feasibility of coastal water quality mapping using satellite remote sensing images. Water quality sampling campaigns were conducted over a coastal area in northern Taiwan for measurements of three water quality variables including Secchi disk depth, turbidity, and total suspended solids. SPOT satellite images nearly concurrent with the water quality sampling campaigns were also acquired. A spectral reflectance estimation scheme proposed in this study was applied to SPOT multispectral images for estimation of the sea surface reflectance. Two models, univariate and multivariate, for water quality estimation using the sea surface reflectance derived from SPOT images were established. The multivariate model takes into consideration the wavelength-dependent combined effect of individual seawater constituents on the sea surface reflectance and is superior over the univariate model. Finally, quantitative coastal water quality mapping was accomplished by substituting the pixel-specific spectral reflectance into the multivariate water quality estimation model.
Gaussian Copula multivariate modeling for texture image retrieval using wavelet transforms.
Lasmar, Nour-Eddine; Berthoumieu, Yannick
2014-05-01
In the framework of texture image retrieval, a new family of stochastic multivariate modeling is proposed based on Gaussian Copula and wavelet decompositions. We take advantage of the copula paradigm, which makes it possible to separate dependence structure from marginal behavior. We introduce two new multivariate models using, respectively, generalized Gaussian and Weibull densities. These models capture both the subband marginal distributions and the correlation between wavelet coefficients. We derive, as a similarity measure, a closed form expression of the Jeffrey divergence between Gaussian copula-based multivariate models. Experimental results on well-known databases show significant improvements in retrieval rates using the proposed method compared with the best known state-of-the-art approaches. PMID:24686281
Nonlinear Latent Curve Models for Multivariate Longitudinal Data
ERIC Educational Resources Information Center
Blozis, Shelley A.; Conger, Katherine J.; Harring, Jeffrey R.
2007-01-01
Latent curve models have become a useful approach to analyzing longitudinal data, due in part to their allowance of and emphasis on individual differences in features that describe change. Common applications of latent curve models in developmental studies rely on polynomial functions, such as linear or quadratic functions. Although useful for…
Modeling the Pineapple Express phenomenon via Multivariate Extreme Value Theory
NASA Astrophysics Data System (ADS)
Weller, G.; Cooley, D. S.
2011-12-01
The pineapple express (PE) phenomenon is responsible for producing extreme winter precipitation events in the coastal and mountainous regions of the western United States. Because the PE phenomenon is also associated with warm temperatures, the heavy precipitation and associated snowmelt can cause destructive flooding. In order to study impacts, it is important that regional climate models from NARCCAP are able to reproduce extreme precipitation events produced by PE. We define a daily precipitation quantity which captures the spatial extent and intensity of precipitation events produced by the PE phenomenon. We then use statistical extreme value theory to model the tail dependence of this quantity as seen in an observational data set and each of the six NARCCAP regional models driven by NCEP reanalysis. We find that most NCEP-driven NARCCAP models do exhibit tail dependence between daily model output and observations. Furthermore, we find that not all extreme precipitation events are pineapple express events, as identified by Dettinger et al. (2011). The synoptic-scale atmospheric processes that drive extreme precipitation events produced by PE have only recently begun to be examined. Much of the current work has focused on pattern recognition, rather than quantitative analysis. We use daily mean sea-level pressure (MSLP) fields from NCEP to develop a "pineapple express index" for extreme precipitation, which exhibits tail dependence with our observed precipitation quantity for pineapple express events. We build a statistical model that connects daily precipitation output from the WRFG model, daily MSLP fields from NCEP, and daily observed precipitation in the western US. Finally, we use this model to simulate future observed precipitation based on WRFG output driven by the CCSM model, and our pineapple express index derived from future CCSM output. Our aim is to use this model to develop a better understanding of the frequency and intensity of extreme
Hypoglycemia Early Alarm Systems Based On Multivariable Models
Turksoy, Kamuran; Bayrak, Elif S; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick; Cinar, Ali
2013-01-01
Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter-/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence. PMID:24187436
Modeling a multivariable reactor and on-line model predictive control.
Yu, D W; Yu, D L
2005-10-01
A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown. PMID:16294779
Storm Water Management Model Climate Adjustment Tool (SWMM-CAT)
The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations. SWMM, first released in 1971, models hydrology and hydrauli...
Unified Model for Academic Competence, Social Adjustment, and Psychopathology.
ERIC Educational Resources Information Center
Schaefer, Earl S.; And Others
A unified conceptual model is needed to integrate the extensive research on (1) social competence and adaptive behavior, (2) converging conceptualizations of social adjustment and psychopathology, and (3) emerging concepts and measures of academic competence. To develop such a model, a study was conducted in which teacher ratings were collected on…
IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data
ERIC Educational Resources Information Center
Wang, Lijuan
2010-01-01
This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…
Computer-Aided Decisions in Human Services: Expert Systems and Multivariate Models.
ERIC Educational Resources Information Center
Sicoly, Fiore
1989-01-01
This comparison of two approaches to the development of computerized supports for decision making--expert systems and multivariate models--focuses on computerized systems that assist professionals with tasks related to diagnosis or classification in human services. Validation of both expert systems and statistical models is emphasized. (39…
MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)
Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...
Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.
Lee, Jaeyoung; Abdel-Aty, Mohamed; Jiang, Ximiao
2015-05-01
Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode. PMID:25790973
Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods
ERIC Educational Resources Information Center
Zhang, Ying
2011-01-01
Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…
Multivariate modeling of settling depth of apple fruit (Red Delicious variety) in water.
Kheiralipour, Kamran; Marzbani, Farshid
2016-03-01
Settling depth of apple was determined by a water column and a digital camera. The depth was experimentally modeled using multivariate regression using a coded program in MATLAB software. The best models were based on the density, dropping height volume/mass with coefficient of determination and mean square error of 0.90 and 4.08, respectively. PMID:27004104
NASA Astrophysics Data System (ADS)
Alih, Ekele; Ong, Hong Choon
2014-07-01
The application of Ordinary Least Squares (OLS) to a single equation assumes among others, that the predictor variables are truly exogenous; that there is only one-way causation between the dependent variable yi and the predictor variables xij. If this is not true and the xij 'S are at the same time determined by yi, the OLS assumption will be violated and a single equation method will give biased and inconsistent parameter estimates. The OLS also suffers a huge set back in the presence of contaminated data. In order to rectify these problems, simultaneous equation models have been introduced as well as robust regression. In this paper, we construct a simultaneous equation model with variables that exhibit simultaneous dependence and we proposed a robust multivariate regression procedure for estimating the parameters of such models. The performance of the robust multivariate regression procedure was examined and compared with the OLS multivariate regression technique and the Three-Stage Least squares procedure (3SLS) using numerical simulation experiment. The performance of the robust multivariate regression and (3SLS) were approximately equally better than OLS when there is no contamination in the data. Nevertheless, when contaminations occur in the data, the robust multivariate regression outperformed the 3SLS and OLS.
A Multivariate Model of Stakeholder Preference for Lethal Cat Management
Wald, Dara M.; Jacobson, Susan K.
2014-01-01
Identifying stakeholder beliefs and attitudes is critical for resolving management conflicts. Debate over outdoor cat management is often described as a conflict between two groups, environmental advocates and animal welfare advocates, but little is known about the variables predicting differences among these critical stakeholder groups. We administered a mail survey to randomly selected stakeholders representing both of these groups (n = 1,596) in Florida, where contention over the management of outdoor cats has been widespread. We used a structural equation model to evaluate stakeholder intention to support non-lethal management. The cognitive hierarchy model predicted that values influenced beliefs, which predicted general and specific attitudes, which in turn, influenced behavioral intentions. We posited that specific attitudes would mediate the effect of general attitudes, beliefs, and values on management support. Model fit statistics suggested that the final model fit the data well (CFI = 0.94, RMSEA = 0.062). The final model explained 74% of the variance in management support, and positive attitudes toward lethal management (humaneness) had the largest direct effect on management support. Specific attitudes toward lethal management and general attitudes toward outdoor cats mediated the relationship between positive (p<0.05) and negative cat-related impact beliefs (p<0.05) and support for management. These results supported the specificity hypothesis and the use of the cognitive hierarchy to assess stakeholder intention to support non-lethal cat management. Our findings suggest that stakeholders can simultaneously perceive both positive and negative beliefs about outdoor cats, which influence attitudes toward and support for non-lethal management. PMID:24736744
Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang
2010-07-01
We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286
Modelling household finances: A Bayesian approach to a multivariate two-part model
Brown, Sarah; Ghosh, Pulak; Su, Li; Taylor, Karl
2016-01-01
We contribute to the empirical literature on household finances by introducing a Bayesian multivariate two-part model, which has been developed to further our understanding of household finances. Our flexible approach allows for the potential interdependence between the holding of assets and liabilities at the household level and also encompasses a two-part process to allow for differences in the influences on asset or liability holding and on the respective amounts held. Furthermore, the framework is dynamic in order to allow for persistence in household finances over time. Our findings endorse the joint modelling approach and provide evidence supporting the importance of dynamics. In addition, we find that certain independent variables exert different influences on the binary and continuous parts of the model thereby highlighting the flexibility of our framework and revealing a detailed picture of the nature of household finances. PMID:27212801
Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.
2014-01-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281
NASA Astrophysics Data System (ADS)
Grujic, O.; Caers, J.
2014-12-01
Modern approaches to uncertainty quantification in the subsurface rely on complex procedures of geological modeling combined with numerical simulation of flow & transport. This approach requires long computational times rendering any full Monte Carlo simulation infeasible, in particular solving the flow & transport problem takes hours of computing time in real field problems. This motivated the development of model selection methods aiming to identify a small subset of models that capture important statistics of a larger ensemble of geological model realization. A recent method, based on model selection in metric space, termed distance-kernel method (DKM) allows selecting representative models though kernel k-medoid clustering. The distance defining the metric space is usually based on some approximate flow model. However, the output of an approximate flow model can be multi-variate (reporting heads/pressures, saturation, rates). In addition, the modeler may have information from several other approximate models (e.g. upscaled models) or summary statistical information about geological heterogeneity that could allow for a more accurate selection. In an effort to perform model selection based on multivariate attributes, we rely on functional data analysis which allows for an exploitation of covariances between time-varying multivariate numerical simulation output. Based on mixed functional principal component analysis, we construct a lower dimensional space in which kernel k-medoid clustering is used for model selection. In this work we demonstrate the functional approach on a complex compositional flow problem where the geological uncertainty consists of channels with uncertain spatial distribution of facies, proportions, orientations and geometries. We illustrate that using multivariate attributes and multiple approximate models provides accuracy improvement over using a single attribute.
Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models.
Nortey, Ezekiel Nn; Ngoh, Delali D; Doku-Amponsah, Kwabena; Ofori-Boateng, Kenneth
2015-01-01
This paper was aimed at investigating the volatility and conditional relationship among inflation rates, exchange rates and interest rates as well as to construct a model using multivariate GARCH DCC and BEKK models using Ghana data from January 1990 to December 2013. The study revealed that the cumulative depreciation of the cedi to the US dollar from 1990 to 2013 is 7,010.2% and the yearly weighted depreciation of the cedi to the US dollar for the period is 20.4%. There was evidence that, the fact that inflation rate was stable, does not mean that exchange rates and interest rates are expected to be stable. Rather, when the cedi performs well on the forex, inflation rates and interest rates react positively and become stable in the long run. The BEKK model is robust to modelling and forecasting volatility of inflation rates, exchange rates and interest rates. The DCC model is robust to model the conditional and unconditional correlation among inflation rates, exchange rates and interest rates. The BEKK model, which forecasted high exchange rate volatility for the year 2014, is very robust for modelling the exchange rates in Ghana. The mean equation of the DCC model is also robust to forecast inflation rates in Ghana. PMID:25741459
Modeling of turbulent supersonic H2-air combustion with a multivariate beta PDF
NASA Technical Reports Server (NTRS)
Baurle, R. A.; Hassan, H. A.
1993-01-01
Recent calculations of turbulent supersonic reacting shear flows using an assumed multivariate beta PDF (probability density function) resulted in reduced production rates and a delay in the onset of combustion. This result is not consistent with available measurements. The present research explores two possible reasons for this behavior: use of PDF's that do not yield Favre averaged quantities, and the gradient diffusion assumption. A new multivariate beta PDF involving species densities is introduced which makes it possible to compute Favre averaged mass fractions. However, using this PDF did not improve comparisons with experiment. A countergradient diffusion model is then introduced. Preliminary calculations suggest this to be the cause of the discrepancy.
Using Bibliotherapy to Help Children Adjust to Changing Role Models.
ERIC Educational Resources Information Center
Pardeck, John T.; Pardeck, Jean A.
One technique for helping children adjust to changing role models is bibliotherapy--the use of children's books to facilitate identification with and exploration of sex role behavior. Confronted with change in various social systems, particularly the family, children are faced with conflicts concerning their sex role development. The process…
Catastrophe, Chaos, and Complexity Models and Psychosocial Adjustment to Disability.
ERIC Educational Resources Information Center
Parker, Randall M.; Schaller, James; Hansmann, Sandra
2003-01-01
Rehabilitation professionals may unknowingly rely on stereotypes and specious beliefs when dealing with people with disabilities, despite the formulation of theories that suggest new models of the adjustment process. Suggests that Catastrophe, Chaos, and Complexity Theories hold considerable promise in this regard. This article reviews these…
NASA Astrophysics Data System (ADS)
Leeds, W. B.; Wikle, C. K.
2012-12-01
Spatio-temporal statistical models, and in particular Bayesian hierarchical models (BHMs), have become increasingly popular as means of representing natural processes such as climate and weather that evolve over space and time. Hierarchical models make it possible to specify separate, conditional probability distributions that account for uncertainty in the observations, the underlying process, and parameters in situations when specifying these sources of uncertainty in a joint probability distribution may be difficult. As a result, BHMs are a natural setting for climatologists, meteorologists, and other environmental scientists to incorporate scientific information (e.g., PDEs, IDEs, etc.) a priori into a rigorous statistical framework that accounts for error in measurements, uncertainty in the understanding of the true underlying process, and uncertainty in the parameters that describe the process. While much work has been done in the development of statistical models for linear dynamic spatio-temporal processes, statistical modeling for nonlinear (and particularly, multivariate nonlinear) spatio-temporal dynamical processes is still a relatively open area of inquiry. As a result, general statistical models for environmental scientists to model complicated nonlinear processes is limited. We address this limitation in the methodology by introducing a multivariate "general quadratic nonlinear" framework for modeling multivariate, nonlinear spatio-temporal random processes inside of a BHM in a way that is especially applicable for problems in the ocean and atmospheric sciences. We show that in addition to the fact that this model addresses the previously mentioned sources of uncertainty for a wide spectrum of multivariate, nonlinear spatio-temporal processes, it is also a natural framework for data assimilation, allowing for the fusing of observations with computer models, computer model emulators, computer model output, or "mechanistically motivated" statistical
ERIC Educational Resources Information Center
Tchumtchoua, Sylvie; Dey, Dipak K.
2012-01-01
This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…
A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence
ERIC Educational Resources Information Center
McKinney, Cliff; Renk, Kimberly
2011-01-01
Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…
ERIC Educational Resources Information Center
MacIntosh, Randall
1997-01-01
Presents KANT, a FORTRAN 77 software program that tests assumptions of multivariate normality in a data set. Based on the test developed by M. V. Mardia (1985), the KANT program is useful for those engaged in structural equation modeling with latent variables. (SLD)
A General Multivariate Latent Growth Model with Applications to Student Achievement
ERIC Educational Resources Information Center
Bianconcini, Silvia; Cagnone, Silvia
2012-01-01
The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…
Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms
ERIC Educational Resources Information Center
Anderson, John R.
2012-01-01
Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…
The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples
ERIC Educational Resources Information Center
Avetisyan, Marianna; Fox, Jean-Paul
2012-01-01
In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…
A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers
ERIC Educational Resources Information Center
Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.
2009-01-01
Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…
Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity
ERIC Educational Resources Information Center
Dinov, Ivo D.; Christou, Nicolas
2011-01-01
This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…
Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI
Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.
2015-01-01
In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490
Multivariable Model for Time to First Treatment in Patients With Chronic Lymphocytic Leukemia
Wierda, William G.; O'Brien, Susan; Wang, Xuemei; Faderl, Stefan; Ferrajoli, Alessandra; Do, Kim-Anh; Garcia-Manero, Guillermo; Cortes, Jorge; Thomas, Deborah; Koller, Charles A.; Burger, Jan A.; Lerner, Susan; Schlette, Ellen; Abruzzo, Lynne; Kantarjian, Hagop M.; Keating, Michael J.
2011-01-01
Purpose The clinical course for patients with chronic lymphocytic leukemia (CLL) is diverse; some patients have indolent disease, never needing treatment, whereas others have aggressive disease requiring early treatment. We continue to use criteria for active disease to initiate therapy. Multivariable analysis was performed to identify prognostic factors independently associated with time to first treatment for patients with CLL. Patients and Methods Traditional laboratory, clinical prognostic, and newer prognostic factors such as fluorescent in situ hybridization (FISH), IGHV mutation status, and ZAP-70 expression evaluated at first patient visit to MD Anderson Cancer Center were correlated by multivariable analysis with time to first treatment. This multivariable model was used to develop a nomogram—a weighted tool to calculate 2- and 4-year probability of treatment and estimate median time to first treatment. Results There were 930 previously untreated patients who had traditional and new prognostic factors evaluated; they did not have active CLL requiring initiation of treatment within 3 months of first visit and were observed for time to first treatment. The following were independently associated with shorter time to first treatment: three involved lymph node sites, increased size of cervical lymph nodes, presence of 17p deletion or 11q deletion by FISH, increased serum lactate dehydrogenase, and unmutated IGHV mutation status. Conclusion We developed a multivariable model that incorporates traditional and newer prognostic factors to identify patients at high risk for progression to treatment. This model may be useful to identify patients for early interventional trials. PMID:21969505
Development of a charge adjustment model for cardiac catheterization.
Brennan, Andrew; Gauvreau, Kimberlee; Connor, Jean; O'Connell, Cheryl; David, Sthuthi; Almodovar, Melvin; DiNardo, James; Banka, Puja; Mayer, John E; Marshall, Audrey C; Bergersen, Lisa
2015-02-01
A methodology that would allow for comparison of charges across institutions has not been developed for catheterization in congenital heart disease. A single institution catheterization database with prospectively collected case characteristics was linked to hospital charges related and limited to an episode of care in the catheterization laboratory for fiscal years 2008-2010. Catheterization charge categories (CCC) were developed to group types of catheterization procedures using a combination of empiric data and expert consensus. A multivariable model with outcome charges was created using CCC and additional patient and procedural characteristics. In 3 fiscal years, 3,839 cases were available for analysis. Forty catheterization procedure types were categorized into 7 CCC yielding a grouper variable with an R (2) explanatory value of 72.6%. In the final CCC, the largest proportion of cases was in CCC 2 (34%), which included diagnostic cases without intervention. Biopsy cases were isolated in CCC 1 (12%), and percutaneous pulmonary valve placement alone made up CCC 7 (2%). The final model included CCC, number of interventions, and cardiac diagnosis (R (2) = 74.2%). Additionally, current financial metrics such as APR-DRG severity of illness and case mix index demonstrated a lack of correlation with CCC. We have developed a catheterization procedure type financial grouper that accounts for the diverse case population encountered in catheterization for congenital heart disease. CCC and our multivariable model could be used to understand financial characteristics of a population at a single point in time, longitudinally, and to compare populations. PMID:25113520
NASA Astrophysics Data System (ADS)
Ng, W.; Rasmussen, P. F.; Panu, U. S.
2009-12-01
Stochastic weather modeling is subject to a number of challenges including varied spatial-dependency and the existence of missing observations. Daily precipitation possesses unique statistical characteristics in distribution, such as the existence of high frequency of zero records and the high skewness of the distribution of precipitation amount. To address for these difficulties, a methodology based on the multivariate truncated Normal distribution model is proposed. The methodology transforms the skewed distribution of precipitation amounts at multiple sites into a multivariate Normal distribution model. The missing observations are then be estimated through the conditional mean and variance obtained from the multivariate Normal distribution model. The adequacy of the proposed model structure was first verified using a synthetic data set. Subsequently, 30 years of historical daily precipitation records from 10 Canadian meteorological stations were used to evaluate the performance of the model. The result of the evaluation shows that the proposed model reasonably can preserve the statistical characteristics of the historical records in estimated the missing records at multiple sites.
Applying the multivariate time-rescaling theorem to neural population models
Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon
2011-01-01
Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436
Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy
Wolfrum, E.; Payne, C.; Stefaniak, T.; Rooney, W.; Dighe, N.; Bean, B.; Dahlberg, J.
2013-03-01
NREL developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.
Kay, D; McDonald, A
1983-01-01
This paper reports on the calibration and use of a multiple regression model designed to predict concentrations of Escherichia coli and total coliforms in two upland British impoundments. The multivariate approach has improved predictive capability over previous univariate linear models because it includes predictor variables for the timing and magnitude of hydrological input to the reservoirs and physiochemical parameters of water quality. The significance of these results for catchment management research is considered. PMID:6639016
ERIC Educational Resources Information Center
Pakenham, Kenneth I.; Samios, Christina; Sofronoff, Kate
2005-01-01
The present study examined the applicability of the double ABCX model of family adjustment in explaining maternal adjustment to caring for a child diagnosed with Asperger syndrome. Forty-seven mothers completed questionnaires at a university clinic while their children were participating in an anxiety intervention. The children were aged between…
NASA Astrophysics Data System (ADS)
Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali
2011-02-01
Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.
NASA Astrophysics Data System (ADS)
Golay, Jean; Kanevski, Mikhaïl
2013-04-01
The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal
Vickers, Andrew J; Cronin, Angel M; Kattan, Michael W; Gonen, Mithat; Scardino, Peter T; Milowsky, Matthew I.; Dalbagni, Guido; Bochner, Bernard H.
2009-01-01
Background Multivariable prediction models have been shown to predict cancer outcomes more accurately than cancer stage. The effects on clinical management are unclear. We aimed to determine whether a published multivariable prediction model for bladder cancer (“bladder nomogram”) improves medical decision making, using referral for adjuvant chemotherapy as a model. Methods We analyzed data from an international cohort study of 4462 patients undergoing cystectomy without chemotherapy 1969 – 2004. The number of patients eligible for chemotherapy was determined using pathologic stage criteria (lymph node positive or stage pT3 or pT4), and for three cut-offs on the bladder nomogram (10%, 25% and 70% risk of recurrence with surgery alone). The number of recurrences was calculated by applying a relative risk reduction to eligible patients' baseline risk. Clinical net benefit was then calculated by combining recurrences and treatments, weighting the latter by a factor related to drug tolerability. Results A nomogram cut-off outperformed pathologic stage for chemotherapy for every scenario of drug effectiveness and tolerability. For a drug with a relative risk of 0.80, where clinicians would treat no more than 20 patients to prevent one recurrence, use of the nomogram was equivalent to a strategy that resulted in 60 fewer chemotherapy treatments per 1000 patients without any increase in recurrence rates. Conclusions Referring cystectomy patients to adjuvant chemotherapy on the basis of a multivariable model is likely to lead to better patient outcomes than the use of pathological stage. Further research is warranted to evaluate the clinical effects of multivariable prediction models. PMID:19823979
2014-01-01
Background Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models. Methods We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures. Results 11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models. Conclusions The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling
Hieke, Stefanie; Benner, Axel; Schlenk, Richard F.; Schumacher, Martin; Bullinger, Lars; Binder, Harald
2016-01-01
Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses. PMID:27159447
Chen, Gang; Adleman, Nancy E; Saad, Ziad S; Leibenluft, Ellen; Cox, Robert W
2014-10-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in
Franco-Pedroso, Javier; Ramos, Daniel; Gonzalez-Rodriguez, Joaquin
2016-01-01
In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (Cllr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints. PMID:26901680
A multivariate conditional model for streamflow prediction and spatial precipitation refinement
NASA Astrophysics Data System (ADS)
Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui
2015-10-01
The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.
NASA Astrophysics Data System (ADS)
Rupšys, P.
2015-10-01
A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE.
Rupšys, P.
2015-10-28
A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE.
Bradshaw, Sam; Mason, Shelley S.; Looft, Fred J.
2010-01-01
Tactile sensation is a complex manifestation of mechanical stimuli applied to the skin. At the most fundamental level of the somatosensory system is the cutaneous mechanoreceptor. The objective here was to establish a framework for modeling afferent mechanoreceptor behavior as a nanoscale biosensor under dynamic compressive loads using multivariate regression techniques. A multivariate logistical model was chosen because the system contains continuous input variables and a singular binary-output variable corresponding to the nerve action potential. Subsequently, this method was used to quantify the sensitivity of ten rapidly adapting afferents from rat hairy skin due to the stimulus metrics of compressive stress, strain, their respective time derivatives, and interactions. In vitro experiments involving compressive stimulation of isolated afferents using pseudorandom and nonrepeating noise sequences were completed. An analysis of the data was performed using multivariate logistical regression producing odds ratios (ORs) as a metric associated with mechanotransduction. It was determined that cutaneous mechanoreceptors are preferentially sensitive to stress (mean ORmax = 26.10), stress rate (mean ORmax = 15.03), strain (mean ORmax = 12.01), and strain rate (mean ORmax = 7.29) typically occurring within 7.3 ms of the nerve response. As a novel approach to receptor characterization, this analytical framework was validated for the multiple-input, binary-output neural system. PMID:21197157
Forecasting of municipal solid waste quantity in a developing country using multivariate grey models
Intharathirat, Rotchana; Abdul Salam, P.; Kumar, S.; Untong, Akarapong
2015-05-15
Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.
Multivariate model to characterise relations between maize mutant starches and hydrolysis kinetics.
Kansou, Kamal; Buléon, Alain; Gérard, Catherine; Rolland-Sabaté, Agnès
2015-11-20
The many studies about amylolysis have collected considerable information regarding the contribution of the starch physico-chemical properties. But the inherent elaborate and variable structure of granular starch and, consequently, the multifactorial condition of the system hinders the interpretation of the experimental results. The immediate benefit of multivariate statistical analysis approaches with that regard is twofold: considering the factors, possibly interrelated, all together and not independently, providing a first estimation of the magnitude and confidence level of the relations between factors and amylolysis kinetic parameters. Based on data of amylolysis of 13 starch samples from wild type, single and double mutants of maize by porcine pancreatic α-amylase (PPA), a multivariate analysis is proposed. Amylolysis progress-curves were fitted by a Weibull function, as proposed in a previous work, to extract three kinetic parameters: the reaction rate coefficient during the first time-unit, k, the reaction rate retardation over time, h, and the final hydrolysis extent, X∞. Multivariate models relate the macromolecular composition and the fractions of crystalline polymorphic types to the kinetic parameters. h and X∞ are found to be highly related to the measured properties. Thus the amylose content appears to be significantly correlated to the hydrolysis rate retardation, which sheds some light on the probable contribution of the amylose molecules contained in the granules. The multivariate models give correct prediction performances except for k whose a part of variability remains unexplained. A further analysis points out the extent of the characterisation effort of the granule structure needed to extend the fraction of explained variability. PMID:26344307
Modarres, Reza; Ouarda, Taha B M J; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre
2014-07-01
Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56% of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk. PMID:23722925
NASA Astrophysics Data System (ADS)
Modarres, Reza; Ouarda, Taha B. M. J.; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre
2014-07-01
Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMA X-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56 % of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.
NASA Astrophysics Data System (ADS)
Libera, D.; Arumugam, S.
2015-12-01
Water quality observations are usually not available on a continuous basis because of the expensive cost and labor requirements so calibrating and validating a mechanistic model is often difficult. Further, any model predictions inherently have bias (i.e., under/over estimation) and require techniques that preserve the long-term mean monthly attributes. This study suggests and compares two multivariate bias-correction techniques to improve the performance of the SWAT model in predicting daily streamflow, TN Loads across the southeast based on split-sample validation. The first approach is a dimension reduction technique, canonical correlation analysis that regresses the observed multivariate attributes with the SWAT model simulated values. The second approach is from signal processing, importance weighting, that applies a weight based off the ratio of the observed and model densities to the model data to shift the mean, variance, and cross-correlation towards the observed values. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are also compared with independent estimates from the USGS LOADEST model. Uncertainties in the bias-corrected estimates due to limited water quality observations are also discussed.
Multi-variate models are essential for understanding vertebrate diversification in deep time
Benson, Roger B. J.; Mannion, Philip D.
2012-01-01
Statistical models are helping palaeontologists to elucidate the history of biodiversity. Sampling standardization has been extensively applied to remedy the effects of uneven sampling in large datasets of fossil invertebrates. However, many vertebrate datasets are smaller, and the issue of uneven sampling has commonly been ignored, or approached using pairwise comparisons with a numerical proxy for sampling effort. Although most authors find a strong correlation between palaeodiversity and sampling proxies, weak correlation is recorded in some datasets. This has led several authors to conclude that uneven sampling does not influence our view of vertebrate macroevolution. We demonstrate that multi-variate regression models incorporating a model of underlying biological diversification, as well as a sampling proxy, fit observed sauropodomorph dinosaur palaeodiversity best. This bivariate model is a better fit than separate univariate models, and illustrates that observed palaeodiversity is a composite pattern, representing a biological signal overprinted by variation in sampling effort. Multi-variate models and other approaches that consider sampling as an essential component of palaeodiversity are central to gaining a more complete understanding of deep time vertebrate diversification. PMID:21697163
NASA Astrophysics Data System (ADS)
Ghosh, S.; Reichenbach, P.; Rossi, M.; Guzzetti, F.; van Westen, C.; Carranza, E. J. M.
2009-04-01
The results of multivariate landslide statistical susceptibility models are highly sensitive to the type of statistical and spatial distribution of the mass movement used as grouping variable, and to the type of geofactors used as explanatory variables. Different classification of landslide data set could result in different model performance and validation fit. Exploiting a discriminant analysis (DA) and a logistic regression (LR) models, we prepared different landslide susceptibility zonation for a study area around Kurseong town in the Darjeeling Himalaya region, Eastern India. To prepare the models, we used as training data set, 342 shallow translational rock slides and 168 shallow translational debris slides, which occurred between 1968 and 2003. To validate the models we used a different set of landslide that occurred between 2004 and 2007. 62 relevant factors including morphometric and geo-environmental parameters were used as explanatory variables. We present and discuss the performance and the validation results of the landslide susceptibility zonation prepared with the two different statistical multivariate models using as grouping variables - the rock slides data set, the debris slides data set and the two type of landslides data set together. The discriminate analysis performs better than the logistic regression and this is probably due to the: a) lack of coherence in the selected training data set and the corresponding explanatory variables; b) landslide type classification problems; c) frequency distribution of landslide/no-landslide mapping units.
Haaland, David M.
1999-07-14
The analysis precision of any multivariate calibration method will be severely degraded if unmodeled sources of spectral variation are present in the unknown sample spectra. This paper describes a synthetic method for correcting for the errors generated by the presence of unmodeled components or other sources of unmodeled spectral variation. If the spectral shape of the unmodeled component can be obtained and mathematically added to the original calibration spectra, then a new synthetic multivariate calibration model can be generated to accommodate the presence of the unmodeled source of spectral variation. This new method is demonstrated for the presence of unmodeled temperature variations in the unknown sample spectra of dilute aqueous solutions of urea, creatinine, and NaCl. When constant-temperature PLS models are applied to spectra of samples of variable temperature, the standard errors of prediction (SEP) are approximately an order of magnitude higher than that of the original cross-validated SEPs of the constant-temperature partial least squares models. Synthetic models using the classical least squares estimates of temperature from pure water or variable-temperature mixture sample spectra reduce the errors significantly for the variable temperature samples. Spectrometer drift adds additional error to the analyte determinations, but a method is demonstrated that can minimize the effect of drift on prediction errors through the measurement of the spectra of a small subset of samples during both calibration and prediction. In addition, sample temperature can be predicted with high precision with this new synthetic model without the need to recalibrate using actual variable-temperature sample data. The synthetic methods eliminate the need for expensive generation of new calibration samples and collection of their spectra. The methods are quite general and can be applied using any known source of spectral variation and can be used with any multivariate
Fuzzy modeling with multivariate membership functions: gray-box identification and control design.
Abonyi, J; Babuska, R; Szeifert, F
2001-01-01
A novel framework for fuzzy modeling and model-based control design is described. The fuzzy model is of the Takagi-Sugeno (TS) type with constant consequents. It uses multivariate antecedent membership functions obtained by Delaunay triangulation of their characteristic points. The number and position of these points are determined by an iterative insertion algorithm. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. Finally, methods for control design through linearization and inversion of this model are developed. The proposed techniques are demonstrated by means of two benchmark examples: identification of the well-known Box-Jenkins gas furnace and inverse model-based control of a pH process. The obtained results are compared with results from the literature. PMID:18244840
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van't
2012-03-15
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
NASA Technical Reports Server (NTRS)
MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.
2005-01-01
Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.
Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.
2016-01-01
We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times.
On the Bayesian Treed Multivariate Gaussian Process with Linear Model of Coregionalization
Konomi, Bledar A.; Karagiannis, Georgios; Lin, Guang
2015-02-01
The Bayesian treed Gaussian process (BTGP) has gained popularity in recent years because it provides a straightforward mechanism for modeling non-stationary data and can alleviate computational demands by fitting models to less data. The extension of BTGP to the multivariate setting requires us to model the cross-covariance and to propose efficient algorithms that can deal with trans-dimensional MCMC moves. In this paper we extend the cross-covariance of the Bayesian treed multivariate Gaussian process (BTMGP) to that of linear model of Coregionalization (LMC) cross-covariances. Different strategies have been developed to improve the MCMC mixing and invert smaller matrices in the Bayesian inference. Moreover, we compare the proposed BTMGP with existing multiple BTGP and BTMGP in test cases and multiphase flow computer experiment in a full scale regenerator of a carbon capture unit. The use of the BTMGP with LMC cross-covariance helped to predict the computer experiments relatively better than existing competitors. The proposed model has a wide variety of applications, such as computer experiments and environmental data. In the case of computer experiments we also develop an adaptive sampling strategy for the BTMGP with LMC cross-covariance function.
Intharathirat, Rotchana; Abdul Salam, P; Kumar, S; Untong, Akarapong
2015-05-01
In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435-44,994 tonnes per day in 2013 to 55,177-56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period. PMID:25704925
An interface model for dosage adjustment connects hematotoxicity to pharmacokinetics.
Meille, C; Iliadis, A; Barbolosi, D; Frances, N; Freyer, G
2008-12-01
When modeling is required to describe pharmacokinetics and pharmacodynamics simultaneously, it is difficult to link time-concentration profiles and drug effects. When patients are under chemotherapy, despite the huge amount of blood monitoring numerations, there is a lack of exposure variables to describe hematotoxicity linked with the circulating drug blood levels. We developed an interface model that transforms circulating pharmacokinetic concentrations to adequate exposures, destined to be inputs of the pharmacodynamic process. The model is materialized by a nonlinear differential equation involving three parameters. The relevance of the interface model for dosage adjustment is illustrated by numerous simulations. In particular, the interface model is incorporated into a complex system including pharmacokinetics and neutropenia induced by docetaxel and by cisplatin. Emphasis is placed on the sensitivity of neutropenia with respect to the variations of the drug amount. This complex system including pharmacokinetic, interface, and pharmacodynamic hematotoxicity models is an interesting tool for analysis of hematotoxicity induced by anticancer agents. The model could be a new basis for further improvements aimed at incorporating new experimental features. PMID:19107581
NASA Astrophysics Data System (ADS)
Gautam, N.; Rasmussen, P. F.
2005-05-01
Stochastic weather generators are frequently used in climate change studies to simulate input to hydrologic models. In this presentation, we focus on the particular problem of simulating daily precipitation at multiple stations in a region for which records are available. Daily precipitation is a highly intermittent process, highly variable in space, and typically has a highly skewed distribution. A stochastic precipitation model should ideally preserve the regional pattern of intermittence, the autocorrelation, the cross-correlation, and the marginal distributions of observed precipitation. For this purpose, we employed a multivariate autoregressive model. Below zero-values were considered days with no rain. To preserve the marginal distributions of observed precipitation at different stations some prior transformation of data was required. The presentation will describe the experience gained from applying the model to precipitation records in Canada. Focus will be on analytical model properties, methods of parameter estimation, and the preservation of observed statistics in the application.
Gilbert, P. B.
2014-01-01
Summary In randomized placebo-controlled preventive HIV vaccine efficacy trials, an objective is to evaluate the relationship between vaccine efficacy to prevent infection and genetic distances of the exposing HIV strains to the multiple HIV sequences included in the vaccine construct, where the set of genetic distances is considered as the continuous multivariate ‘mark’ observed in infected subjects only. This research develops a multivariate mark-specific hazard ratio model in the competing risks failure time analysis framework for the assessment of mark-specific vaccine efficacy. It allows improved efficiency of estimation by employing the semiparametric method of maximum profile likelihood estimation in the vaccine-to-placebo mark density ratio model. The model also enables the use of a more efficient estimation method for the overall log hazard ratio in the Cox model. Additionally, we propose testing procedures to evaluate two relevant hypotheses concerning mark-specific vaccine efficacy. The asymptotic properties and finite-sample performance of the inferential procedures are investigated. Finally, we apply the proposed methods to data collected in the Thai RV144 HIV vaccine efficacy trial. PMID:23421613
A pairwise likelihood-based approach for changepoint detection in multivariate time series models
Ma, Ting Fung; Yau, Chun Yip
2016-01-01
This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method. PMID:27279666
On the interpretation of weight vectors of linear models in multivariate neuroimaging.
Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix
2014-02-15
The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward
Cella, Laura; Liuzzi, Raffaele; Conson, Manuel; D’Avino, Vittoria; Salvatore, Marco; Pacelli, Roberto
2013-10-01
Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced asymptomatic heart valvular defects (RVD). Methods and Materials: Fifty-six patients treated with sequential chemoradiation therapy for Hodgkin lymphoma (HL) were retrospectively reviewed for RVD events. Clinical information along with whole heart, cardiac chambers, and lung dose distribution parameters was collected, and the correlations to RVD were analyzed by means of Spearman's rank correlation coefficient (Rs). For the selection of the model order and parameters for NTCP modeling, a multivariate logistic regression method using resampling techniques (bootstrapping) was applied. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results: When we analyzed the whole heart, a 3-variable NTCP model including the maximum dose, whole heart volume, and lung volume was shown to be the optimal predictive model for RVD (Rs = 0.573, P<.001, AUC = 0.83). When we analyzed the cardiac chambers individually, for the left atrium and for the left ventricle, an NTCP model based on 3 variables including the percentage volume exceeding 30 Gy (V30), cardiac chamber volume, and lung volume was selected as the most predictive model (Rs = 0.539, P<.001, AUC = 0.83; and Rs = 0.557, P<.001, AUC = 0.82, respectively). The NTCP values increase as heart maximum dose or cardiac chambers V30 increase. They also increase with larger volumes of the heart or cardiac chambers and decrease when lung volume is larger. Conclusions: We propose logistic NTCP models for RVD considering not only heart irradiation dose but also the combined effects of lung and heart volumes. Our study establishes the statistical evidence of the indirect effect of lung size on radio-induced heart toxicity.
Lee, S. H.; van der Werf, J. H. J.
2016-01-01
Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2. Contact: hong.lee@une.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26755623
Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks
NASA Astrophysics Data System (ADS)
Kreibich, Heidi; Schröter, Kai
2015-04-01
Decisions on flood risk management and adaptation are increasingly based on risk analyses. Such analyses are associated with considerable uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention recently, they are hardly applied in flood damage assessments. Most of the damage models usually applied in standard practice have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. This presentation will show approaches for probabilistic, multi-variate flood damage modelling on the micro- and meso-scale and discuss their potential and limitations. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B. (2014): How useful are complex flood damage models? - Water Resources Research, 50, 4, p. 3378-3395.
Multivariate model of female black bear habitat use for a Geographic Information System
Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.
1993-01-01
Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.
Prediction of lip response to orthodontic treatment using a multivariable regression model
Shirvani, Amin; Sadeghian, Saeid; Abbasi, Safieh
2016-01-01
Background: This was a retrospective cephalometric study to develop a more precise estimation of soft tissue changes related to underlying tooth movment than simple relatioship betweenhard and soft tissues. Materials and Methods: The lateral cephalograms of 61 adult patients undergoing orthodontic treatment (31 = premolar extraction, 31 = nonextraction) were obtained, scanned and digitized before and immediately after the end of treatment. Hard and soft tissues, angular and linear measures were calculated by Viewbox 4.0 software. The changes of the values were analyzed using paired t-test. The accuracy of predictions of soft tissue changes were compared with two methods: (1) Use of ratios of the means of soft tissue to hard tissue changes (Viewbox 4.0 Software), (2) use of stepwise multivariable regression analysis to create prediction equations for soft tissue changes at superior labial sulcus, labrale superius, stomion superius, inferior labial sulcus, labrale inferius, stomion inferius (all on a horizontal plane). Results: Stepwise multiple regressions to predict lip movements showed strong relations for the upper lip (adjusted R2 = 0.92) and the lower lip (adjusted R2 = 0.91) in the extraction group. Regression analysis showed slightly weaker relations in the nonextraction group. Conclusion: Within the limitation of this study, multiple regression technique was slightly more accurate than the ratio of mean prediction (Viewbox4.0 software) and appears to be useful in the prediction of soft tissue changes. As the variability of the predicted individual outcome seems to be relatively high, caution should be taken in predicting hard and soft tissue positional changes. PMID:26962314
Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms
Anderson, John R.
2011-01-01
Multivariate pattern analysis can be combined with hidden Markov model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first “mind reading” application involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second “model discovery” application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. PMID:21820455
Valle, Denis; Baiser, Benjamin; Woodall, Christopher W; Chazdon, Robin
2014-01-01
We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances. PMID:25328064
Ecological prediction with nonlinear multivariate time-frequency functional data models
Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.
2013-01-01
Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.
Developing a multivariate electronic medical record integration model for primary health care.
Lau, Francis; Price, Morgan; Lesperance, Mary
2013-01-01
This paper describes the development of a multivariate electronic medical record (EMR) integration model for the primary health care setting. Our working hypothesis is that an integrated EMR is associated with high quality primary health care. Our assumption is that EMR integration should be viewed as a form of complex intervention with multiple interacting components that can impact the quality of care. Depending on how well the EMR is integrated in the practice setting, one can expect a corresponding change in the quality of care as measured through a set of primary health care quality indicators. To test the face validity of this model, a Delphi study is being planned where health care providers and information technology professionals involved with EMR adoption are polled for their feedback. This model has the potential to quantify and explain the factors that influence successful EMR integration to improve primary health care. PMID:23388317
Valle, Denis; Baiser, Benjamin; Woodall, Christopher W; Chazdon, Robin
2014-12-01
We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances. PMID:25328064
Wu, Chuanli; Gao, Yuexia; Hua, Tianqi; Xu, Chenwu
2016-01-01
Background It is challenging to deal with mixture models when missing values occur in clustering datasets. Methods and Results We propose a dynamic clustering algorithm based on a multivariate Gaussian mixture model that efficiently imputes missing values to generate a “pseudo-complete” dataset. Parameters from different clusters and missing values are estimated according to the maximum likelihood implemented with an expectation-maximization algorithm, and multivariate individuals are clustered with Bayesian posterior probability. A simulation showed that our proposed method has a fast convergence speed and it accurately estimates missing values. Our proposed algorithm was further validated with Fisher’s Iris dataset, the Yeast Cell-cycle Gene-expression dataset, and the CIFAR-10 images dataset. The results indicate that our algorithm offers highly accurate clustering, comparable to that using a complete dataset without missing values. Furthermore, our algorithm resulted in a lower misjudgment rate than both clustering algorithms with missing data deleted and with missing-value imputation by mean replacement. Conclusion We demonstrate that our missing-value imputation clustering algorithm is feasible and superior to both of these other clustering algorithms in certain situations. PMID:27552203
NASA Astrophysics Data System (ADS)
Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza
2014-10-01
The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of
Freitas, Mirlaine R; Barigye, Stephen J; Daré, Joyce K; Freitas, Matheus P
2016-06-01
The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r(2) = 0.79, q(2) = 0.70 and rtest(2) = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 - 0.00233 × X9774 - 0.00070 × X813 - 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides. PMID:26971171
Disaster Hits Home: A Model of Displaced Family Adjustment after Hurricane Katrina
ERIC Educational Resources Information Center
Peek, Lori; Morrissey, Bridget; Marlatt, Holly
2011-01-01
The authors explored individual and family adjustment processes among parents (n = 30) and children (n = 55) who were displaced to Colorado after Hurricane Katrina. Drawing on in-depth interviews with 23 families, this article offers an inductive model of displaced family adjustment. Four stages of family adjustment are presented in the model: (a)…
ERIC Educational Resources Information Center
Tay, Louis; Drasgow, Fritz
2012-01-01
Two Monte Carlo simulation studies investigated the effectiveness of the mean adjusted X[superscript 2]/df statistic proposed by Drasgow and colleagues and, because of problems with the method, a new approach for assessing the goodness of fit of an item response theory model was developed. It has been previously recommended that mean adjusted…
El-Basyouny, Karim; Barua, Sudip; Islam, Md Tazul
2014-12-01
Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition. PMID:25190632
Rahman, Ziyaur; Mohammad, Adil; Siddiqui, Akhtar; Khan, Mansoor A
2015-12-01
The focus of the present investigation was to explore the use of solid-state nuclear magnetic resonance ((13)C ssNMR) and X-ray powder diffraction (XRPD) for quantification of nimodipine polymorphs (form I and form II) crystallized in a cosolvent formulation. The cosolvent formulation composed of polyethylene glycol 400, glycerin, water, and 2.5% drug, and was stored at 5°C for the drug crystallization. The (13)C ssNMR and XRPD data of the sample matrices containing varying percentages of nimodipine form I and form II were collected. Univariate and multivariate models were developed using the data. Least square method was used for the univariate model generation. Partial least square and principle component regressions were used for the multivariate models development. The univariate models of the (13)C ssNMR were better than the XRPD as indicated by statistical parameters such as correlation coefficient, R (2), root mean square error, and standard error. On the other hand, the XRPD multivariate models were better than the (13)C ssNMR as indicated by precision and accuracy parameters. Similar values were predicted by the univariate and multivariate models for independent samples. In conclusion, the univariate and multivariate models of (13)C ssNMR and XRPD can be used to quantitate nimodipine polymorphs. PMID:25956485
Wolfrum, E. J.; Sluiter, A. D.
2009-01-01
We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert M.
2013-01-01
A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.
Warnell, I; Chincholkar, M; Eccles, M
2015-01-01
Predicting risk of perioperative mortality after oesophagectomy for cancer may assist patients to make treatment choices and allow balanced comparison of providers. The aim of this systematic review of multivariate prediction models is to report their performance in new patients, and compare study methods against current recommendations. We used PRISMA guidelines and searched Medline, Embase, and standard texts from 1990 to 2012. Inclusion criteria were English language articles reporting development and validation of prediction models of perioperative mortality after open oesophagectomy. Two reviewers screened articles and extracted data for methods, results, and potential biases. We identified 11 development, 10 external validation, and two clinical impact studies. Overestimation of predicted mortality was common (5-200% error), discrimination was poor to moderate (area under receiver operator curves ranged from 0.58 to 0.78), and reporting of potential bias was poor. There were potentially important case mix differences between modelling and validation samples, and sample sizes were considerably smaller than is currently recommended. Steyerberg and colleagues' model used the most 'transportable' predictors and was validated in the largest sample. Most models have not been adequately validated and reported performance has been unsatisfactory. There is a need to clarify definition, effect size, and selection of currently available candidate predictors for inclusion in prediction models, and to identify new ones strongly associated with outcome. Adoption of prediction models into practice requires further development and validation in well-designed large sample prospective studies. PMID:25231768
NASA Technical Reports Server (NTRS)
Belcastro, Christine M.
1998-01-01
Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.
NASA Astrophysics Data System (ADS)
Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.
2014-04-01
An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p < 0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.
Hynds, Paul; Misstear, Bruce D; Gill, Laurence W; Murphy, Heather M
2014-04-01
An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p=0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p<0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts. PMID:24583518
Multi-variate spatial explicit constraining of a large scale hydrological model
NASA Astrophysics Data System (ADS)
Rakovec, Oldrich; Kumar, Rohini; Samaniego, Luis
2016-04-01
Increased availability and quality of near real-time data should target at better understanding of predictive skills of distributed hydrological models. Nevertheless, predictions of regional scale water fluxes and states remains of great challenge to the scientific community. Large scale hydrological models are used for prediction of soil moisture, evapotranspiration and other related water states and fluxes. They are usually properly constrained against river discharge, which is an integral variable. Rakovec et al (2016) recently demonstrated that constraining model parameters against river discharge is necessary, but not a sufficient condition. Therefore, we further aim at scrutinizing appropriate incorporation of readily available information into a hydrological model that may help to improve the realism of hydrological processes. It is important to analyze how complementary datasets besides observed streamflow and related signature measures can improve model skill of internal model variables during parameter estimation. Among those products suitable for further scrutiny are for example the GRACE satellite observations. Recent developments of using this dataset in a multivariate fashion to complement traditionally used streamflow data within the distributed model mHM (www.ufz.de/mhm) are presented. Study domain consists of 80 European basins, which cover a wide range of distinct physiographic and hydrologic regimes. First-order data quality check ensures that heavily human influenced basins are eliminated. For river discharge simulations we show that model performance of discharge remains unchanged when complemented by information from the GRACE product (both, daily and monthly time steps). Moreover, the GRACE complementary data lead to consistent and statistically significant improvements in evapotranspiration estimates, which are evaluated using an independent gridded FLUXNET product. We also show that the choice of the objective function used to estimate
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
Application of multivariate storage model to quantify trends in seasonally frozen soil
NASA Astrophysics Data System (ADS)
Woody, Jonathan; Wang, Yan; Dyer, Jamie
2016-06-01
This article presents a study of the ground thermal regime recorded at 11 stations in the North Dakota Agricultural Network. Particular focus is placed on detecting trends in the annual ground freeze process portion of the ground thermal regime's daily temperature signature. A multivariate storage model from queuing theory is fit to a quantity of estimated daily depths of frozen soil. Statistical inference on a trend parameter is obtained by minimizing a weighted sum of squares of a sequence of daily one-step-ahead predictions. Standard errors for the trend estimates are presented. It is shown that the daily quantity of frozen ground experienced at these 11 sites exhibited a negative trend over the observation period.
Web-based tools for modelling and analysis of multivariate data: California ozone pollution activity
Dinov, Ivo D.; Christou, Nicolas
2014-01-01
This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting and statistical inference on these data are presented. All components of this case study (data, tools, activity) are freely available online at: http://wiki.stat.ucla.edu/socr/index.php/SOCR_MotionCharts_CAOzoneData. Several types of exploratory (motion charts, box-and-whisker plots, spider charts) and quantitative (inference, regression, analysis of variance (ANOVA)) data analyses tools are demonstrated. Two specific human health related questions (temporal and geographic effects of ozone pollution) are discussed as motivational challenges. PMID:24465054
Giacomo, Della Riccia; Stefania, Del Zotto
2013-12-15
Fumonisins are mycotoxins produced by Fusarium species that commonly live in maize. Whereas fungi damage plants, fumonisins cause disease both to cattle breedings and human beings. Law limits set fumonisins tolerable daily intake with respect to several maize based feed and food. Chemical techniques assure the most reliable and accurate measurements, but they are expensive and time consuming. A method based on Near Infrared spectroscopy and multivariate statistical regression is described as a simpler, cheaper and faster alternative. We apply Partial Least Squares with full cross validation. Two models are described, having high correlation of calibration (0.995, 0.998) and of validation (0.908, 0.909), respectively. Description of observed phenomenon is accurate and overfitting is avoided. Screening of contaminated maize with respect to European legal limit of 4 mg kg(-1) should be assured. PMID:23993617
Mac Nally, Ralph; Thomson, James R.; Kimmerer, Wim J.; Feyrer, Frederick; Newman, Ken B.; Sih, Andy; Bennett, William A.; Brown, Larry; Fleishman, Erica; Culberson, Steven D.; Castillo, Gonzalo
2010-01-01
Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2 isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey). Our results were relatively robust with respect to the form of stock–recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state–space models that describe more fully the life-history dynamics of the declining species.
NASA Astrophysics Data System (ADS)
Pajor, A.
2006-11-01
In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility (SV) models to describe the conditional correlations between stock index returns. We consider four tri-variate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example we study tri-variate volatility models for the daily log returns on the WIG, S&P 500, and FTSE 100 indexes. In order to formally compare the relative explanatory power of SV specifications we use the Bayesian principles of comparing statistic models. Our results are based on the Bayes factors and implemented through Markov Chain Monte Carlo techniques. The results indicate that the most adequate specifications are those that allow for time-varying conditional correlations and that have as many latent processes as there are conditional variances and covariances. The empirical results clearly show that the data strongly reject the assumption of constant conditional correlations.
Multivariate probabilistic projections using imperfect climate models part I: outline of methodology
NASA Astrophysics Data System (ADS)
Sexton, David M. H.; Murphy, James M.; Collins, Mat; Webb, Mark J.
2012-06-01
We demonstrate a method for making probabilistic projections of climate change at global and regional scales, using examples consisting of the equilibrium response to doubled CO2 concentrations of global annual mean temperature and regional climate changes in summer and winter temperature and precipitation over Northern Europe and England-Wales. This method combines information from a perturbed physics ensemble, a set of international climate models, and observations. Our approach is based on a multivariate Bayesian framework which enables the prediction of a joint probability distribution for several variables constrained by more than one observational metric. This is important if different sets of impacts scientists are to use these probabilistic projections to make coherent forecasts for the impacts of climate change, by inputting several uncertain climate variables into their impacts models. Unlike a single metric, multiple metrics reduce the risk of rewarding a model variant which scores well due to a fortuitous compensation of errors rather than because it is providing a realistic simulation of the observed quantity. We provide some physical interpretation of how the key metrics constrain our probabilistic projections. The method also has a quantity, called discrepancy, which represents the degree of imperfection in the climate model i.e. it measures the extent to which missing processes, choices of parameterisation schemes and approximations in the climate model affect our ability to use outputs from climate models to make inferences about the real system. Other studies have, sometimes without realising it, treated the climate model as if it had no model error. We show that omission of discrepancy increases the risk of making over-confident predictions. Discrepancy also provides a transparent way of incorporating improvements in subsequent generations of climate models into probabilistic assessments. The set of international climate models is used to derive
ERIC Educational Resources Information Center
Timmerman, Marieke E.; Kiers, Henk A. L.
2003-01-01
Discusses a class of four simultaneous component models for the explanatory analysis of multivariate time series collected from more than one subject simultaneously. Shows how the models can be ordered hierarchically and illustrates their use through an empirical example. (SLD)
Bell, Susan P; Schnelle, John; Nwosu, Samuel K; Schildcrout, Jonathan; Goggins, Kathryn; Cawthon, Courtney; Mixon, Amanda S; Vasilevskis, Eduard E; Kripalani, Sunil
2015-01-01
Objectives To identify vulnerable cardiovascular patients in the hospital using a self-reported function-based screening tool. Participants Prospective observational cohort study of 445 individuals aged ≥65 years admitted to a university medical centre hospital within the USA with acute coronary syndrome and/or decompensated heart failure. Methods Participants completed an inperson interview during hospitalisation, which included vulnerable functional status using the Vulnerable Elders Survey (VES-13), sociodemographic, healthcare utilisation practices and clinical patient-specific measures. A multivariable proportional odds logistic regression model examined associations between VES-13 and prior healthcare utilisation, as well as other coincident medical and psychosocial risk factors for poor outcomes in cardiovascular disease. Results Vulnerability was highly prevalent (54%) and associated with a higher number of clinic visits, emergency room visits and hospitalisations (all p<0.001). A multivariable analysis demonstrating a 1-point increase in VES-13 (vulnerability) was independently associated with being female (OR 1.55, p=0.030), diagnosis of heart failure (OR 3.11, p<0.001), prior hospitalisations (OR 1.30, p<0.001), low social support (OR 1.42, p=0.007) and depression (p<0.001). A lower VES-13 score (lower vulnerability) was associated with increased health literacy (OR 0.70, p=0.002). Conclusions Vulnerability to functional decline is highly prevalent in hospitalised older cardiovascular patients and was associated with patient risk factors for adverse outcomes and an increased use of healthcare services. PMID:26316650
NASA Technical Reports Server (NTRS)
Crutcher, H. L.; Falls, L. W.
1976-01-01
Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.
Technology Transfer Automated Retrieval System (TEKTRAN)
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...
Michalareas, George; Schoffelen, Jan-Mathijs; Paterson, Gavin; Gross, Joachim
2013-01-01
Abstract In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22328419
Nieto, P J García; Antón, J C Álvarez; Vilán, J A Vilán; García-Gonzalo, E
2015-05-01
The aim of this research work is to build a regression model of air quality by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (northern Spain) at a local scale. To accomplish the objective of this study, the experimental data set made up of nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and dust (PM10) was collected over 3 years (2006-2008). The US National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the MARS technique, conclusions of this research work are exposed. PMID:25414030
Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model
Jiang, J; Zhang, Q; Ma, L; Li, J; Wang, Z; Liu, J-F
2015-01-01
Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding. PMID:25873147
Hackstadt, Amber J.; Peng, Roger D.
2014-01-01
Summary Time series studies have suggested that air pollution can negatively impact health. These studies have typically focused on the total mass of fine particulate matter air pollution or the individual chemical constituents that contribute to it, and not source-specific contributions to air pollution. Source-specific contribution estimates are useful from a regulatory standpoint by allowing regulators to focus limited resources on reducing emissions from sources that are major contributors to air pollution and are also desired when estimating source-specific health effects. However, researchers often lack direct observations of the emissions at the source level. We propose a Bayesian multivariate receptor model to infer information about source contributions from ambient air pollution measurements. The proposed model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of air pollution. The proposed model is used to perform source apportionment analyses for two distinct locations in the United States (Boston, Massachusetts and Phoenix, Arizona). Our results mirror previous source apportionment analyses that did not utilize the information from national databases and provide additional information about uncertainty that is relevant to the estimation of health effects. PMID:25309119
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models
Finley, Andrew O.; Banerjee, Sudipto; Carlin, Bradley P.
2010-01-01
Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude–longitude, Easting–Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example. PMID:21494410
Impact of Fractionation and Dose in a Multivariate Model for Radiation-Induced Chest Wall Pain
Din, Shaun U.; Williams, Eric L.; Jackson, Andrew; Rosenzweig, Kenneth E.; Wu, Abraham J.; Foster, Amanda; Yorke, Ellen D.; Rimner, Andreas
2015-10-01
Purpose: To determine the role of patient/tumor characteristics, radiation dose, and fractionation using the linear-quadratic (LQ) model to predict stereotactic body radiation therapy–induced grade ≥2 chest wall pain (CWP2) in a larger series and develop clinically useful constraints for patients treated with different fraction numbers. Methods and Materials: A total of 316 lung tumors in 295 patients were treated with stereotactic body radiation therapy in 3 to 5 fractions to 39 to 60 Gy. Absolute dose–absolute volume chest wall (CW) histograms were acquired. The raw dose-volume histograms (α/β = ∞ Gy) were converted via the LQ model to equivalent doses in 2-Gy fractions (normalized total dose, NTD) with α/β from 0 to 25 Gy in 0.1-Gy steps. The Cox proportional hazards (CPH) model was used in univariate and multivariate models to identify and assess CWP2 exposed to a given physical and NTD. Results: The median follow-up was 15.4 months, and the median time to development of CWP2 was 7.4 months. On a univariate CPH model, prescription dose, prescription dose per fraction, number of fractions, D83cc, distance of tumor to CW, and body mass index were all statistically significant for the development of CWP2. Linear-quadratic correction improved the CPH model significance over the physical dose. The best-fit α/β was 2.1 Gy, and the physical dose (α/β = ∞ Gy) was outside the upper 95% confidence limit. With α/β = 2.1 Gy, V{sub NTD99Gy} was most significant, with median V{sub NTD99Gy} = 31.5 cm{sup 3} (hazard ratio 3.87, P<.001). Conclusion: There were several predictive factors for the development of CWP2. The LQ-adjusted doses using the best-fit α/β = 2.1 Gy is a better predictor of CWP2 than the physical dose. To aid dosimetrists, we have calculated the physical dose equivalent corresponding to V{sub NTD99Gy} = 31.5 cm{sup 3} for the 3- to 5-fraction groups.
Application of Multivariate Modeling for Radiation Injury Assessment: A Proof of Concept
Bolduc, David L.; Villa, Vilmar; Sandgren, David J.; Ledney, G. David; Blakely, William F.; Bünger, Rolf
2014-01-01
Multivariate radiation injury estimation algorithms were formulated for estimating severe hematopoietic acute radiation syndrome (H-ARS) injury (i.e., response category three or RC3) in a rhesus monkey total-body irradiation (TBI) model. Classical CBC and serum chemistry blood parameters were examined prior to irradiation (d 0) and on d 7, 10, 14, 21, and 25 after irradiation involving 24 nonhuman primates (NHP) (Macaca mulatta) given 6.5-Gy 60Co Υ-rays (0.4 Gy min−1) TBI. A correlation matrix was formulated with the RC3 severity level designated as the “dependent variable” and independent variables down selected based on their radioresponsiveness and relatively low multicollinearity using stepwise-linear regression analyses. Final candidate independent variables included CBC counts (absolute number of neutrophils, lymphocytes, and platelets) in formulating the “CBC” RC3 estimation algorithm. Additionally, the formulation of a diagnostic CBC and serum chemistry “CBC-SCHEM” RC3 algorithm expanded upon the CBC algorithm model with the addition of hematocrit and the serum enzyme levels of aspartate aminotransferase, creatine kinase, and lactate dehydrogenase. Both algorithms estimated RC3 with over 90% predictive power. Only the CBC-SCHEM RC3 algorithm, however, met the critical three assumptions of linear least squares demonstrating slightly greater precision for radiation injury estimation, but with significantly decreased prediction error indicating increased statistical robustness. PMID:25165485
Wang, Xiuquan; Huang, Guohe; Zhao, Shan; Guo, Junhong
2015-09-01
This paper presents an open-source software package, rSCA, which is developed based upon a stepwise cluster analysis method and serves as a statistical tool for modeling the relationships between multiple dependent and independent variables. The rSCA package is efficient in dealing with both continuous and discrete variables, as well as nonlinear relationships between the variables. It divides the sample sets of dependent variables into different subsets (or subclusters) through a series of cutting and merging operations based upon the theory of multivariate analysis of variance (MANOVA). The modeling results are given by a cluster tree, which includes both intermediate and leaf subclusters as well as the flow paths from the root of the tree to each leaf subcluster specified by a series of cutting and merging actions. The rSCA package is a handy and easy-to-use tool and is freely available at http://cran.r-project.org/package=rSCA . By applying the developed package to air quality management in an urban environment, we demonstrate its effectiveness in dealing with the complicated relationships among multiple variables in real-world problems. PMID:25966889
NASA Astrophysics Data System (ADS)
Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; Spence, Harlan E.
2015-12-01
The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. To provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraft were used as the predictors. Model explanatory parameters were selected from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L ≧ 4.8 and L ≧ 5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≦ L ≦ 6, while for the GEO flux prediction, the KP index is better than Dst. A test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.
NASA Astrophysics Data System (ADS)
Ayoko, Godwin A.; Singh, Kirpal; Balerea, Steven; Kokot, Serge
2007-03-01
SummaryPhysico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia-nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.
Multivariate Multi-data Assimilation System in Regional Model with High Resolution
NASA Astrophysics Data System (ADS)
Benkiran, M.; Chanut, J.; Giraud St Albin, S.; Drillet, Y.
2010-12-01
Mercator Ocean has developed a regional North East Shelf forecasting system over the North East Atlantic, taking advantage of the recent developments in NEMO (1/12°). This regional forecasting system uses boundary conditions from the operational real-time Mercator Ocean North Atlantic high resolution system (1/12°). The assimilation component of the Mercator Ocean system, is based on a reduced-order Kalman filter (the SEEK or Singular Extended Evolutive Kalman filter). The error statistics are represented in a sub-space spanned by a small number of dominant 3D error directions. The data assimilation system allows to constrain the model in a multivariate way with Sea Surface Temperature (RTG-SST), together with all available satellite Sea Level Anomalies, and with in situ observations from the CORIOLIS database, including ARGO floats temperature and salinity measurements.At last, we used PALM coupler which provides a general structure for a modular implementation of a data assimilation system, and makes easier the changes in the analysis algorithm. We will confront the results obtained with the regional forecast system (1/12°) with IAU (Incremental Analysis Updates) to the ones obtained with Mercator Ocean North Atlantic high resolution system (1/12°).
Multivariate Multi-Data Assimilation System in Regional Model With High Resolution
NASA Astrophysics Data System (ADS)
Benkiran, M.; Chanut, J.; Greiner, E.; Giraud St Albin, S.; Drillet, Y.
2008-12-01
Mercator Ocean has developed a regional North East Shelf forecasting system over the North East Atlantic, taking advantage of the recent developments in NEMO (1/12). This regional forecasting system uses boundary conditions from the operational real-time Mercator Ocean North Atlantic high resolution system (1/12). The assimilation component of the Mercator Ocean system, is based on a reduced-order Kalman filter (the SEEK or Singular Extended Evolutive Kalman filter). The error statistics are represented in a sub-space spanned by a small number of dominant 3D error directions. The data assimilation system allows to constrain the model in a multivariate way with Sea Surface Temperature (RTG-SST), together with all available satellite Sea Level Anomalies, and with in situ observations from the CORIOLIS database, including ARGO floats temperature and salinity measurements.At last, we used PALM coupler which provides a general structure for a modular implementation of a data assimilation system, and makes easier the changes in the analysis algorithm. We will confront the results obtained with the regional forecast system (1/12) with IAU (Incremental Analysis Updates) to the ones obtained with Mercator Ocean North Atlantic high resolution system (1/12).
Multivariate Multi-data Assimilation System in Regional Model with High Resolution
NASA Astrophysics Data System (ADS)
Benkiran, M.; Bourdalle-Badie, R.; Drillet, Y.; Greiner, E.; Chanut, J.
2009-12-01
Mercator Ocean has developed a regional North East Shelf forecasting system over the North East Atlantic, taking advantage of the recent developments in NEMO (1/12°). This regional forecasting system uses boundary conditions from the operational real-time Mercator Ocean North Atlantic high resolution system (1/12°). The assimilation component of the Mercator Ocean system, is based on a reduced-order Kalman filter (the SEEK or Singular Extended Evolutive Kalman filter). The error statistics are represented in a sub-space spanned by a small number of dominant 3D error directions. The data assimilation system allows to constrain the model in a multivariate way with Sea Surface Temperature (RTG-SST), together with all available satellite Sea Level Anomalies, and with in situ observations from the CORIOLIS database, including ARGO floats temperature and salinity measurements.At last, we used PALM coupler which provides a general structure for a modular implementation of a data assimilation system, and makes easier the changes in the analysis algorithm. We will confront the results obtained with the regional forecast system (1/12°) with IAU (Incremental Analysis Updates) to the ones obtained with Mercator Ocean North Atlantic high resolution system (1/12°).
Multivariate time series modeling of short-term system scale irrigation demand
NASA Astrophysics Data System (ADS)
Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara
2015-12-01
Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as
Snell, Kym I.E.; Hua, Harry; Debray, Thomas P.A.; Ensor, Joie; Look, Maxime P.; Moons, Karel G.M.; Riley, Richard D.
2016-01-01
Objectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of “good” performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. Results In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of “good” performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of “good” performance. Conclusion Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. PMID:26142114
Multivariate dynamical systems models for estimating causal interactions in fMRI
Ryali, Srikanth; Supekar, Kaustubh; Chen, Tianwen; Menon, Vinod
2010-01-01
Analysis of dynamical interactions between distributed brain areas is of fundamental importance for understanding cognitive information processing. However, estimating dynamic causal interactions between brain regions using functional magnetic resonance imaging (fMRI) poses several unique challenges. For one, fMRI measures Blood Oxygenation Level Dependent (BOLD) signals, rather than the underlying latent neuronal activity. Second, regional variations in the hemodynamic response function (HRF) can significantly influence estimation of casual interactions between them. Third, causal interactions between brain regions can change with experimental context over time. To overcome these problems, we developed a novel state-space Multivariate Dynamical Systems (MDS) model to estimate intrinsic and experimentally-induced modulatory causal interactions between multiple brain regions. A probabilistic graphical framework is then used to estimate the parameters of MDS as applied to fMRI data. We show that MDS accurately takes into account regional variations in the HRF and estimates dynamic causal interactions at the level of latent signals. We develop and compare two estimation procedures using maximum likelihood estimation (MLE) and variational Bayesian (VB) approaches for inferring model parameters. Using extensive computer simulations, we demonstrate that, compared to Granger causal analysis (GCA), MDS exhibits superior performance for a wide range of signal to noise ratios (SNRs), sample length and network size. Our simulations also suggest that GCA fails to uncover causal interactions when there is a conflict between the direction of intrinsic and modulatory influences. Furthermore, we show that MDS estimation using VB methods is more robust and performs significantly better at low SNRs and shorter time series than MDS with MLE. Our study suggests that VB estimation of MDS provides a robust method for estimating and interpreting causal network interactions in fMRI data
Multivariate calibration modeling of liver oxygen saturation using near-infrared spectroscopy
NASA Astrophysics Data System (ADS)
Cingo, Ndumiso A.; Soller, Babs R.; Puyana, Juan C.
2000-05-01
The liver has been identified as an ideal site to spectroscopically monitor for changes in oxygen saturation during liver transplantation and shock because it is susceptible to reduced blood flow and oxygen transport. Near-IR spectroscopy, combined with multivariate calibration techniques, has been shown to be a viable technique for monitoring oxygen saturation changes in various organs in a minimally invasive manner. The liver has a dual system circulation. Blood enters the liver through the portal vein and hepatic artery, and leaves through the hepatic vein. Therefore, it is of utmost importance to determine how the liver NIR spectroscopic information correlates with the different regions of the hepatic lobule as the dual circulation flows from the presinusoidal space into the post sinusoidal region of the central vein. For NIR spectroscopic information to reliably represent the status of liver oxygenation, the NIR oxygen saturation should best correlate with the post-sinusoidal region. In a series of six pigs undergoing induced hemorrhagic chock, NIR spectra collected from the liver were used together with oxygen saturation reference data from the hepatic and portal veins, and an average of the two to build partial least-squares regression models. Results obtained from these models show that the hepatic vein and an average of the hepatic and portal veins provide information that is best correlate with NIR spectral information, while the portal vein reference measurement provides poorer correlation and accuracy. These results indicate that NIR determination of oxygen saturation in the liver can provide an assessment of liver oxygen utilization.
Multivariate analysis of groundwater quality and modeling impact of ground heat pump system
NASA Astrophysics Data System (ADS)
Thuyet, D. Q.; Saito, H.; Muto, H.; Saito, T.; Hamamoto, S.; Komatsu, T.
2013-12-01
The ground source heat pump system (GSHP) has recently become a popular building heating or cooling method, especially in North America, Western Europe, and Asia, due to advantages in reducing energy consumption and greenhouse gas emission. Because of the stability of the ground temperature, GSHP can effectively exchange the excess or demand heat of the building to the ground during the building air conditioning in the different seasons. The extensive use of GSHP can potentially disturb subsurface soil temperature and thus the groundwater quality. Therefore the assessment of subsurface thermal and environmental impacts from the GSHP operations is necessary to ensure sustainable use of GSHP system as well as the safe use of groundwater resources. This study aims to monitor groundwater quality during GSHP operation and to develop a numerical model to assess changes in subsurface soil temperature and in groundwater quality as affected by GSHP operation. A GSHP system was installed in Fuchu city, Tokyo, and consists of two closed double U-tubes (50-m length) buried vertically in the ground with a distance of 7.3 m from each U-tube located outside a building. An anti-freezing solution was circulated inside the U-tube for exchanging the heat between the building and the ground. The temperature at every 5-m depth and the groundwater quality including concentrations of 16 trace elements, pH, EC, Eh and DO in the shallow aquifer (32-m depth) and the deep aquifer (44-m depth) were monitored monthly since 2012, in an observation well installed 3 m from the center of the two U-tubes.Temporal variations of each element were evaluated using multivariate analysis and geostatistics. A three-dimensional heat exchange model was developed in COMSOL Multiphysics4.3b to simulate the heat exchange processes in subsurface soils. Results showed the difference in groundwater quality between the shallow and deep aquifers to be significant for some element concentrations and DO, but
Sediment fingerprinting experiments to test the sensitivity of multivariate mixing models
NASA Astrophysics Data System (ADS)
Gaspar, Leticia; Blake, Will; Smith, Hugh; Navas, Ana
2014-05-01
(e.g. P). In general, the best fits between actual and modeled proportions were found using a set of nine tracer properties (Sr, Rb, Fe, Ti, Ca, Al, P, Si, K, Si) that were derived using DFA coupled with a multivariate stepwise algorithm, with errors between real and estimated value that did not exceed 6.7 % and values of GOF above 94.5 %. The second set of experiments aimed to explore the sensitivity of model output to variability in the particle size of source materials assuming that a degree of fluvial sorting of the resulting mixture took place. Most particle size correction procedures assume grain size affects are consistent across sources and tracer properties which is not always the case. Consequently, the < 40 µm fraction of selected soil mixtures was analysed to simulate the effect of selective fluvial transport of finer particles and the results were compared to those for source materials. Preliminary findings from this experiment demonstrate the sensitivity of the numerical mixing model outputs to different particle size distributions of source material and the variable impact of fluvial sorting on end member signatures used in mixing models. The results suggest that particle size correction procedures require careful scrutiny in the context of variable source characteristics.
Technology Transfer Automated Retrieval System (TEKTRAN)
We examined multivariate relationships in structural carbohydrates plus lignin (STC) and non-structural (NSC) carbohydrates and their impact on C:N ratio and the dynamics of active (ka) and passive (kp) residue decomposition of alfalfa, corn, soybean, cuphea and switchgrass as candidates in diverse ...
Technology Transfer Automated Retrieval System (TEKTRAN)
Wheat grain attributes that influence tortilla quality are not fully understood. This impedes genetic improvement efforts to develop wheat varieties for the growing market. This study used a multivariate discriminant analysis to predict tortilla quality using a set of 16 variables derived from kerne...
