ALONSO ABAD, Ariel; Rodriguez, O.; TIBALDI, Fabian; CORTINAS ABRAHANTES, Jose
2002-01-01
In medical studies the categorical endpoints are quite often. Even though nowadays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main results are discussed. multivariate analysis; multivariate logistic regression; multicategor...
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...
Liu, Zitao; Hauskrecht, Milos
2017-11-01
Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.
Multivariate analysis: models and method
International Nuclear Information System (INIS)
Sanz Perucha, J.
1990-01-01
Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis
Multivariate covariance generalized linear models
DEFF Research Database (Denmark)
Bonat, W. H.; Jørgensen, Bent
2016-01-01
are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...
A "Model" Multivariable Calculus Course.
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…
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...
Model Checking Multivariate State Rewards
DEFF Research Database (Denmark)
Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis
2010-01-01
We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...
Multivariate pluvial flood damage models
International Nuclear Information System (INIS)
Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom
2015-01-01
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
Multivariate pluvial flood damage models
Energy Technology Data Exchange (ETDEWEB)
Van Ootegem, Luc [HIVA — University of Louvain (Belgium); SHERPPA — Ghent University (Belgium); Verhofstadt, Elsy [SHERPPA — Ghent University (Belgium); Van Herck, Kristine; Creten, Tom [HIVA — University of Louvain (Belgium)
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.
The value of multivariate model sophistication
DEFF Research Database (Denmark)
Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco
2014-01-01
We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...
Moons, Karel G. M.; Altman, Douglas G.; Reitsma, Johannes B.; Collins, Gary S.
Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include
Multivariate generalized linear mixed models using R
Berridge, Damon Mark
2011-01-01
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...
Ranking multivariate GARCH models by problem dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2010-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to
Preliminary Multivariable Cost Model for Space Telescopes
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Multivariate Option Pricing Using Dynamic Copula Models
van den Goorbergh, R.W.J.; Genest, C.; Werker, B.J.M.
2003-01-01
This paper examines the behavior of multivariate option prices in the presence of association between the underlying assets.Parametric families of copulas offering various alternatives to the normal dependence structure are used to model this association, which is explicitly assumed to vary over
Modeling Covariance Breakdowns in Multivariate GARCH
Jin, Xin; Maheu, John M
2014-01-01
This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Multivariate Markov chain modeling for stock markets
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.
Crane cabins' interior space multivariate anthropometric modeling.
Essdai, Ahmed; Spasojević Brkić, Vesna K; Golubović, Tamara; Brkić, Aleksandar; Popović, Vladimir
2018-01-01
Previous research has shown that today's crane cabins fail to meet the needs of a large proportion of operators. Performance and financial losses and effects on safety should not be overlooked as well. The first aim of this survey is to model the crane cabin interior space using up-to-date crane operator anthropometric data and to compare the multivariate and univariate method anthropometric models. The second aim of the paper is to define the crane cabin interior space dimensions that enable anthropometric convenience. To facilitate the cabin design, the anthropometric dimensions of 64 crane operators in the first sample and 19 more in the second sample were collected in Serbia. The multivariate anthropometric models, spanning 95% of the population on the basis of a set of 8 anthropometric dimensions, have been developed. The percentile method was also used on the same set of data. The dimensions of the interior space, necessary for the accommodation of the crane operator, are 1174×1080×1865 mm. The percentiles results for the 5th and 95th model are within the obtained dimensions. The results of this study may prove useful to crane cabin designers in eliminating anthropometric inconsistencies and improving the health of operators, but can also aid in improving the safety, performance and financial results of the companies where crane cabins operate.
Validation of models with multivariate output
International Nuclear Information System (INIS)
Rebba, Ramesh; Mahadevan, Sankaran
2006-01-01
This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung; Park, Eun Sug
2013-01-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air
Multivariate Heteroscedasticity Models for Functional Brain Connectivity
Directory of Open Access Journals (Sweden)
Christof Seiler
2017-12-01
Full Text Available Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI. We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
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. © 2015, The International Biometric Society.
A short note on multivariate dependence modeling
Czech Academy of Sciences Publication Activity Database
Bína, V.; Jiroušek, Radim
2013-01-01
Roč. 49, č. 3 (2013), s. 420-432 ISSN 0023-5954 Grant - others:GA ČR(CZ) GAP403/12/2175 Program:GA Institutional support: RVO:67985556 Keywords : multivariate distribution * dependence * copula Subject RIV: IN - Informatics, Computer Science Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2014/MTR/jirousek-0427848.pdf
Multivariate linear models and repeated measurements revisited
DEFF Research Database (Denmark)
Dalgaard, Peter
2009-01-01
Methods for generalized analysis of variance based on multivariate normal theory have been known for many years. In a repeated measurements context, it is most often of interest to consider transformed responses, typically within-subject contrasts or averages. Efficiency considerations leads...... to sphericity assumptions, use of F tests and the Greenhouse-Geisser and Huynh-Feldt adjustments to compensate for deviations from sphericity. During a recent implementation of such methods in the R language, the general structure of such transformations was reconsidered, leading to a flexible specification...
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
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…
A Range-Based Multivariate Model for Exchange Rate Volatility
B. Tims (Ben); R.J. Mahieu (Ronald)
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are
A Multivariate Approach to Functional Neuro Modeling
DEFF Research Database (Denmark)
Mørch, Niels J.S.