Bilgel, Murat; Prince, Jerry L; Wong, Dean F; Resnick, Susan M; Jedynak, Bruno M
2016-07-01
It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm(3). These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. We apply our method to studying cortical β-amyloid deposition, a hallmark of preclinical Alzheimer's disease, as measured using positron emission tomography. Results on 104 individuals with a total of 300 visits suggest that precuneus is the earliest cortical region to
A MULTIVARIATE FIT LUMINOSITY FUNCTION AND WORLD MODEL FOR LONG GAMMA-RAY BURSTS
Shahmoradi, Amir
2013-04-01
It is proposed that the luminosity function, the rest-frame spectral correlations, and distributions of cosmological long-duration (Type-II) gamma-ray bursts (LGRBs) may be very well described as a multivariate log-normal distribution. This result is based on careful selection, analysis, and modeling of LGRBs' temporal and spectral variables in the largest catalog of GRBs available to date: 2130 BATSE GRBs, while taking into account the detection threshold and possible selection effects. Constraints on the joint rest-frame distribution of the isotropic peak luminosity (L{sub iso}), total isotropic emission (E{sub iso}), the time-integrated spectral peak energy (E{sub p,z}), and duration (T{sub 90,z}) of LGRBs are derived. The presented analysis provides evidence for a relatively large fraction of LGRBs that have been missed by the BATSE detector with E{sub iso} extending down to {approx}10{sup 49} erg and observed spectral peak energies (E{sub p} ) as low as {approx}5 keV. LGRBs with rest-frame duration T{sub 90,z} {approx}< 1 s or observer-frame duration T{sub 90} {approx}< 2 s appear to be rare events ({approx}< 0.1% chance of occurrence). The model predicts a fairly strong but highly significant correlation ({rho} = 0.58 {+-} 0.04) between E{sub iso} and E{sub p,z} of LGRBs. Also predicted are strong correlations of L{sub iso} and E{sub iso} with T{sub 90,z} and moderate correlation between L{sub iso} and E{sub p,z}. The strength and significance of the correlations found encourage the search for underlying mechanisms, though undermine their capabilities as probes of dark energy's equation of state at high redshifts. The presented analysis favors-but does not necessitate-a cosmic rate for BATSE LGRBs tracing metallicity evolution consistent with a cutoff Z/Z{sub Sun} {approx} 0.2-0.5, assuming no luminosity-redshift evolution.
Yu, P.
2008-01-01
More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.
NASA Astrophysics Data System (ADS)
McFadden, Fiona J. Stevens; Welch, Barry J.; Austin, Pual C.
2006-02-01
This paper investigates the application of multivariable model-based control to improve the regulatory control of electrolyte temperature, aluminum fluoride concentration, liquidus temperature, superheat, and electrolyte height. Also examined are therappropriateness of different control structures and the possible inclusion of recently developed sensors for alumina concentration and individual cell duct flowrate, temperature, and heat loss. For the smelter in this study, the maximum improvement possible with a multivariable model-based controller is predicted to be 30 40% reduction in standard deviation in electrolyte temperature, aluminum fluoride concentration, liquidus temperature, and superheat, and around half this for electrolyte height. Three control structures were found to be appropriate; all are different than the existing control structure, which was found to be suboptimal. Linear Quadratic Gaussian controllers were designed for each control structure and their predicted performance compared.
Covariate-Adjusted Linear Mixed Effects Model with an Application to Longitudinal Data
Nguyen, Danh V.; Şentürk, Damla; Carroll, Raymond J.
2009-01-01
Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We propose a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalizing covariate. The proposed approach involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline confounding covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modeled nonparametrically via smooth unknown functions. In addition to covariate-adjusted estimation of fixed/population parameters and random effects, an estimation procedure for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency and convergence rates of the proposed estimators are also established. An application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index, illustrates the proposed methodology. PMID:19266053
NASA Astrophysics Data System (ADS)
Bernasocchi, M.; Coltekin, A.; Gruber, S.
2012-07-01
In environmental change studies, often multiple variables are measured or modelled, and temporal information is essential for the task. These multivariate geographic time-series datasets are often big and difficult to analyse. While many established methods such as PCP (parallel coordinate plots), STC (space-time cubes), scatter-plots and multiple (linked) visualisations help provide more information, we observe that most of the common geovisual analytics suits do not include three-dimensional (3D) visualisations. However, in many environmental studies, we hypothesize that the addition of 3D terrain visualisations along with appropriate data plots and two-dimensional views can help improve the analysts' ability to interpret the spatial relevance better. To test our ideas, we conceptualize, develop, implement and evaluate a geovisual analytics toolbox in a user-centred manner. The conceptualization of the tool is based on concrete user needs that have been identified and collected during informal brainstorming sessions and in a structured focus group session prior to the development. The design process, therefore, is based on a combination of user-centred design with a requirement analysis and agile development. Based on the findings from this phase, the toolbox was designed to have a modular structure and was built on open source geographic information systems (GIS) program Quantum GIS (QGIS), thus benefiting from existing GIS functionality. The modules include a globe view for 3D terrain visualisation (OSGEarth), a scattergram, a time vs. value plot, and a 3D helix visualisation as well as the possibility to view the raw data. The visualisation frame allows real-time linking of these representations. After the design and development stage, a case study was created featuring data from Zermatt valley and the toolbox was evaluated based on expert interviews. Analysts performed multiple spatial and temporal tasks with the case study using the toolbox. The expert
A New Climate Adjustment Tool: An update to EPA’s Storm Water Management Model
The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations.
Liu, Xianhua; Wang, Lili
2015-01-01
A series of ultraviolet-visible (UV-Vis) spectra from seawater samples collected from sites along the coastline of Tianjin Bohai Bay in China were subjected to multivariate partial least squares (PLS) regression analysis. Calibration models were developed for monitoring chemical oxygen demand (COD) and concentrations of total organic carbon (TOC). Three different PLS models were developed using the spectra from raw samples (Model-1), diluted samples (Model-2), and diluted and raw samples combined (Model-3). Experimental results showed that: (i) possible nonlinearities in the signal concentration relationships were well accounted for by the multivariate PLS model; (ii) the predicted values of COD and TOC fit the analytical values well; the high correlation coefficients and small root mean squared error of cross-validation (RMSECV) showed that this method can be used for seawater quality monitoring; and (iii) compared with Model-1 and Model-2, Model-3 had the highest coefficient of determination (R2) and the lowest number of latent variables. This latter finding suggests that only large data sets that include data representing different combinations of conditions (i.e., various seawater matrices) will produce stable site-specific regressions. The results of this study illustrate the effectiveness of the proposed method and its potential for use as a seawater quality monitoring technique. PMID:26442484
NASA Technical Reports Server (NTRS)
Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)
2001-01-01
A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.
NASA Astrophysics Data System (ADS)
Kisi, Ozgur
2015-09-01
Pan evaporation (Ep) modeling is an important issue in reservoir management, regional water resources planning and evaluation of drinking-water supplies. The main purpose of this study is to investigate the accuracy of least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5Tree) in modeling Ep. The first part of the study focused on testing the ability of the LSSVM, MARS and M5Tree models in estimating the Ep data of Mersin and Antalya stations located in Mediterranean Region of Turkey by using cross-validation method. The LSSVM models outperformed the MARS and M5Tree models in estimating Ep of Mersin and Antalya stations with local input and output data. The average root mean square error (RMSE) of the M5Tree and MARS models was decreased by 24-32.1% and 10.8-18.9% using LSSVM models for the Mersin and Antalya stations, respectively. The ability of three different methods was examined in estimation of Ep using input air temperature, solar radiation, relative humidity and wind speed data from nearby station in the second part of the study (cross-station application without local input data). The results showed that the MARS models provided better accuracy than the LSSVM and M5Tree models with respect to RMSE, mean absolute error (MAE) and determination coefficient (R2) criteria. The average RMSE accuracy of the LSSVM and M5Tree was increased by 3.7% and 16.5% using MARS. In the case of without local input data, the average RMSE accuracy of the LSSVM and M5Tree was respectively increased by 11.4% and 18.4% using MARS. In the third part of the study, the ability of the applied models was examined in Ep estimation using input and output data of nearby station. The results reported that the MARS models performed better than the other models with respect to RMSE, MAE and R2 criteria. The average RMSE of the LSSVM and M5Tree was respectively decreased by 54% and 3.4% using MARS. The overall results indicated that
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, Anne B.; Lizarraga, Joy S.
1996-01-01
Statistical operations termed model-adjustment procedures can be used to incorporate local data into existing regression modes to improve the predication of urban-runoff quality. Each procedure is a form of regression analysis in which the local data base is used as a calibration data set; the resulting adjusted regression models can then be used to predict storm-runoff quality at unmonitored sites. Statistical tests of the calibration data set guide selection among proposed procedures.
Modeling of an Adjustable Beam Solid State Light Project
NASA Technical Reports Server (NTRS)
Clark, Toni
2015-01-01
This proposal is for the development of a computational model of a prototype variable beam light source using optical modeling software, Zemax Optics Studio. The variable beam light source would be designed to generate flood, spot, and directional beam patterns, while maintaining the same average power usage. The optical model would demonstrate the possibility of such a light source and its ability to address several issues: commonality of design, human task variability, and light source design process improvements. An adaptive lighting solution that utilizes the same electronics footprint and power constraints while addressing variability of lighting needed for the range of exploration tasks can save costs and allow for the development of common avionics for lighting controls.
Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio
2016-09-01
Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). PMID:27041319
Circumplex and Spherical Models for Child School Adjustment and Competence.
ERIC Educational Resources Information Center
Schaefer, Earl S.; Edgerton, Marianna
The goal of this study is to broaden the scope of a conceptual model for child behavior by analyzing constructs relevant to cognition, conation, and affect. Two samples were drawn from school populations. For the first sample, 28 teachers from 8 rural, suburban, and urban schools rated 193 kindergarten children. Each teacher rated up to eight…
A General Linear Model Approach to Adjusting the Cumulative GPA.
ERIC Educational Resources Information Center
Young, John W.
A general linear model (GLM), using least-squares techniques, was used to develop a criterion measure to replace freshman year grade point average (GPA) in college admission predictive validity studies. Problems with the use of GPA include those associated with the combination of grades from different courses and disciplines into a single measure,…
Oskrochi, Gholamreza; Lesaffre, Emmanuel; Oskrochi, Youssof; Shamley, Delva
2016-01-01
In this study, four major muscles acting on the scapula were investigated in patients who had been treated in the last six years for unilateral carcinoma of the breast. Muscle activity was assessed by electromyography during abduction and adduction of the affected and unaffected arms. The main principal aim of the study was to compare shoulder muscle activity in the affected and unaffected shoulder during elevation of the arm. A multivariate linear mixed model was introduced and applied to address the principal aims. The result of fitting this model to the data shows a huge improvement as compared to the alternatives. PMID:26950134
Oskrochi, Gholamreza; Lesaffre, Emmanuel; Oskrochi, Youssof; Shamley, Delva
2016-01-01
In this study, four major muscles acting on the scapula were investigated in patients who had been treated in the last six years for unilateral carcinoma of the breast. Muscle activity was assessed by electromyography during abduction and adduction of the affected and unaffected arms. The main principal aim of the study was to compare shoulder muscle activity in the affected and unaffected shoulder during elevation of the arm. A multivariate linear mixed model was introduced and applied to address the principal aims. The result of fitting this model to the data shows a huge improvement as compared to the alternatives. PMID:26950134
Small Bowel Obstruction—Who Needs an Operation? A Multivariate Prediction Model
Eiken, Patrick W.; Bannon, Michael P.; Heller, Stephanie F.; Lohse, Christine M.; Huebner, Marianne; Sarr, Michael G.
2016-01-01
Background Proper management of small bowel obstruction (SBO) requires a methodology to prevent nontherapeutic laparotomy while minimizing the chance of overlooking strangulation obstruction causing intestinal ischemia. Our aim was to identify preoperative risk factors associated with strangulating SBO and to develop a model to predict the need for operative intervention in the presence of an SBO. Our hypothesis was that free intraperitoneal fluid on computed tomography (CT) is associated with the presence of bowel ischemia and need for exploration. Methods We reviewed 100 consecutive patients with SBO, all of whom had undergone CT that was reviewed by a radiologist blinded to outcome. The need for operative management was confirmed retrospectively by four surgeons based on operative findings and the patient’s clinical course. Results Patients were divided into two groups: group 1, who required operative management on retrospective review, and group 2 who did not. Four patients who were treated nonoperatively had ischemia or died of malignant SBO and were then included in group 1; two patients who had a nontherapeutic exploration were included in group 2. On univariate analysis, the need for exploration (n = 48) was associated (p < 0.05) with a history of malignancy (29% vs. 12%), vomiting (85% vs. 63%), and CT findings of either free intraperitoneal fluid (67% vs. 31%), mesenteric edema (67% vs. 37%), mesenteric vascular engorgement (85% vs. 67%), small bowel wall thickening (44% vs. 25%) or absence of the “small bowel feces sign” (so-called fecalization) (10% vs. 29%). Ischemia (n = 11) was associated (p < 0.05 each) with peritonitis (36% vs. 1%), free intraperitoneal fluid (82% vs. 44%), serum lactate concentration (2.7 ± 1.6 vs. 1.3 ± 0.6 mmol/l), mesenteric edema (91% vs. 46%), closed loop obstruction (27% vs. 2%), pneumatosis intestinalis (18% vs. 0%), and portal venous gas (18% vs. 0%). On multivariate analysis, free intraperitoneal fluid [odds ratio
Cross-Correlations and Joint Gaussianity in Multivariate Level Crossing Models
2014-01-01
A variety of phenomena in physical and biological sciences can be mathematically understood by considering the statistical properties of level crossings of random Gaussian processes. Notably, a growing number of these phenomena demand a consideration of correlated level crossings emerging from multiple correlated processes. While many theoretical results have been obtained in the last decades for individual Gaussian level-crossing processes, few results are available for multivariate, jointly correlated threshold crossings. Here, we address bivariate upward crossing processes and derive the corresponding bivariate Central Limit Theorem as well as provide closed-form expressions for their joint level-crossing correlations. PMID:24742344
Lewin, M.D.; Sarasua, S.; Jones, P.A. . Div. of Health Studies)
1999-07-01
For the purpose of examining the association between blood lead levels and household-specific soil lead levels, the authors used a multivariate linear regression model to find a slope factor relating soil lead levels to blood lead levels. They used previously collected data from the Agency for Toxic Substances and Disease Registry's (ATSDR's) multisite lead and cadmium study. The data included in the blood lead measurements of 1,015 children aged 6--71 months, and corresponding household-specific environmental samples. The environmental samples included lead in soil, house dust, interior paint, and tap water. After adjusting for income, education or the parents, presence of a smoker in the household, sex, and dust lead, and using a double log transformation, they found a slope factor of 0.1388 with a 95% confidence interval of 0.09--0.19 for the dose-response relationship between the natural log of the soil lead level and the natural log of the blood lead level. The predicted blood lead level corresponding to a soil lead level of 500 mg/kg was 5.99 [micro]g/kg with a 95% prediction interval of 2.08--17.29. Predicted values and their corresponding prediction intervals varied by covariate level. The model shows that increased soil lead level is associated with elevated blood leads in children, but that predictions based on this regression model are subject to high levels of uncertainty and variability.
Comparison of the Properties of Regression and Categorical Risk-Adjustment Models
Averill, Richard F.; Muldoon, John H.; Hughes, John S.
2016-01-01
Clinical risk-adjustment, the ability to standardize the comparison of individuals with different health needs, is based upon 2 main alternative approaches: regression models and clinical categorical models. In this article, we examine the impact of the differences in the way these models are constructed on end user applications. PMID:26945302
ERIC Educational Resources Information Center
Olejnik, Stephen; Mills, Jamie; Keselman, Harvey
2000-01-01
Evaluated the use of Mallow's C(p) and Wherry's adjusted R squared (R. Wherry, 1931) statistics to select a final model from a pool of model solutions using computer generated data. Neither statistic identified the underlying regression model any better than, and usually less well than, the stepwise selection method, which itself was poor for…
NASA Astrophysics Data System (ADS)
Khoshravesh, Mojtaba; Sefidkouhi, Mohammad Ali Gholami; Valipour, Mohammad
2015-12-01
The proper evaluation of evapotranspiration is essential in food security investigation, farm management, pollution detection, irrigation scheduling, nutrient flows, carbon balance as well as hydrologic modeling, especially in arid environments. To achieve sustainable development and to ensure water supply, especially in arid environments, irrigation experts need tools to estimate reference evapotranspiration on a large scale. In this study, the monthly reference evapotranspiration was estimated by three different regression models including the multivariate fractional polynomial (MFP), robust regression, and Bayesian regression in Ardestan, Esfahan, and Kashan. The results were compared with Food and Agriculture Organization (FAO)-Penman-Monteith (FAO-PM) to select the best model. The results show that at a monthly scale, all models provided a closer agreement with the calculated values for FAO-PM (R 2 > 0.95 and RMSE < 12.07 mm month-1). However, the MFP model gives better estimates than the other two models for estimating reference evapotranspiration at all stations.
Interpreting support vector machine models for multivariate group wise analysis in neuroimaging
Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos
2015-01-01
Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913
Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.
Gaonkar, Bilwaj; T Shinohara, Russell; Davatzikos, Christos
2015-08-01
Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913
NASA Astrophysics Data System (ADS)
Sabourin, Anne; Renard, Benjamin
2015-12-01
The estimation of extreme flood quantiles is challenging due to the relative scarcity of extreme data compared to typical target return periods. Several approaches have been developed over the years to face this challenge, including regional estimation and the use of historical flood data. This paper investigates the combination of both approaches using a multivariate peaks-over-threshold model that allows estimating altogether the intersite dependence structure and the marginal distributions at each site. The joint distribution of extremes at several sites is constructed using a semiparametric Dirichlet Mixture model. The existence of partially missing and censored observations (historical data) is accounted for within a data augmentation scheme. This model is applied to a case study involving four catchments in Southern France, for which historical data are available since 1604. The comparison of marginal estimates from four versions of the model (with or without regionalizing the shape parameter; using or ignoring historical floods) highlights significant differences in terms of return level estimates. Moreover, the availability of historical data on several nearby catchments allows investigating the asymptotic dependence properties of extreme floods. Catchments display a significant amount of asymptotic dependence, calling for adapted multivariate statistical models.
Wang, Yiyi; Kockelman, Kara M
2013-11-01
This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. PMID:24036167
Multivariate Intraclass Correlation.
ERIC Educational Resources Information Center
Wiley, David E.; Hawkes, Thomas H.
This paper is an explication of a statistical model which will permit an interpretable intraclass correlation coefficient that is negative, and a generalized extension of that model to cover a multivariate problem. The methodological problem has its practical roots in an attempt to find a statistic which could indicate the degree of similarity or…
Milly, P.C.D.; Dunne, K.A.
2011-01-01
Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median 211%) caused by the hydrologic model's apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen-Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climatechange impacts on water. Copyright ?? 2011, Paper 15-001; 35,952 words, 3 Figures, 0 Animations, 1 Tables.
Hobolth, Asger; Siren, Jukka
2016-04-01
We consider the diffusion approximation of the multivariate Wright-Fisher process with mutation. Analytically tractable formulas for the first-and second-order moments of the allele frequency distribution are derived, and the moments are subsequently used to better understand key population genetics parameters and modeling frameworks. In particular we investigate the behavior of the expected homozygosity (the probability that two randomly sampled genes are identical) in the transient and stationary phases, and how appropriate the Dirichlet distribution is for modeling the allele frequency distribution at different evolutionary time scales. We find that the Dirichlet distribution is adequate for the pure drift model (no mutations allowed), but the distribution is not sufficiently flexible for more general mutation models. We suggest a new hierarchical Beta distribution for the allele frequencies in the Wright-Fisher process with a mutation model on the nucleotide level that distinguishes between transitions and transversions. PMID:26612605
Milly, Paul C.; Dunne, Krista A.
2011-01-01
Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median -11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.
Block adjustment of Chang'E-1 images based on rational function model
NASA Astrophysics Data System (ADS)
Liu, Bin; Liu, Yiliang; Di, Kaichang; Sun, Xiliang
2014-05-01
Chang'E-1(CE-1) is the first lunar orbiter of China's lunar exploration program. The CCD camera carried by CE-1 has acquired stereo images covering the entire lunar surface. Block adjustment and 3D mapping using CE-1 images are of great importance for morphological and other scientific research of the Moon. Traditional block adjustment based on rigorous sensor model is complicated due to a large number of parameters and possible correlations among them. To tackle this problem, this paper presents a block adjustment method using Rational Function Model (RFM). The RFM parameters are generated based on rigorous sensor model using virtual grid of control points. Afterwards, the RFM based block adjustment solves the refinement parameters through a least squares solution. Experimental results using CE-1 images located in Sinus Irdium show that the RFM can fit the rigorous sensor model with a high precision of 1% pixel level. Through the RFM-based block adjustment, the back-projection residuals in image space can be reduced from around 1.5 pixels to sub-pixel., indicating that RFM can replace rigorous sensor model for geometric processing of lunar images.
Phillips, Joe Scutt; Patterson, Toby A; Leroy, Bruno; Pilling, Graham M; Nicol, Simon J
2015-07-01
Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification. We use high-resolution behavioral data from bio-loggers attached to free-roaming pelagic tuna as an example. Observed patterns are assumed to be generated by an unseen Markov process that switches between several multivariate-normal distributions. Our approach is assessed in two parts. The first uses simulation experiments, from which the ability of the HMM to estimate known parameter values is examined using artificial time series of data consistent with hypotheses about pelagic predator foraging ecology. The second is the application to time series of continuous vertical movement data from yellowfin and bigeye tuna taken from tuna tagging experiments. These data were compressed into summary metrics capturing the variation of patterns in diving behavior and formed into a multivariate time series used to estimate a HMM. Each observation was associated with covariate information incorporating the effect of day and night on behavioral switching. Known parameter values were well recovered by the HMMs in our simulation experiments, resulting in mean correct classification rates of 90-97%, although some variance-covariance parameters were estimated less accurately. HMMs with two distinct behavioral states were selected for every time series of real tuna data, predicting a shallow warm state, which was similar across all individuals, and a deep colder state, which was more variable. Marked diurnal behavioral switching was predicted, consistent with many previous empirical studies on tuna. HMMs provide easily interpretable models for the objective classification of
Multivariate Regression with Calibration*
Liu, Han; Wang, Lie; Zhao, Tuo
2014-01-01
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861
NASA Astrophysics Data System (ADS)
Chun, K. P.; Wheater, H. S.; Barr, A. G.
2014-11-01
Multiple variables from simulated climate fields are widely used in hydrological and ecological models and climate impact assessments, yet the importance of multivariate climate relationships is not widely recognised. This study evaluates climatic outputs from the Canadian Coupled Global Climate Model (CGCM3) in the southern boreal forests of western Canada, by comparing the simulated multivariate relationships with those observed at three representative forest sites. Monthly mean data for five near-surface climate variables (net radiation RN, air temperature TA, relative humidity RH, wind speed WS and surface pressure P) are analysed and compared using visual inspection, hypothesis testing and principal component analysis. The projections of the 1st and 2nd principal components, which explain about 75% of the variation in the data, show remarkable similarities in the observations from the three forest sites (with some subtle differences between the evergreen and deciduous plant functional types), but some broad differences between the observations and model outputs. The model reproduces the observed relationships among RN, TA and P, but not between RH or WS and the other variables. In particular, RH is strongly and negatively related to TA and RN in the forest observations but independent in the model outputs; RH is negatively related to WS in the observations but positively related in the model output; and P is uncoupled from the other variables in the observations but negatively related to RH and WS in the model output. The broad scope of the differences indicates a divergence of process representation at large time and space scales. We explore possible reasons for the observed discrepancies, which indicate some limitations in using climate model outputs directly to drive hydrological models.
Richardson, David B.; Laurier, Dominique; Schubauer-Berigan, Mary K.; Tchetgen, Eric Tchetgen; Cole, Stephen R.
2014-01-01
Workers' smoking histories are not measured in many occupational cohort studies. Here we discuss the use of negative control outcomes to detect and adjust for confounding in analyses that lack information on smoking. We clarify the assumptions necessary to detect confounding by smoking and the additional assumptions necessary to indirectly adjust for such bias. We illustrate these methods using data from 2 studies of radiation and lung cancer: the Colorado Plateau cohort study (1950–2005) of underground uranium miners (in which smoking was measured) and a French cohort study (1950–2004) of nuclear industry workers (in which smoking was unmeasured). A cause-specific relative hazards model is proposed for estimation of indirectly adjusted associations. Among the miners, the proposed method suggests no confounding by smoking of the association between radon and lung cancer—a conclusion supported by adjustment for measured smoking. Among the nuclear workers, the proposed method suggests substantial confounding by smoking of the association between radiation and lung cancer. Indirect adjustment for confounding by smoking resulted in an 18% decrease in the adjusted estimated hazard ratio, yet this cannot be verified because smoking was unmeasured. Assumptions underlying this method are described, and a cause-specific proportional hazards model that allows easy implementation using standard software is presented. PMID:25245043
Attar-Schwartz, Shalhevet
2015-09-01
Warm and emotionally close relationships with parents and grandparents have been found in previous studies to be linked with better adolescent adjustment. The present study, informed by Family Systems Theory and Intergenerational Solidarity Theory, uses a moderated mediation model analyzing the contribution of the dynamics of these intergenerational relationships to adolescent adjustment. Specifically, it examines the mediating role of emotional closeness to the closest grandparent in the relationship between emotional closeness to a parent (the offspring of the closest grandparent) and adolescent adjustment difficulties. The model also examines the moderating role of emotional closeness to parents in the relationship between emotional closeness to grandparents and adjustment difficulties. The study was based on a sample of 1,405 Jewish Israeli secondary school students (ages 12-18) who completed a structured questionnaire. It was found that emotional closeness to the closest grandparent was more strongly associated with reduced adjustment difficulties among adolescents with higher levels of emotional closeness to their parents. In addition, adolescent adjustment and emotional closeness to parents was partially mediated by emotional closeness to grandparents. Examining the family conditions under which adolescents' relationships with grandparents is stronger and more beneficial for them can help elucidate variations in grandparent-grandchild ties and expand our understanding of the mechanisms that shape child outcomes. PMID:26237053
Kim, Bong Mann; Lee, Seung-Bok; Kim, Jin Young; Kim, Sunwha; Seo, Jihoon; Bae, Gwi-Nam; Lee, Ji Yi
2016-02-01
Understanding the geographic source contributions by particulate polycyclic aromatic hydrocarbons (PAHs) is important for the Korean peninsula due to its downwind location from source areas. Regional influence of particulate PAHs was previously identified using diagnostic ratios applied to mobile source dominated roadside sampling data (Kim et al., 2012b). However, no study has yet been conducted to quantify the regional source contributions. We applied a multivariate receptor modeling tool to identify and quantify the regional source contributions to particulate PAHs in Seoul. Sampling of roadside particulate PAHs was conducted in Seoul, Korea for four years between May 2005 and April 2009, and data analysis was performed with a new multivariate receptor model, Solver for Mixture Problem (SMP). The SMP model identified two sources, local mobile source and transported regional source, and quantified their source contributions. Analysis of the particulate PAHs data reveals three types of episodic periods: a high regional source contribution period with one case, a high mobile source contribution period with three cases, and a normal contribution period with eight cases. Four-year average particulate PAHs source contributions from the two sources are 4.6 ng m(-3) and 10.7 ng m(-3) for regional and mobile sources, respectively and equivalent to 30% and 70% of the total estimated contribution from each of these sources. PMID:26473551
Hagar, Yolanda C; Harvey, Danielle J; Beckett, Laurel A
2016-08-30
We develop a multivariate cure survival model to estimate lifetime patterns of colorectal cancer screening. Screening data cover long periods of time, with sparse observations for each person. Some events may occur before the study begins or after the study ends, so the data are both left-censored and right-censored, and some individuals are never screened (the 'cured' population). We propose a multivariate parametric cure model that can be used with left-censored and right-censored data. Our model allows for the estimation of the time to screening as well as the average number of times individuals will be screened. We calculate likelihood functions based on the observations for each subject using a distribution that accounts for within-subject correlation and estimate parameters using Markov chain Monte Carlo methods. We apply our methods to the estimation of lifetime colorectal cancer screening behavior in the SEER-Medicare data set. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26990553
Modeling Quality-Adjusted Life Expectancy Loss Resulting from Tobacco Use in the United States
ERIC Educational Resources Information Center
Kaplan, Robert M.; Anderson, John P.; Kaplan, Cameron M.
2007-01-01
Purpose: To describe the development of a model for estimating the effects of tobacco use upon Quality Adjusted Life Years (QALYs) and to estimate the impact of tobacco use on health outcomes for the United States (US) population using the model. Method: We obtained estimates of tobacco consumption from 6 years of the National Health Interview…
Evaluation of the Stress Adjustment and Adaptation Model among Families Reporting Economic Pressure
ERIC Educational Resources Information Center
Vandsburger, Etty; Biggerstaff, Marilyn A.
2004-01-01
This research evaluates the Stress Adjustment and Adaptation Model (double ABCX model) examining the effects resiliency resources on family functioning when families experience economic pressure. Families (N = 128) with incomes at or below the poverty line from a rural area of a southern state completed measures of perceived economic pressure,…
A Model of Divorce Adjustment for Use in Family Service Agencies.
ERIC Educational Resources Information Center
Faust, Ruth Griffith
1987-01-01
Presents a combined educationally and therapeutically oriented model of treatment to (1) control and lessen disruptive experiences associated with divorce; (2) enable individuals to improve their skill in coping with adjustment reactions to divorce; and (3) modify the pressures and response of single parenthood. Describes the model's four-session…
ERIC Educational Resources Information Center
Nettles, Saundra Murray; Caughy, Margaret O'Brien; O'Campo, Patricia J.
2008-01-01
Examining recent research on neighborhood influences on child development, this review focuses on social influences on school adjustment in the early elementary years. A model to guide community research and intervention is presented. The components of the model of integrated processes are neighborhoods and their effects on academic outcomes and…
Risk adjustment of Medicare capitation payments using the CMS-HCC model.
Pope, Gregory C; Kautter, John; Ellis, Randall P; Ash, Arlene S; Ayanian, John Z; Lezzoni, Lisa I; Ingber, Melvin J; Levy, Jesse M; Robst, John
2004-01-01
This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed. PMID:15493448
Community Influences on Adjustment in First Grade: An Examination of an Integrated Process Model
ERIC Educational Resources Information Center
Caughy, Margaret O'Brien; Nettles, Saundra M.; O'Campo, Patricia J.
2007-01-01
We examined the impact of neighborhood characteristics both directly and indirectly as mediated by parent coaching and the parent/child affective relationship on behavioral and school adjustment in a sample of urban dwelling first graders. We used structural equations modeling to assess model fit and estimate direct, indirect, and total effects of…
Zhan, Xianyuan; Aziz, H. M. Abdul; Ukkusuri, Satish V.
2015-11-19
Our study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and takes account of the over dispersion in the data that leads to a superior data fitting. But, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a parallel sampling scheme is introduced to improve the original Markov Chainmore » Monte Carlo (MCMC) estimation approach of the MVPLN model, which significantly reduces the model estimation time. Two MVPLN models are developed using the pedestrian vehicle crash data collected in New York City from 2002 to 2006, and the highway-injury data from Washington State (5-year data from 1990 to 1994) The Deviance Information Criteria (DIC) is used to evaluate the model fitting. The estimation results show that the MVPLN models provide a superior fit over univariate Poisson-lognormal (PLN), univariate Poisson, and Negative Binomial models. Moreover, the correlations among the latent effects of different severity levels are found significant in both datasets that justifies the importance of jointly modeling crash frequency and severity accounting for correlations.« less
Zhan, Xianyuan; Aziz, H. M. Abdul; Ukkusuri, Satish V.
2015-11-19
Our study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and takes account of the over dispersion in the data that leads to a superior data fitting. But, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a parallel sampling scheme is introduced to improve the original Markov Chain Monte Carlo (MCMC) estimation approach of the MVPLN model, which significantly reduces the model estimation time. Two MVPLN models are developed using the pedestrian vehicle crash data collected in New York City from 2002 to 2006, and the highway-injury data from Washington State (5-year data from 1990 to 1994) The Deviance Information Criteria (DIC) is used to evaluate the model fitting. The estimation results show that the MVPLN models provide a superior fit over univariate Poisson-lognormal (PLN), univariate Poisson, and Negative Binomial models. Moreover, the correlations among the latent effects of different severity levels are found significant in both datasets that justifies the importance of jointly modeling crash frequency and severity accounting for correlations.
Contact angle adjustment in equation-of-state-based pseudopotential model
NASA Astrophysics Data System (ADS)
Hu, Anjie; Li, Longjian; Uddin, Rizwan; Liu, Dong
2016-05-01
The single component pseudopotential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many studies, it has been claimed that this model can be stable for density ratios larger than 1000. However, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in the present work show that, by applying the original contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with our contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension.
Huang, Lam O.; Infante-Rivard, Claire; Labbe, Aurélie
2016-01-01
Transmission of the two parental alleles to offspring deviating from the Mendelian ratio is termed Transmission Ratio Distortion (TRD), occurs throughout gametic and embryonic development. TRD has been well-studied in animals, but remains largely unknown in humans. The Transmission Disequilibrium Test (TDT) was first proposed to test for association and linkage in case-trios (affected offspring and parents); adjusting for TRD using control-trios was recommended. However, the TDT does not provide risk parameter estimates for different genetic models. A loglinear model was later proposed to provide child and maternal relative risk (RR) estimates of disease, assuming Mendelian transmission. Results from our simulation study showed that case-trios RR estimates using this model are biased in the presence of TRD; power and Type 1 error are compromised. We propose an extended loglinear model adjusting for TRD. Under this extended model, RR estimates, power and Type 1 error are correctly restored. We applied this model to an intrauterine growth restriction dataset, and showed consistent results with a previous approach that adjusted for TRD using control-trios. Our findings suggested the need to adjust for TRD in avoiding spurious results. Documenting TRD in the population is therefore essential for the correct interpretation of genetic association studies.
A multivariate quadrature based approach for LES based supersonic combustion modeling
NASA Astrophysics Data System (ADS)
Donde, Pratik; Koo, Heeseok; Raman, Venkat
2010-11-01
The direct quadrature method of moments (DQMOM) was developed to solve high-dimensional probability density function (PDF) equations that arise in the description of turbulent combustion. This method is particularly useful in shock-containing supersonic internal flows such as those encountered in scramjet engines. In the DQMOM approach, the PDF is described in terms of a finite number of weighted delta functions whose weights and locations in composition space are obtained by solving specific transport equations. Since this approach is fully Eulerian in nature, it is advantageous compared to conventional Lagrangian methods used for solving the PDF transport equation. However, implementation of this formulation in the context of the large eddy simulation (LES) methodology leads to large numerical errors. For instance, the high-resolution numerical schemes used in LES lead to non-realizable and diffusive evolution of the DQMOM equations. Here, we propose a novel semi-discrete quadrature method of moments (SeQMOM) that overcomes this problem. A decoupling procedure is used to extend this method to multivariate PDF descriptions. The numerical implementation in LES as well as validation exercises will be presented.
Applying a multivariate statistical analysis model to evaluate the water quality of a watershed.
Wu, Edward Ming-Yang; Kuo, Shu-Lung
2012-12-01
Multivariate statistics have been applied to evaluate the water quality data collected at six monitoring stations in the Feitsui Reservoir watershed of Taipei, Taiwan. The objective is to evaluate the mutual correlations among the various water quality parameters to reveal the primary factors that affect reservoir water quality, and the differences among the various water quality parameters in the watershed. In this study, using water quality samples collected over a period of two and a half years will effectively raise the efficacy and reliability of the factor analysis results. This will be a valuable reference for managing water pollution in the watershed. Additionally, results obtained using the proposed theory and method to analyze and interpret statistical data must be examined to verify their similarity to field data collected on the stream geographical and geological characteristics, the physical and chemical phenomena of stream self-purification, and the stream hydrological phenomena. In this research, the water quality data has been collected over two and a half years so that sufficient sets of water quality data are available to increase the stability, effectiveness, and reliability of the final factor analysis results. These data sets can be valuable references for managing, regulating, and remediating water pollution in a reservoir watershed. PMID:23342938
NASA Astrophysics Data System (ADS)
Sipes, T.; Karimabadi, H.; Imber, S. M.; Slavin, J. A.; Pothier, N. M.; Coeli, R.
2013-12-01
Advances in data collection and data storage technologies have made the assembly of multivariate time series data more common. Data analysis and extraction of knowledge from such massive and complex datasets encountered in space physics today present a major obstacle to fully utilizing our vast data repositories and to scientific progress. In the previous years we introduced a time series classification tool MineTool-TS [Karimabadi et al, 2009] and its extension to simulation and streaming data [Sipes& Karimabadi, 2012, 2013]. In this work we demonstrate the applicability and real world utility of the predictive models created using the tool to scoring and labeling of a large dataset of unseen, streaming data. Predictive models that are created are based on the assumption that the training data used to create them is a true representative of the population. Multivariate time series datasets are also characterized by large amounts of variability and potential background noise. Moreover, there are multiple issues being raised by the streaming nature of the data. In this work we illustrate how we dealt with these challenges and demonstrate the results in a study of flux ropes in the plasma sheet. We have used an iterative process of building a predictive model using the original labeled training set, tested it on a week worth of streaming data, had the results checked by a scientific expert in the domain, and fed the results and the labels back into the training set, creating a large training set and using it to produce the final model. This final model was then put to use to predict a very large, unseen, six month period of streaming data. In this work we present the results of our machine learning approach to automatically detect flux ropes in spacecraft data.
Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin
2013-01-01
In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436
Cummings, E. Mark; Merrilees, Christine E.; Schermerhorn, Alice C.; Goeke-Morey, Marcie C.; Shirlow, Peter; Cairns, Ed
2013-01-01
Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family and child psychological processes in child adjustment, supporting study of inter-relations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M= 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland completed measures of community discord, family relations, and children’s regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members’ reports of current sectarian and non-sectarian antisocial behavior. Interparental conflict and parental monitoring and children’s emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children’s adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world. PMID:20423550
Cummings, E Mark; Merrilees, Christine E; Schermerhorn, Alice C; Goeke-Morey, Marcie C; Shirlow, Peter; Cairns, Ed
2010-05-01
Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family, and child psychological processes in child adjustment, supporting study of interrelations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M = 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland, completed measures of community discord, family relations, and children's regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members' reports of current sectarian antisocial behavior and nonsectarian antisocial behavior. Interparental conflict and parental monitoring and children's emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children's adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world. PMID:20423550
NASA Astrophysics Data System (ADS)
Zhang, Xingwu; Gao, Robert X.; Yan, Ruqiang; Chen, Xuefeng; Sun, Chuang; Yang, Zhibo
2016-08-01
Crack is one of the crucial causes of structural failure. A methodology for quantitative crack identification is proposed in this paper based on multivariable wavelet finite element method and particle swarm optimization. First, the structure with crack is modeled by multivariable wavelet finite element method (MWFEM) so that the vibration parameters of the first three natural frequencies in arbitrary crack conditions can be obtained, which is named as the forward problem. Second, the structure with crack is tested to obtain the vibration parameters of first three natural frequencies by modal testing and advanced vibration signal processing method. Then, the analyzed and measured first three natural frequencies are combined together to obtain the location and size of the crack by using particle swarm optimization. Compared with traditional wavelet finite element method, MWFEM method can achieve more accurate vibration analysis results because it interpolates all the solving variables at one time, which makes the MWFEM-based method to improve the accuracy in quantitative crack identification. In the end, the validity and superiority of the proposed method are verified by experiments of both cantilever beam and simply supported beam.
Sinha, Sarita; Basant, Ankita; Malik, Amrita; Singh, Kunwar P
2009-07-01
Biochemical changes in the plants of Pistia stratiotes L., a free floating macrophyte exposed to different concentrations of hexavalent chromium (0, 10, 40, 60, 80 and 160 microM) for 48, 96 and 144 h were studied. Chromium-induced oxidative stress in macrophyte was investigated using the multivariate modeling approaches. Cluster analysis rendered two fairly distinct clusters (roots and shoots) of similar characteristics in terms of their biochemical responses. Discriminant analysis identified ascorbate peroxidase (APX) as discriminating variable between the root and shoot tissues. Principal components analysis results suggested that malondialdehyde (MDA), superoxide dismutase (SOD), APX, non-protein thiols (NP-SH), cysteine, ascorbic acid, and Cr-accumulation are dominant in root tissues, whereas, protein and guaiacol peroxidase (GPX) in shoots of the plant. Discriminant partial least squares analysis results further confirmed that MDA, SOD, NP-SH, cysteine, GPX, APX, ascorbic acid and Cr-accumulation dominated in the root tissues, while protein in the shoot. Three-way analysis helped in visualizing simultaneous influence of metal concentration and exposure duration on biochemical variables in plant tissues. The multivariate approaches, thus, allowed for the interpretation of the induced biochemical changes in the plant tissues exposed to chromium, which otherwise using the conventional approaches is difficult. PMID:19396544
Kapur, Kush; Li, Xue; Blood, Emily A; Hedeker, Donald
2015-02-20
In the statistical literature, the methods to understand the relationship of explanatory variables on each individual outcome variable are well developed and widely applied. However, in most health-related studies given the technological advancement and sophisticated methods of obtaining and storing data, a need to perform joint analysis of multivariate outcomes while explaining the impact of predictors simultaneously and accounting for all the correlations is in high demand. In this manuscript, we propose a generalized approach within a Bayesian framework that models the changes in the variation in terms of explanatory variables and captures the correlations between the multivariate continuous outcomes by the inclusion of random effects at both the location and scale levels. We describe the use of a spherical transformation for the correlations between the random location and scale effects in order to apply separation strategy for prior elicitation while ensuring positive semi-definiteness of the covariance matrix. We present the details of our approach using an example from an ecological momentary assessment study on adolescents. PMID:25409923
ERIC Educational Resources Information Center
Bernard, Lori L.; Guarnaccia, Charles A.
2003-01-01
Purpose: Caregiver bereavement adjustment literature suggests opposite models of impact of role strain on bereavement adjustment after care-recipient death--a Complicated Grief Model and a Relief Model. This study tests these competing models for husband and adult-daughter caregivers of breast cancer hospice patients. Design and Methods: This…
NASA Astrophysics Data System (ADS)
George, Rani
The purpose of this dissertation is to simultaneously examine individual change in students' attitudes toward science and attitudes about the utility of science over the middle and high school years using multivariate latent variable growth modeling. A second goal is to identify the time-invariant and time-varying predictors of attitudes toward science and attitudes about the utility of science using data from the Longitudinal Study of American Youth. The conceptual framework for this dissertation is based on Klopfer's (1971) structure of the affective domain related to science and the concept of the multidimensionality of science-related attitudes. The results of the multivariate model showed that over the middle and high school years, changes in attitudes toward science are positively related to changes in attitudes about the utility of science. This means that more positive attitudes toward science are associated with positive attitudes about the utility of science. Science self-concept was found to be the strongest predictor of both the attitudinal variables. Teacher encouragement of science was another significant predictor of both the attitudinal variables, while peer attitudes had a greater influence on attitudes toward science. Achievement Motivation was found to have a small, but consistently significant, influence on students' attitudes about the utility of science. The effect of the parent variable was found to be quite small and statistically nonsignificant, with the exception of the seventh grade. Boys were found to have higher initial status on both the attitudinal variables in the seventh grade. However, boys had a steeper drop in attitudes toward science, while the drop in attitudes about the utility of science was faster for the girls. Also it was found that students in Metropolitan and Rural schools have less positive attitudes toward science and the utility of science in the seventh grade compared to students in Suburban schools. Multivariate
NASA Astrophysics Data System (ADS)
Mfumu Kihumba, Antoine; Ndembo Longo, Jean; Vanclooster, Marnik
2016-03-01
A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.
Kiang, Lisa; Witkow, Melissa R; Thompson, Taylor L
2016-07-01
The model minority image is a common and pervasive stereotype that Asian American adolescents must navigate. Using multiwave data from 159 adolescents from Asian American backgrounds (mean age at initial recruitment = 15.03, SD = .92; 60 % female; 74 % US-born), the current study targeted unexplored aspects of the model minority experience in conjunction with more traditionally measured experiences of negative discrimination. When examining normative changes, perceptions of model minority stereotyping increased over the high school years while perceptions of discrimination decreased. Both experiences were not associated with each other, suggesting independent forms of social interactions. Model minority stereotyping generally promoted academic and socioemotional adjustment, whereas discrimination hindered outcomes. Moreover, in terms of academic adjustment, the model minority stereotype appears to protect against the detrimental effect of discrimination. Implications of the complex duality of adolescents' social interactions are discussed. PMID:26251100
A Four-Part Model of Autonomy during Emerging Adulthood: Associations with Adjustment
ERIC Educational Resources Information Center
Lamborn, Susie D.; Groh, Kelly
2009-01-01
We found support for a four-part model of autonomy that links connectedness, separation, detachment, and agency to adjustment during emerging adulthood. Based on self-report surveys of 285 American college students, expected associations among the autonomy variables were found. In addition, agency, as measured by self-reliance, predicted lower…
A Threshold Model of Social Support, Adjustment, and Distress after Breast Cancer Treatment
ERIC Educational Resources Information Center
Mallinckrodt, Brent; Armer, Jane M.; Heppner, P. Paul
2012-01-01
This study examined a threshold model that proposes that social support exhibits a curvilinear association with adjustment and distress, such that support in excess of a critical threshold level has decreasing incremental benefits. Women diagnosed with a first occurrence of breast cancer (N = 154) completed survey measures of perceived support…
Berry, Brandon; Moretto, Justin; Matthews, Thomas; Smelko, John; Wiltberger, Kelly
2015-01-01
Multi-component, multi-scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single-scale and combination-scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single-scale and combination-scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale-dependencies that hindered model quality in cross-scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross-scale applications predicting against manufacturing scale batches. PMID:25504860
A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model
Technology Transfer Automated Retrieval System (TEKTRAN)
Proper parameterization enables hydrological models to make reliable estimates of non-point source pollution for effective control measures. The automatic calibration of hydrologic models requires significant computational power limiting its application. The study objective was to develop and eval...
Chang, Jui-Yang; Pigorini, Andrea; Massimini, Marcello; Tononi, Giulio; Nobili, Lino; Van Veen, Barry D.
2012-01-01
A multivariate autoregressive (MVAR) model with exogenous inputs (MVARX) is developed for describing the cortical interactions excited by direct electrical current stimulation of the cortex. Current stimulation is challenging to model because it excites neurons in multiple locations both near and distant to the stimulation site. The approach presented here models these effects using an exogenous input that is passed through a bank of filters, one for each channel. The filtered input and a random input excite a MVAR system describing the interactions between cortical activity at the recording sites. The exogenous input filter coefficients, the autoregressive coefficients, and random input characteristics are estimated from the measured activity due to current stimulation. The effectiveness of the approach is demonstrated using intracranial recordings from three surgical epilepsy patients. We evaluate models for wakefulness and NREM sleep in these patients with two stimulation levels in one patient and two stimulation sites in another resulting in a total of 10 datasets. Excellent agreement between measured and model-predicted evoked responses is obtained across all datasets. Furthermore, one-step prediction is used to show that the model also describes dynamics in pre-stimulus and evoked recordings. We also compare integrated information—a measure of intracortical communication thought to reflect the capacity for consciousness—associated with the network model in wakefulness and sleep. As predicted, higher information integration is found in wakefulness than in sleep for all five cases. PMID:23226122
NASA Astrophysics Data System (ADS)
Naipal, V.; Reick, C.; Pongratz, J.; Van Oost, K.
2015-03-01
Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale, and this limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is due to its simple structure and empirical basis a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial scale applications often rely on coarse data input, which is not compatible with the local scale at which the model is parameterized. This study aimed at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting soil erosion rates to extensive empirical databases on soil erosion from the USA and Europe. Adjusting the topographical factor required scaling of slope according to the fractal method, which resulted in improved topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that are in good comparison with high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted RUSLE model not only shows a global higher mean soil erosion rate but also more variability in the soil erosion rates. Comparison to empirical datasets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional
Multivariate Strategies in Functional Magnetic Resonance Imaging
ERIC Educational Resources Information Center
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.
Multivariate modeling of PCB bioaccumulation in three-spined stickleback (Gasterosteus aculeatus)
Bavel, B. van; Andersson, P.; Wingfors, H.; Bergqvist, P.A.; Rappe, C.; Tysklind, M.; Aahgren, J.; Norrgren, L.
1996-06-01
Three-spined sticklebacks were exposed to 20 polychlorinated biphenyls (PCBs) through gastrointestinal intake. The PCBs, viz. 2,2{prime},3,4-TeCB, 2,2{prime},4,6{prime}-TeCB, 2,3,3{prime},5{prime}-TeCB, 2,3,4,4{prime}-TeCB, 2,3{prime},4,5{prime}-TeCB, 3,3{prime},4,5-TeCB, 2,2{prime},3,4{prime},6-PeCB, 2,2{prime},4,4{prime},5-PeCB, 2,2{prime},4,6,6{prime}-PeCB, 2,3,3{prime},5,6-PeCB, 2,3,4,4{prime},6-PeCB, 3,3{prime}4,4{prime}5-PeCB, 2,2{prime},3,4,5,6{prime}-HxCB, 2,2{prime},4,4{prime},5,5{prime}-HxCB, 3,3{prime},4,4{prime},5,5{prime}-HxCB, 2,2{prime},3,3{prime},4,5,6-HpCB, 2,2{prime},3,4,4{prime},6,6{prime}-HpCB, 2,2{prime},3,4{prime},5,6,6{prime}-HpCB, 2,3,3{prime},4,4{prime},5,6-HpCB, and 2,3,3{prime},4{prime},5,5{prime},6-HpCB, were selected by means of a full factorial design in combination with principal component analyses based on several physicochemical properties of tetra- through hepta-PCBs. After exposure to the training set, pooled samples of the sticklebacks were analyzed by GC-MS after lipid determination and cleanup using Florisil open-column chromatography. Bioaccumulation factors (BAFs) were calculated on a lipid basis by dividing the PCB concentrations in the fish by the respective concentrations in the feed. Higher chlorinated PCBs showed, in general, higher bioaccumulation than the lower chlorinated congeners. Polychlorinated biphenyls with vicinal hydrogens in the meta- and para-positions exhibited low bioaccumulation. Finally, 18 of the 20 measured BAFs were used to calculate a multivariate quantitative structure-activity relationship (QSAR) by means of partial least squares fitting to latent structures. By applying this QSAR, the bioaccumulation potentials of 136 nontested tetra- through hepta-PCB congeners were predicted.
Bonne, François; Bonnay, Patrick; Bradu, Benjamin
2014-01-29
In this paper, a multivariable model-based non-linear controller for Warm Compression Stations (WCS) is proposed. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to have precise control of every pressure in normal operation or to stabilize and control the cryoplant under high variation of thermal loads (such as a pulsed heat load expected to take place in future fusion reactors such as those expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details how to set the WCS model up to synthesize the Linear Quadratic Optimal feedback gain and how to use it. After preliminary tuning at CEA-Grenoble on the 400W@1.8K helium test facility, the controller has been implemented on a Schneider PLC and fully tested first on the CERN's real-time simulator. Then, it was experimentally validated on a real CERN cryoplant. The efficiency of the solution is experimentally assessed using a reasonable operating scenario of start and stop of compressors and cryogenic turbines. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.
NASA Astrophysics Data System (ADS)
Bonne, François; Alamir, Mazen; Bonnay, Patrick; Bradu, Benjamin
2014-01-01
In this paper, a multivariable model-based non-linear controller for Warm Compression Stations (WCS) is proposed. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to have precise control of every pressure in normal operation or to stabilize and control the cryoplant under high variation of thermal loads (such as a pulsed heat load expected to take place in future fusion reactors such as those expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details how to set the WCS model up to synthesize the Linear Quadratic Optimal feedback gain and how to use it. After preliminary tuning at CEA-Grenoble on the 400W@1.8K helium test facility, the controller has been implemented on a Schneider PLC and fully tested first on the CERN's real-time simulator. Then, it was experimentally validated on a real CERN cryoplant. The efficiency of the solution is experimentally assessed using a reasonable operating scenario of start and stop of compressors and cryogenic turbines. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.
An improved bundle adjustment model and algorithm with novel block matrix partition method
NASA Astrophysics Data System (ADS)
Xia, Zemin; Li, Zhongwei; Zhong, Kai
2014-11-01
Sparse bundle adjustment is widely applied in computer vision and photogrammetry. However, existing implementation is based on the model of n 3D points projecting onto m different camera imaging planes at m positions, which can't be applied to commonly monocular, binocular or trinocular imaging systems. A novel design and implementation of bundle adjustment algorithm is proposed in this paper, which is based on n 3D points projecting onto the same camera imaging plane at m positions .To improve the performance of the algorithm, a novel sparse block matrix partition method is proposed. Experiments show that the improved bundle adjustment is effective, robust and has a better tolerance to pixel coordinates error.
NASA Astrophysics Data System (ADS)
Naipal, V.; Reick, C.; Pongratz, J.; Van Oost, K.
2015-09-01
Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale. This limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is, due to its simple structure and empirical basis, a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial-scale applications often rely on coarse data input, which is not compatible with the local scale on which the model is parameterized. Our study aims at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting erosion rates to extensive empirical databases from the USA and Europe. By scaling the slope according to the fractal method to adjust the topographical factor, we managed to improve the topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that compared well to high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted version shows a global higher mean erosion rate and more variability in the erosion rates. Comparison to empirical data sets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional differences with the empirical databases, the results indicate that the
NASA Astrophysics Data System (ADS)
Lautz, L. K.; Hoke, G. D.; Lu, Z.; Siegel, D. I.
2013-12-01
Hydraulic fracturing has the potential to introduce saline water into the environment due to migration of deep formation water to shallow aquifers and/or discharge of flowback water to the environment during transport and disposal. It is challenging to definitively identify whether elevated salinity is associated with hydraulic fracturing, in part, due to the real possibility of other anthropogenic sources of salinity in the human-impacted watersheds in which drilling is taking place and some formation water present naturally in shallow groundwater aquifers. We combined new and published chemistry data for private drinking water wells sampled across five southern New York (NY) counties overlying the Marcellus Shale (Broome, Chemung, Chenango, Steuben, and Tioga). Measurements include Cl, Na, Br, I, Ca, Mg, Ba, SO4, and Sr. We compared this baseline groundwater quality data in NY, now under a moratorium on hydraulic fracturing, with published chemistry data for 6 different potential sources of elevated salinity in shallow groundwater, including Appalachian Basin formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. A multivariate random number generator was used to create a synthetic, low salinity (< 20 mg/L Cl) groundwater data set (n=1000) based on the statistical properties of the observed low salinity groundwater. The synthetic, low salinity groundwater was then artificially mixed with variable proportions of different potential sources of salinity to explore chemical differences between groundwater impacted by formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. We then trained a multivariate, discriminant analysis model on the resulting data set to classify observed high salinity groundwater (> 20 mg/L Cl) as being affected by formation water, road salt, septic effluent, landfill leachate, animal waste, or water softeners. Single elements or pairs of
Teece, Lucy; Dennis, Martin S.; Roffe, Christine
2016-01-01
Introduction Various prognostic models have been developed for acute stroke, including one based on age and five binary variables (‘six simple variables’ model; SSVMod) and one based on age plus scores on the National Institutes of Health Stroke Scale (NIHSSMod). The aims of this study were to externally validate and recalibrate these models, and to compare their predictive ability in relation to both survival and independence. Methods Data from a large clinical trial of oxygen therapy (n = 8003) were used to determine the discrimination and calibration of the models, using C-statistics, calibration plots, and Hosmer-Lemeshow statistics. Methods of recalibration in the large and logistic recalibration were used to update the models. Results For discrimination, both models functioned better for survival (C-statistics between .802 and .837) than for independence (C-statistics between .725 and .735). Both models showed slight shortcomings with regard to calibration, over-predicting survival and under-predicting independence; the NIHSSMod performed slightly better than the SSVMod. For the most part, there were only minor differences between ischaemic and haemorrhagic strokes. Logistic recalibration successfully updated the models for a clinical trial population. Conclusions Both prognostic models performed well overall in a clinical trial population. The choice between them is probably better based on clinical and practical considerations than on statistical considerations. PMID:27227988
Development of Control Models and a Robust Multivariable Controller for Surface Shape Control
Winters, S
2003-06-18
Surface shape control techniques are applied to many diverse disciplines, such as adaptive optics, noise control, aircraft flutter control and satellites, with an objective to achieve a desirable shape for an elastic body by the application of distributed control forces. Achieving the desirable shape is influenced by many factors, such as, actuator locations, sensor locations, surface precision and controller performance. Building prototypes to complete design optimizations or controller development can be costly or impractical. This shortfall, puts significant value in developing accurate modeling and control simulation approaches. This thesis focuses on the field of adaptive optics, although these developments have the potential for application in many other fields. A static finite element model is developed and validated using a large aperture interferometer system. This model is then integrated into a control model using a linear least squares algorithm and Shack-Hartmann sensor. The model is successfully exercised showing functionality for various wavefront aberrations. Utilizing a verified model shows significant value in simulating static surface shape control problems with quantifiable uncertainties. A new dynamic model for a seven actuator deformable mirror is presented and its accuracy is proven through experiment. Bond graph techniques are used to generate the state space model of the multi-actuator deformable mirror including piezo-electric actuator dynamics. Using this verified model, a robust multi-input multi-output (MIMO) H{sub {infinity}} controller is designed and implemented. This controller proved superior performance as compared to a standard proportional-integral controller (PI) design.
ERIC Educational Resources Information Center
Grimm, Kevin J.; An, Yang; McArdle, John J.; Zonderman, Alan B.; Resnick, Susan M.
2012-01-01
Latent difference score models (e.g., McArdle & Hamagami, 2001) are extended to include effects from prior changes to subsequent changes. This extension of latent difference scores allows for testing hypotheses where recent changes, as opposed to recent levels, are a primary predictor of subsequent changes. These models are applied to bivariate…
NASA Technical Reports Server (NTRS)
Morelli, E. A.; Proffitt, M. S.
1999-01-01
The data for longitudinal non-dimensional, aerodynamic coefficients in the High Speed Research Cycle 2B aerodynamic database were modeled using polynomial expressions identified with an orthogonal function modeling technique. The discrepancy between the tabular aerodynamic data and the polynomial models was tested and shown to be less than 15 percent for drag, lift, and pitching moment coefficients over the entire flight envelope. Most of this discrepancy was traced to smoothing local measurement noise and to the omission of mass case 5 data in the modeling process. A simulation check case showed that the polynomial models provided a compact and accurate representation of the nonlinear aerodynamic dependencies contained in the HSR Cycle 2B tabular aerodynamic database.
MacDonald, Shannon E; Schopflocher, Donald P; Vaudry, Wendy
2014-01-01
Children who begin but do not fully complete the recommended series of childhood vaccines by 2 y of age are a much larger group than those who receive no vaccines. While parents who refuse all vaccines typically express concern about vaccine safety, it is critical to determine what influences parents of ‘partially’ immunized children. This case-control study examined whether parental concern about vaccine safety was responsible for partial immunization, and whether other personal or system-level factors played an important role. A random sample of parents of partially and completely immunized 2 y old children were selected from a Canadian regional immunization registry and completed a postal survey assessing various personal and system-level factors. Unadjusted odds ratios (OR) and adjusted ORs (aOR) were calculated with logistic regression. While vaccine safety concern was associated with partial immunization (OR 7.338, 95% CI 4.138– 13.012), other variables were more strongly associated and reduced the strength of the relationship between concern and partial immunization in multivariable analysis (aOR 2.829, 95% CI 1.151 – 6.957). Other important factors included perceived disease susceptibility and severity (aOR 4.629, 95% CI 2.017 – 10.625), residential mobility (aOR 3.908, 95% CI 2.075 – 7.358), daycare use (aOR 0.310, 95% CI 0.144 - 0.671), number of needles administered at each visit (aOR 7.734, 95% CI 2.598 – 23.025) and access to a regular physician (aOR 0.219, 95% CI 0.057 – 0.846). While concern about vaccine safety may be addressed through educational strategies, this study suggests that additional program and policy-level strategies may positively impact immunization uptake. PMID:25483477
Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings
NASA Astrophysics Data System (ADS)
Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia
2014-09-01
In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.
On the need of multi-variables measurements for a proper assessment of hydrological models
NASA Astrophysics Data System (ADS)
Baroni, G.; Graeff, T.; Oswald, S. E.
2012-12-01
The assessment of hydrological model is often limited by the number of data available. For this, in most of the cases, the hydrological models are often calibrated and validated considering just one single hydrological process. Depending on the case study and the model used examples mainly refer to the use of discharge data, soil moisture or groundwater level. However, several authors have underlined the limit and inconsistence of this approach with multi-input output models because a good simulation may be obtained on the basis of internal errors compensation. To further explore this issue, an uncertainty and global sensitivity analysis is conducted on SHETRAN, a fully distributed physical-based hydrological model. The model is applied in a small (~1.4 km2) mountainous catchment with agricultural land use located in central Germany (Schaefertal, Harz Mountains). Input and parameters are considered as major sources of uncertainty in the framework. A global sensitivity analysis based on the method of Sobol/Saltelli is used to find the most important sources of uncertainty. The results show the presence of compensating errors by the different processes considered i.e. evapotranspiration, percolation, soil moisture and discharge. Complementarily, the sources of uncertainty are founded to be specific for each process considered. In this way, it is showed how a coupled multi-objective sensitivity and calibration analysis should be used for a proper assessment of the model.
NASA Technical Reports Server (NTRS)
Baheti, R. S.
1982-01-01
The magnetic suspension and balance system for an airplane model in a large wind tunnel is considered. In this system, superconducting coils generate magnetic forces and torques on the magnetized soft iron core of the airplane model. The control system is a position servo where the airplane model, with six degrees of feedom, follows the reference static or dynamic input commands. The controller design, based on the characteristic loci method, minimizes the effects of aerodynamic and inertial cross-couplings, and provides the specified dynamic response.
Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James
2015-03-25
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.
Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James
2015-03-25
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer tomore » true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.« less
DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA*
Erosheva, Elena A.; Fienberg, Stephen E.; Joutard, Cyrille
2008-01-01
Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population of elderly adults. We consider an extract of functional disability data from the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) model. We first describe GoM as an individual-level mixture model that allows individuals to have partial membership in several mixture components simultaneously. We then prove the equivalence between individual-level and population-level mixture models, and use this property to develop a Markov Chain Monte Carlo algorithm for Bayesian estimation of the model. We use our approach to analyze functional disability data from the NLTCS. PMID:21687832
An appraisal-based coping model of attachment and adjustment to arthritis.
Sirois, Fuschia M; Gick, Mary L
2016-05-01
Guided by pain-related attachment models and coping theory, we used structural equation modeling to test an appraisal-based coping model of how insecure attachment was linked to arthritis adjustment in a sample of 365 people with arthritis. The structural equation modeling analyses revealed indirect and direct associations of anxious and avoidant attachment with greater appraisals of disease-related threat, less perceived social support to deal with this threat, and less coping efficacy. There was evidence of reappraisal processes for avoidant but not anxious attachment. Findings highlight the importance of considering attachment style when assessing how people cope with the daily challenges of arthritis. PMID:24984717
Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model
NASA Astrophysics Data System (ADS)
Sedighi, Vahid; Fridrich, Jessica; Cogranne, Rémi
2015-03-01
The vast majority of steganographic schemes for digital images stored in the raster format limit the amplitude of embedding changes to the smallest possible value. In this paper, we investigate the possibility to further improve the empirical security by allowing the embedding changes in highly textured areas to have a larger amplitude and thus embedding there a larger payload. Our approach is entirely model driven in the sense that the probabilities with which the cover pixels should be changed by a certain amount are derived from the cover model to minimize the power of an optimal statistical test. The embedding consists of two steps. First, the sender estimates the cover model parameters, the pixel variances, when modeling the pixels as a sequence of independent but not identically distributed generalized Gaussian random variables. Then, the embedding change probabilities for changing each pixel by 1 or 2, which can be transformed to costs for practical embedding using syndrome-trellis codes, are computed by solving a pair of non-linear algebraic equations. Using rich models and selection-channel-aware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and S-UNIWARD.
Cassidy, Adam R
2016-01-01
The objective of this study was to establish latent executive function (EF) and psychosocial adjustment factor structure, to examine associations between EF and psychosocial adjustment, and to explore potential development differences in EF-psychosocial adjustment associations in healthy children and adolescents. Using data from the multisite National Institutes of Health (NIH) magnetic resonance imaging (MRI) Study of Normal Brain Development, the current investigation examined latent associations between theoretically and empirically derived EF factors and emotional and behavioral adjustment measures in a large, nationally representative sample of children and adolescents (7-18 years old; N = 352). Confirmatory factor analysis (CFA) was the primary method of data analysis. CFA results revealed that, in the whole sample, the proposed five-factor model (Working Memory, Shifting, Verbal Fluency, Externalizing, and Internalizing) provided a close fit to the data, χ(2)(66) = 114.48, p < .001; RMSEA = .046; NNFI = .973; CFI = .980. Significant negative associations were demonstrated between Externalizing and both Working Memory and Verbal Fluency (p < .01) factors. A series of increasingly restrictive tests led to the rejection of the hypothesis of invariance, thereby precluding formal statistical examination of age-related differences in latent EF-psychosocial adjustment associations. Findings indicate that childhood EF skills are best conceptualized as a constellation of interconnected yet distinguishable cognitive self-regulatory skills. Individual differences in certain domains of EF track meaningfully and in expected directions with emotional and behavioral adjustment indices. Externalizing behaviors, in particular, are associated with latent Working Memory and Verbal Fluency factors. PMID:25569593
2013-01-01
Background Outbreaks of infectious pancreatic necrosis (IPN) in Atlantic salmon can result in reduced growth rates in a fraction of the surviving fish (runts). Genetic and environmental variation also affects growth rates within different categories of healthy animals and runts, which complicates identification of runts. Mixture models are commonly used to identify the underlying structures in such data, and the aim of this study was to develop Bayesian mixture models for the genetic analysis of health status (runt/healthy) of surviving fish from an IPN outbreak. Methods Five statistical models were tested on data consisting of 10 972 fish that died and 3959 survivors with recorded growth data. The most complex models (4 and 5) were multivariate normal-binary mixture models including growth, sexual maturity and field survival traits. Growth rate and liability of sexual maturation were treated as two-component normal mixtures, assuming phenotypes originated from two potentially overlapping distributions, (runt/normal). Runt status was an unobserved binary trait. These models were compared to mixture models with fewer traits (Models 2 and 3) and a classical linear animal model for growth (Model 1). Results Assuming growth as a mixture trait improved the predictive ability of the statistical model considerably (Model 2 vs. 1). The final models (4 and 5) yielded the following results: estimated (underlying) heritabilities were moderate for growth in healthy fish (0.32 ± 0.04 and 0.35 ± 0.05), runt status (0.39 ± 0.07 and 0.36 ± 0.08) and sexual maturation (0.33 ± 0.05), and high for field survival (0.47 ± 0.03 and 0.48 ± 0.03). Growth in healthy animals, runt status and survival showed consistent favourable genetic associations. Sexual maturation showed an unfavourable non-significant genetic correlation with runt status, but favourable genetic correlations with other traits. The estimated fraction of healthy fish was 81-85%. The
Kalivas, John H; Héberger, Károly; Andries, Erik
2015-04-15
Most multivariate calibration methods require selection of tuning parameters, such as partial least squares (PLS) or the Tikhonov regularization variant ridge regression (RR). Tuning parameter values determine the direction and magnitude of respective model vectors thereby setting the resultant predication abilities of the model vectors. Simultaneously, tuning parameter values establish the corresponding bias/variance and the underlying selectivity/sensitivity tradeoffs. Selection of the final tuning parameter is often accomplished through some form of cross-validation and the resultant root mean square error of cross-validation (RMSECV) values are evaluated. However, selection of a "good" tuning parameter with this one model evaluation merit is almost impossible. Including additional model merits assists tuning parameter selection to provide better balanced models as well as allowing for a reasonable comparison between calibration methods. Using multiple merits requires decisions to be made on how to combine and weight the merits into an information criterion. An abundance of options are possible. Presented in this paper is the sum of ranking differences (SRD) to ensemble a collection of model evaluation merits varying across tuning parameters. It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired. Essentially, the user's preference for the degree of balance between bias and variance ultimately decides the merits used in SRD and hence, the tuning parameter values ranked lowest by SRD for automatic selection. The SRD process is also shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection. Because SRD evaluates consistency across multiple merits, decisions on how to combine and weight merits are avoided. To demonstrate the utility of SRD, a near infrared spectral data set and a
Dagne, Getachew; Huang, Yangxin
2016-01-01
Censored data are characteristics of many bioassays in HIV/AIDS studies where assays may not be sensitive enough to determine gradations in viral load determination among those below a detectable threshold. Not accounting for such left-censoring appropriately can lead to biased parameter estimates in most data analysis. To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew distributions. Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable. A Bayesian modeling approach via Markov Chain Monte Carlo (MCMC) algorithm is used to estimate model parameters. The proposed methods are illustrated using real data from an HIV/AIDS study. PMID:22992288
Dagne, Getachew; Huang, Yangxin
2012-01-01
Censored data are characteristics of many bioassays in HIV/AIDS studies where assays may not be sensitive enough to determine gradations in viral load determination among those below a detectable threshold. Not accounting for such left-censoring appropriately can lead to biased parameter estimates in most data analysis. To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew distributions. Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable. A Bayesian modeling approach via Markov Chain Monte Carlo (MCMC) algorithm is used to estimate model parameters. The proposed methods are illustrated using real data from an HIV/AIDS study. PMID:22992288
Multi-variable mathematical models for the air-cathode microbial fuel cell system
Ou, Shiqi; Kashima, Hiroyuki; Aaron, Douglas S.; Regan, John M.; Mench, Matthew M.
2016-03-10
This research adopted the version control system into the model construction for the single chamber air-cathode microbial fuel cell (MFC) system, to understand the interrelation of biological, chemical, and electrochemical reactions. The anodic steady state model was used to consider the chemical species diffusion and electric migration influence to the MFC performance. In the cathodic steady state model, the mass transport and reactions in a multi-layer, abiotic cathode and multi-bacteria cathode biofilm were simulated. Transport of hydroxide was assumed for cathodic pH change. This assumption is an alternative to the typical notion of proton consumption during oxygen reduction to explainmore » elevated cathode pH. The cathodic steady state model provided the power density and polarization curve performance results that can be compared to an experimental MFC system. Another aspect we considered was the relative contributions of platinum catalyst and microbes on the cathode to the oxygen reduction reaction (ORR). We found simulation results showed that the biocatalyst in a cathode that includes a Pt/C catalyst likely plays a minor role in ORR, contributing up to 8% of the total power calculated by the models.« less
Multi-variable mathematical models for the air-cathode microbial fuel cell system
NASA Astrophysics Data System (ADS)
Ou, Shiqi; Kashima, Hiroyuki; Aaron, Douglas S.; Regan, John M.; Mench, Matthew M.
2016-05-01
This research adopted the version control system into the model construction for the single chamber air-cathode microbial fuel cell (MFC) system, to understand the interrelation of biological, chemical, and electrochemical reactions. The anodic steady state model was used to consider the chemical species diffusion and electric migration influence to the MFC performance. In the cathodic steady state model, the mass transport and reactions in a multi-layer, abiotic cathode and multi-bacteria cathode biofilm were simulated. Transport of hydroxide was assumed for cathodic pH change. This assumption is an alternative to the typical notion of proton consumption during oxygen reduction to explain elevated cathode pH. The cathodic steady state model provided the power density and polarization curve performance results that can be compared to an experimental MFC system. Another aspect considered was the relative contributions of platinum catalyst and microbes on the cathode to the oxygen reduction reaction (ORR). Simulation results showed that the biocatalyst in a cathode that includes a Pt/C catalyst likely plays a minor role in ORR, contributing up to 8% of the total power calculated by the models.
Hadjipantelis, P. Z.; Aston, J. A. D.; Müller, H. G.; Evans, J. P.
2015-01-01
Mandarin Chinese is characterized by being a tonal language; the pitch (or F 0) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample F 0 contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online. PMID:26692591
NASA Astrophysics Data System (ADS)
Zu, Theresah N. K.; Liu, Sanchao; Germane, Katherine L.; Servinsky, Matthew D.; Gerlach, Elliot S.; Mackie, David M.; Sund, Christian J.
2016-05-01
The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the well-known Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q2Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.
Scrutiny of Appropriate Model Error Specification in Multivariate Assimilation Framework using mHM
NASA Astrophysics Data System (ADS)
Rakovec, O.; Noh, S. J.; Kumar, R.; Samaniego, L. E.
2015-12-01
Reliable and accurate predictions of regional scale water fluxes and states is of great challenge to the scientific community. Several sectors of society (municipalities, agriculture, energy, etc.) may benefit from successful solutions to appropriately quantify uncertainties in hydro-meteorological prediction systems, with particular attention to extreme weather conditions.Increased availability and quality of near real-time data enables better understanding of predictive skill of forecasting frameworks. To address this issue, automatic model-observation integrations are required for appropriate model initializations. In this study, the effects of noise specification on the quality of hydrological forecasts is scrutinized via a data assimilation system. This framework has been developed by incorporating the mesoscale hydrologic model (mHM, {http://www.ufz.de/mhm) with particle filtering (PF) approach used for model state updating. In comparison with previous works, lag PF is considered to better account for the response times of internal hydrologic processes.The objective of this study is to assess the benefits of model state updating for prediction of water fluxes and states up to 3-month ahead forecast using particle filtering. The efficiency of this system is demonstrated in 10 large European basins. We evaluate the model skill for five assimilation scenarios using observed (1) discharge (Q); (2) MODIS evapotranspiration (ET); (3) GRACE terrestrial total water storage (TWS) anomaly; (4) ESA-CCI soil moisture; and (5) the combination of Q, ET, TWS, and SM in a hindcast experiment (2004-2010). The effects of error perturbations for both, the analysis and the forecasts are presented, and optimal trade-offs are discussed. While large perturbations are preferred for the analysis time step, steep deterioration is observed for longer lead times, for which more conservative error measures should be considered. From all the datasets, complementary GRACE TWS data together
NASA Astrophysics Data System (ADS)
Shareef, Muntadher A.; Toumi, Abdelmalek; Khenchaf, Ali
2014-10-01
Remote sensing is one of the most important tools for monitoring and assisting to estimate and predict Water Quality parameters (WQPs). The traditional methods used for monitoring pollutants are generally relied on optical images. In this paper, we present a new approach based on the Synthetic Aperture Radar (SAR) images which we used to map the region of interest and to estimate the WQPs. To achieve this estimation quality, the texture analysis is exploited to improve the regression models. These models are established and developed to estimate six common concerned water quality parameters from texture parameters extracted from Terra SAR-X data. In this purpose, the Gray Level Cooccurrence Matrix (GLCM) is used to estimate several regression models using six texture parameters such as contrast, correlation, energy, homogeneity, entropy and variance. For each predicted model, an accuracy value is computed from the probability value given by the regression analysis model of each parameter. In order to validate our approach, we have used tow dataset of water region for training and test process. To evaluate and validate the proposed model, we applied it on the training set. In the last stage, we used the fuzzy K-means clustering to generalize the water quality estimation on the whole of water region extracted from segmented Terra SAR-X image. Also, the obtained results showed that there are a good statistical correlation between the in situ water quality and Terra SAR-X data, and also demonstrated that the characteristics obtained by texture analysis are able to monitor and predicate the distribution of WQPs in large rivers with high accuracy.