1998-01-01
by the application of linear and more flexible, nonlinear microscopic regression models to a real-world dataset. The dependency of model performance, as quantified by generalization error, on model flexibility and training set size is demonstrated, leading to the important realization that no uniformly optimal model......, provides the basis for a generalization theoretical framework relating model performance to model complexity and dataset size. Briefly summarized the major topics discussed in the thesis include: - An introduction of the representation of functional datasets by pairs of neuronal activity patterns...... exists. - Model visualization and interpretation techniques. The simplicity of this task for linear models contrasts the difficulties involved when dealing with nonlinear models. Finally, a visualization technique for nonlinear models is proposed. A single observation emerges from the thesis...
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
Modelling and Forecasting Multivariate Realized Volatility
DEFF Research Database (Denmark)
Chiriac, Roxana; Voev, Valeri
. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...
Modelling the Covariance Structure in Marginal Multivariate Count Models
DEFF Research Database (Denmark)
Bonat, W. H.; Olivero, J.; Grande-Vega, M.
2017-01-01
The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined...... be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited...... with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson...
A multivariate time series approach to modeling and forecasting demand in the emergency department.
Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L
2009-02-01
The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.
Sparse Multivariate Modeling: Priors and Applications
DEFF Research Database (Denmark)
Henao, Ricardo
This thesis presents a collection of statistical models that attempt to take advantage of every piece of prior knowledge available to provide the models with as much structure as possible. The main motivation for introducing these models is interpretability since in practice we want to be able...... a general yet self-contained description of every model in terms of generative assumptions, interpretability goals, probabilistic formulation and target applications. Case studies, benchmark results and practical details are also provided as appendices published elsewhere, containing reprints of peer...
A Range-Based Multivariate Model for Exchange Rate Volatility
Tims, Ben; Mahieu, Ronald
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...
Global Nonlinear Model Identification with Multivariate Splines
De Visser, C.C.
2011-01-01
At present, model based control systems play an essential role in many aspects of modern society. Application areas of model based control systems range from food processing to medical imaging, and from process control in oil refineries to the flight control systems of modern aircraft. Central to a
A Multivariate Model of Physics Problem Solving
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 Non-Symmetric Stochastic Models for Spatial Dependence Models
Haslauer, C. P.; Bárdossy, A.
2017-12-01
A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.
Modelling and Forecasting Multivariate Realized Volatility
DEFF Research Database (Denmark)
Halbleib, Roxana; Voev, Valeri
2011-01-01
This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical appl...
Multivariate Density Modeling for Retirement Finance
Rook, Christopher J.
2017-01-01
Prior to the financial crisis mortgage securitization models increased in sophistication as did products built to insure against losses. Layers of complexity formed upon a foundation that could not support it and as the foundation crumbled the housing market followed. That foundation was the Gaussian copula which failed to correctly model failure-time correlations of derivative securities in duress. In retirement, surveys suggest the greatest fear is running out of money and as retirement dec...
Multivariable Wind Modeling in State Space
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Pedersen, B. J.
2011-01-01
Turbulence of the incoming wind field is of paramount importance to the dynamic response of wind turbines. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper an empirical...... for the vector turbulence process incorporating its phase spectrum in one stage, and its results are compared with a conventional ARMA modeling method....... the succeeding state space and ARMA modeling of the turbulence rely on the positive definiteness of the cross-spectral density matrix, the problem with the non-positive definiteness of such matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross...
A simplified parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.
A tridiagonal parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.
Structural Equation Modeling of Multivariate Time Series
du Toit, Stephen H. C.; Browne, Michael W.
2007-01-01
The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…
Multivariate Term Structure Models with Level and Heteroskedasticity Effects
DEFF Research Database (Denmark)
Christiansen, Charlotte
2005-01-01
The paper introduces and estimates a multivariate level-GARCH model for the long rate and the term-structure spread where the conditional volatility is proportional to the ãth power of the variable itself (level effects) and the conditional covariance matrix evolves according to a multivariate GA...... and the level model. GARCH effects are more important than level effects. The results are robust to the maturity of the interest rates. Udgivelsesdato: MAY...
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes
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.
Complications from arteriovenous malformation radiosurgery: multivariate analysis and risk modeling
International Nuclear Information System (INIS)
Flickinger, John C.; Kondziolka, Douglas; Pollock, Bruce E.; Maitz, Ann H.; Lunsford, L. Dade
1997-01-01
Purpose/Objective: To assess the relationships of radiosurgery treatment parameters to the development of complications from radiosurgery for arteriovenous malformations (AVM). Methods and Materials: We evaluated follow-up imaging and clinical data in 307 AVM patients who received gamma knife radiosurgery at the University of Pittsburgh between 1987 and 1993. All patients had regular clinical or imaging follow up for a minimum of 2 years (range: 24-96 months, median = 44 months). Results: Post-radiosurgical imaging (PRI) changes developed in 30.5% of patients with regular follow-up magnetic resonance imaging, and were symptomatic in 10.7% of all patients at 7 years. PRI changes resolved within 3 years developed significantly less often (p = 0.0274) in patients with symptoms (52.8%) compared to asymptomatic patients (94.8%). The 7-year actuarial rate for developing persistent symptomatic PRI changes was 5.05%. Multivariate logistic regression modeling found that the 12 Gy volume was the only independent variable that correlated significantly with PRI changes (p < 0.0001) while symptomatic PRI changes were correlated with both 12 Gy volume (p = 0.0013) and AVM location (p 0.0066). Conclusion: Complications from AVM radiosurgery can be predicted with a statistical model relating the risks of developing symptomatic post-radiosurgical imaging changes to 12 Gy treatment volume and location
Multivariate statistical modelling based on generalized linear models
Fahrmeir, Ludwig
1994-01-01
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...
A generalized multivariate regression model for modelling ocean wave heights
Wang, X. L.; Feng, Y.; Swail, V. R.
2012-04-01
In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.
Univariate and Multivariate Specification Search Indices in Covariance Structure Modeling.
Hutchinson, Susan R.
1993-01-01
Simulated population data were used to compare relative performances of the modification index and C. Chou and P. M. Bentler's Lagrange multiplier test (a multivariate generalization of a modification index) for four levels of model misspecification. Both indices failed to recover the true model except at the lowest level of misspecification. (SLD)
Multivariate operational risk: dependence modelling with Lévy copulas
Böcker, K. and Klüppelberg, C.
2015-01-01
Simultaneous modelling of operational risks occurring in different event type/business line cells poses the challenge for operational risk quantification. Invoking the new concept of L´evy copulas for dependence modelling yields simple approximations of high quality for multivariate operational VAR.
Robust Ranking of Multivariate GARCH Models by Problem Dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2012-01-01
textabstractDuring the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models,
Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2011-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models,
Modeling rainfall-runoff relationship using multivariate GARCH model
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.
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung
2013-08-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.
Critical elements on fitting the Bayesian multivariate Poisson Lognormal model
Zamzuri, Zamira Hasanah binti
2015-10-01
Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.
Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits
DEFF Research Database (Denmark)
Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo
2014-01-01
A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...
Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits
DEFF Research Database (Denmark)
Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo
2013-01-01
A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...
Copula Based Factorization in Bayesian Multivariate Infinite Mixture Models
Martin Burda; Artem Prokhorov
2012-01-01
Bayesian nonparametric models based on infinite mixtures of density kernels have been recently gaining in popularity due to their flexibility and feasibility of implementation even in complicated modeling scenarios. In economics, they have been particularly useful in estimating nonparametric distributions of latent variables. However, these models have been rarely applied in more than one dimension. Indeed, the multivariate case suffers from the curse of dimensionality, with a rapidly increas...
Multivariate Variance Targeting in the BEKK-GARCH Model
DEFF Research Database (Denmark)
Pedersen, Rasmus Søndergaard; Rahbek, Anders
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By de…nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modi…ed like- lihood function, or estimating function, corresponding...
Multivariate Variance Targeting in the BEKK-GARCH Model
DEFF Research Database (Denmark)
Pedersen, Rasmus Søndergaard; Rahbek, Anders
2014-01-01
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By definition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modified likelihood function, or estimating function, corresponding...
Multivariate Variance Targeting in the BEKK-GARCH Model
DEFF Research Database (Denmark)
Pedersen, Rasmus Søndergaard; Rahbek, Anders
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By de…nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modi…ed likelihood function, or estimating function, corresponding...
Multivariate time series modeling of selected childhood diseases in ...
African Journals Online (AJOL)
This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...
A joint model for multivariate hierarchical semicontinuous data with replications.
Kassahun-Yimer, Wondwosen; Albert, Paul S; Lipsky, Leah M; Nansel, Tonja R; Liu, Aiyi
2017-01-01
Longitudinal data are often collected in biomedical applications in such a way that measurements on more than one response are taken from a given subject repeatedly overtime. For some problems, these multiple profiles need to be modeled jointly to get insight on the joint evolution and/or association of these responses over time. In practice, such longitudinal outcomes may have many zeros that need to be accounted for in the analysis. For example, in dietary intake studies, as we focus on in this paper, some food components are eaten daily by almost all subjects, while others are consumed episodically, where individuals have time periods where they do not eat these components followed by periods where they do. These episodically consumed foods need to be adequately modeled to account for the many zeros that are encountered. In this paper, we propose a joint model to analyze multivariate hierarchical semicontinuous data characterized by many zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components. The novelty of the proposed approach is that it incorporates: (1) multivariate, possibly correlated, response variables; (2) within subject correlation resulting from repeated measurements taken from each subject; (3) many zero observations; (4) overdispersion; and (5) replicate measurements at each visit time.
Multivariate Product-Shot-noise Cox Point Process Models
DEFF Research Database (Denmark)
Jalilian, Abdollah; Guan, Yongtao; Mateu, Jorge
We introduce a new multivariate product-shot-noise Cox process which is useful for model- ing multi-species spatial point patterns with clustering intra-specific interactions and neutral, negative or positive inter-specific interactions. The auto and cross pair correlation functions of the process...... can be obtained in closed analytical forms and approximate simulation of the process is straightforward. We use the proposed process to model interactions within and among five tree species in the Barro Colorado Island plot....
Emulating facial biomechanics using multivariate partial least squares surrogate models
Martens, Harald; Wu, Tim; Hunter, Peter; Mithraratne, Kumar
2014-01-01
This is the author’s final, accepted and refereed manuscript to the article. Locked until 2015-05-06 A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-mode...
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.
Music Genre Classification using the multivariate AR feature integration model
DEFF Research Database (Denmark)
Ahrendt, Peter; Meng, Anders
2005-01-01
informative decisions about musical genre. For the MIREX music genre contest several authors derive long time features based either on statistical moments and/or temporal structure in the short time features. In our contribution we model a segment (1.2 s) of short time features (texture) using a multivariate...... autoregressive model. Other authors have applied simpler statistical models such as the mean-variance model, which also has been included in several of this years MIREX submissions, see e.g. Tzanetakis (2005); Burred (2005); Bergstra et al. (2005); Lidy and Rauber (2005)....