ERIC Educational Resources Information Center
Pedras, Melvin J.
The model used in a multivariate fashion to reorganize the Department of Industrial Technology Education at the University of Idaho thereby undergoing a test for effectiveness is presented. This model is a product of a seminar held in West Germany in 1986 in which a group of professional educators from several countries produced a generic model…
Technology Transfer Automated Retrieval System (TEKTRAN)
This paper assesses the impact of different likelihood functions in identifying sensitive parameters of the highly parameterized, spatially distributed Soil and Water Assessment Tool (SWAT) watershed model for multiple variables at multiple sites. The global one-factor-at-a-time (OAT) method of Morr...
Multivariate Dynamic Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts
NASA Technical Reports Server (NTRS)
Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah
2014-01-01
The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in integrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.
Multivariate Dynamical Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts
NASA Technical Reports Server (NTRS)
Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah
2014-01-01
The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in intergrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.
ERIC Educational Resources Information Center
Day, David M.; Hunt, Ann C.
1996-01-01
The predictive validity of a hypothesized model of 5 factors associated with the development of juvenile delinquency was evaluated with 68 children (ages 6-11) who had been referred for delinquent behavior. Analysis of clinical files indicated that aggressiveness and variety of conduct problems accounted for 31% of variance in delinquent behavior.…
ERIC Educational Resources Information Center
Henson, Robin K.
In General Linear Model (GLM) analyses, it is important to interpret structure coefficients, along with standardized weights, when evaluating variable contribution to observed effects. Although often used in canonical correlation analysis, structure coefficients are less frequently used in multiple regression and several other multivariate…
Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...
Celaya-Padilla, José; Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Galvan-Tejada, Jorge; Treviño, Victor; Tamez-Peña, José
2015-01-01
Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets. The method coregisters the left and right mammograms, extracts image features, and classifies the subjects into risk of having malignant calcifications (CS), malignant masses (MS), and healthy subject (HS). In this study, 449 subjects (197 CS, 207 MS, and 45 HS) from a public database were used to train and evaluate the CADx. Percentile-rank (p-rank) and z-normalizations were used. For the p-rank, the CS versus HS model achieved a cross-validation accuracy of 0.797 with an area under the receiver operating characteristic curve (AUC) of 0.882; the MS versus HS model obtained an accuracy of 0.772 and an AUC of 0.842. For the z-normalization, the CS versus HS model achieved an accuracy of 0.825 with an AUC of 0.882 and the MS versus HS model obtained an accuracy of 0.698 and an AUC of 0.807. The proposed method has the potential to rank cases with high probability of malignant findings aiding in the prioritization of radiologists work list. PMID:26240818
Kautter, John; Pope, Gregory C; Ingber, Melvin; Freeman, Sara; Patterson, Lindsey; Cohen, Michael; Keenan, Patricia
2014-01-01
Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula. PMID:25360387
Kautter, John; Pope, Gregory C; Ingber, Melvin; Freeman, Sara; Patterson, Lindsey; Cohen, Michael; Keenan, Patricia
2014-01-01
Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula. PMID:25360387
Lithium-ion Open Circuit Voltage (OCV) curve modelling and its ageing adjustment
NASA Astrophysics Data System (ADS)
Lavigne, L.; Sabatier, J.; Francisco, J. Mbala; Guillemard, F.; Noury, A.
2016-08-01
This paper is a contribution to lithium-ion batteries modelling taking into account aging effects. It first analyses the impact of aging on electrode stoichiometry and then on lithium-ion cell Open Circuit Voltage (OCV) curve. Through some hypotheses and an appropriate definition of the cell state of charge, it shows that each electrode equilibrium potential, but also the whole cell equilibrium potential can be modelled by a polynomial that requires only one adjustment parameter during aging. An adjustment algorithm, based on the idea that for two fixed OCVs, the state of charge between these two equilibrium states is unique for a given aging level, is then proposed. Its efficiency is evaluated on a battery pack constituted of four cells.
A multivariate/chemical mass balance model for air pollution in China: A hybrid methodology
Zelenka, M.P.
1992-01-01
This research explores the possibility of using a two step method of identifying and quantifying air pollution emissions in an urban environment. The procedure uses a mathematical model called Target Transformation Factor Analysis (TTFA) to estimate source profiles using ambient trace element air concentration data. A source profile is analogous to a fingerprint since it is unique to each source of air pollution. It is important to use source profiles that are measured or estimated for the specific location under study. The profiles estimated by TTFA are then employed in a Chemical Mass Balance (CMB) source apportionment analysis for the airshed. Other known sources are estimated using source signatures from the literature. Applying the TTFA and CMB models in this fashion is called receptor modeling. Generically, a receptor model is the combination of measured air pollution concentration data with a numerical technique which apportions the measured air pollution among distinct source types. The results show that TTFA can be used to provide quantitative estimates of air pollution source profiles for an urban center in China. The number of profiles for unique source types was limited for this data set since emissions from certain types of sources co-varied during each sampling day. Consequently, the CMB analyses that applied the TTFA source profiles needed to be supplemented with standard US EPA source profiles. The application of TTFA for estimating source profiles from ambient data and the subsequent use of those profiles in CMB analyses with source profiles obtained from the EPA's source library can improve the statistical quality of the source apportionment analysis. TTFA can identify source categories of airborne pollution for specific cities, as well as give quantitative data on the composition of the emissions from those source types.
NASA Astrophysics Data System (ADS)
Mfumu Kihumba, Antoine; Vanclooster, Marnik
2013-04-01
Drinking water in Kinshasa, the capital of the Democratic Republic of Congo, is provided by extracting groundwater from the local aquifer, particularly in peripheral areas. The exploited groundwater body is mainly unconfined and located within a continuous detrital aquifer, primarily composed of sedimentary formations. However, the aquifer is subjected to an increasing threat of anthropogenic pollution pressure. Understanding the detailed origin of this pollution pressure is important for sustainable drinking water management in Kinshasa. The present study aims to explain the observed nitrate pollution problem, nitrate being considered as a good tracer for other pollution threats. The analysis is made in terms of physical attributes that are readily available using a statistical modelling approach. For the nitrate data, use was made of a historical groundwater quality assessment study, for which the data were re-analysed. The physical attributes are related to the topography, land use, geology and hydrogeology of the region. Prior to the statistical modelling, intrinsic and specific vulnerability for nitrate pollution was assessed. This vulnerability assessment showed that the alluvium area in the northern part of the region is the most vulnerable area. This area consists of urban land use with poor sanitation. Re-analysis of the nitrate pollution data demonstrated that the spatial variability of nitrate concentrations in the groundwater body is high, and coherent with the fragmented land use of the region and the intrinsic and specific vulnerability maps. For the statistical modeling use was made of multiple regression and regression tree analysis. The results demonstrated the significant impact of land use variables on the Kinshasa groundwater nitrate pollution and the need for a detailed delineation of groundwater capture zones around the monitoring stations. Key words: Groundwater , Isotopic, Kinshasa, Modelling, Pollution, Physico-chemical.
Zhou, Chengfeng; Jiang, Wei; Cheng, Qingzheng; Via, Brian K.
2015-01-01
This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm−1 from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry. PMID:26576321
Zhou, Chengfeng; Jiang, Wei; Cheng, Qingzheng; Via, Brian K
2015-01-01
This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm(-1) from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry. PMID:26576321
Update on Multi-Variable Parametric Cost Models for Ground and Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Henrichs, Todd; Luedtke, Alexander; West, Miranda
2012-01-01
Parametric cost models can be used by designers and project managers to perform relative cost comparisons between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. This paper reports on recent revisions and improvements to our ground telescope cost model and refinements of our understanding of space telescope cost models. One interesting observation is that while space telescopes are 50X to 100X more expensive than ground telescopes, their respective scaling relationships are similar. Another interesting speculation is that the role of technology development may be different between ground and space telescopes. For ground telescopes, the data indicates that technology development tends to reduce cost by approximately 50% every 20 years. But for space telescopes, there appears to be no such cost reduction because we do not tend to re-fly similar systems. Thus, instead of reducing cost, 20 years of technology development may be required to enable a doubling of space telescope capability. Other findings include: mass should not be used to estimate cost; spacecraft and science instrument costs account for approximately 50% of total mission cost; and, integration and testing accounts for only about 10% of total mission cost.
NASA Astrophysics Data System (ADS)
Chung, Sang Yong
2015-04-01
Multivariate statistical methods and inverse geochemical modelling were used to assess the hydrogeochemical processes of groundwater in Nakdong River basin. The study area is located in a part of Nakdong River basin, the Busan Metropolitan City, Kora. Quaternary deposits forms Samrak Park region and are underlain by intrusive rocks of Bulkuksa group and sedimentary rocks of Yucheon group in the Cretaceous Period. The Samrak park region is acting as two aquifer systems of unconfined aquifer and confined aquifer. The unconfined aquifer consists of upper sand, and confined aquifer is comprised of clay, lower sand, gravel, weathered rock. Porosity and hydraulic conductivity of the area is 37 to 59% and 1.7 to 200m/day, respectively. Depth of the wells ranges from 9 to 77m. Piper's trilinear diagram, CaCl2 type was useful for unconfined aquifer and NaCl type was dominant for confined aquifer. By hierarchical cluster analysis (HCA), Group 1 and Group 2 are fully composed of unconfined aquifer and confined aquifer, respectively. In factor analysis (FA), Factor 1 is described by the strong loadings of EC, Na, K, Ca, Mg, Cl, HCO3, SO4 and Si, and Factor 2 represents the strong loadings of pH and Al. Base on the Gibbs diagram, the unconfined and confined aquifer samples are scattered discretely in the rock and evaporation areas. The principal hydrogeochemical processes occurring in the confined and unconfined aquifers are the ion exchange due to the phenomena of freshening under natural recharge and water-rock interactions followed by evaporation and dissolution. The saturation index of minerals such as Ca-montmorillonite, dolomite and calcite represents oversaturated, and the albite, gypsum and halite show undersaturated. Inverse geochemical modeling using PHREEQC code demonstrated that relatively few phases were required to derive the differences in groundwater chemistry along the flow path in the area. It also suggested that dissolution of carbonate and ion exchange
Lestander, Torbjörn A; Rhén, Christofer
2005-08-01
The multitude of biofuels in use and their widely different characteristics stress the need for improved characterisation of their chemical and physical properties. Industrial use of biofuels further demands rapid characterisation methods suitable for on-line measurements. The single most important property in biofuels is the calorific value. This is influenced by moisture and ash content as well as the chemical composition of the dry biomass. Near infrared (NIR) spectroscopy and bi-orthogonal partial least squares (BPLS) regression were used to model moisture and ash content as well as gross calorific value in ground samples of stem and branches wood. Samples from 16 individual trees of Norway spruce were artificially moistened into five classes (10, 20, 30, 40 and 50%). Three different models for decomposition of the spectral variation into structure and noise were applied. In total 16 BPLS models were used, all of which showed high accuracy in prediction for a test set and they explained 95.4-99.8% of the reference variable variation. The models for moisture content were spanned by the O-H and C-H overtones, i.e. between water and organic matter. The models for ash content appeared to be based on interactions in carbon chains. For calorific value the models was spanned by C-H stretching, by O-H stretching and bending and by combinations of O-H and C-O stretching. Also -C=C- bonds contributed in the prediction of calorific value. This study illustrates the possibility of using the NIR technique in combination with multivariate calibration to predict economically important properties of biofuels and to interpret models. This concept may also be applied for on-line prediction in processes to standardize biofuels or in biofuelled plants for process monitoring. PMID:16021218
NASA Technical Reports Server (NTRS)
Keppenne, Christian L.; Rienecker, Michele M.; Koblinsky, Chester (Technical Monitor)
2001-01-01
A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been implemented for the Poseidon ocean circulation model and tested with a Pacific Basin model configuration. There are about two million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF configuration are discussed. It is shown that the regionalization of the background covariances; has a negligible impact on the quality of the analyses. The parallel algorithm is very efficient for large numbers of observations but does not scale well beyond 100 PEs at the current model resolution. On a platform with distributed memory, memory rather than speed is the limiting factor.
Ma, Jianming; Kockelman, Kara M; Damien, Paul
2008-05-01
Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves. PMID:18460364
Modeling of topographic effects on Antarctic sea ice using multivariate adaptive regression splines
NASA Technical Reports Server (NTRS)
De Veaux, Richard D.; Gordon, Arnold L.; Comiso, Joey C.; Bacherer, Nadine E.
1993-01-01
The role of seafloor topography in the spatial variations of the southern ocean sea ice cover as observed (every other day) by the Nimbus 7 scanning multichannel microwave radiometer satellite in the years 1980, 1983, and 1984 is studied. Bottom bathymetry can affect sea ice surface characteristics because of the basically barotropic circulation of the ocean south of the Antarctic Circumpolar current. The main statistical tool used to quantify this effect is a local nonparametric regression model of sea ice concentration as a function of the depth and its first two derivatives in both meridional and zonal directions. First, we model the relationship of bathymetry to sea ice concentration in two sudy areas, one over the Maud Rise and the other over the Ross Sea shelf region. The multiple correlation coefficient is found to average 44% in the Maud Rise study area and 62% in the Ross Sea study area over the years 1980, 1983, and 1984. Second, a strategy of dividing the entire Antarctic region into an overlapping mosaic of small areas, or windows is considered. Keeping the windows small reduces the correlation of bathymetry with other factors such as wind, sea temperature, and distance to the continent. We find that although the form of the model varies from window to window due to the changing role of other relevant environmental variables, we are left with a spatially consistent ordering of the relative importance of the topographic predictors. For a set of three representative days in the Austral winter of 1980, the analysis shows that an average of 54% of the spatial variation in sea ice concentration over the entire ice cover can be attributed to topographic variables. The results thus support the hypothesis that there is a sea ice to bottom bathymetry link. However this should not undermine the considerable influence of wind, current, and temperature which affect the ice distribution directly and are partly responsible for the observed bathymetric effects.
Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale
NASA Astrophysics Data System (ADS)
Kreibich, Heidi; Schröter, Kai; Merz, Bruno
2016-05-01
Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.
Li, Li; Brumback, Babette A; Weppelmann, Thomas A; Morris, J Glenn; Ali, Afsar
2016-08-15
Motivated by an investigation of the effect of surface water temperature on the presence of Vibrio cholerae in water samples collected from different fixed surface water monitoring sites in Haiti in different months, we investigated methods to adjust for unmeasured confounding due to either of the two crossed factors site and month. In the process, we extended previous methods that adjust for unmeasured confounding due to one nesting factor (such as site, which nests the water samples from different months) to the case of two crossed factors. First, we developed a conditional pseudolikelihood estimator that eliminates fixed effects for the levels of each of the crossed factors from the estimating equation. Using the theory of U-Statistics for independent but non-identically distributed vectors, we show that our estimator is consistent and asymptotically normal, but that its variance depends on the nuisance parameters and thus cannot be easily estimated. Consequently, we apply our estimator in conjunction with a permutation test, and we investigate use of the pigeonhole bootstrap and the jackknife for constructing confidence intervals. We also incorporate our estimator into a diagnostic test for a logistic mixed model with crossed random effects and no unmeasured confounding. For comparison, we investigate between-within models extended to two crossed factors. These generalized linear mixed models include covariate means for each level of each factor in order to adjust for the unmeasured confounding. We conduct simulation studies, and we apply the methods to the Haitian data. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26892025
Hyper-Fit: Fitting Linear Models to Multidimensional Data with Multivariate Gaussian Uncertainties
NASA Astrophysics Data System (ADS)
Robotham, A. S. G.; Obreschkow, D.
2015-09-01
Astronomical data is often uncertain with errors that are heteroscedastic (different for each data point) and covariant between different dimensions. Assuming that a set of D-dimensional data points can be described by a (D - 1)-dimensional plane with intrinsic scatter, we derive the general likelihood function to be maximised to recover the best fitting model. Alongside the mathematical description, we also release the hyper-fit package for the R statistical language (http://github.com/asgr/hyper.fit) and a user-friendly web interface for online fitting (http://hyperfit.icrar.org). The hyper-fit package offers access to a large number of fitting routines, includes visualisation tools, and is fully documented in an extensive user manual. Most of the hyper-fit functionality is accessible via the web interface. In this paper, we include applications to toy examples and to real astronomical data from the literature: the mass-size, Tully-Fisher, Fundamental Plane, and mass-spin-morphology relations. In most cases, the hyper-fit solutions are in good agreement with published values, but uncover more information regarding the fitted model.
A multivariate quadrature based moment method for LES based modeling of supersonic combustion
NASA Astrophysics Data System (ADS)
Donde, Pratik; Koo, Heeseok; Raman, Venkat
2012-07-01
The transported probability density function (PDF) approach is a powerful technique for large eddy simulation (LES) based modeling of scramjet combustors. In this approach, a high-dimensional transport equation for the joint composition-enthalpy PDF needs to be solved. Quadrature based approaches provide deterministic Eulerian methods for solving the joint-PDF transport equation. In this work, it is first demonstrated that the numerical errors associated with LES require special care in the development of PDF solution algorithms. The direct quadrature method of moments (DQMOM) is one quadrature-based approach developed for supersonic combustion modeling. This approach is shown to generate inconsistent evolution of the scalar moments. Further, gradient-based source terms that appear in the DQMOM transport equations are severely underpredicted in LES leading to artificial mixing of fuel and oxidizer. To overcome these numerical issues, a semi-discrete quadrature method of moments (SeQMOM) is formulated. The performance of the new technique is compared with the DQMOM approach in canonical flow configurations as well as a three-dimensional supersonic cavity stabilized flame configuration. The SeQMOM approach is shown to predict subfilter statistics accurately compared to the DQMOM approach.
Accounting for sex differences in PTSD: A multi-variable mediation model
Christiansen, Dorte M.; Hansen, Maj
2015-01-01
Background Approximately twice as many females as males are diagnosed with posttraumatic stress disorder (PTSD). However, little is known about why females report more PTSD symptoms than males. Prior studies have generally focused on few potential mediators at a time and have often used methods that were not ideally suited to test for mediation effects. Prior research has identified a number of individual risk factors that may contribute to sex differences in PTSD severity, although these cannot fully account for the increased symptom levels in females when examined individually. Objective The present study is the first to systematically test the hypothesis that a combination of pre-, peri-, and posttraumatic risk factors more prevalent in females can account for sex differences in PTSD severity. Method The study was a quasi-prospective questionnaire survey assessing PTSD and related variables in 73.3% of all Danish bank employees exposed to bank robbery during the period from April 2010 to April 2011. Participants filled out questionnaires 1 week (T1, N=450) and 6 months after the robbery (T2, N=368; 61.1% females). Mediation was examined using an analysis designed specifically to test a multiple mediator model. Results Females reported more PTSD symptoms than males and higher levels of neuroticism, depression, physical anxiety sensitivity, peritraumatic fear, horror, and helplessness (the A2 criterion), tonic immobility, panic, dissociation, negative posttraumatic cognitions about self and the world, and feeling let down. These variables were included in the model as potential mediators. The combination of risk factors significantly mediated the association between sex and PTSD severity, accounting for 83% of the association. Conclusions The findings suggest that females report more PTSD symptoms because they experience higher levels of associated risk factors. The results are relevant to other trauma populations and to other trauma-related psychiatric disorders
NASA Astrophysics Data System (ADS)
Müller, Marc F.; Thompson, Sally E.
2013-10-01
Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Sparse ground-based gauge networks do not provide a robust basis for interpolation, and the reliability of remote sensing products, although improving, is still imperfect. Current techniques to estimate precipitation rely on combining these different kinds of measurements to correct the bias in the satellite observations. We propose a novel procedure that, unlike existing techniques, (i) allows correcting the possibly confounding effects of different sources of errors in satellite estimates, (ii) explicitly accounts for the spatial heterogeneity of the biases and (iii) allows the use of non overlapping historical observations. The proposed method spatially aggregates and interpolates gauge data at the satellite grid resolution by focusing on parameters that describe the frequency and intensity of the rainfall observed at the gauges. The resulting gridded parameters can then be used to adjust the probability density function of satellite rainfall observations at each grid cell, accounting for spatial heterogeneity. Unlike alternate methods, we explicitly adjust biases on rainfall frequency in addition to its intensity. Adjusted rainfall distributions can then readily be applied as input in stochastic rainfall generators or frequency domain hydrological models. Finally, we also provide a procedure to use them to correct remotely sensed rainfall time series. We apply the method to adjust the distributions of daily rainfall observed by the TRMM satellite in Nepal, which exemplifies the challenges associated with a sparse gauge network and large biases due to complex topography. In a cross-validation analysis on daily rainfall from TRMM 3B42 v6, we find that using a small subset of the available gauges, the proposed method outperforms local rainfall estimations using the complete network of available gauges to directly interpolate local rainfall or correct TRMM by adjusting
Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.
Qiao, Hong; Xi, Xuanyang; Li, Yinlin; Wu, Wei; Li, Fengfu
2015-11-01
Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational
NASA Astrophysics Data System (ADS)
Brix, H.; Menemenlis, D.; Hill, C.; Dutkiewicz, S.; Jahn, O.; Wang, D.; Bowman, K.; Zhang, H.
2015-11-01
The NASA Carbon Monitoring System (CMS) Flux Project aims to attribute changes in the atmospheric accumulation of carbon dioxide to spatially resolved fluxes by utilizing the full suite of NASA data, models, and assimilation capabilities. For the oceanic part of this project, we introduce ECCO2-Darwin, a new ocean biogeochemistry general circulation model based on combining the following pre-existing components: (i) a full-depth, eddying, global-ocean configuration of the Massachusetts Institute of Technology general circulation model (MITgcm), (ii) an adjoint-method-based estimate of ocean circulation from the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project, (iii) the MIT ecosystem model "Darwin", and (iv) a marine carbon chemistry model. Air-sea gas exchange coefficients and initial conditions of dissolved inorganic carbon, alkalinity, and oxygen are adjusted using a Green's Functions approach in order to optimize modeled air-sea CO2 fluxes. Data constraints include observations of carbon dioxide partial pressure (pCO2) for 2009-2010, global air-sea CO2 flux estimates, and the seasonal cycle of the Takahashi et al. (2009) Atlas. The model sensitivity experiments (or Green's Functions) include simulations that start from different initial conditions as well as experiments that perturb air-sea gas exchange parameters and the ratio of particulate inorganic to organic carbon. The Green's Functions approach yields a linear combination of these sensitivity experiments that minimizes model-data differences. The resulting initial conditions and gas exchange coefficients are then used to integrate the ECCO2-Darwin model forward. Despite the small number (six) of control parameters, the adjusted simulation is significantly closer to the data constraints (37% cost function reduction, i.e., reduction in the model-data difference, relative to the baseline simulation) and to independent observations (e.g., alkalinity). The adjusted air-sea gas
Chan, C M; Baratta, F S; Ozzello, L; Ceriani, R L
1994-01-01
Monoclonal antibody (MoAb) BrE-3, an anti-human milk fat globule (HMFG) MoAb, is used here as a novel prognostic indicator for survival and relapse time in patients with infiltrating ductal carcinoma of the breast. A scoring system (4-Score method) was developed to this effect that measured, in a statistically reliable fashion, the level of expression of the epitope for MoAb BrE-3 in the cytoplasm and membranes of breast carcinoma cells in paraffin-embedded sections. In univariate analysis, data obtained by the 4-Score Method as well as data from traditional prognostic indicators (tumor size, axillary node status, and grade of differentiation) were found to be associated with patient survival and relapse. In multivariate analysis, using a Cox proportional hazards regression model, levels of expression of BrE-3 epitope plus tumor size and axillary node status were weighted and combined in an Individual Linear Composite Prognostic Score (ILCPS) that had a high level of association with survival and relapse time in this sample model of patients with infiltrating ductal carcinoma of the breast. This level of association was found to be higher than the level of association for any other combination of traditional or 4-Score method variables. The level of expression of BrE-3 significantly adds to the prognostic capacity of traditional prognostic markers for infiltrating ductal carcinoma of the breast. PMID:7981443
Kim, Hyunwoo J.; Adluru, Nagesh; Collins, Maxwell D.; Chung, Moo K.; Bendlin, Barbara B.; Johnson, Sterling C.; Davidson, Richard J.; Singh, Vikas
2014-01-01
Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi, yi) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xi ∈ R, against a manifold-valued variable, yi ∈ . We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression —a manifold-valued dependent variable against multiple independent variables, i.e., f : Rn → . Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm. PMID:25580070
NASA Astrophysics Data System (ADS)
Bogenschutz, P.; Gettelman, A.; Larson, V. E.; Morrison, H.; Chen, C. C.; Thayer-Calder, K.; Craig, C.
2014-12-01
Supported by funding through a Climate Process Team (CPT), we have implemented a multi-variate probability density function (PDF) cloud and turbulence scheme into NCAR's Community Atmosphere Model (CAM). The parameterization is known as Cloud Layers Unified by Bi-normals (CLUBB) and is an incomplete third-order turbulence closure centered around a double-Gaussian assumed PDF. CLUBB replaces the existing planetary boundary layer, shallow convection, and cloud macrophysics schemes in CAM with a unified parameterization that drives one double moment microphysics scheme. This presentation documents the performance of CAM-CLUBB for both prescribed sea surface temeprature (SST) and coupled simulations. We will discuss the improved mean state climate, such as improved stratocumulus to cumulus transitions, that can result when compared to CAM5. In addition, CAM-CLUBB is able to improve many long-standing issues that many general circulation models (GCMs) struggle to realistically simulate; such as the Madden-Julian Oscillation (MJO), diurnal cycle of precipitation, and the frequency and intensity of precipitation. We will also discuss preliminary work being done to use CLUBB as a deep convection scheme in CAM.
Zammit-Mangion, Andrew; Rougier, Jonathan; Schön, Nana; Lindgren, Finn; Bamber, Jonathan
2015-01-01
Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60 m. An ice sheet contributes to sea-level rise (SLR) when its rate of ice discharge and/or surface melting exceeds accumulation through snowfall. Constraining the contribution of the ice sheets to present-day SLR is vital both for coastal development and planning, and climate projections. Information on various ice sheet processes is available from several remote sensing data sets, as well as in situ data such as global positioning system data. These data have differing coverage, spatial support, temporal sampling and sensing characteristics, and thus, it is advantageous to combine them all in a single framework for estimation of the SLR contribution and the assessment of processes controlling mass exchange with the ocean. In this paper, we predict the rate of height change due to salient geophysical processes in Antarctica and use these to provide estimates of SLR contribution with associated uncertainties. We employ a multivariate spatio-temporal model, approximated as a Gaussian Markov random field, to take advantage of differing spatio-temporal properties of the processes to separate the causes of the observed change. The process parameters are estimated from geophysical models, while the remaining parameters are estimated using a Markov chain Monte Carlo scheme, designed to operate in a high-performance computing environment across multiple nodes. We validate our methods against a separate data set and compare the results to those from studies that invariably employ numerical model outputs directly. We conclude that it is possible, and insightful, to assess Antarctica's contribution without explicit use of numerical models. Further, the results obtained here can be used to test the geophysical numerical models for which in situ data are hard to obtain. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd. PMID:25937792
A finite element model updating technique for adjustment of parameters near boundaries
NASA Astrophysics Data System (ADS)
Gwinn, Allen Fort, Jr.
Even though there have been many advances in research related to methods of updating finite element models based on measured normal mode vibration characteristics, there is yet to be a widely accepted method that works reliably with a wide range of problems. This dissertation focuses on the specific class of problems having to do with changes in stiffness near the clamped boundary of plate structures. This class of problems is especially important as it relates to the performance of turbine engine blades, where a change in stiffness at the base of the blade can be indicative of structural damage. The method that is presented herein is a new technique for resolving the differences between the physical structure and the finite element model. It is a semi-iterative technique that incorporates a "physical expansion" of the measured eigenvectors along with appropriate scaling of these expanded eigenvectors into an iterative loop that uses the Engel's model modification method to then calculate adjusted stiffness parameters for the finite element model. Three example problems are presented that use eigenvalues and mass normalized eigenvectors that have been calculated from experimentally obtained accelerometer readings. The test articles that were used were all thin plates with one edge fully clamped. They each had a cantilevered length of 8.5 inches and a width of 4 inches. The three plates differed from one another in thickness from 0.100 inches to 0.188 inches. These dimensions were selected in order to approximate a gas turbine engine blade. The semi-iterative modification technique is shown to do an excellent job of calculating the necessary adjustments to the finite element model so that the analytically determined eigenvalues and eigenvectors for the adjusted model match the corresponding values from the experimental data with good agreement. Furthermore, the semi-iterative method is quite robust. For the examples presented here, the method consistently converged
Jensen, Dan B; Hogeveen, Henk; De Vries, Albert
2016-09-01
Rapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes' theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of
Interfacial free energy adjustable phase field crystal model for homogeneous nucleation.
Guo, Can; Wang, Jincheng; Wang, Zhijun; Li, Junjie; Guo, Yaolin; Huang, Yunhao
2016-05-18
To describe the homogeneous nucleation process, an interfacial free energy adjustable phase-field crystal model (IPFC) was proposed by reconstructing the energy functional of the original phase field crystal (PFC) methodology. Compared with the original PFC model, the additional interface term in the IPFC model effectively can adjust the magnitude of the interfacial free energy, but does not affect the equilibrium phase diagram and the interfacial energy anisotropy. The IPFC model overcame the limitation that the interfacial free energy of the original PFC model is much less than the theoretical results. Using the IPFC model, we investigated some basic issues in homogeneous nucleation. From the viewpoint of simulation, we proceeded with an in situ observation of the process of cluster fluctuation and obtained quite similar snapshots to colloidal crystallization experiments. We also counted the size distribution of crystal-like clusters and the nucleation rate. Our simulations show that the size distribution is independent of the evolution time, and the nucleation rate remains constant after a period of relaxation, which are consistent with experimental observations. The linear relation between logarithmic nucleation rate and reciprocal driving force also conforms to the steady state nucleation theory. PMID:27117814
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models.
Krivitsky, Pavel N; Handcock, Mark S; Morris, Martina
2011-07-01
Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS). PMID:21691424
Remote Sensing-based Methodologies for Snow Model Adjustments in Operational Streamflow Prediction
NASA Astrophysics Data System (ADS)
Bender, S.; Miller, W. P.; Bernard, B.; Stokes, M.; Oaida, C. M.; Painter, T. H.
2015-12-01
Water management agencies rely on hydrologic forecasts issued by operational agencies such as NOAA's Colorado Basin River Forecast Center (CBRFC). The CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate research-oriented, remotely-sensed snow data into CBRFC operations and to improve the accuracy of CBRFC forecasts. The partnership has yielded valuable analysis of snow surface albedo as represented in JPL's MODIS Dust Radiative Forcing in Snow (MODDRFS) data, across the CBRFC's area of responsibility. When dust layers within a snowpack emerge, reducing the snow surface albedo, the snowmelt rate may accelerate. The CBRFC operational snow model (SNOW17) is a temperature-index model that lacks explicit representation of snowpack surface albedo. CBRFC forecasters monitor MODDRFS data for emerging dust layers and may manually adjust SNOW17 melt rates. A technique was needed for efficient and objective incorporation of the MODDRFS data into SNOW17. Initial development focused in Colorado, where dust-on-snow events frequently occur. CBRFC forecasters used retrospective JPL-CBRFC analysis and developed a quantitative relationship between MODDRFS data and mean areal temperature (MAT) data. The relationship was used to generate adjusted, MODDRFS-informed input for SNOW17. Impacts of the MODDRFS-SNOW17 MAT adjustment method on snowmelt-driven streamflow prediction varied spatially and with characteristics of the dust deposition events. The largest improvements occurred in southwestern Colorado, in years with intense dust deposition events. Application of the method in other regions of Colorado and in "low dust" years resulted in minimal impact. The MODDRFS-SNOW17 MAT technique will be implemented in CBRFC operations in late 2015, prior to spring 2016 runoff. Collaborative investigation of remote sensing-based adjustment methods for the CBRFC operational hydrologic forecasting environment will continue over the next several years.
Andrade, A I A S S; Stigter, T Y
2013-04-01
In this study multivariate and geostatistical methods are jointly applied to model the spatial and temporal distribution of arsenic (As) concentrations in shallow groundwater as a function of physicochemical, hydrogeological and land use parameters, as well as to assess the related uncertainty. The study site is located in the Mondego River alluvial body in Central Portugal, where maize, rice and some vegetable crops dominate. In a first analysis scatter plots are used, followed by the application of principal component analysis to two different data matrices, of 112 and 200 samples, with the aim of detecting associations between As levels and other quantitative parameters. In the following phase explanatory models of As are created through factorial regression based on correspondence analysis, integrating both quantitative and qualitative parameters. Finally, these are combined with indicator-geostatistical techniques to create maps indicating the predicted probability of As concentrations in groundwater exceeding the current global drinking water guideline of 10 μg/l. These maps further allow assessing the uncertainty and representativeness of the monitoring network. A clear effect of the redox state on the presence of As is observed, and together with significant correlations with dissolved oxygen, nitrate, sulfate, iron, manganese and alkalinity, points towards the reductive dissolution of Fe (hydr)oxides as the essential mechanism of As release. The association of high As values with rice crop, known to promote reduced environments due to ponding, further corroborates this hypothesis. An additional source of As from fertilizers cannot be excluded, as the correlation with As is higher where rice is associated with vegetables, normally associated with higher fertilization rates. The best explanatory model of As occurrence integrates the parameters season, crop type, well and water depth, nitrate and Eh, though a model without the last two parameters also gives
NASA Astrophysics Data System (ADS)
Venkatapathi, Murugesan; Rajwa, Bartek; Ragheb, Kathy; Banada, Padmapriya P.; Lary, Todd; Robinson, J. Paul; Hirleman, E. Daniel
2008-02-01
We describe a model-based instrument design combined with a statistical classification approach for the development and realization of high speed cell classification systems based on light scatter. In our work, angular light scatter from cells of four bacterial species of interest, Bacillus subtilis, Escherichia coli, Listeria innocua, and Enterococcus faecalis, was modeled using the discrete dipole approximation. We then optimized a scattering detector array design subject to some hardware constraints, configured the instrument, and gathered experimental data from the relevant bacterial cells. Using these models and experiments, it is shown that optimization using a nominal bacteria model (i.e., using a representative size and refractive index) is insufficient for classification of most bacteria in realistic applications. Hence the computational predictions were constituted in the form of scattering-data-vector distributions that accounted for expected variability in the physical properties between individual bacteria within the four species. After the detectors were optimized using the numerical results, they were used to measure scatter from both the known control samples and unknown bacterial cells. A multivariate statistical method based on a support vector machine (SVM) was used to classify the bacteria species based on light scatter signatures. In our final instrument, we realized correct classification of B. subtilis in the presence of E. coli,L. innocua, and E. faecalis using SVM at 99.1%, 99.6%, and 98.5%, respectively, in the optimal detector array configuration. For comparison, the corresponding values for another set of angles were only 69.9%, 71.7%, and 70.2% using SVM, and more importantly, this improved performance is consistent with classification predictions.
“A model of mother-child Adjustment in Arab Muslim Immigrants to the US”
Hough, Edythe s; Templin, Thomas N; Kulwicki, Anahid; Ramaswamy, Vidya; Katz, Anne
2009-01-01
We examined the mother-child adjustment and child behavior problems in Arab Muslim immigrant families residing in the U.S.A. The sample of 635 mother-child dyads was comprised of mothers who emigrated from 1989 or later and had at least one early adolescent child between the ages of 11 to 15 years old who was also willing to participate. Arabic speaking research assistants collected the data from the mothers and children using established measures of maternal and child stressors, coping, and social support; maternal distress; parent-child relationship; and child behavior problems. A structural equation model (SEM) was specified a priori with 17 predicted pathways. With a few exceptions, the final SEM model was highly consistent with the proposed model and had a good fit to the data. The model accounted for 67% of the variance in child behavior problems. Child stressors, mother-child relationship, and maternal stressors were the causal variables that contributed the most to child behavior problems. The model also accounted for 27% of the variance in mother-child relationship. Child active coping, child gender, mother’s education, and maternal distress were all predictive of the mother-child relationship. Mother-child relationship also mediated the effects of maternal distress and child active coping on child behavior problems. These findings indicate that immigrant mothers contribute greatly to adolescent adjustment, both as a source of risk and protection. These findings also suggest that intervening with immigrant mothers to reduce their stress and strengthening the parent-child relationship are two important areas for promoting adolescent adjustment. PMID:19758737
A spatial model of bird abundance as adjusted for detection probability
Gorresen, P.M.; Mcmillan, G.P.; Camp, R.J.; Pratt, T.K.