Identification of multivariate models for noise analysis of nuclear plant
International Nuclear Information System (INIS)
Zwingelstein, G.C.; Upadhyaya, B.R.
1979-01-01
During the normal operation of a pressurized water reactor, neutron noise analysis with multivariate autoregressive procedures in a valuable diagnostic tool to extract dynamic characteristics for incipient failure detection. The first part of the paper will describe in details the equations for estimating the multivariate autoregressive model matrices and the structure of various matrices. The matrices are estimated by solving a set of matrix operations, called Yule-Walker equations. The selection of optimal model order will also be discussed. Once the optimal parameter set is obtained, simple and fast calculations are used to determine the auto power spectral density, cross spectra, coherence function, phase. In addition the spectra may be decomposed into components being contributed from different noise sources. An application using neutron flux data collected on a nuclear plant will illustrate the efficiency of the method
MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA
Daniel BRÎNDESCU – OLARIU
2016-01-01
The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, ...
Multivariable robust adaptive controller using reduced-order model
Directory of Open Access Journals (Sweden)
Wei Wang
1990-04-01
Full Text Available In this paper a multivariable robust adaptive controller is presented for a plant with bounded disturbances and unmodeled dynamics due to plant-model order mismatches. The robust stability of the closed-loop system is achieved by using the normalization technique and the least squares parameter estimation scheme with dead zones. The weighting polynomial matrices are incorporated into the control law, so that the open-loop unstable or/and nonminimum phase plants can be handled.
Various forms of indexing HDMR for modelling multivariate classification problems
Energy Technology Data Exchange (ETDEWEB)
Aksu, Çağrı [Bahçeşehir University, Information Technologies Master Program, Beşiktaş, 34349 İstanbul (Turkey); Tunga, M. Alper [Bahçeşehir University, Software Engineering Department, Beşiktaş, 34349 İstanbul (Turkey)
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.
Multivariable Parametric Cost Model for Ground Optical Telescope Assembly
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.
Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly
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.
A multivariate model for predicting segmental body composition.
Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice
2013-12-01
The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.
The multivariate supOU stochastic volatility model
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole; Stelzer, Robert
Using positive semidefinite supOU (superposition of Ornstein-Uhlenbeck type) processes to describe the volatility, we introduce a multivariate stochastic volatility model for financial data which is capable of modelling long range dependence effects. The finiteness of moments and the second order...... structure of the volatility, the log returns, as well as their "squares" are discussed in detail. Moreover, we give several examples in which long memory effects occur and study how the model as well as the simple Ornstein-Uhlenbeck type stochastic volatility model behave under linear transformations....... In particular, the models are shown to be preserved under invertible linear transformations. Finally, we discuss how (sup)OU stochastic volatility models can be combined with a factor modelling approach....
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu
2014-06-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.
A Cyber-Attack Detection Model Based on Multivariate Analyses
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.
Algorithm of Dynamic Model Structural Identification of the Multivariable Plant
Directory of Open Access Journals (Sweden)
Л.М. Блохін
2004-02-01
Full Text Available The new algorithm of dynamic model structural identification of the multivariable stabilized plant with observable and unobservable disturbances in the regular operating modes is offered in this paper. With the help of the offered algorithm it is possible to define the “perturbed” models of dynamics not only of the plant, but also the dynamics characteristics of observable and unobservable casual disturbances taking into account the absence of correlation between themselves and control inputs with the unobservable perturbations.
Emulating facial biomechanics using multivariate partial least squares surrogate models.
Wu, Tim; Martens, Harald; Hunter, Peter; Mithraratne, Kumar
2014-11-01
A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input-output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time. Copyright © 2014 John Wiley & Sons, Ltd.
Multivariable control system for dynamic PEM fuel cell model
International Nuclear Information System (INIS)
Tanislav, Vasile; Carcadea, Elena; Capris, Catalin; Culcer, Mihai; Raceanu, Mircea
2010-01-01
Full text: The main objective of this work was to develop a multivariable control system of robust type for a PEM fuel cells assembly. The system will be used in static and mobile applications for different values of power, generated by a fuel cell assembly of up to 10 kW. Intermediate steps were accomplished: a study of a multivariable control strategy for a PEM fuel cell assembly; a mathematic modeling of mass and heat transfer inside of fuel cell assembly, defining the response function to hydrogen and oxygen/air mass flow and inlet pressure changes; a testing stand for fuel cell assembly; experimental determinations of transient response for PEM fuel cell assembly, and more others. To define the multivariable control system for a PEM fuel cell assembly the parameters describing the system were established. Also, there were defined the generic mass and energy balance equations as functions of derivative of m i , in and m i , out , representing the mass going into and out from the fuel cell, while Q in is the enthalpy and Q out is the enthalpy of the unused reactant gases and heat produced by the product, Q dis is the heat dissipated to the surroundings, Q c is the heat taken away from the stack by active cooling and W el is the electricity generated. (authors)
Multivariate moment closure techniques for stochastic kinetic models
International Nuclear Information System (INIS)
Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.
2015-01-01
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
Energy Technology Data Exchange (ETDEWEB)
Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H., E-mail: m.stumpf@imperial.ac.uk [Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ (United Kingdom)
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.
Preference learning with evolutionary Multivariate Adaptive Regression Spline model
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll
2015-01-01
This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...
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.
Moser, Dominik A; Doucet, Gaelle E; Lee, Won Hee; Rasgon, Alexander; Krinsky, Hannah; Leibu, Evan; Ing, Alex; Schumann, Gunter; Rasgon, Natalie; Frangou, Sophia
2018-04-01
Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P nonimaging variables examined, age (r = -0.53), IQ (r = 0.36), and body mass index (r = -0.25) were associated
Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
Directory of Open Access Journals (Sweden)
A. Alexandre Trindade
2003-02-01
Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.
MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA
Directory of Open Access Journals (Sweden)
Daniel BRÎNDESCU – OLARIU
2016-06-01
Full Text Available The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, developed through discriminant analysis. Financial ratios are employed as explanatory variables within the model. The study has included 53,252 yearly financial statements from the period 2007 – 2010, with the state of the companies being monitored until the end of 2012. It thus employs the largest sample ever used in Romanian research in the field of bankruptcy prediction, not targeting high levels of accuracy over isolated samples, but reliability and ease of use over the entire population.
Clustering Multivariate Time Series Using Hidden Markov Models
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Shima Ghassempour
2014-03-01
Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.
Models and analysis for multivariate failure time data
Shih, Joanna Huang
The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the
Multivariate Frequency-Severity Regression Models in Insurance
Directory of Open Access Journals (Sweden)
Edward W. Frees
2016-02-01
Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.
Hidden Markov latent variable models with multivariate longitudinal data.
Song, Xinyuan; Xia, Yemao; Zhu, Hongtu
2017-03-01
Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use. © 2016, The International Biometric Society.
Optimisation of Marine Boilers using Model-based Multivariable Control
DEFF Research Database (Denmark)
Solberg, Brian
Traditionally, marine boilers have been controlled using classical single loop controllers. To optimise marine boiler performance, reduce new installation time and minimise the physical dimensions of these large steel constructions, a more comprehensive and coherent control strategy is needed....... This research deals with the application of advanced control to a specific class of marine boilers combining well-known design methods for multivariable systems. This thesis presents contributions for modelling and control of the one-pass smoke tube marine boilers as well as for hybrid systems control. Much...... of the focus has been directed towards water level control which is complicated by the nature of the disturbances acting on the system as well as by low frequency sensor noise. This focus was motivated by an estimated large potential to minimise the boiler geometry by reducing water level fluctuations...
Nonstationary multivariate modeling of cerebral autoregulation during hypercapnia.
Kostoglou, Kyriaki; Debert, Chantel T; Poulin, Marc J; Mitsis, Georgios D
2014-05-01
We examined the time-varying characteristics of cerebral autoregulation and hemodynamics during a step hypercapnic stimulus by using recursively estimated multivariate (two-input) models which quantify the dynamic effects of mean arterial blood pressure (ABP) and end-tidal CO2 tension (PETCO2) on middle cerebral artery blood flow velocity (CBFV). Beat-to-beat values of ABP and CBFV, as well as breath-to-breath values of PETCO2 during baseline and sustained euoxic hypercapnia were obtained in 8 female subjects. The multiple-input, single-output models used were based on the Laguerre expansion technique, and their parameters were updated using recursive least squares with multiple forgetting factors. The results reveal the presence of nonstationarities that confirm previously reported effects of hypercapnia on autoregulation, i.e. a decrease in the MABP phase lead, and suggest that the incorporation of PETCO2 as an additional model input yields less time-varying estimates of dynamic pressure autoregulation obtained from single-input (ABP-CBFV) models. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.
Bhattacharjee, T.; Kumar, P.; Fillipe, L.
2018-02-01
Vibrational spectroscopy, especially FTIR and Raman, has shown enormous potential in disease diagnosis, especially in cancers. Their potential for detecting varied pathological conditions are regularly reported. However, to prove their applicability in clinics, large multi-center multi-national studies need to be undertaken; and these will result in enormous amount of data. A parallel effort to develop analytical methods, including user-friendly software that can quickly pre-process data and subject them to required multivariate analysis is warranted in order to obtain results in real time. This study reports a MATLAB based script that can automatically import data, preprocess spectra— interpolation, derivatives, normalization, and then carry out Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) of the first 10 PCs; all with a single click. The software has been verified on data obtained from cell lines, animal models, and in vivo patient datasets, and gives results comparable to Minitab 16 software. The software can be used to import variety of file extensions, asc, .txt., .xls, and many others. Options to ignore noisy data, plot all possible graphs with PCA factors 1 to 5, and save loading factors, confusion matrices and other parameters are also present. The software can provide results for a dataset of 300 spectra within 0.01 s. We believe that the software will be vital not only in clinical trials using vibrational spectroscopic data, but also to obtain rapid results when these tools get translated into clinics.
Multivariate Birnbaum-Saunders Distributions: Modelling and Applications
Directory of Open Access Journals (Sweden)
Robert G. Aykroyd
2018-03-01
Full Text Available Since its origins and numerous applications in material science, the Birnbaum–Saunders family of distributions has now found widespread uses in some areas of the applied sciences such as agriculture, environment and medicine, as well as in quality control, among others. It is able to model varied data behaviour and hence provides a flexible alternative to the most usual distributions. The family includes Birnbaum–Saunders and log-Birnbaum–Saunders distributions in univariate and multivariate versions. There are now well-developed methods for estimation and diagnostics that allow in-depth analyses. This paper gives a detailed review of existing methods and of relevant literature, introducing properties and theoretical results in a systematic way. To emphasise the range of suitable applications, full analyses are included of examples based on regression and diagnostics in material science, spatial data modelling in agricultural engineering and control charts for environmental monitoring. However, potential future uses in new areas such as business, economics, finance and insurance are also discussed. This work is presented to provide a full tool-kit of novel statistical models and methods to encourage other researchers to implement them in these new areas. It is expected that the methods will have the same positive impact in the new areas as they have had elsewhere.