2009-01-01
Modeling the spatial distribution of animals can be complicated by spatial and temporal effects (i.e. spatial autocorrelation and trends in abundance over time) and other factors such as imperfect detection probabilities and observation-related nuisance variables. Recent advances in modeling have demonstrated various approaches that handle most of these factors but which require a degree of sampling effort (e.g. replication) not available to many field studies. We present a two-step approach that addresses these challenges to spatially model species abundance. Habitat, spatial and temporal variables were handled with a Bayesian approach which facilitated modeling hierarchically structured data. Predicted abundance was subsequently adjusted to account for imperfect detection and the area effectively sampled for each species. We provide examples of our modeling approach for two endemic Hawaiian nectarivorous honeycreepers: 'i'iwi Vestiaria coccinea and 'apapane Himatione sanguinea. ?? 2009 Ecography.
Technology Transfer Automated Retrieval System (TEKTRAN)
Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...
ERIC Educational Resources Information Center
Chiarello, Lisa A.; Palisano, Robert J.; Bartlett, Doreen J.; McCoy, Sarah Westcott
2011-01-01
A multivariate model of determinants of change in gross-motor ability and engagement in self-care and play provides physical and occupational therapists a framework for decisions on interventions and supports for young children with cerebral palsy and their families. Aspects of the child, family ecology, and rehabilitation and community services…
Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik
2013-01-01
The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia. PMID:24023854
NASA Astrophysics Data System (ADS)
Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka
2009-11-01
A study was made of the link between time of day, weather variables and the hourly content of certain fungal spores in the atmosphere of the city of Szczecin, Poland, in 2004-2007. Sampling was carried out with a Lanzoni 7-day-recording spore trap. The spores analysed belonged to the taxa Alternaria and Cladosporium. These spores were selected both for their allergenic capacity and for their high level presence in the atmosphere, particularly during summer. Spearman correlation coefficients between spore concentrations, meteorological parameters and time of day showed different indices depending on the taxon being analysed. Relative humidity (RH), air temperature, air pressure and clouds most strongly and significantly influenced the concentration of Alternaria spores. Cladosporium spores correlated less strongly and significantly than Alternaria. Multivariate regression tree analysis revealed that, at air pressures lower than 1,011 hPa the concentration of Alternaria spores was low. Under higher air pressure spore concentrations were higher, particularly when RH was lower than 36.5%. In the case of Cladosporium, under higher air pressure (>1,008 hPa), the spores analysed were more abundant, particularly after 0330 hours. In artificial neural networks, RH, air pressure and air temperature were the most important variables in the model for Alternaria spore concentration. For Cladosporium, clouds, time of day, air pressure, wind speed and dew point temperature were highly significant factors influencing spore concentration. The maximum abundance of Cladosporium spores in air fell between 1200 and 1700 hours.
Jiao, Shengwu; Guo, Yumin; Huettmann, Falk; Lei, Guangchun
2014-07-01
Avian nest-site selection is an important research and management subject. The hooded crane (Grus monacha) is a vulnerable (VU) species according to the IUCN Red List. Here, we present the first long-term Chinese legacy nest data for this species (1993-2010) with publicly available metadata. Further, we provide the first study that reports findings on multivariate nest habitat preference using such long-term field data for this species. Our work was carried out in Northeastern China, where we found and measured 24 nests and 81 randomly selected control plots and their environmental parameters in a vast landscape. We used machine learning (stochastic boosted regression trees) to quantify nest selection. Our analysis further included varclust (R Hmisc) and (TreenNet) to address statistical correlations and two-way interactions. We found that from an initial list of 14 measured field variables, water area (+), water depth (+) and shrub coverage (-) were the main explanatory variables that contributed to hooded crane nest-site selection. Agricultural sites played a smaller role in the selection of these nests. Our results are important for the conservation management of cranes all over East Asia and constitute a defensible and quantitative basis for predictive models. PMID:25001914
Observational Constraint of Aerosol Effects on the CMIP5 Inter-model Spread of Adjusted Forcings
NASA Astrophysics Data System (ADS)
Chen, J.; Wennberg, P. O.; Jiang, J. H.; Su, H.; Bordoni, S.
2013-12-01
The simulated global-mean temperature (GMT) change over the past 150 years is quite consistent across CMIP5 climate models and also consistent with the observations. However, the predicted future GMT under the identical CO2 forcing is divergent. This paradox is partly due to the errors in the predicted GMT produced by historical greenhouse gas (GHG) forcing being compensated by the parameterization of aerosol cloud radiative forcing. Historical increases in anthropogenic aerosols exert an overall (but highly uncertain) cooling effect in the climate system, which partially offsets the warming due to well mixed greenhouse gases (WMGHGs). Because aerosol concentrations are predicted to eventually decrease in future scenarios, climate change becomes dominated by warming due to the WMGHG. This change in the relative importance of forcing by aerosol versus WMGHG makes apparent the substantial differences in prediction of climate by WMGHG forcing. Here we investigate the role of aerosols in the context of adjusted forcing changes in the historical runs and the effect of aerosols on the cloud feedback. Our preliminary results suggest that models which are more sensitive to the increase in concentration of CO2 have a larger aerosol radiative cooling effect. By comparing the historicalMisc runs and historicalGHG runs, we find that aerosols exert a potential impact on the cloud adjusted forcings, especially shortwave cloud adjusted forcings. We use the CLIPSO, MISR and CERES data as the benchmark to evaluate the present aerosol simulations. Using satellite observations to assess the relative reliability of the different model responses and to constrain the simulated aerosol radiative forcing will contribute significantly to reducing the across model spread in future climate simulations and identifying some missing physical processes.
Multivariant function model generation
NASA Technical Reports Server (NTRS)
1974-01-01
The development of computer programs applicable to space vehicle guidance was conducted. The subjects discussed are as follows: (1) determination of optimum reentry trajectories, (2) development of equations for performance of trajectory computation, (3) vehicle control for fuel optimization, (4) development of equations for performance trajectory computations, (5) applications and solution of Hamilton-Jacobi equation, and (6) stresses in dome shaped shells with discontinuities at the apex.
Glacial isostatic adjustment using GNSS permanent stations and GIA modelling tools
NASA Astrophysics Data System (ADS)
Kollo, Karin; Spada, Giorgio; Vermeer, Martin
2013-04-01
Glacial Isostatic Adjustment (GIA) affects the Earth's mantle in areas which were once ice covered and the process is still ongoing. In this contribution we focus on GIA processes in Fennoscandian and North American uplift regions. In this contribution we use horizontal and vertical uplift rates from Global Navigation Satellite System (GNSS) permanent stations. For Fennoscandia the BIFROST dataset (Lidberg, 2010) and North America the dataset from Sella, 2007 were used respectively. We perform GIA modelling with the SELEN program (Spada and Stocchi, 2007) and we vary ice model parameters in space in order to find ice model which suits best with uplift values obtained from GNSS time series analysis. In the GIA modelling, the ice models ICE-5G (Peltier, 2004) and the ice model denoted as ANU05 ((Fleming and Lambeck, 2004) and references therein) were used. As reference, the velocity field from GNSS permanent station time series was used for both target areas. Firstly the sensitivity to the harmonic degree was tested in order to reduce the computation time. In the test, nominal viscosity values and pre-defined lithosphere thicknesses models were used, varying maximum harmonic degree values. Main criteria for choosing the suitable harmonic degree was chi-square fit - if the error measure does not differ more than 10%, then one might use as well lower harmonic degree value. From this test, maximum harmonic degree of 72 was chosen to perform calculations, as the larger value did not significantly modify the results obtained, as well the computational time for observations was kept reasonable. Secondly the GIA computations were performed to find the model, which could fit with highest probability to the GNSS-based velocity field in the target areas. In order to find best fitting Earth viscosity parameters, different viscosity profiles for the Earth models were tested and their impact on horizontal and vertical velocity rates from GIA modelling was studied. For every
Elizur, Y; Ziv, M
2001-01-01
While heterosexist family undermining has been demonstrated to be a developmental risk factor in the life of persons with same-gender orientation, the issue of protective family factors is both controversial and relatively neglected. In this study of Israeli gay males (N = 114), we focused on the interrelations of family support, family acceptance and family knowledge of gay orientation, and gay male identity formation, and their effects on mental health and self-esteem. A path model was proposed based on the hypotheses that family support, family acceptance, family knowledge, and gay identity formation have an impact on psychological adjustment, and that family support has an effect on gay identity formation that is mediated by family acceptance. The assessment of gay identity formation was based on an established stage model that was streamlined for cross-cultural practice by defining three basic processes of same-gender identity formation: self-definition, self-acceptance, and disclosure (Elizur & Mintzer, 2001). The testing of our conceptual path model demonstrated an excellent fit with the data. An alternative model that hypothesized effects of gay male identity on family acceptance and family knowledge did not fit the data. Interpreting these results, we propose that the main effect of family support/acceptance on gay identity is related to the process of disclosure, and that both general family support and family acceptance of same-gender orientation play a significant role in the psychological adjustment of gay men. PMID:11444052
Principal Component Analysis of breast DCE-MRI Adjusted with a Model Based Method
Eyal, Erez.; Badikhi, Daria; Furman-Haran, Edna; Kelcz, Fredrick; Kirshenbaum, Kevin J.; Degani, Hadassa
2010-01-01
Purpose To investigate a fast, objective and standardized method for analyzing breast DCE-MRI applying principal component analysis (PCA) adjusted with a model based method. Materials and Methods 3D gradient-echo dynamic contrast-enhanced breast images of 31 malignant and 38 benign lesions, recorded on a 1.5 Tesla scanner were retrospectively analyzed by PCA and by the model based three-time-point (3TP) method. Results Intensity scaled (IS) and enhancement scaled (ES) datasets were reduced by PCA yielding a 1st IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors that captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions enabling calculation of a general rotated eigenvector base. ROC curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the 1st rotated eigenvector to detect lesions (AUC>0.97) and of the 2nd rotated eigenvector to differentiate malignancy from benignancy (AUC=0.87). Conclusion PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI. PMID:19856419
NASA Astrophysics Data System (ADS)
Park, Seung Shik; Pancras, J. Patrick; Ondov, John; Poor, Noreen
2005-04-01
A new multivariate pseudodeterministic receptor model (PDRM), combining mass balance and Gaussian plume dispersion equations, was developed to exploit highly time-resolved ambient measurements of SO2 and particulate pollutants influencing air quality at a site in Sydney, Florida, during the Tampa Bay Regional Aerosol Chemistry Experiment (BRACE) in May 2002. The PDRM explicitly exploits knowledge of the number and locations of major stationary sources, source and transport wind directions, stack gas emission parameters, and meteorological plume dispersion parameters during sample collections to constrain solutions for individual sources. Model outputs include average emission rates and time-resolved ambient concentrations for each of the measured species and time-resolved meteorological dispersion factors for each of the sources. The model was applied to ambient Federal Reference Method SO2 and 30-min elemental measurements during an 8.5-hour period when winds swept a 70° sector containing six large stationary sources. Agreement between predicted and observed ambient SO2 concentrations was extraordinarily good: The correlation coefficient (R2) was 0.97, their ratio was 1.00 ± 0.18, and predicted SO2 emission rates for each of four large utility sources lie within 8% of their average continuous emission monitor values. Mean fractional bias, normalized mean square error, and the fractions of the predictions within a factor of 2 of the observed values are -2.7, 0.9, and 94%, respectively. For elemental markers of coal-fired (As and Se) and oil-fired (Ni) power plant emissions the average ratio of predicted and observed concentrations was 1.02 ± 0.18 for As, 0.96 ± 0.17 for Se, and 0.99 ± 0.41 for Ni, indicating that the six sources located in the wind sector between approximately 200° and 260° well accounted for background-corrected concentrations measured at the sampling site. Model results were relatively insensitive to the choice of upper bound used to
Ryali, Srikanth; Ian Shih, Yen-Yu; Chen, Tianwen; Kochalka, John; Albaugh, Daniel; Fang, Zhongnan; Supekar, Kaustubh; Lee, Jin Hyung; Menon, Vinod
2016-01-01
State-space multivariate dynamical systems (MDS) (Ryali et al., 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods is poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More
Ryali, Srikanth; Shih, Yen-Yu Ian; Chen, Tianwen; Kochalka, John; Albaugh, Daniel; Fang, Zhongnan; Supekar, Kaustubh; Lee, Jin Hyung; Menon, Vinod
2016-05-15
State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort, optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in f
Goeritz, Marie L.; Marder, Eve
2014-01-01
We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. PMID:25008414
Obtaining diverse behaviors in a climate model without the use of flux adjustment
NASA Astrophysics Data System (ADS)
Yamazaki, K.; Rowlands, D. J.; Williamson, D.; Allen, M.
2011-12-01
Efforts have been made in past research to attain a wide range of atmosphere and ocean model behaviors by perturbing the model physics of Global Climate Models. However, obtaining a large spread of behaviors of the ocean model has so far been unsuccessful. In an ongoing project within RAPID-WATCH, physical parameters of HadCM3 have been perturbed within plausible ranges across the Latin-hypercube to generate a 10,000 member ensemble, which have been running on the distributed computing platform of climateprediction.net. In this work we resample and run a second, 20,000 member ensemble of model variants that have been identified not to drift significantly away from a realistic initial base state, a key step since we are not using flux adjustment. To this end, they are conditioned on the diagnosed fluxes from the first ensemble by statistical methods to sample regions of parameter space that are predicted to exhibit low top-of-atmosphere (TOA) flux imbalance. Specifically, we constrain the distribution of outgoing longwave radiation (OLR) and reflected shortwave radiation (RSR) by laying an uncertainty ellipse at the 99% significance level, using the error analysis from Tett et al. (2011), over the standard configuration. In addition, parameters are sampled to generate a wide spread in estimated climate sensitivities, informed by results from a separate, coupled atmosphere-thermodynamic ocean coupled model ensemble. The results from the conditioned ensemble show that its members have successfully attained the distribution of OLR and RSR very similar to those predicted, while exhibiting a wide range of behaviors in both the atmosphere and the ocean. The spread of estimated effective climate sensitivity with the balanced TOA fluxes shows that the range of sensitivities of the conditioned ensemble is substantially smaller than that obtained with flux adjustment, but still as large or larger than the range in an ensemble of opportunity. This confirms that flux adjustment
Wheelock, Åsa M; Wheelock, Craig E
2013-11-01
Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of 'omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In 'omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these "short-and-wide" datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with 'omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex 'omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research
Okazaki, Shuntaro; Hirotani, Masako; Koike, Takahiko; Bosch-Bayard, Jorge; Takahashi, Haruka K.; Hashiguchi, Maho; Sadato, Norihiro
2015-01-01
People’s behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction—two individuals influencing one another—or in one direction—one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another’s head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR), the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one’s postural sway is explained by that of the other’s and how visual information (sighted vs. blindfolded) interacts with paired participants’ postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the behavioral
O'Muircheartaigh, Jonathan; Marquand, Andre; Hodkinson, Duncan J; Krause, Kristina; Khawaja, Nadine; Renton, Tara F; Huggins, John P; Vennart, William; Williams, Steven C R; Howard, Matthew A
2015-02-01
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. We combined these acquisition and analysis methodologies in a well-characterized postsurgical pain model. The principal aims were (1) to assess the classification accuracy of rCBF indices acquired prior to and following surgical intervention and (2) to optimise the amount of data required to maintain accurate classification. Twenty male volunteers, requiring bilateral, lower jaw third molar extraction (TME), underwent ASL examination prior to and following individual left and right TME, representing presurgical and postsurgical states, respectively. Six ASL time points were acquired at each exam. Each ASL image was preceded by visual analogue scale assessments of alertness and subjective pain experiences. Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting. PMID:25307488
Modeling fluvial incision and transient landscape evolution: Influence of dynamic channel adjustment
NASA Astrophysics Data System (ADS)
Attal, M.; Tucker, G. E.; Whittaker, A. C.; Cowie, P. A.; Roberts, G. P.
2008-09-01
Channel geometry exerts a fundamental control on fluvial processes. Recent work has shown that bedrock channel width depends on a number of parameters, including channel slope, and is not solely a function of drainage area as is commonly assumed. The present work represents the first attempt to investigate the consequences of dynamic, gradient-sensitive channel adjustment for drainage-basin evolution. We use the Channel-Hillslope Integrated Landscape Development (CHILD) model to analyze the response of a catchment to a given tectonic perturbation, using, as a template, the topography of a well-documented catchment in the footwall of an active normal fault in the Apennines (Italy) that is known to be undergoing a transient response to tectonic forcing. We show that the observed transient response can be reproduced to first order with a simple detachment-limited fluvial incision law. Transient landscape is characterized by gentler gradients and a shorter response time when dynamic channel adjustment is allowed. The differences in predicted channel geometry between the static case (width dependent solely on upstream area) and dynamic case (width dependent on both drainage area and channel slope) lead to contrasting landscape morphologies when integrated at the scale of a whole catchment, particularly in presence of strong tilting and/or pronounced slip-rate acceleration. Our results emphasize the importance of channel width in controlling fluvial processes and landscape evolution. They stress the need for using a dynamic hydraulic scaling law when modeling landscape evolution, particularly when the relative uplift field is nonuniform.
NASA Astrophysics Data System (ADS)
Asong, Zilefac E.; Khaliq, M. N.; Wheater, H. S.
2016-02-01
Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971-2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.
Primer on multivariate calibration
Thomas, E.V. )
1994-08-01
In analytical chemistry, calibration is the procedure that relates instrumental measurements to an analyte of interest. Typically, instrumental measurements are obtained from specimens in which the amount (or level) of the analyte has been determined by some independent and inherently accurate assay (e.g., wet chemistry). Together, the instrumental measurements and results from the independent assays are used to construct a model that relates the analyte level to the instrumental measurements. The advent of high-speed digital computers has greatly increased data acquisition and analysis capabilities and has provided the analytical chemist with opportunities to use many measurements - perhaps hundreds - for calibrating an instrument (e.g., absorbances at multiple wave-lengths). To take advantage of this technology, however, new methods (i.e., multivariate calibration methods) were needed for analyzing and modeling the experimental data. The purpose of this report is to introduce several evolving multivariate calibration methods and to present some important issues regarding their use. 30 refs., 7 figs.
A model of the western Laurentide Ice Sheet, using observations of glacial isostatic adjustment
NASA Astrophysics Data System (ADS)
Gowan, Evan J.; Tregoning, Paul; Purcell, Anthony; Montillet, Jean-Philippe; McClusky, Simon
2016-05-01
We present the results of a new numerical model of the late glacial western Laurentide Ice Sheet, constrained by observations of glacial isostatic adjustment (GIA), including relative sea level indicators, uplift rates from permanent GPS stations, contemporary differential lake level change, and postglacial tilt of glacial lake level indicators. The later two datasets have been underutilized in previous GIA based ice sheet reconstructions. The ice sheet model, called NAICE, is constructed using simple ice physics on the basis of changing margin location and basal shear stress conditions in order to produce ice volumes required to match GIA. The model matches the majority of the observations, while maintaining a relatively realistic ice sheet geometry. Our model has a peak volume at 18,000 yr BP, with a dome located just east of Great Slave Lake with peak thickness of 4000 m, and surface elevation of 3500 m. The modelled ice volume loss between 16,000 and 14,000 yr BP amounts to about 7.5 m of sea level equivalent, which is consistent with the hypothesis that a large portion of Meltwater Pulse 1A was sourced from this part of the ice sheet. The southern part of the ice sheet was thin and had a low elevation profile. This model provides an accurate representation of ice thickness and paleo-topography, and can be used to assess present day uplift and infer past climate.
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, A.B.; Sisolak, J.K.
1993-01-01
Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for
Multivariate respiratory motion prediction
NASA Astrophysics Data System (ADS)
Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.
2014-10-01
In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.
Introduction to multivariate discrimination
NASA Astrophysics Data System (ADS)
Kégl, Balázs
2013-07-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
Liu, Yan; Cai, Wensheng; Shao, Xueguang
2016-12-01
Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses. PMID:27380302
Adjusting Satellite Rainfall Error in Mountainous Areas for Flood Modeling Applications
NASA Astrophysics Data System (ADS)
Zhang, X.; Anagnostou, E. N.; Astitha, M.; Vergara, H. J.; Gourley, J. J.; Hong, Y.
2014-12-01
This study aims to investigate the use of high-resolution Numerical Weather Prediction (NWP) for evaluating biases of satellite rainfall estimates of flood-inducing storms in mountainous areas and associated improvements in flood modeling. Satellite-retrieved precipitation has been considered as a feasible data source for global-scale flood modeling, given that satellite has the spatial coverage advantage over in situ (rain gauges and radar) observations particularly over mountainous areas. However, orographically induced heavy precipitation events tend to be underestimated and spatially smoothed by satellite products, which error propagates non-linearly in flood simulations.We apply a recently developed retrieval error and resolution effect correction method (Zhang et al. 2013*) on the NOAA Climate Prediction Center morphing technique (CMORPH) product based on NWP analysis (or forecasting in the case of real-time satellite products). The NWP rainfall is derived from the Weather Research and Forecasting Model (WRF) set up with high spatial resolution (1-2 km) and explicit treatment of precipitation microphysics.In this study we will show results on NWP-adjusted CMORPH rain rates based on tropical cyclones and a convective precipitation event measured during NASA's IPHEX experiment in the South Appalachian region. We will use hydrologic simulations over different basins in the region to evaluate propagation of bias correction in flood simulations. We show that the adjustment reduced the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH rainfall bias, which results in significant improvement in flood peak simulations. Further study over Blue Nile Basin (western Ethiopia) will be investigated and included in the presentation. *Zhang, X. et al. 2013: Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas. J. Hydrometeor, 14, 1844-1858.
Parameter Sensitivity in Multivariate Methods
ERIC Educational Resources Information Center
Green, Bert F., Jr.
1977-01-01
Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)
Núñez-Dominguez, R; Van Vleck, L D; Boldman, K G; Cundiff, L V
1993-09-01
The purpose of this study was to estimate the correlation between the expression of genes from sires in purebred and crossbred progeny (rPC) and in Hereford and Angus F1 calves (rHA). Performance traits were weights at birth, 200 d, and 365 d. Progeny from Hereford, Polled Hereford, and Angus bulls mated to Hereford or Angus cows were used to estimate rPC. Progeny from Charolais, Shorthorn, Simmental, Limousin, Maine-Anjou, Chianina, Gelbvieh, Tarentaise, and Salers bulls mated to Hereford or Angus cows were used to estimate rHA. Performances in purebreds (P) and crosses (C) or in Hereford (H) and Angus (A) F1 calves were treated as separate traits. A multivariate animal model with birth year-cow age-sex subclasses, random correlated direct and maternal additive genetic effects, and maternal permanent environmental effects was used. Separate analyses were done by breed of sire. A derivative-free algorithm was used to obtain REML estimates of (co)variance components. Weighted averages across breeds of estimates of heritability for P, C, H, and A were, respectively, .61, .51, .47, and .40 for birth weight, .41, .46, .37, and .34 for weaning weight, and .50, .49, .42, and .46 for yearling weight. Estimates of rPC ranged from .88 to .97, .55 to .94, and .68 to .86 for weights at birth, 200 d, and 365 d, respectively. Estimates of rHA ranged from .43 to .99, .56 to .95, and .50 to .98 for weights at birth, 200 d, and 365 d, respectively. Weighted averages of estimates of rPC and rHA across sire breeds were, respectively, .93 and .85 for birth weight, .77 and .73 for weaning weight, and .76 and .86 for yearling weight. These results indicate that ranking of sires producing purebreds or crosses, or crossbred calves from different breeds of dams, is approximately the same for birth and yearling weights, but some reranking might occur for weaning weight. PMID:8407645
NASA Astrophysics Data System (ADS)
Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur
2014-05-01
Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos
Lehmann, E. D.; Deutsch, T.
1992-01-01
Joe Daniels is a 41 year old, 76kg male, insulin-treated diabetic patient who was diagnosed as being diabetic in 1972, at the age of 22. Joe recently found that he was having hypoglycaemic symptoms. Using self-monitoring blood glucose equipment glycaemic levels below 3.0 mmol/l were recorded at least once a week while hyperglycaemic readings (> 16 mmol/l) were observed 2-3 times per week. Joe came into hospital to have his glycaemic control improved as doctors were concerned about the risks of him suffering a serious hypoglycaemic attack. Using some of the data collected by Joe while in hospital we will demonstrate how a computer model of glucose-insulin interaction in type I diabetes can be used interactively to teach diabetic patients about their diabetes and educate them to adjust their own insulin injections and diet. PMID:1482868
UPDATING THE FREIGHT TRUCK STOCK ADJUSTMENT MODEL: 1997 VEHICLE INVENTORY AND USE SURVEY DATA
Davis, S.C.
2000-11-16
The Energy Information Administration's (EIA's) National Energy Modeling System (NEMS) Freight Truck Stock Adjustment Model (FTSAM) was created in 1995 relying heavily on input data from the 1992 Economic Census, Truck Inventory and Use Survey (TIUS). The FTSAM is part of the NEMS Transportation Sector Model, which provides baseline energy projections and analyzes the impacts of various technology scenarios on consumption, efficiency, and carbon emissions. The base data for the FTSAM can be updated every five years as new Economic Census information is released. Because of expertise in using the TIUS database, Oak Ridge National Laboratory (ORNL) was asked to assist the EIA when the new Economic Census data were available. ORNL provided the necessary base data from the 1997 Vehicle Inventory and Use Survey (VIUS) and other sources to update the FTSAM. The next Economic Census will be in the year 2002. When those data become available, the EIA will again want to update the FTSAM using the VIUS. This report, which details the methodology of estimating and extracting data from the 1997 VIUS Microdata File, should be used as a guide for generating the data from the next VIUS so that the new data will be as compatible as possible with the data in the model.
NASA Astrophysics Data System (ADS)
Ticlavilca, A. M.; McKee, M.; Walker, W.
2009-12-01
This research presents a model that simultaneously forecasts streamflow one and two days ahead, and water loss/gain in a river reach between two reservoirs one day ahead and for the next two days. The reservoir operator can take into account these real-time predictions and decide whether to increase/decrease the releases from the upstream reservoir in order to compensate the water loss/gain and manage the streamflow entering the downstream reservoir efficiently. The model inputs are the past daily data of climate (maximum and minimum temperature), streamflow, reservoir releases, water loss/gain in the river, and irrigation canal diversions. The model is developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a multivariate Bayesian regression approach. Based on this Bayesian approach, a predictive confidence interval is obtained from the model that captures the uncertainty of both the model and the data. The model is applied to the river system located in the Lower Sevier River Basin near Delta, Utah. The results show that the model learns the input-output patterns with good accuracy. A bootstrap analysis is used to guarantee robustness of the estimated model parameters. Test results demonstrate good performance of predictions and statistics that indicate robust model generalization abilities.
Lo, Kenneth; Gottardo, Raphael
2012-01-01
Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components. PMID:22125375
Punamäki, R L; Qouta, S; el Sarraj, E
1997-08-01
The relations between traumatic events, perceived parenting styles, children's resources, political activity, and psychological adjustment were examined among 108 Palestinian boys and girls of 11-12 years of age. The results showed that exposure to traumatic events increased psychological adjustment problems directly and via 2 mediating paths. First, the more traumatic events children had experienced, the more negative parenting they experienced. And, the poorer they perceived parenting, the more they suffered from high neuroticism and low self-esteem. Second, the more traumatic events children had experienced, the more political activity they showed, and the more active they were, the more they suffered from psychological adjustment problems. Good perceived parenting protected children's psychological adjustment by making them less vulnerable in two ways. First, traumatic events decreased their intellectual, creative, and cognitive resources, and a lack of resources predicted many psychological adjustment problems in a model excluding perceived parenting. Second, political activity increased psychological adjustment problems in the same model, but not in the model including good parenting. PMID:9306648
The Trauma Outcome Process Assessment Model: A Structural Equation Model Examination of Adjustment
ERIC Educational Resources Information Center
Borja, Susan E.; Callahan, Jennifer L.
2009-01-01
This investigation sought to operationalize a comprehensive theoretical model, the Trauma Outcome Process Assessment, and test it empirically with structural equation modeling. The Trauma Outcome Process Assessment reflects a robust body of research and incorporates known ecological factors (e.g., family dynamics, social support) to explain…
A new glacial isostatic adjustment model of the Innuitian Ice Sheet, Arctic Canada
NASA Astrophysics Data System (ADS)
Simon, K. M.; James, T. S.; Dyke, A. S.
2015-07-01
A reconstruction of the Innuitian Ice Sheet (IIS) is developed that incorporates first-order constraints on its spatial extent and history as suggested by regional glacial geology studies. Glacial isostatic adjustment modelling of this ice sheet provides relative sea-level predictions that are in good agreement with measurements of post-glacial sea-level change at 18 locations. The results indicate peak thicknesses of the Innuitian Ice Sheet of approximately 1600 m, up to 400 m thicker than the minimum peak thicknesses estimated from glacial geology studies, but between approximately 1000 to 1500 m thinner than the peak thicknesses present in previous GIA models. The thickness history of the best-fit Innuitian Ice Sheet model developed here, termed SJD15, differs from the ICE-5G reconstruction and provides an improved fit to sea-level measurements from the lowland sector of the ice sheet. Both models provide a similar fit to relative sea-level measurements from the alpine sector. The vertical crustal motion predictions of the best-fit IIS model are in general agreement with limited GPS observations, after correction for a significant elastic crustal response to present-day ice mass change. The new model provides approximately 2.7 m equivalent contribution to global sea-level rise, an increase of +0.6 m compared to the Innuitian portion of ICE-5G. SJD15 is qualitatively more similar to the recent ICE-6G ice sheet reconstruction, which appears to also include more spatially extensive ice cover in the Innuitian region than ICE-5G.
NASA Astrophysics Data System (ADS)
Yang, M. F.
In this research we present a stylized model to find the optimal strategy for integrated vendor-buyer inventory model with fuzzy annual demand and fuzzy adjustable production rate. This model with such consideration is based on the total cost optimization under a common stock strategy. However, the supposition of known annual demand and adjustable production rate in most related publications may not be realistic. This paper proposes the triangular fuzzy number of annual demand and adjustable production rate and then employs the signed distance, to find the estimation of the common total cost in the fuzzy sense and derives the corresponding optimal buyer`s quantity consequently and the integer number of lots in which the items are delivered from the vendor to the purchaser. A numerical example is provided and the results of fuzzy and crisp models are compared.
NASA Astrophysics Data System (ADS)
Root, Bart; Tarasov, Lev; van der Wal, Wouter
2014-05-01
The global ice budget is still under discussion because the observed 120-130 m eustatic sea level equivalent since the Last Glacial Maximum (LGM) can not be explained by the current knowledge of land-ice melt after the LGM. One possible location for the missing ice is the Barents Sea Region, which was completely covered with ice during the LGM. This is deduced from relative sea level observations on Svalbard, Novaya Zemlya and the North coast of Scandinavia. However, there are no observations in the middle of the Barents Sea that capture the post-glacial uplift. With increased precision and longer time series of monthly gravity observations of the GRACE satellite mission it is possible to constrain Glacial Isostatic Adjustment in the center of the Barents Sea. This study investigates the extra constraint provided by GRACE data for modeling the past ice geometry in the Barents Sea. We use CSR release 5 data from February 2003 to July 2013. The GRACE data is corrected for the past 10 years of secular decline of glacier ice on Svalbard, Novaya Zemlya and Frans Joseph Land. With numerical GIA models for a radially symmetric Earth, we model the expected gravity changes and compare these with the GRACE observations after smoothing with a 250 km Gaussian filter. The comparisons show that for the viscosity profile VM5a, ICE-5G has too strong a gravity signal compared to GRACE. The regional calibrated ice sheet model (GLAC) of Tarasov appears to fit the amplitude of the GRACE signal. However, the GRACE data are very sensitive to the ice-melt correction, especially for Novaya Zemlya. Furthermore, the ice mass should be more concentrated to the middle of the Barents Sea. Alternative viscosity models confirm these conclusions.
Four-dimensional data assimilation applied to photochemical air quality modeling is used to suggest adjustments to the emissions inventory of the Atlanta, Georgia metropolitan area. In this approach, a three-dimensional air quality model, coupled with direct sensitivity analys...
A Class of Elementary Particle Models Without Any Adjustable Real Parameters
NASA Astrophysics Data System (ADS)
't Hooft, Gerard
2011-12-01
Conventional particle theories such as the Standard Model have a number of freely adjustable coupling constants and mass parameters, depending on the symmetry algebra of the local gauge group and the representations chosen for the spinor and scalar fields. There seems to be no physical principle to determine these parameters as long as they stay within certain domains dictated by the renormalization group. Here however, reasons are given to demand that, when gravity is coupled to the system, local conformal invariance should be a spontaneously broken exact symmetry. The argument has to do with the requirement that black holes obey a complementarity principle relating ingoing observers to outside observers, or equivalently, initial states to final states. This condition fixes all parameters, including masses and the cosmological constant. We suspect that only examples can be found where these are all of order one in Planck units, but the values depend on the algebra chosen. This paper combines findings reported in two previous preprints (G. 't Hooft in arXiv:1009.0669 [gr-qc], 2010; arXiv:1011.0061 [gr-qc], 2010) and puts these in a clearer perspective by shifting the emphasis towards the implications for particle models.
Testing the compatibility of constraints for parameters of a geodetic adjustment model
NASA Astrophysics Data System (ADS)
Lehmann, Rüdiger; Neitzel, Frank
2013-06-01
Geodetic adjustment models are often set up in a way that the model parameters need to fulfil certain constraints. The normalized Lagrange multipliers have been used as a measure of the strength of constraint in such a way that if one of them exceeds in magnitude a certain threshold then the corresponding constraint is likely to be incompatible with the observations and the rest of the constraints. We show that these and similar measures can be deduced as test statistics of a likelihood ratio test of the statistical hypothesis that some constraints are incompatible in the same sense. This has been done before only for special constraints (Teunissen in Optimization and Design of Geodetic Networks, pp. 526-547, 1985). We start from the simplest case, that the full set of constraints is to be tested, and arrive at the advanced case, that each constraint is to be tested individually. Every test is worked out both for a known as well as for an unknown prior variance factor. The corresponding distributions under null and alternative hypotheses are derived. The theory is illustrated by the example of a double levelled line.
Kendall, W.L.; Hines, J.E.; Nichols, J.D.
2003-01-01
Matrix population models are important tools for research and management of populations. Estimating the parameters of these models is an important step in applying them to real populations. Multistate capture-recapture methods have provided a useful means for estimating survival and parameters of transition between locations or life history states but have mostly relied on the assumption that the state occupied by each detected animal is known with certainty. Nevertheless, in some cases animals can be misclassified. Using multiple capture sessions within each period of interest, we developed a method that adjusts estimates of transition probabilities for bias due to misclassification. We applied this method to 10 years of sighting data for a population of Florida manatees (Trichechus manatus latirostris) in order to estimate the annual probability of transition from nonbreeding to breeding status. Some sighted females were unequivocally classified as breeders because they were clearly accompanied by a first-year calf. The remainder were classified, sometimes erroneously, as nonbreeders because an attendant first-year calf was not observed or was classified as more than one year old. We estimated a conditional breeding probability of 0.31 + 0.04 (estimate + 1 SE) when we ignored misclassification bias, and 0.61 + 0.09 when we accounted for misclassification.
Soares, Ana Paula; Guisande, M Adelina; Diniz, António M; Almeida, Leandro S
2006-05-01
This article presents a model of interaction of personal and contextual variables in the prediction of academic performance and psychosocial development of Portuguese college students. The sample consists of 560 first-year college students of the University of Minho. The path analysis results suggest that initial expectations of the students' involvement in academic life constituted an effective predictor of their involvement during their first year; as well as the social climate of the classroom influenced their involvement, well-being and levels of satisfaction obtained. However, these relationships were not strong enough to influence the criterion variables integrated in the model (academic performance and psychosocial development). Academic performance was predicted by the high school grades and college entrance examination scores, and the level of psychosocial development was determined by the level of development showed at the time they entered college. Though more research is needed, these results point to the importance of students' pre-college characteristics when we are considering the quality of their college adjustment process. PMID:17296040
Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Sahimi, Muhammad
2016-03-01
In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.
NASA Astrophysics Data System (ADS)
Han, X.; Li, X.; He, G.; Kumbhar, P.; Montzka, C.; Kollet, S.; Miyoshi, T.; Rosolem, R.; Zhang, Y.; Vereecken, H.; Franssen, H.-J. H.