Production optimisation in the petrochemical industry by hierarchical multivariate modelling
Energy Technology Data Exchange (ETDEWEB)
Andersson, Magnus; Furusjoe, Erik; Jansson, Aasa
2004-06-01
This project demonstrates the advantages of applying hierarchical multivariate modelling in the petrochemical industry in order to increase knowledge of the total process. The models indicate possible ways to optimise the process regarding the use of energy and raw material, which is directly linked to the environmental impact of the process. The refinery of Nynaes Refining AB (Goeteborg, Sweden) has acted as a demonstration site in this project. The models developed for the demonstration site resulted in: Detection of an unknown process disturbance and suggestions of possible causes; Indications on how to increase the yield in combination with energy savings; The possibility to predict product quality from on-line process measurements, making the results available at a higher frequency than customary laboratory analysis; Quantification of the gradually lowered efficiency of heat transfer in the furnace and increased fuel consumption as an effect of soot build-up on the furnace coils; Increased knowledge of the relation between production rate and the efficiency of the heat exchangers. This report is one of two reports from the project. It contains a technical discussion of the result with some degree of detail. A shorter and more easily accessible report is also available, see IVL report B1586-A.
Optimal non-periodic inspection for a multivariate degradation model
Barker, C.T.; Newby, M.J.
2009-01-01
We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined
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.
Xuan Chi; Barry Goodwin
2012-01-01
Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...
Directory of Open Access Journals (Sweden)
Valéria Rosa Lopes
2014-02-01
Full Text Available This work had the aim to evaluate the genetic divergence in sugarcane clones using the methodology of graphic dispersion by principal components analysis associated to linear mixed models, indentifying the more divergent and productive genotypes with more precision, for a subsequent combination. 138 sugarcane clones of the RB97 series of the Sugarcane Breeding Program of the Universidade Federal do Parana, more two standard cultivars were evaluated in three environments, with two replications. The two first components explained 96% of the total variation, sufficiently for explaining the divergence found. The variable that contributed the most to de divergence was kilogram of brix per plot (BKP followed by brix, mass of 10 stalks and number of stalks per plot. The more divergent sugarcane clones were RB975008, RB975112, RB975019, RB975153 and RB975067 and the more productive clones were RB975269, RB977533, RB975102, RB975317 and RB975038.
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
multivariate time series modeling of selected childhood diseases
African Journals Online (AJOL)
2016-06-17
Jun 17, 2016 ... KEYWORDS: Multivariate Approach, Pre-whitening, Vector Time Series, .... Alternatively, the process may be written in mean adjusted form as .... The AIC criterion asymptotically over estimates the order with positive probability, whereas the BIC and HQC criteria ... has the same asymptotic distribution as Ǫ.
Multivariate models to classify Tuscan virgin olive oils by zone.
Directory of Open Access Journals (Sweden)
Alessandri, Stefano
1999-10-01
Full Text Available In order to study and classify Tuscan virgin olive oils, 179 samples were collected. They were obtained from drupes harvested during the first half of November, from three different zones of the Region. The sampling was repeated for 5 years. Fatty acids, phytol, aliphatic and triterpenic alcohols, triterpenic dialcohols, sterols, squalene and tocopherols were analyzed. A subset of variables was considered. They were selected in a preceding work as the most effective and reliable, from the univariate point of view. The analytical data were transformed (except for the cycloartenol to compensate annual variations, the mean related to the East zone was subtracted from each value, within each year. Univariate three-class models were calculated and further variables discarded. Then multivariate three-zone models were evaluated, including phytol (that was always selected and all the combinations of palmitic, palmitoleic and oleic acid, tetracosanol, cycloartenol and squalene. Models including from two to seven variables were studied. The best model shows by-zone classification errors less than 40%, by-zone within-year classification errors that are less than 45% and a global classification error equal to 30%. This model includes phytol, palmitic acid, tetracosanol and cycloartenol.
Para estudiar y clasificar aceites de oliva vírgenes Toscanos, se utilizaron 179 muestras, que fueron obtenidas de frutos recolectados durante la primera mitad de Noviembre, de tres zonas diferentes de la Región. El muestreo fue repetido durante 5 años. Se analizaron ácidos grasos, fitol, alcoholes alifáticos y triterpénicos, dialcoholes triterpénicos, esteroles, escualeno y tocoferoles. Se consideró un subconjunto de variables que fueron seleccionadas en un trabajo anterior como el más efectivo y fiable, desde el punto de vista univariado. Los datos analíticos se transformaron (excepto para el cicloartenol para compensar las variaciones anuales, rest
Multivariate η-μ fading distribution with arbitrary correlation model
Ghareeb, Ibrahim; Atiani, Amani
2018-03-01
An extensive analysis for the multivariate ? distribution with arbitrary correlation is presented, where novel analytical expressions for the multivariate probability density function, cumulative distribution function and moment generating function (MGF) of arbitrarily correlated and not necessarily identically distributed ? power random variables are derived. Also, this paper provides exact-form expression for the MGF of the instantaneous signal-to-noise ratio at the combiner output in a diversity reception system with maximal-ratio combining and post-detection equal-gain combining operating in slow frequency nonselective arbitrarily correlated not necessarily identically distributed ?-fading channels. The average bit error probability of differentially detected quadrature phase shift keying signals with post-detection diversity reception system over arbitrarily correlated and not necessarily identical fading parameters ?-fading channels is determined by using the MGF-based approach. The effect of fading correlation between diversity branches, fading severity parameters and diversity level is studied.
Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group
DEFF Research Database (Denmark)
Christophersen, J; Menné, T; Tanghøj, P
1989-01-01
The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients...... patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba......-mix, colophony, and formaldehyde), one or more individual factors were of significance for the risk of being sensitized, except for chromate and formaldehyde. It is concluded that patch test results can be compared only after stratification of the material or by multivariate analysis....
A new multivariate zero-adjusted Poisson model with applications to biomedicine.
Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen
2018-05-25
Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Joint density of eigenvalues in spiked multivariate models.
Dharmawansa, Prathapasinghe; Johnstone, Iain M
2014-01-01
The classical methods of multivariate analysis are based on the eigenvalues of one or two sample covariance matrices. In many applications of these methods, for example to high dimensional data, it is natural to consider alternative hypotheses which are a low rank departure from the null hypothesis. For rank one alternatives, this note provides a representation for the joint eigenvalue density in terms of a single contour integral. This will be of use for deriving approximate distributions for likelihood ratios and 'linear' statistics used in testing.
DEFF Research Database (Denmark)
Ørregård Nielsen, Morten
2015-01-01
the multivariate non-cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge...
Multivariate zero-inflated modeling with latent predictors: Modeling feedback behavior
Fox, Gerardus J.A.
2013-01-01
In educational studies, the use of computer-based assessments leads to the collection of multiple outcomes to assess student performance. The student-specific outcomes are correlated and often measured in different scales, such as continuous and count outcomes. A multivariate zero-inflated model
International Nuclear Information System (INIS)
Cella, Laura; Liuzzi, Raffaele; Conson, Manuel; D’Avino, Vittoria; Salvatore, Marco; Pacelli, Roberto
2013-01-01
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
M. Asai (Manabu); M.J. McAleer (Michael)
2016-01-01
textabstractThe paper derives a Multivariate Asymmetric Long Memory conditional volatility model with Exogenous Variables (X), or the MALMX model, with dynamic conditional correlations, appropriate regularity conditions, and associated asymptotic theory. This enables checking of internal consistency
DEFF Research Database (Denmark)
Ørregård Nielsen, Morten
This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses...... the multivariate non-cointegrated fractional ARIMA model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probablity, thus making...
Modelling and Multi-Variable Control of Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, Lars Finn Slot; Holm, J. R.
2003-01-01
In this paper a dynamic model of a 1:1 refrigeration system is presented. The main modelling effort has been concentrated on a lumped parameter model of a shell and tube condenser. The model has shown good resemblance with experimental data from a test rig, regarding as well the static as the dyn......In this paper a dynamic model of a 1:1 refrigeration system is presented. The main modelling effort has been concentrated on a lumped parameter model of a shell and tube condenser. The model has shown good resemblance with experimental data from a test rig, regarding as well the static...... as the dynamic behavior. Based on this model the effects of the cross couplings has been examined. The influence of the cross couplings on the achievable control performance has been investigated. A MIMO controller is designed and the performance is compared with the control performance achieved by using...
Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model
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.
Estimation of a multivariate mean under model selection uncertainty
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2014-05-01
Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty. When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.
Multivariate Hawkes process models of the occurrence of regulatory elements
DEFF Research Database (Denmark)
Carstensen, L; Sandelin, A; Winther, Ole
2010-01-01
distribution of the occurrences of these TREs along the genome. RESULTS: We present a model of TRE occurrences known as the Hawkes process. We illustrate the use of this model by analyzing two different publically available data sets. We are able to model, in detail, how the occurrence of one TRE is affected....... For each of the two data sets we provide two results: first, a qualitative description of the dependencies among the occurrences of the TREs, and second, quantitative results on the favored or avoided distances between the different TREs. CONCLUSIONS: The Hawkes process is a novel way of modeling the joint...
Preliminary Multi-Variable Cost Model for Space Telescopes
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.
Multivariate Modelling of Extreme Load Combinations for Wind Turbines
DEFF Research Database (Denmark)
Dimitrov, Nikolay Krasimirov
2015-01-01
into a periodic part and a perturbation term, where each part has a known probability distribution. The proposed model shows good agreement with simulated data under stationary conditions, and a design load envelope based on this model is comparable to the load envelope estimated using the standard procedure...
Evaluation of multivariate calibration models transferred between spectroscopic instruments
DEFF Research Database (Denmark)
Eskildsen, Carl Emil Aae; Hansen, Per W.; Skov, Thomas
2016-01-01
In a setting where multiple spectroscopic instruments are used for the same measurements it may be convenient to develop the calibration model on a single instrument and then transfer this model to the other instruments. In the ideal scenario, all instruments provide the same predictions for the ......In a setting where multiple spectroscopic instruments are used for the same measurements it may be convenient to develop the calibration model on a single instrument and then transfer this model to the other instruments. In the ideal scenario, all instruments provide the same predictions...... for the same samples using the transferred model. However, sometimes the success of a model transfer is evaluated by comparing the transferred model predictions with the reference values. This is not optimal, as uncertainties in the reference method will impact the evaluation. This paper proposes a new method...... for calibration model transfer evaluation. The new method is based on comparing predictions from different instruments, rather than comparing predictions and reference values. A total of 75 flour samples were available for the study. All samples were measured on ten near infrared (NIR) instruments from two...