2015-08-01
Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. Multivariate data assimilation refers to the simultaneous assimilation of observation data from multiple model state variables into a simulation model. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. We developed an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with the C++ and Fortran programming languages. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be introduced by perturbed atmospheric forcing data, and represented by perturbed soil and vegetation parameters and model initial conditions. The Community Land Model (CLM) was integrated as the model operator. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. The Community Microwave Emission Modelling platform (CMEM), COsmic-ray Soil Moisture Interaction Code (COSMIC) and the Two-Source Formulation (TSF) were integrated as observation operators for the assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy has been evaluated in several assimilation studies of neutron count intensity (soil moisture), L-band brightness temperature and land surface temperature. DasPy is parallelized using the hybrid Message Passing Interface and Open Multi-Processing techniques. All the input and output data flows are organized efficiently
Glacial isostatic adjustment in Fennoscandia from GRACE data and comparison with geodynamical models
NASA Astrophysics Data System (ADS)
Steffen, Holger; Denker, Heiner; Müller, Jürgen
2008-10-01
The Earth's gravity field observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission shows variations due to the integral effect of mass variations in the atmosphere, hydrosphere and geosphere. Several institutions, such as the GeoForschungsZentrum (GFZ) Potsdam, the University of Texas at Austin, Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL), Pasadena, provide GRACE monthly solutions, which differ slightly due to the application of different reduction models and centre-specific processing schemes. The GRACE data are used to investigate the mass variations in Fennoscandia, an area which is strongly influenced by glacial isostatic adjustment (GIA). Hence the focus is set on the computation of secular trends. Different filters (e.g. isotropic and non-isotropic filters) are discussed for the removal of high frequency noise to permit the extraction of the GIA signal. The resulting GRACE based mass variations are compared to global hydrology models (WGHM, LaDWorld) in order to (a) separate possible hydrological signals and (b) validate the hydrology models with regard to long period and secular components. In addition, a pattern matching algorithm is applied to localise the uplift centre, and finally the GRACE signal is compared with the results from a geodynamical modelling. The GRACE data clearly show temporal gravity variations in Fennoscandia. The secular variations are in good agreement with former studies and other independent data. The uplift centre is located over the Bothnian Bay, and the whole uplift area comprises the Scandinavian Peninsula and Finland. The secular variations derived from the GFZ, CSR and JPL monthly solutions differ up to 20%, which is not statistically significant, and the largest signal of about 1.2 μGal/year is obtained from the GFZ solution. Besides the GIA signal, two peaks with positive trend values of about 0.8 μGal/year exist in central eastern Europe, which are not GIA-induced, and
NASA Astrophysics Data System (ADS)
Menna, F.; Nocerino, E.; Troisi, S.; Remondino, F.
2015-04-01
The surveying and 3D modelling of objects that extend both below and above the water level, such as ships, harbour structures, offshore platforms, are still an open issue. Commonly, a combined and simultaneous survey is the adopted solution, with acoustic/optical sensors respectively in underwater and in air (most common) or optical/optical sensors both below and above the water level. In both cases, the system must be calibrated and a ship is to be used and properly equipped with also a navigation system for the alignment of sequential 3D point clouds. Such a system is usually highly expensive and has been proved to work with still structures. On the other hand for free floating objects it does not provide a very practical solution. In this contribution, a flexible, low-cost alternative for surveying floating objects is presented. The method is essentially based on photogrammetry, employed for surveying and modelling both the emerged and submerged parts of the object. Special targets, named Orientation Devices, are specifically designed and adopted for the successive alignment of the two photogrammetric models (underwater and in air). A typical scenario where the proposed procedure can be particularly suitable and effective is the case of a ship after an accident whose damaged part is underwater and necessitate to be measured (Figure 1). The details of the mathematical procedure are provided in the paper, together with a critical explanation of the results obtained from the adoption of the method for the survey of a small pleasure boat in floating condition.
An assessment of the ICE6G_C(VM5a) glacial isostatic adjustment model
NASA Astrophysics Data System (ADS)
Purcell, A.; Tregoning, P.; Dehecq, A.
2016-05-01
The recent release of the next-generation global ice history model, ICE6G_C(VM5a), is likely to be of interest to a wide range of disciplines including oceanography (sea level studies), space gravity (mass balance studies), glaciology, and, of course, geodynamics (Earth rheology studies). In this paper we make an assessment of some aspects of the ICE6G_C(VM5a) model and show that the published present-day radial uplift rates are too high along the eastern side of the Antarctic Peninsula (by ˜8.6 mm/yr) and beneath the Ross Ice Shelf (by ˜5 mm/yr). Furthermore, the published spherical harmonic coefficients—which are meant to represent the dimensionless present-day changes due to glacial isostatic adjustment (GIA)—contain excessive power for degree ≥90, do not agree with physical expectations and do not represent accurately the ICE6G_C(VM5a) model. We show that the excessive power in the high-degree terms produces erroneous uplift rates when the empirical relationship of Purcell et al. (2011) is applied, but when correct Stokes coefficients are used, the empirical relationship produces excellent agreement with the fully rigorous computation of the radial velocity field, subject to the caveats first noted by Purcell et al. (2011). Using the Australian National University (ANU) groups CALSEA software package, we recompute the present-day GIA signal for the ice thickness history and Earth rheology used by Peltier et al. (2015) and provide dimensionless Stokes coefficients that can be used to correct satellite altimetry observations for GIA over oceans and by the space gravity community to separate GIA and present-day mass balance change signals. We denote the new data sets as ICE6G_ANU.
Modeling Fluvial Incision and Transient Landscape Evolution: Influence of Dynamic Channel Adjustment
NASA Astrophysics Data System (ADS)
Attal, M.; Tucker, G. E.; Cowie, P. A.; Whittaker, A. C.; Roberts, G. P.
2007-12-01
Channel geometry exerts a fundamental control on fluvial processes. Recent work has shown that bedrock channel width (W) depends on a number of parameters, including channel slope, and is not only a function of drainage area (A) as is commonly assumed. The present work represents the first attempt to investigate the consequences, for landscape evolution, of using a static expression of channel width (W ~ A0.5) versus a relationship that allows channels to dynamically adjust to changes in slope. We consider different models for the evolution of the channel geometry, including constant width-to-depth ratio (after Finnegan et al., Geology, v. 33, no. 3, 2005), and width-to-depth ratio varying as a function of slope (after Whittaker et al., Geology, v. 35, no. 2, 2007). We use the Channel-Hillslope Integrated Landscape Development (CHILD) model to analyze the response of a catchment to a given tectonic disturbance. The topography of a catchment in the footwall of an active normal fault in the Apennines (Italy) is used as a template for the study. We show that, for this catchment, the transient response can be fairly well reproduced using a simple detachment-limited fluvial incision law. We also show that, depending on the relationship used to express channel width, initial steady-state topographies differ, as do transient channel width, slope, and the response time of the fluvial system. These differences lead to contrasting landscape morphologies when integrated at the scale of a whole catchment. Our results emphasize the importance of channel width in controlling fluvial processes and landscape evolution. They stress the need for using a dynamic hydraulic scaling law when modeling landscape evolution, particularly when the uplift field is non-uniform.
Glacial isostatic adjustment on 3-D Earth models: a finite-volume formulation
NASA Astrophysics Data System (ADS)
Latychev, Konstantin; Mitrovica, Jerry X.; Tromp, Jeroen; Tamisiea, Mark E.; Komatitsch, Dimitri; Christara, Christina C.
2005-05-01
We describe and present results from a finite-volume (FV) parallel computer code for forward modelling the Maxwell viscoelastic response of a 3-D, self-gravitating, elastically compressible Earth to an arbitrary surface load. We implement a conservative, control volume discretization of the governing equations using a tetrahedral grid in Cartesian geometry and a low-order, linear interpolation. The basic starting grid honours all major radial discontinuities in the Preliminary Reference Earth Model (PREM), and the models are permitted arbitrary spatial variations in viscosity and elastic parameters. These variations may be either continuous or discontinuous at a set of grid nodes forming a 3-D surface within the (regional or global) modelling domain. In the second part of the paper, we adopt the FV methodology and a spherically symmetric Earth model to generate a suite of predictions sampling a broad class of glacial isostatic adjustment (GIA) data types (3-D crustal motions, long-wavelength gravity anomalies). These calculations, based on either a simple disc load history or a global Late Pleistocene ice load reconstruction (ICE-3G), are benchmarked against predictions generated using the traditional normal-mode approach to GIA. The detailed comparison provides a guide for future analyses (e.g. what grid resolution is required to obtain a specific accuracy?) and it indicates that discrepancies in predictions of 3-D crustal velocities less than 0.1 mm yr-1 are generally obtainable for global grids with ~3 × 106 nodes; however, grids of higher resolution are required to predict large-amplitude (>1 cm yr-1) radial velocities in zones of peak post-glacial uplift (e.g. James bay) to the same level of absolute accuracy. We conclude the paper with a first application of the new formulation to a 3-D problem. Specifically, we consider the impact of mantle viscosity heterogeneity on predictions of present-day 3-D crustal motions in North America. In these tests, the
Assessment of an adjustment factor to model radar range dependent error
NASA Astrophysics Data System (ADS)
Sebastianelli, S.; Russo, F.; Napolitano, F.; Baldini, L.
2012-09-01
Quantitative radar precipitation estimates are affected by errors determined by many causes such as radar miscalibration, range degradation, attenuation, ground clutter, variability of Z-R relation, variability of drop size distribution, vertical air motion, anomalous propagation and beam-blocking. Range degradation (including beam broadening and sampling of precipitation at an increasing altitude)and signal attenuation, determine a range dependent behavior of error. The aim of this work is to model the range-dependent error through an adjustment factor derived from the G/R ratio trend against the range, where G and R are the corresponding rain gauge and radar rainfall amounts computed at each rain gauge location. Since range degradation and signal attenuation effects are negligible close to the radar, resultsshowthatwithin 40 km from radar the overall range error is independent of the distance from Polar 55C and no range-correction is needed. Nevertheless, up to this distance, the G/R ratiocan showa concave trend with the range, which is due to the melting layer interception by the radar beam during stratiform events.
Comparison of Two Foreign Body Retrieval Devices with Adjustable Loops in a Swine Model
Konya, Andras
2006-12-15
The purpose of the study was to compare two similar foreign body retrieval devices, the Texan{sup TM} (TX) and the Texan LONGhorn{sup TM} (TX-LG), in a swine model. Both devices feature a {<=}30-mm adjustable loop. Capture times and total procedure times for retrieving foreign bodies from the infrarenal aorta, inferior vena cava, and stomach were compared. All attempts with both devices (TX, n = 15; TX-LG, n = 14) were successful. Foreign bodies in the vasculature were captured quickly using both devices (mean {+-} SD, 88 {+-} 106 sec for TX vs 67 {+-} 42 sec for TX-LG) with no significant difference between them. The TX-LG, however, allowed significantly better capture times than the TX in the stomach (p = 0.022), Overall, capture times for the TX-LG were significantly better than for the TX (p = 0.029). There was no significant difference between the total procedure times in any anatomic region. TX-LG performed significantly better than the TX in the stomach and therefore overall. The better torque control and maneuverability of TX-LG resulted in better performance in large anatomic spaces.
Glacial isostatic adjustment model with composite 3-D Earth rheology for Fennoscandia
NASA Astrophysics Data System (ADS)
van der Wal, Wouter; Barnhoorn, Auke; Stocchi, Paolo; Gradmann, Sofie; Wu, Patrick; Drury, Martyn; Vermeersen, Bert
2013-07-01
Models for glacial isostatic adjustment (GIA) can provide constraints on rheology of the mantle if past ice thickness variations are assumed to be known. The Pleistocene ice loading histories that are used to obtain such constraints are based on an a priori 1-D mantle viscosity profile that assumes a single deformation mechanism for mantle rocks. Such a simplified viscosity profile makes it hard to compare the inferred mantle rheology to inferences from seismology and laboratory experiments. It is unknown what constraints GIA observations can provide on more realistic mantle rheology with an ice history that is not based on an a priori mantle viscosity profile. This paper investigates a model for GIA with a new ice history for Fennoscandia that is constrained by palaeoclimate proxies and glacial sediments. Diffusion and dislocation creep flow law data are taken from a compilation of laboratory measurements on olivine. Upper-mantle temperature data sets down to 400 km depth are derived from surface heatflow measurements, a petrochemical model for Fennoscandia and seismic velocity anomalies. Creep parameters below 400 km are taken from an earlier study and are only varying with depth. The olivine grain size and water content (a wet state, or a dry state) are used as free parameters. The solid Earth response is computed with a global spherical 3-D finite-element model for an incompressible, self-gravitating Earth. We compare predictions to sea level data and GPS uplift rates in Fennoscandia. The objective is to see if the mantle rheology and the ice model is consistent with GIA observations. We also test if the inclusion of dislocation creep gives any improvements over predictions with diffusion creep only, and whether the laterally varying temperatures result in an improved fit compared to a widely used 1-D viscosity profile (VM2). We find that sea level data can be explained with our ice model and with information on mantle rheology from laboratory experiments
NASA Astrophysics Data System (ADS)
Hermann, Albert J.; Gibson, Georgina A.; Bond, Nicholas A.; Curchitser, Enrique N.; Hedstrom, Kate; Cheng, Wei; Wang, Muyin; Stabeno, Phyllis J.; Eisner, Lisa; Cieciel, Kristin D.
2013-10-01
Coupled physical/biological models can be used to downscale global climate change to the ecology of subarctic regions, and to explore the bottom-up and top-down effects of that change on the spatial structure of subarctic ecosystems—for example, the relative dominance of large vs. small zooplankton in relation to ice cover. Here we utilize a multivariate statistical approach to extract the emergent properties of a coupled physical/biological hindcast of the Bering Sea for years 1970-2009, which includes multiple episodes of warming and cooling (e.g. the recent cooling of 2005-2009), and a multidecadal regional forecast of the coupled models, driven by an IPCC global model forecast of 2010-2040. Specifically, we employ multivariate empirical orthogonal function (EOF) analysis to derive the spatial covariance among physical and biological timeseries from our simulations. These are compared with EOFs derived from spatially gridded measurements of the region, collected during multiyear field programs. The model replicates observed relationships among temperature and salinity, as well as the observed inverse correlation between temperature and large crustacean zooplankton on the southeastern Bering Sea shelf. Predicted future warming of the shelf is accompanied by a northward shift in both pelagic and benthic biomass.
NASA Astrophysics Data System (ADS)
Addor, Nans; Rohrer, Marco; Furrer, Reinhard; Seibert, Jan
2016-03-01
Bias adjustment methods usually do not account for the origins of biases in climate models and instead perform empirical adjustments. Biases in the synoptic circulation are for instance often overlooked when postprocessing regional climate model (RCM) simulations driven by general circulation models (GCMs). Yet considering atmospheric circulation helps to establish links between the synoptic and the regional scale, and thereby provides insights into the physical processes leading to RCM biases. Here we investigate how synoptic circulation biases impact regional climate simulations and influence our ability to mitigate biases in precipitation and temperature using quantile mapping. We considered 20 GCM-RCM combinations from the ENSEMBLES project and characterized the dominant atmospheric flow over the Alpine domain using circulation types. We report in particular a systematic overestimation of the frequency of westerly flow in winter. We show that it contributes to the generalized overestimation of winter precipitation over Switzerland, and this wet regional bias can be reduced by improving the simulation of synoptic circulation. We also demonstrate that statistical bias adjustment relying on quantile mapping is sensitive to circulation biases, which leads to residual errors in the postprocessed time series. Overall, decomposing GCM-RCM time series using circulation types reveals connections missed by analyses relying on monthly or seasonal values. Our results underscore the necessity to better diagnose process misrepresentation in climate models to progress with bias adjustment and impact modeling.
An assessment of the ICE6G_C (VM5A) glacial isostatic adjustment model
NASA Astrophysics Data System (ADS)
Purcell, Anthony; Tregoning, Paul; Dehecq, Amaury
2016-04-01
The recent release of the next-generation global ice history model, ICE6G_C(VM5a) [Peltier et al., 2015, Argus et al. 2014] is likely to be of interest to a wide range of disciplines including oceanography (sea level studies), space gravity (mass balance studies), glaciology and, of course, geodynamics (Earth rheology studies). In this presentation I will assess some aspects of the ICE6G_C(VM5a) model and the accompanying published data sets. I will demonstrate that the published present-day radial uplift rates are too high along the eastern side of the Antarctic Peninsula (by ˜8.6 mm/yr) and beneath the Ross Ice Shelf (by ˜5 mm/yr). Further, the published spherical harmonic coefficients - which are meant to represent the dimensionless present-day changes due to glacial isostatic adjustment (GIA) - will be shown to contain excessive power for degree ≥ 90, to be physically implausible and to not represent accurately the ICE6G_C(VM5a) model. The excessive power in the high degree terms produces erroneous uplift rates when the empirical relationship of Purcell et al. [2011] is applied but, when correct Stokes' coefficients are used, the empirical relationship will be shown to produce excellent agreement with the fully rigorous computation of the radial velocity field, subject to the caveats first noted by Purcell et al. [2011]. Finally, a global radial velocity field for the present-day GIA signal, and corresponding Stoke's coefficients will be presented for the ICE6GC ice model history using the VM5a rheology model. These results have been obtained using the ANU group's CALSEA software package and can be used to correct satellite altimetry observations for GIA over oceans and by the space gravity community to separate GIA and present-day mass balance change signals without any of the shortcomings of the previously published data-sets. We denote the new data sets ICE6G_ANU.
Multivariate meta-analysis using individual participant data.
Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R
2015-06-01
When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484
Bazzazian, S; Besharat, M A
2012-01-01
The aim of this study was to develop and test a model of adjustment to type I diabetes. Three hundred young adults (172 females and 128 males) with type I diabetes were asked to complete the Adult Attachment Inventory (AAI), the Brief Illness Perception Questionnaire (Brief IPQ), Task-oriented subscale of the Coping Inventory for Stressful Situations (CISS), D-39, and well-being subscale of the Mental Health Inventory (MHI). HbA1c was obtained from laboratory examination. Results from structural equation analysis partly supported the hypothesized model. Secure and avoidant attachment styles were found to have effects on illness perception, ambivalent attachment style did not have significant effect on illness perception. Three attachment styles had significant effect on task-oriented coping strategy. Avoidant attachment had negative direct effect on adjustment too. Regression effects of illness perception and task-oriented coping strategy on adjustment were positive. Therefore, positive illness perception and more usage of task-oriented coping strategy predict better adjustment to diabetes. So, the results confirmed the theoretical bases and empirical evidence of effectiveness of attachment styles in adjustment to chronic disease and can be helpful in devising preventive policies, determining high-risk maladjusted patients, and planning special psychological treatment. PMID:21678193
Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?
ERIC Educational Resources Information Center
Thompson, Bruce
Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…
NASA Astrophysics Data System (ADS)
Belousov, V. I.; Ezhela, V. V.; Kuyanov, Yu. V.; Tkachenko, N. P.
2015-12-01
The experience of using the dynamic atlas of the experimental data and mathematical models of their description in the problems of adjusting parametric models of observable values depending on kinematic variables is presented. The functional possibilities of an image of a large number of experimental data and the models describing them are shown by examples of data and models of observable values determined by the amplitudes of elastic scattering of hadrons. The Internet implementation of an interactive tool DaMoScope and its interface with the experimental data and codes of adjusted parametric models with the parameters of the best description of data are schematically shown. The DaMoScope codes are freely available.
Belousov, V. I.; Ezhela, V. V.; Kuyanov, Yu. V. Tkachenko, N. P.
2015-12-15
The experience of using the dynamic atlas of the experimental data and mathematical models of their description in the problems of adjusting parametric models of observable values depending on kinematic variables is presented. The functional possibilities of an image of a large number of experimental data and the models describing them are shown by examples of data and models of observable values determined by the amplitudes of elastic scattering of hadrons. The Internet implementation of an interactive tool DaMoScope and its interface with the experimental data and codes of adjusted parametric models with the parameters of the best description of data are schematically shown. The DaMoScope codes are freely available.
ERIC Educational Resources Information Center
Rulison, Kelly L.; Gest, Scott D.; Loken, Eric; Welsh, Janet A.
2010-01-01
The association between affiliating with aggressive peers and behavioral, social and psychological adjustment was examined. Students initially in 3rd, 4th, and 5th grade (N = 427) were followed biannually through 7th grade. Students' peer-nominated groups were identified. Multilevel modeling was used to examine the independent contributions of…
The Effectiveness of the Strength-Centered Career Adjustment Model for Dual-Career Women in Taiwan
ERIC Educational Resources Information Center
Wang, Yu-Chen; Tien, Hsiu-Lan Shelley
2011-01-01
The authors investigated the effectiveness of a Strength-Centered Career Adjustment Model for dual-career women (N = 28). Fourteen women in the experimental group received strength-centered career counseling for 6 to 8 sessions; the 14 women in the control group received test services in 1 to 2 sessions. All participants completed the Personal…
ERIC Educational Resources Information Center
Hawkins, Amy L.; Haskett, Mary E.
2014-01-01
Background: Abused children's internal working models (IWM) of relationships are known to relate to their socioemotional adjustment, but mechanisms through which negative representations increase vulnerability to maladjustment have not been explored. We sought to expand the understanding of individual differences in IWM of abused children and…
NASA Astrophysics Data System (ADS)
Shi, Yuning; Davis, Kenneth J.; Zhang, Fuqing; Duffy, Christopher J.; Yu, Xuan
2015-09-01
The capability of an ensemble Kalman filter (EnKF) to simultaneously estimate multiple parameters in a physically-based land surface hydrologic model using multivariate field observations is tested at a small watershed (0.08 km2). Multivariate, high temporal resolution, in situ measurements of discharge, water table depth, soil moisture, and sensible and latent heat fluxes encompassing five months of 2009 are assimilated. It is found that, for five out of the six parameters, the EnKF estimated parameter values from different test cases converge strongly, and the estimates after convergence are close to the manually calibrated parameter values. The EnKF estimated parameters and manually calibrated parameters yield similar model performance, but the EnKF sequential method significantly decreases the time and labor required for calibration. The results demonstrate that, given a limited number of multi-state, site-specific observations, an automated sequential calibration method (EnKF) can be used to optimize physically-based land surface hydrologic models.
Hoos, Anne B.; Patel, Anant R.
1996-01-01
Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Parmar, Kulwinder Singh
2016-03-01
This study investigates the accuracy of least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in modeling river water pollution. Various combinations of water quality parameters, Free Ammonia (AMM), Total Kjeldahl Nitrogen (TKN), Water Temperature (WT), Total Coliform (TC), Fecal Coliform (FC) and Potential of Hydrogen (pH) monitored at Nizamuddin, Delhi Yamuna River in India were used as inputs to the applied models. Results indicated that the LSSVM and MARS models had almost same accuracy and they performed better than the M5Tree model in modeling monthly chemical oxygen demand (COD). The average root mean square error (RMSE) of the LSSVM and M5Tree models was decreased by 1.47% and 19.1% using MARS model, respectively. Adding TC input to the models did not increase their accuracy in modeling COD while adding FC and pH inputs to the models generally decreased the accuracy. The overall results indicated that the MARS and LSSVM models could be successfully used in estimating monthly river water pollution level by using AMM, TKN and WT parameters as inputs.
NASA Astrophysics Data System (ADS)
Barbeira, Paulo J. S.; Paganotti, Rosilene S. N.; Ássimos, Ariane A.
2013-10-01
This study had the objective of determining the content of dry extract of commercial alcoholic extracts of bee propolis through Partial Least Squares (PLS) multivariate calibration and electronic spectroscopy. The PLS model provided a good prediction of dry extract content in commercial alcoholic extracts of bee propolis in the range of 2.7 a 16.8% (m/v), presenting the advantage of being less laborious and faster than the traditional gravimetric methodology. The PLS model was optimized with outlier detection tests according to the ASTM E 1655-05. In this study it was possible to verify that a centrifugation stage is extremely important in order to avoid the presence of waxes, resulting in a more accurate model. Around 50% of the analyzed samples presented content of dry extract lower than the value established by Brazilian legislation, in most cases, the values found were different from the values claimed in the product's label.
Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed
2017-01-01
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. PMID:27423110
Data Assimilation and Adjusted Spherical Harmonic Model of VTEC Map over Thailand
NASA Astrophysics Data System (ADS)
Klinngam, Somjai; Maruyama, Takashi; Tsugawa, Takuya; Ishii, Mamoru; Supnithi, Pornchai; Chiablaem, Athiwat
2016-07-01
The global navigation satellite system (GNSS) and high frequency (HF) communication are vulnerable to the ionospheric irregularities, especially when the signal travels through the low-latitude region and around the magnetic equator known as equatorial ionization anomaly (EIA) region. In order to study the ionospheric effects to the communications performance in this region, the regional map of the observed total electron content (TEC) can show the characteristic and irregularities of the ionosphere. In this work, we develop the two-dimensional (2D) map of vertical TEC (VTEC) over Thailand using the adjusted spherical harmonic model (ASHM) and the data assimilation technique. We calculate the VTEC from the receiver independent exchange (RINEX) files recorded by the dual-frequency global positioning system (GPS) receivers on July 8th, 2012 (quiet day) at 12 stations around Thailand: 0° to 25°E and 95°N to 110°N. These stations are managed by Department of Public Works and Town & Country Planning (DPT), Thailand, and the South East Asia Low-latitude ionospheric Network (SEALION) project operated by National Institute of Information and Communications Technology (NICT), Japan, and King Mongkut's Institute of Technology Ladkrabang (KMITL). We compute the median observed VTEC (OBS-VTEC) in the grids with the spatial resolution of 2.5°x5° in latitude and longitude and time resolution of 2 hours. We assimilate the OBS-VTEC with the estimated VTEC from the International Reference Ionosphere model (IRI-VTEC) as well as the ionosphere map exchange (IONEX) files provided by the International GNSS Service (IGS-VTEC). The results show that the estimation of the 15-degree ASHM can be improved when both of IRI-VTEC and IGS-VTEC are weighted by the latitude-dependent factors before assimilating with the OBS-VTEC. However, the IRI-VTEC assimilation can improve the ASHM estimation more than the IGS-VTEC assimilation. Acknowledgment: This work is partially funded by the
Multivariate mixtures of Erlangs for density estimation under censoring.
Verbelen, Roel; Antonio, Katrien; Claeskens, Gerda
2016-07-01
Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets. PMID:26340888
Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan
2014-01-01
Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions
Conger, R D; Patterson, G R; Ge, X
1995-02-01
In this study of parental stress and adolescent adjustment, experiences of negative life events during the recent past were used to generate a measure of acute stress. In addition, multiple indicators based on reports from various informants were used to estimate latent constructs for parental depression, discipline practices, and adolescent adjustment. Employing 2 independent samples of families from 2 different regions of the country (rural Iowa and a medium-sized city in Oregon), structural equation models were used to test the hypothesis that in intact families acute stress experienced by parents is linked to boys' adjustment (average age equaled 11.8 years in the Oregon sample, 12.7 years in the Iowa sample) through 2 different causal mechanisms. The findings showed that parental stress was related to adjustment through stress-related parental depression that is, in turn, correlated with disrupted discipline practices. Poor discipline appears to provide the direct link with developmental outcomes. The structural equation model (SEM) used to test the proposed mediational process was consistent with the data for mothers and boys from both the Oregon and the Iowa samples. The similarity in results was less clear for fathers and boys. Implications of these results for future replication studies are discussed. PMID:7497831
Telzer, Eva H; Yuen, Cynthia; Gonzales, Nancy; Fuligni, Andrew J
2016-07-01
The acculturation gap-distress model purports that immigrant children acculturate faster than do their parents, resulting in an acculturation gap that leads to family and youth maladjustment. However, empirical support for the acculturation gap-distress model has been inconclusive. In the current study, 428 Mexican-American adolescents (50.2 % female) and their primary caregivers independently completed questionnaires assessing their levels of American and Mexican cultural orientation, family functioning, and youth adjustment. Contrary to the acculturation gap-distress model, acculturation gaps were not associated with poorer family or youth functioning. Rather, adolescents with higher levels of Mexican cultural orientations showed positive outcomes, regardless of their parents' orientations to either American or Mexican cultures. Findings suggest that youths' heritage cultural maintenance may be most important for their adjustment. PMID:26759225
A Key Challenge in Global HRM: Adding New Insights to Existing Expatriate Spouse Adjustment Models
ERIC Educational Resources Information Center
Gupta, Ritu; Banerjee, Pratyush; Gaur, Jighyasu
2012-01-01
This study is an attempt to strengthen the existing knowledge about factors affecting the adjustment process of the trailing expatriate spouse and the subsequent impact of any maladjustment or expatriate failure. We conducted a qualitative enquiry using grounded theory methodology with 26 Indian spouses who had to deal with their partner's…
ERIC Educational Resources Information Center
Donaldson, Tarryn; Earl, Joanne K.; Muratore, Alexa M.
2010-01-01
Extending earlier research, this study explores individual (e.g. demographic and health characteristics), psychosocial (e.g. mastery and planning) and organizational factors (e.g. conditions of workforce exit) influencing retirement adjustment. Survey data were collected from 570 semi-retired and retired men and women aged 45 years and older.…
ERIC Educational Resources Information Center
Wong, Jessica Y.; Earl, Joanne K.
2009-01-01
This cross-sectional study examines three predictors of retirement adjustment: individual (demographic and health), psychosocial (work centrality), and organizational (conditions of workforce exit). It also examines the effect of work centrality on post-retirement activity levels. Survey data was collected from 394 retirees (aged 45-93 years).…
ERIC Educational Resources Information Center
Ray, Corey E.; Elliott, Stephen N.
2006-01-01
This study examined the hypothesized relationship between social adjustment, as measured by perceived social support, self-concept, and social skills, and performance on academic achievement tests. Participants included 27 teachers and 77 fourth- and eighth-grade students with diverse academic and behavior competencies. Teachers were asked to…
Divorce Stress and Adjustment Model: Locus of Control and Demographic Predictors.
ERIC Educational Resources Information Center
Barnet, Helen Smith
This study depicts the divorce process over three time periods: predivorce decision phase, divorce proper, and postdivorce. Research has suggested that persons with a more internal locus of control experience less intense and shorter intervals of stress during the divorce proper and better postdivorce adjustment than do persons with a more…
ERIC Educational Resources Information Center
Sonderegger, Robi; Barrett, Paula M.; Creed, Peter A.
2004-01-01
Building on previous cultural adjustment profile work by Sonderegger and Barrett (2004), the aim of this study was to propose an organised structure for a number of single risk factors that have been linked to acculturative-stress in young migrants. In recognising that divergent situational characteristics (e.g., school level, gender, residential…
ERIC Educational Resources Information Center
Asberg, Kia K.; Bowers, Clint; Renk, Kimberly; McKinney, Cliff
2008-01-01
Today's society puts constant demands on the time and resources of all individuals, with the resulting stress promoting a decline in psychological adjustment. Emerging adults are not exempt from this experience, with an alarming number reporting excessive levels of stress and stress-related problems. As a result, the present study addresses the…
Barks, C.S.
1995-01-01
Storm-runoff water-quality data were used to verify and, when appropriate, adjust regional regression models previously developed to estimate urban storm- runoff loads and mean concentrations in Little Rock, Arkansas. Data collected at 5 representative sites during 22 storms from June 1992 through January 1994 compose the Little Rock data base. Comparison of observed values (0) of storm-runoff loads and mean concentrations to the predicted values (Pu) from the regional regression models for nine constituents (chemical oxygen demand, suspended solids, total nitrogen, total ammonia plus organic nitrogen as nitrogen, total phosphorus, dissolved phosphorus, total recoverable copper, total recoverable lead, and total recoverable zinc) shows large prediction errors ranging from 63 to several thousand percent. Prediction errors for six of the regional regression models are less than 100 percent, and can be considered reasonable for water-quality models. Differences between 0 and Pu are due to variability in the Little Rock data base and error in the regional models. Where applicable, a model adjustment procedure (termed MAP-R-P) based upon regression with 0 against Pu was applied to improve predictive accuracy. For 11 of the 18 regional water-quality models, 0 and Pu are significantly correlated, that is much of the variation in 0 is explained by the regional models. Five of these 11 regional models consistently overestimate O; therefore, MAP-R-P can be used to provide a better estimate. For the remaining seven regional models, 0 and Pu are not significanfly correlated, thus neither the unadjusted regional models nor the MAP-R-P is appropriate. A simple estimator, such as the mean of the observed values may be used if the regression models are not appropriate. Standard error of estimate of the adjusted models ranges from 48 to 130 percent. Calibration results may be biased due to the limited data set sizes in the Little Rock data base. The relatively large values of
NASA Astrophysics Data System (ADS)
Scradeanu, D.; Pagnejer, M.
2012-04-01
The purpose of the works is to evaluate the uncertainty of the hydrodynamic model for a multilayered geological structure, a potential trap for carbon dioxide storage. The hydrodynamic model is based on a conceptual model of the multilayered hydrostructure with three components: 1) spatial model; 2) parametric model and 3) energy model. The necessary data to achieve the three components of the conceptual model are obtained from: 240 boreholes explored by geophysical logging and seismic investigation, for the first two components, and an experimental water injection test for the last one. The hydrodinamic model is a finite difference numerical model based on a 3D stratigraphic model with nine stratigraphic units (Badenian and Oligocene) and a 3D multiparameter model (porosity, permeability, hydraulic conductivity, storage coefficient, leakage etc.). The uncertainty of the two 3D models was evaluated using multivariate geostatistical tools: a)cross-semivariogram for structural analysis, especially the study of anisotropy and b)cokriging to reduce estimation variances in a specific situation where is a cross-correlation between a variable and one or more variables that are undersampled. It has been identified important differences between univariate and bivariate anisotropy. The minimised uncertainty of the parametric model (by cokriging) was transferred to hydrodynamic model. The uncertainty distribution of the pressures generated by the water injection test has been additional filtered by the sensitivity of the numerical model. The obtained relative errors of the pressure distribution in the hydrodynamic model are 15-20%. The scientific research was performed in the frame of the European FP7 project "A multiple space and time scale approach for the quantification of deep saline formation for CO2 storage(MUSTANG)".
Farjam, R; Pramanik, P; Srinivasan, A; Chapman, C; Tsien, C; Lawrence, T; Cao, Y
2014-06-15
Purpose: Vascular injury could be a cause of hippocampal dysfunction leading to late neurocognitive decline in patients receiving brain radiotherapy (RT). Hence, our aim was to develop a multivariate interaction model for characterization of hippocampal vascular dose-response and early prediction of radiation-induced late neurocognitive impairments. Methods: 27 patients (17 males and 10 females, age 31–80 years) were enrolled in an IRB-approved prospective longitudinal study. All patients were diagnosed with a low-grade glioma or benign tumor and treated by 3-D conformal or intensity-modulated RT with a median dose of 54 Gy (50.4–59.4 Gy in 1.8− Gy fractions). Six DCE-MRI scans were performed from pre-RT to 18 months post-RT. DCE data were fitted to the modified Toft model to obtain the transfer constant of gadolinium influx from the intravascular space into the extravascular extracellular space, Ktrans, and the fraction of blood plasma volume, Vp. The hippocampus vascular property alterations after starting RT were characterized by changes in the hippocampal mean values of, μh(Ktrans)τ and μh(Vp)τ. The dose-response, Δμh(Ktrans/Vp)pre->τ, was modeled using a multivariate linear regression considering integrations of doses with age, sex, hippocampal laterality and presence of tumor/edema near a hippocampus. Finally, the early vascular dose-response in hippocampus was correlated with neurocognitive decline 6 and 18 months post-RT. Results: The μh(Ktrans) increased significantly from pre-RT to 1 month post-RT (p<0.0004). The multivariate model showed that the dose effect on Δμh(Ktrans)pre->1M post-RT was interacted with sex (p<0.0007) and age (p<0.00004), with the dose-response more pronounced in older females. Also, the vascular dose-response in the left hippocampus of females was significantly correlated with memory function decline at 6 (r = − 0.95, p<0.0006) and 18 (r = −0.88, p<0.02) months post-RT. Conclusion: The hippocampal vascular
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet
2010-05-01
This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross
NASA Astrophysics Data System (ADS)
Mikkonen, S.; Korhonen, H.; Romakkaniemi, S.; Smith, J. N.; Joutsensaari, J.; Lehtinen, K. E. J.; Hamed, A.; Breider, T. J.; Birmili, W.; Spindler, G.; Plass-Duelmer, C.; Facchini, M. C.; Laaksonen, A.
2011-01-01
Measurements of aerosol size distribution and different gas and meteorological parameters, made in three polluted sites in Central and Southern Europe: Po Valley, Italy, Melpitz and Hohenpeissenberg in Germany, were analysed for this study to examine which of the meteorological and trace gas variables affect the number concentration of Aitken (Dp= 50 nm) particles. The aim of our study was to predict the number concentration of 50 nm particles by a combination of in-situ meteorological and gas phase parameters. The statistical model needs to describe, amongst others, the factors affecting the growth of newly formed aerosol particles (below 10 nm) to 50 nm size, but also sources of direct particle emissions in that size range. As the analysis method we used multivariate nonlinear mixed effects model. Hourly averages of gas and meteorological parameters measured at the stations were used as predictor variables; the best predictive model was attained with a combination of relative humidity, new particle formation event probability, temperature, condensation sink and concentrations of SO2, NO2 and ozone. The seasonal variation was also taken into account in the mixed model structure. Model simulations with the Global Model of Aerosol Processes (GLOMAP) indicate that the parameterization can be used as a part of a larger atmospheric model to predict the concentration of climatically active particles. As an additional benefit, the introduced model framework is, in theory, applicable for any kind of measured aerosol parameter.
NASA Astrophysics Data System (ADS)
Mikkonen, S.; Korhonen, H.; Romakkaniemi, S.; Smith, J. N.; Joutsensaari, J.; Lehtinen, K. E. J.; Hamed, A.; Breider, T. J.; Birmili, W.; Spindler, G.; Plass-Duelmer, C.; Facchini, M. C.; Laaksonen, A.
2010-08-01
Measurements of aerosol size-distribution and different gas and meteorological parameters, made in three polluted sites in Central- and Southern Europe: Po Valley, Italy, Melpitz and Hohenpeissenberg in Germany, were analysed for this study to examine which of the meteorological and trace gas variables affect the number concentration of Aitken (Dp=50 nm) particles. The aim of our study was to predict the number concentration of 50 nm particles by a combination of in-situ meteorological and gas phase parameters. The statistical model needs to describe, amongst others, the factors affecting the growth of newly formed aerosol particles (below 10 nm) to 50 nm size, but also sources of direct particle emissions in that size range. As the analysis method we used multivariate nonlinear mixed effects model. Hourly averages of gas and meteorological parameters measured at the stations were used as predictor variables; the best predictive model was attained with a combination of relative humidity, new particle formation event probability, temperature, condensation sink and concentrations of SO2, NO2 and ozone. The seasonal variation was also taken into account in the mixed model structure. Model simulations with the Global Model of Aerosol Processes (GLOMAP) indicate that the parameterization can be used as a part of a larger atmospheric model to predict the concentration of climatically active particles. As an additional benefit, the introduced model framework is, in theory, applicable for any kind of measured aerosol parameter.
CLUSTERING CRITERIA AND MULTIVARIATE NORMAL MIXTURES
New clustering criteria for use when a mixture of multivariate normal distributions is an appropriate model are presented. They are derived from maximum likelihood and Bayesian approaches corresponding to different assumptions about the covariance matrices of the mixture componen...
Chauvin, Benoît; Kasselouri, Athena; Chaminade, Pierre; Quiameso, Rita; Nicolis, Ioannis; Maillard, Philippe; Prognon, Patrice
2011-10-31
Tetrapyrrole rings possess four nitrogen atoms, two of which act as Bröndsted bases in acidic media. The two protonation steps occur on a close pH range, particularly in the case of meso-tetraphenylporphyrin (TPP) derivatives. If the cause of this phenomenon is well known--a protonation-induced distortion of the porphyrin ring--data on stepwise protonation constants and on electronic absorption spectra of monoprotonated TPPs are sparse. A multivariate approach has been systematically applied to a series of glycoconjugated and hydroxylated TPPs, potential anticancer drugs usable in Photodynamic Therapy. The dual purpose was determination of protonation constants and linking substitution with basicity. Hard-modeling version of MCR-ALS (Multivariate Curve Resolution Alternating Least Squares) has given access to spectra and distribution profile of pure components. Spectra of monoprotonated species (H(3)TPP(+)) in solution resemble those of diprotonated species (H(4)TPP(2+)), mainly differing by a slight blue-shift of bands. Overlap of H(3)TPP(+) and H(4)TPP(2+) spectra reinforces the difficulty to evidence an intermediate form only present in low relative abundance. Depending on macrocycle substitution, pK values ranged from 3.5±0.1 to 5.1±0.1 for the first protonation and from 3.2±0.2 to 4.9±0.1 for the second one. Inner nitrogens' basicity is affected by position, number and nature of peripheral substituents depending on their electrodonating character. pK values have been used to establish a predictive Multiple Linear Regression (MLR) model, relying on atom-type electrotopological indices. This model accurately describes our results and should be applied to new TPP derivatives in a drug-design perspective. PMID:21962373
NASA Astrophysics Data System (ADS)
Cotroneo, Vincenzo; Davis, William N.; Reid, Paul B.; Schwartz, Daniel A.; Trolier-McKinstry, Susan; Wilke, Rudeger H. T.
2011-09-01
The present generation of X-ray telescopes emphasizes either high image quality (e.g. Chandra with sub-arc second resolution) or large effective area (e.g. XMM-Newton), while future observatories under consideration (e.g. Athena, AXSIO) aim to greatly enhance the effective area, while maintaining moderate (~10 arc-seconds) image quality. To go beyond the limits of present and planned missions, the use of thin adjustable optics for the control of low-order figure error is needed to obtain the high image quality of precisely figured mirrors along with the large effective area of thin mirrors. The adjustable mirror prototypes under study at Smithsonian Astrophysical Observatory are based on two different principles and designs: 1) thin film lead-zirconate-titanate (PZT) piezoelectric actuators directly deposited on the mirror back surface, with the strain direction parallel to the glass surface (for sub-arc-second angular resolution and large effective area), and 2) conventional leadmagnesium- niobate (PMN) electrostrictive actuators with their strain direction perpendicular to the mirror surface (for 3-5 arc second resolution and moderate effective area). We have built and operated flat test mirrors of these adjustable optics. We present the comparison between theoretical influence functions as obtained by finite element analysis and the measured influence functions obtained from the two test configurations.
Shapiro, Y; Moran, D; Epstein, Y; Stroschein, L; Pandolf, K B
1995-05-01
Under outdoor conditions this model was over estimating sweat loss response in shaded (low solar radiation) environments, and underestimating the response when solar radiation was high (open field areas). The present study was conducted in order to adjust the model to be applicable under outdoor environmental conditions. Four groups of fit acclimated subjects participated in the study. They were exposed to three climatic conditions (30 degrees, 65% rh; 31 degrees C, 40% rh; and 40 degrees C, 20% rh) and three levels of metabolic rate (100, 300 and 450 W) in shaded and sunny areas while wearing shorts, cotton fatigues (BDUs) or protective garments. The original predictive equation for sweat loss was adjusted for the outdoor conditions by evaluating separately the radiative heat exchange, short-wave absorption in the body and long-wave emission from the body to the atmosphere and integrating them in the required evaporation component (Ereq) of the model, as follows: Hr = 1.5SL0.6/I(T) (watt) H1 = 0.047Me.th/I(T) (watt), where SL is solar radiation (W.m-2), Me.th is the Stephan Boltzman constant, and I(T) is the effective clothing insulation coefficient. This adjustment revealed a high correlation between the measured and expected values of sweat loss (r = 0.99, p < 0.0001). PMID:7737107
Multivariate Data EXplorer (MDX)
Steed, Chad Allen
2012-08-01
The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.
Kasprzyk, Idalia; Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka; Wolski, Tomasz
2011-02-01
Ganoderma spores are one of the most airspora abundant taxa in many regions of the world, and are considered to be important allergens. The aerobiology of Ganoderma basidiospores in two cities in Poland was examined using the volumetric method, (Burkard and Lanzonii Spore Traps), from selected days in 2004, 2005 and 2006. Spores of Ganoderma were present in the atmosphere from June to November, with peak concentrations generally occurring from late July to mid-October. ANN (artificial neural network) and MRT (multivariate regression trees), models indicated that atmospheric phenomenon, hour and relative humidity were the most important variables influencing spore content. The remaining variables (air temperature, dew point, air pressure, wind speed and wind direction), also contributed to the high network performance, (ratio above 1), but their impact was less distinct. Those results are consistent with the Spearman's rank correlation analysis. PMID:21183203
Stirrup, Oliver T; Babiker, Abdel G; Carpenter, James R; Copas, Andrew J
2016-04-30
Longitudinal data are widely analysed using linear mixed models, with 'random slopes' models particularly common. However, when modelling, for example, longitudinal pre-treatment CD4 cell counts in HIV-positive patients, the incorporation of non-stationary stochastic processes such as Brownian motion has been shown to lead to a more biologically plausible model and a substantial improvement in model fit. In this article, we propose two further extensions. Firstly, we propose the addition of a fractional Brownian motion component, and secondly, we generalise the model to follow a multivariate-t distribution. These extensions are biologically plausible, and each demonstrated substantially improved fit on application to example data from the Concerted Action on SeroConversion to AIDS and Death in Europe study. We also propose novel procedures for residual diagnostic plots that allow such models to be assessed. Cohorts of patients were simulated from the previously reported and newly developed models in order to evaluate differences in predictions made for the timing of treatment initiation under different clinical management strategies. A further simulation study was performed to demonstrate the substantial biases in parameter estimates of the mean slope of CD4 decline with time that can occur when random slopes models are applied in the presence of censoring because of treatment initiation, with the degree of bias found to depend strongly on the treatment initiation rule applied. Our findings indicate that researchers should consider more complex and flexible models for the analysis of longitudinal biomarker data, particularly when there are substantial missing data, and that the parameter estimates from random slopes models must be interpreted with caution. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. PMID:26555755
Technology Transfer Automated Retrieval System (TEKTRAN)
Hydrologic models are essential tools for environmental assessment of agricultural non-point source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, which can limit its application. The study objective was to investigate a cost e...
ERIC Educational Resources Information Center
Whittaker, Tiffany A.; Stapleton, Laura M.
2006-01-01
Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…
NKG201xGIA - first results for a new model of glacial isostatic adjustment in Fennoscandia
NASA Astrophysics Data System (ADS)
Steffen, Holger; Barletta, Valentina; Kollo, Karin; Milne, Glenn A.; Nordman, Maaria; Olsson, Per-Anders; Simpson, Matthew J. R.; Tarasov, Lev; Ågren, Jonas
2016-04-01
Glacial isostatic adjustment (GIA) is a dominant process in northern Europe, which is observed with several geodetic and geophysical methods. The observed land uplift due to this process amounts to about 1 cm/year in the northern Gulf of Bothnia. GIA affects the establishment and maintenance of reliable geodetic and gravimetric reference networks in the Nordic countries. To support a high level of accuracy in the determination of position, adequate corrections have to be applied with dedicated models. Currently, there are efforts within a Nordic Geodetic Commission (NKG) activity towards a model of glacial isostatic adjustment for Fennoscandia. The new model, NKG201xGIA, to be developed in the near future will complement the forthcoming empirical NKG land uplift model, which will substitute the currently used empirical land uplift model NKG2005LU (Ågren & Svensson, 2007). Together, the models will be a reference for vertical and horizontal motion, gravity and geoid change and more. NKG201xGIA will also provide uncertainty estimates for each field. Following former investigations, the GIA model is based on a combination of an ice and an earth model. The selected reference ice model, GLAC, for Fennoscandia, the Barents/Kara seas and the British Isles is provided by Lev Tarasov and co-workers. Tests of different ice and earth models will be performed based on the expertise of each involved modeler. This includes studies on high resolution ice sheets, different rheologies, lateral variations in lithosphere and mantle viscosity and more. This will also be done in co-operation with scientists outside NKG who help in the development and testing of the model. References Ågren, J., Svensson, R. (2007): Postglacial Land Uplift Model and System Definition for the New Swedish Height System RH 2000. Reports in Geodesy and Geographical Information Systems Rapportserie, LMV-Rapport 4, Lantmäteriet, Gävle.
NASA Astrophysics Data System (ADS)
Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad
2015-11-01
One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.
Kleinman, Lawrence C; Norton, Edward C
2009-01-01
Objective To develop and validate a general method (called regression risk analysis) to estimate adjusted risk measures from logistic and other nonlinear multiple regression models. We show how to estimate standard errors for these estimates. These measures could supplant various approximations (e.g., adjusted odds ratio [AOR]) that may diverge, especially when outcomes are common. Study Design Regression risk analysis estimates were compared with internal standards as well as with Mantel–Haenszel estimates, Poisson and log-binomial regressions, and a widely used (but flawed) equation to calculate adjusted risk ratios (ARR) from AOR. Data Collection Data sets produced using Monte Carlo simulations. Principal Findings Regression risk analysis accurately estimates ARR and differences directly from multiple regression models, even when confounders are continuous, distributions are skewed, outcomes are common, and effect size is large. It is statistically sound and intuitive, and has properties favoring it over other methods in many cases. Conclusions Regression risk analysis should be the new standard for presenting findings from multiple regression analysis of dichotomous outcomes for cross-sectional, cohort, and population-based case–control studies, particularly when outcomes are common or effect size is large. PMID:18793213
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va: American Psychiatric Publishing. 2013. Powell AD. Grief, bereavement, and adjustment disorders. In: Stern TA, Rosenbaum ...
NASA Astrophysics Data System (ADS)
Fang, X.; Pomeroy, J. W.; Ellis, C. R.; MacDonald, M. K.; DeBeer, C. M.; Brown, T.
2013-04-01
One of the purposes of the Cold Regions Hydrological Modelling platform (CRHM) is to diagnose inadequacies in the understanding of the hydrological cycle and its simulation. A physically based hydrological model including a full suite of snow and cold regions hydrology processes as well as warm season, hillslope and groundwater hydrology was developed in CRHM for application in the Marmot Creek Research Basin (~ 9.4 km2), located in the Front Ranges of the Canadian Rocky Mountains. Parameters were selected from digital elevation model, forest, soil, and geological maps, and from the results of many cold regions hydrology studies in the region and elsewhere. Non-calibrated simulations were conducted for six hydrological years during the period 2005-2011 and were compared with detailed field observations of several hydrological cycle components. The results showed good model performance for snow accumulation and snowmelt compared to the field observations for four seasons during the period 2007-2011, with a small bias and normalised root mean square difference (NRMSD) ranging from 40 to 42% for the subalpine conifer forests and from 31 to 67% for the alpine tundra and treeline larch forest environments. Overestimation or underestimation of the peak SWE ranged from 1.6 to 29%. Simulations matched well with the observed unfrozen moisture fluctuation in the top soil layer at a lodgepole pine site during the period 2006-2011, with a NRMSD ranging from 17 to 39%, but with consistent overestimation of 7 to 34%. Evaluations of seasonal streamflow during the period 2006-2011 revealed that the model generally predicted well compared to observations at the basin scale, with a NRMSD of 60% and small model bias (1%), while at the sub-basin scale NRMSDs were larger, ranging from 72 to 76%, though overestimation or underestimation for the cumulative seasonal discharge was within 29%. Timing of discharge was better predicted at the Marmot Creek basin outlet, having a Nash
NASA Astrophysics Data System (ADS)
Fang, X.; Pomeroy, J. W.; Ellis, C. R.; MacDonald, M. K.; DeBeer, C. M.; Brown, T.
2012-11-01
One of the purposes of the Cold Regions Hydrological Modelling platform (CRHM) is to diagnose inadequacies in the understanding of the hydrological cycle and its simulation. A physically based hydrological model including a full suite of snow and cold regions hydrology processes as well as warm season, hillslope and groundwater hydrology was developed in CRHM for application in the Marmot Creek Research Basin (~ 9.4km2), located in the Front Ranges of Canadian Rocky Mountains. Parameters were selected from digital elevation model, forest, soil and geological maps, and from the results of many cold regions hydrology studies in the region and elsewhere. Non-calibrated simulations were conducted for six hydrological years during 2005-2011 and were compared with detailed field observations of several hydrological cycle components. Results showed good model performance for snow accumulation and snowmelt compared to the field observations for four seasons during 2007-2011, with a small bias and normalized root mean square difference (NRMSD) ranging from 40 to 42% for the subalpine conifer forests and from 31 to 67% for the alpine tundra and tree-line larch forest environments. Overestimation or underestimation of the peak SWE ranged from 1.6 to 29%. Simulations matched well with the observed unfrozen moisture fluctuation in the top soil layer at a lodgepole pine site during 2006-2011, with a NRMSD ranging from 17% to 39%, but with consistent overestimation of 7 to 34%. Evaluations of seasonal streamflow during 2006-2011 revealed the model generally predicted well compared to observations at the basin scale, with a NRMSD of 77% and small model bias (6%), but at the sub-basin scale NRMSD were larger, ranging from 86 to 106%; though overestimation or underestimation for the cumulative seasonal discharge was within 24%. Timing of discharge was better predicted at the Marmot Creek basin outlet having a Nash-Sutcliffe efficiency (NSE) of 0.31 compared to the outlets of the sub
NASA Astrophysics Data System (ADS)
Pond, G. J.
2005-05-01
There is still debate on the strength of various data analysis tools for assessing biological condition in streams. This study compared two popular assessment approaches (multimetric index and RIVPACS-type O/E model) using macroinvertebrates from Kentucky streams. Data from 557 targeted and randomly selected sites (212 reference, 345 non-reference) sampled between 2000 and 2004 were used in this analysis. The Kentucky Macroinvertebrate Bioassessment Index (MBI) combines seven metrics (total generic richness, EPT generic richness, modified HBI, %Ephemeroptera, %EPT minus Cheumatopsyche, %midges+worms, and %clingers) that are scored by standardizing to the 95th or 5th percentile of the reference distribution and averaged. For comparison, three separate genus-level RIVPACS-type models were constructed (high-, low-, and mixed gradient streams) using four predictive variables (area, latitude, longitude, and week number) and taxa from reference sites. All 3 models preformed well but the low gradient model had the lowest precision. Assessments of non-reference sites based on MBI and O/E scores yielded similar results in terms of discrimination efficiency but the model based on mixed-gradient streams was the least sensitive. Using a subset of data from 84 headwater streams in the Appalachian region, MBI and O/E scores responded almost identically to stressors such as conductivity and habitat degradation.
Kogan, Clark; Kalachev, Leonid; Van Dongen, Hans P. A.
2016-01-01
In study designs with repeated measures for multiple subjects, population models capturing within- and between-subjects variances enable efficient individualized prediction of outcome measures (response variables) by incorporating individuals response data through Bayesian forecasting. When measurement constraints preclude reasonable levels of prediction accuracy, additional (secondary) response variables measured alongside the primary response may help to increase prediction accuracy. We investigate this for the case of substantial between-subjects correlation between primary and secondary response variables, assuming negligible within-subjects correlation. We show how to determine the accuracy of primary response predictions as a function of secondary response observations. Given measurement costs for primary and secondary variables, we determine the number of observations that produces, with minimal cost, a fixed average prediction accuracy for a model of subject means. We illustrate this with estimation of subject-specific sleep parameters using polysomnography and wrist actigraphy. We also consider prediction accuracy in an example time-dependent, linear model and derive equations for the optimal timing of measurements to achieve, on average, the best prediction accuracy. Finally, we examine an example involving a circadian rhythm model and show numerically that secondary variables can improve individualized predictions in this time-dependent nonlinear model as well. PMID:27110271
Li, Weiyong; Worosila, Gregory D
2005-05-13
This research note demonstrates the simultaneous quantitation of a pharmaceutical active ingredient and three excipients in a simulated powder blend containing acetaminophen, Prosolv and Crospovidone. An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples. The samples were prepared by weighing suitable amount of powders into separate 20-mL scintillation vials and were mixed manually. Partial least squares (PLS) regression was used in calibration model development. The models generated accurate results for quantitation of Crospovidone (at 5%, w/w) and magnesium stearate (at 0.5%, w/w). Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50. The models had a small bias for quantitation of acetaminophen (at 30%, w/w) and Prosolv (at 64.5%, w/w) in the blend. The implication of the bias is discussed. PMID:15848006
Multivariate Longitudinal Analysis with Bivariate Correlation Test.
Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory
2016-01-01
In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692
NASA Astrophysics Data System (ADS)
Ostertag, Edwin; Boldrini, Barbara; Luckow, Sabrina; Kessler, Rudolf W.
2012-06-01
Glioblastoma multiforme represents a highly lethal brain tumor. A tumor model has been developed based on the U-251 MG cell line from a human explant. The tumor model simulates different malignancies by controlled expression of the tumor suppressor proteins PTEN and TP53 within the cell lines derived from the wild type. The cells from each different malignant cell line are grown on slides, followed by a paraformaldehyde fixation. UV / VIS and IR spectra are recorded in the cell nuclei. For the differentiation of the cell lines a principal component analysis (PCA) is performed. The PCA demonstrates a good separation of the tumor model cell lines both with UV / VIS spectroscopy and with IR spectroscopy.
NASA Technical Reports Server (NTRS)
Mielke, R. R.; Tung, L. J.; Carraway, P. I., III
1984-01-01
The feasibility of using reduced order models and reduced order observers with eigenvalue/eigenvector assignment procedures is investigated. A review of spectral assignment synthesis procedures is presented. Then, a reduced order model which retains essential system characteristics is formulated. A constant state feedback matrix which assigns desired closed loop eigenvalues and approximates specified closed loop eigenvectors is calculated for the reduced order model. It is shown that the eigenvalue and eigenvector assignments made in the reduced order system are retained when the feedback matrix is implemented about the full order system. In addition, those modes and associated eigenvectors which are not included in the reduced order model remain unchanged in the closed loop full order system. The full state feedback design is then implemented by using a reduced order observer. It is shown that the eigenvalue and eigenvector assignments of the closed loop full order system rmain unchanged when a reduced order observer is used. The design procedure is illustrated by an actual design problem.
NASA Astrophysics Data System (ADS)
Sajdak, Marcin; Smędowski, Łukasz
2013-09-01
The aim of this work was to develop a statistical model which can predict values describing chemical composition of cokes performed in industrial scale. This model was developed on the basis of data that were taken from the production system used in the one of Polish coking plant. Elaborated equation include quality parameters of initial coals that form coal blends as well as contribution of additions such as coke and petrochemical coke. These equations allow to predict chemical composition of coke, e.g. contributions of: sulphur, ash, phosphorus and chlorine within the coke. A model was elaborated with use of STATISTICA 10 program and it is based on factor and multiply regression analyses. These analyses were chosen from among few kinds of regression analyses. They allowed to develop prediction model with the required goodness of fit between calculated and actual values. Goodness of fit was elaborated with: • residuals analyses, • residues normality and predicted normality • mean absolute error • Pearson correlation confidence
NASA Astrophysics Data System (ADS)
Larson, Timothy V.; Covert, David S.; Kim, Eugene; Elleman, Robert; Schreuder, Astrid B.; Lumley, Thomas
2006-05-01
We introduce an extended receptor model, implemented with the multilinear engine ME2, which combines simultaneous but separate filter-based species information with size-resolved particle volume information. Our chemical data set consisted of 24-hour filter measurements reported by the EPA Speciation Trends Network at Beacon Hill in Seattle, Washington, from February 2000 to June 2003. We measured the particle size distribution at this site from December 2000 to April 2002 using a differential mobility particle sizer (DMPS) and an aerodynamic particle sizer (APS). The combined model extends the traditional chemical mass balance approach by including a simultaneous set of conservation equations for both particle mass and volume, linked by a unique value of apparent particle density for each source. The model distinguished three mobile source features, two consistent with previous identifications of "gasoline" and "diesel" sources, and an additional minor feature enriched in EC, Fe and Mn and ultrafine particle mass that would have been difficult to interpret in the absence of particle size information. This study has also demonstrated the feasibility of defining missing mass as an additional variable, and thereby providing additional useful model constraints and eliminating the posthoc regression step that is traditionally used to rescale the results.
Lee, Yeojin; Kim, Jaejin; Lee, Sanguk; Woo, Young-Ah; Chung, Hoeil
2012-01-30
Direct transmission Raman measurements for analysis of pharmaceuticals in capsules are advantageous since they can be used to determine active pharmaceutical ingredient (API) concentrations in a non-destructive manner and with much less fluorescence background interference from the capsules themselves compared to conventional back-scattering measurements. If a single calibration model such as developed from spectra simply collected in glass vials could be used to determine API concentrations of samples contained in capsules of different colors rather than constructing individual models for each capsule color, the utility of transmission measurements would be further enhanced. To evaluate the feasibility, transmission Raman spectra of binary mixtures of ambroxol and lactose were collected in a glass vial and a partial least squares (PLS) model for the determination of ambroxol concentration was developed. Then, the model was directly applied to determine ambroxol concentrations of samples contained in capsules of 4 different colors (blue, green, white and yellow). Although the prediction performance was slightly degraded when the samples were placed in blue or green capsules, due to the presence of weak fluorescence, accurate determination of ambroxol was generally achieved in all cases. The prediction accuracy was also investigated when the thickness of the capsule was varied. PMID:22284467
NASA Technical Reports Server (NTRS)
Mielke, R. R.; Tung, L. J.; Carraway, P. I., III
1985-01-01
The feasibility of using reduced order models and reduced order observers with eigenvalue/eigenvector assignment procedures is investigated. A review of spectral assignment synthesis procedures is presented. Then, a reduced order model which retains essential system characteristics is formulated. A constant state feedback matrix which assigns desired closed loop eigenvalues and approximates specified closed loop eigenvectors is calculated for the reduced order model. It is shown that the eigenvalue and eigenvector assignments made in the reduced order system are retained when the feedback matrix is implemented about the full order system. In addition, those modes and associated eigenvectors which are not included in the reduced order model remain unchanged in the closed loop full order system. The fulll state feedback design is then implemented by using a reduced order observer. It is shown that the eigenvalue and eigenvector assignments of the closed loop full order system remain unchanged when a reduced order observer is used. The design procedure is illustrated by an actual design problem.
Technology Transfer Automated Retrieval System (TEKTRAN)
Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residua...
Mattson, Earl; Smith, Robert; Fujita, Yoshiko; McLing, Travis; Neupane, Ghanashyam; Palmer, Carl; Reed, David; Thompson, Vicki
2015-03-01
The project was aimed at demonstrating that the geothermometric predictions can be improved through the application of multi-element reaction path modeling that accounts for lithologic and tectonic settings, while also accounting for biological influences on geochemical temperature indicators. The limited utilization of chemical signatures by individual traditional geothermometer in the development of reservoir temperature estimates may have been constraining their reliability for evaluation of potential geothermal resources. This project, however, was intended to build a geothermometry tool which can integrate multi-component reaction path modeling with process-optimization capability that can be applied to dilute, low-temperature water samples to consistently predict reservoir temperature within ±30 °C. The project was also intended to evaluate the extent to which microbiological processes can modulate the geochemical signals in some thermal waters and influence the geothermometric predictions.
da Costa, Pedro Beschoren; Granada, Camille E.; Ambrosini, Adriana; Moreira, Fernanda; de Souza, Rocheli; dos Passos, João Frederico M.; Arruda, Letícia; Passaglia, Luciane M. P.
2014-01-01
Plant growth-promoting bacteria can greatly assist sustainable farming by improving plant health and biomass while reducing fertilizer use. The plant-microorganism-environment interaction is an open and complex system, and despite the active research in the area, patterns in root ecology are elusive. Here, we simultaneously analyzed the plant growth-promoting bacteria datasets from seven independent studies that shared a methodology for bioprospection and phenotype screening. The soil richness of the isolate's origin was classified by a Principal Component Analysis. A Categorical Principal Component Analysis was used to classify the soil richness according to isolate's indolic compound production, siderophores production and phosphate solubilization abilities, and bacterial genera composition. Multiple patterns and relationships were found and verified with nonparametric hypothesis testing. Including niche colonization in the analysis, we proposed a model to explain the expression of bacterial plant growth-promoting traits according to the soil nutritional status. Our model shows that plants favor interaction with growth hormone producers under rich nutrient conditions but favor nutrient solubilizers under poor conditions. We also performed several comparisons among the different genera, highlighting interesting ecological interactions and limitations. Our model could be used to direct plant growth-promoting bacteria bioprospection and metagenomic sampling. PMID:25542031
da Costa, Pedro Beschoren; Granada, Camille E; Ambrosini, Adriana; Moreira, Fernanda; de Souza, Rocheli; dos Passos, João Frederico M; Arruda, Letícia; Passaglia, Luciane M P
2014-01-01
Plant growth-promoting bacteria can greatly assist sustainable farming by improving plant health and biomass while reducing fertilizer use. The plant-microorganism-environment interaction is an open and complex system, and despite the active research in the area, patterns in root ecology are elusive. Here, we simultaneously analyzed the plant growth-promoting bacteria datasets from seven independent studies that shared a methodology for bioprospection and phenotype screening. The soil richness of the isolate's origin was classified by a Principal Component Analysis. A Categorical Principal Component Analysis was used to classify the soil richness according to isolate's indolic compound production, siderophores production and phosphate solubilization abilities, and bacterial genera composition. Multiple patterns and relationships were found and verified with nonparametric hypothesis testing. Including niche colonization in the analysis, we proposed a model to explain the expression of bacterial plant growth-promoting traits according to the soil nutritional status. Our model shows that plants favor interaction with growth hormone producers under rich nutrient conditions but favor nutrient solubilizers under poor conditions. We also performed several comparisons among the different genera, highlighting interesting ecological interactions and limitations. Our model could be used to direct plant growth-promoting bacteria bioprospection and metagenomic sampling. PMID:25542031
Tencate, Alister J; Kalivas, John H; White, Alexander J
2016-05-19
New multivariate calibration methods and other processes are being developed that require selection of multiple tuning parameter (penalty) values to form the final model. With one or more tuning parameters, using only one measure of model quality to select final tuning parameter values is not sufficient. Optimization of several model quality measures is challenging. Thus, three fusion ranking methods are investigated for simultaneous assessment of multiple measures of model quality for selecting tuning parameter values. One is a supervised learning fusion rule named sum of ranking differences (SRD). The other two are non-supervised learning processes based on the sum and median operations. The effect of the number of models evaluated on the three fusion rules are also evaluated using three procedures. One procedure uses all models from all possible combinations of the tuning parameters. To reduce the number of models evaluated, an iterative process (only applicable to SRD) is applied and thresholding a model quality measure before applying the fusion rules is also used. A near infrared pharmaceutical data set requiring model updating is used to evaluate the three fusion rules. In this case, calibration of the primary conditions is for the active pharmaceutical ingredient (API) of tablets produced in a laboratory. The secondary conditions for calibration updating is for tablets produced in the full batch setting. Two model updating processes requiring selection of two unique tuning parameter values are studied. One is based on Tikhonov regularization (TR) and the other is a variation of partial least squares (PLS). The three fusion methods are shown to provide equivalent and acceptable results allowing automatic selection of the tuning parameter values. Best tuning parameter values are selected when model quality measures used with the fusion rules are for the small secondary sample set used to form the updated models. In this model updating situation, evaluation of
Multivariate Longitudinal Analysis with Bivariate Correlation Test
Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory
2016-01-01
In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692
Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258
Surolia, Avadhesha
2015-01-01
Biomolecular recognition underlying drug-target interactions is determined by both binding affinity and specificity. Whilst, quantification of binding efficacy is possible, determining specificity remains a challenge, as it requires affinity data for multiple targets with the same ligand dataset. Thus, understanding the interaction space by mapping the target space to model its complementary chemical space through computational techniques are desirable. In this study, active site architecture of FabD drug target in two apicomplexan parasites viz. Plasmodium falciparum (PfFabD) and Toxoplasma gondii (TgFabD) is explored, followed by consensus docking calculations and identification of fifteen best hit compounds, most of which are found to be derivatives of natural products. Subsequently, machine learning techniques were applied on molecular descriptors of six FabD homologs and sixty ligands to induce distinct multivariate partial-least square models. The biological space of FabD mapped by the various chemical entities explain their interaction space in general. It also highlights the selective variations in FabD of apicomplexan parasites with that of the host. Furthermore, chemometric models revealed the principal chemical scaffolds in PfFabD and TgFabD as pyrrolidines and imidazoles, respectively, which render target specificity and improve binding affinity in combination with other functional descriptors conducive for the design and optimization of the leads. PMID:26535573
NASA Astrophysics Data System (ADS)
Thelen, Brian J.; Rickerd, Chris J.; Burns, Joseph W.
2014-06-01
With all of the new remote sensing modalities available, with ever increasing capabilities, there is a constant desire to extend the current state of the art in physics-based feature extraction and to introduce new and innovative techniques that enable the exploitation within and across modalities, i.e., fusion. A key component of this process is finding the associated features from the various imaging modalities that provide key information in terms of exploitative fusion. Further, it is desired to have an automatic methodology for assessing the information in the features from the various imaging modalities, in the presence of uncertainty. In this paper we propose a novel approach for assessing, quantifying, and isolating the information in the features via a joint statistical modeling of the features with the Gaussian Copula framework. This framework allows for a very general modeling of distributions on each of the features while still modeling the conditional dependence between the features, and the final output is a relatively accurate estimate of the information-theoretic J-divergence metric, which is directly related to discriminability. A very useful aspect of this approach is that it can be used to assess which features are most informative, and what is the information content as a function of key uncertainties (e.g., geometry) and collection parameters (e.g., SNR and resolution). We show some results of applying the Gaussian Copula framework and estimating the J-Divergence on HRR data as generated from the AFRL public release data set known as the Backhoe Data Dome.
NASA Astrophysics Data System (ADS)
Dubrovsky, M.; Farda, A.; Huth, R.
2012-12-01
The regional-scale simulations of weather-sensitive processes (e.g. hydrology, agriculture and forestry) for the present and/or future climate often require high resolution meteorological inputs in terms of the time series of selected surface weather characteristics (typically temperature, precipitation, solar radiation, humidity, wind) for a set of stations or on a regular grid. As even the latest Global and Regional Climate Models (GCMs and RCMs) do not provide realistic representation of statistical structure of the surface weather, the model outputs must be postprocessed (downscaled) to achieve the desired statistical structure of the weather data before being used as an input to the follow-up simulation models. One of the downscaling approaches, which is employed also here, is based on a weather generator (WG), which is calibrated using the observed weather series and then modified (in case of simulations for the future climate) according to the GCM- or RCM-based climate change scenarios. The present contribution uses the parametric daily weather generator M&Rfi to follow two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate/CZ (v.2) Regional Climate Model at 25 km resolution. The WG parameters will be derived from the RCM-simulated surface weather series and compared to those derived from observational data in the Czech meteorological stations. The set of WG parameters will include selected statistics of the surface temperature and precipitation (characteristics of the mean, variability, interdiurnal variability and extremes). (2) Testing a potential of RCM output for calibration of the WG for the ungauged locations. The methodology being examined will consist in using the WG, whose parameters are interpolated from the surrounding stations and then corrected based on a RCM-simulated spatial variability. The quality of the weather series produced by the WG calibrated in this way will be assessed in terms
Chang, Pao-Erh Paul; Yang, Jen-Chih Rena; Den, Walter; Wu, Chang-Fu
2014-09-01
Emissions of volatile organic compounds (VOCs) are most frequent environmental nuisance complaints in urban areas, especially where industrial districts are nearby. Unfortunately, identifying the responsible emission sources of VOCs is essentially a difficult task. In this study, we proposed a dynamic approach to gradually confine the location of potential VOC emission sources in an industrial complex, by combining multi-path open-path Fourier transform infrared spectrometry (OP-FTIR) measurement and the statistical method of principal component analysis (PCA). Close-cell FTIR was further used to verify the VOC emission source by measuring emitted VOCs from selected exhaust stacks at factories in the confined areas. Multiple open-path monitoring lines were deployed during a 3-month monitoring campaign in a complex industrial district. The emission patterns were identified and locations of emissions were confined by the wind data collected simultaneously. N,N-Dimethyl formamide (DMF), 2-butanone, toluene, and ethyl acetate with mean concentrations of 80.0 ± 1.8, 34.5 ± 0.8, 103.7 ± 2.8, and 26.6 ± 0.7 ppbv, respectively, were identified as the major VOC mixture at all times of the day around the receptor site. As the toxic air pollutant, the concentrations of DMF in air samples were found exceeding the ambient standard despite the path-average effect of OP-FTIR upon concentration levels. The PCA data identified three major emission sources, including PU coating, chemical packaging, and lithographic printing industries. Applying instrumental measurement and statistical modeling, this study has established a systematic approach for locating emission sources. Statistical modeling (PCA) plays an important role in reducing dimensionality of a large measured dataset and identifying underlying emission sources. Instrumental measurement, however, helps verify the outcomes of the statistical modeling. The field study has demonstrated the feasibility of
NASA Astrophysics Data System (ADS)
De Ketelaere, B.; Vanhoutte, H.; De Baerdemaeker, J.
2003-09-01
In the non-destructive quality assessment of agro-products using vibration analysis, the resonant frequency and the damping of the vibration are the main interest. Those parameters are usually calculated starting from the frequency spectrum, obtained after a fast Fourier transformation (FFT) of the time signal. However, this method faces several drawbacks when applied to short-time signals, as in the case of impact testing of highly damped specimen. An alternative to the FFT method is used for the high-resolution estimation of both resonant frequency and damping. Furthermore, the mass-spring model that is used in the literature for non-destructive quality assessment of various agro-products is extended with the incorporation of the damping and a shape characteristic. As a practical example, eggshell stiffness was estimated using vibration measurements. A data set consisting of 229 eggs was measured. It is shown that both the damping and the shape characteristics are of major importance to explain eggshell strength. This paper makes clear that a univariable model, as is mostly used in the literature, is not always satisfactory to describe the vibration behaviour of biological products.
Multivariate Analysis in Metabolomics
Worley, Bradley; Powers, Robert
2015-01-01
Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916
Multivariate Data EXplorer (MDX)
2012-08-01
The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views wherebymore » selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.« less
Laughlin, D.C.; Grace, J.B.
2006-01-01
Recently, efforts to develop multivariate models of plant species richness have been extended to include systems where trees play important roles as overstory elements mediating the influences of environment and disturbance on understory richness. We used structural equation modeling to examine the relationship of understory vascular plant species richness to understory abundance, forest structure, topographic slope, and surface fire history in lower montane forests on the North Rim of Grand Canyon National Park, USA based on data from eighty-two 0.1 ha plots. The questions of primary interest in this analysis were: (1) to what degree are influences of trees on understory richness mediated by effects on understory abundance? (2) To what degree are influences of fire history on richness mediated by effects on trees and/or understory abundance? (3) Can the influences of fire history on this system be related simply to time-since-fire or are there unique influences associated with long-term fire frequency? The results we obtained are consistent with the following inferences. First, it appears that pine trees had a strong inhibitory effect on the abundance of understory plants, which in turn led to lower understory species richness. Second, richness declined over time since the last fire. This pattern appears to result from several processes, including (1) a post-fire stimulation of germination, (2) a decline in understory abundance, and (3) an increase over time in pine abundance (which indirectly leads to reduced richness). Finally, once time-since-fire was statistically controlled, it was seen that areas with higher fire frequency have lower richness than expected, which appears to result from negative effects on understory abundance, possibly by depletions of soil nutrients from repeated surface fire. Overall, it appears that at large temporal and spatial scales, surface fire plays an important and complex role in structuring understory plant communities in old