Functionally unidimensional item response models for multivariate binary data
DEFF Research Database (Denmark)
Ip, Edward; Molenberghs, Geert; Chen, Shyh-Huei
2013-01-01
The problem of fitting unidimensional item response models to potentially multidimensional data has been extensively studied. The focus of this article is on response data that have a strong dimension but also contain minor nuisance dimensions. Fitting a unidimensional model to such multidimensio......The problem of fitting unidimensional item response models to potentially multidimensional data has been extensively studied. The focus of this article is on response data that have a strong dimension but also contain minor nuisance dimensions. Fitting a unidimensional model...... to such multidimensional data is believed to result in ability estimates that represent a combination of the major and minor dimensions. We conjecture that the underlying dimension for the fitted unidimensional model, which we call the functional dimension, represents a nonlinear projection. In this article we investigate...... tool. An example regarding a construct of desire for physical competency is used to illustrate the functional unidimensional approach....
Modeling the Pineapple Express phenomenon via Multivariate Extreme Value Theory
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
Multivariate Modelling of the Career Intent of Air Force Personnel.
1980-09-01
index (HOPP) was used as a measure of current job satisfaction . As with the Vroom and Fishbein/Graen models, two separate validations were accom...34 Organizational Behavior and Human Performance , 23: 251-267, 1979. Lewis, Logan M. "Expectancy Theory as a Predictive Model of Career Intent, Job Satisfaction ...W. Albright. "Expectancy Theory Predictions of the Satisfaction , Effort, Performance , and Retention of Naval Aviation Officers," Organizational
Modeling and Control of Multivariable Process Using Intelligent Techniques
Directory of Open Access Journals (Sweden)
Subathra Balasubramanian
2010-10-01
Full Text Available For nonlinear dynamic systems, the first principles based modeling and control is difficult to implement. In this study, a fuzzy controller and recurrent fuzzy controller are developed for MIMO process. Fuzzy logic controller is a model free controller designed based on the knowledge about the process. In fuzzy controller there are two types of rule-based fuzzy models are available: one the linguistic (Mamdani model and the other is Takagi–Sugeno model. Of these two, Takagi-Sugeno model (TS has attracted most attention. The fuzzy controller application is limited to static processes due to their feedforward structure. But, most of the real-time processes are dynamic and they require the history of input/output data. In order to store the past values a memory unit is needed, which is introduced by the recurrent structure. The proposed recurrent fuzzy structure is used to develop a controller for the two tank heating process. Both controllers are designed and implemented in a real time environment and their performance is compared.
Multivariate statistical models for disruption prediction at ASDEX Upgrade
International Nuclear Information System (INIS)
Aledda, R.; Cannas, B.; Fanni, A.; Sias, G.; Pautasso, G.
2013-01-01
In this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented. The system consists of a data-based model, which is built using only few input signals coming from successfully terminated pulses. A fault detection and isolation approach has been used, where the prediction is based on the analysis of the residuals of an auto regressive exogenous input model. The prediction performance of the proposed system is encouraging when it is applied to the same set of campaigns used to implement the model. However, the false alarms significantly increase when we tested the system on discharges coming from experimental campaigns temporally far from those used to train the model. This is due to the well know aging effect inherent in the data-based models. The main advantage of the proposed method, with respect to other data-based approaches in literature, is that it does not need data on experiments terminated with a disruption, as it uses a normal operating conditions model. This is a big advantage in the prospective of a prediction system for ITER, where a limited number of disruptions can be allowed
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
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 (pstakeholder 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.
Multivariate modelling with 1H NMR of pleural effusion in murine cerebral malaria
Directory of Open Access Journals (Sweden)
Ghosh Soumita
2011-11-01
Full Text Available Abstract Background Cerebral malaria is a clinical manifestation of Plasmodium falciparum infection. Although brain damage is the predominant pathophysiological complication of cerebral malaria (CM, respiratory distress, acute lung injury, hydrothorax/pleural effusion are also observed in several cases. Immunological parameters have been assessed in pleural fluid in murine models; however there are no reports of characterization of metabolites present in pleural effusion. Methods 1H NMR of the sera and the pleural effusion of cerebral malaria infected mice were analyzed using principal component analysis, orthogonal partial least square analysis, multiway principal component analysis, and multivariate curve resolution. Results It has been observed that there was 100% occurrence of pleural effusion (PE in the mice affected with CM, as opposed to those are non-cerebral and succumbing to hyperparasitaemia (NCM/HP. An analysis of 1H NMR and SDS-PAGE profile of PE and serum samples of each of the CM mice exhibited a similar profile in terms of constituents. Multivariate analysis on these two classes of biofluids was performed and significant differences were detected in concentrations of metabolites. Glucose, creatine and glutamine contents were high in the PE and lipids being high in the sera. Multivariate curve resolution between sera and pleural effusion showed that changes in PE co-varied with that of serum in CM mice. The increase of glucose in PE is negatively correlated to the glucose in serum in CM as obtained from the result of multiway principal component analysis. Conclusions This study reports for the first time, the characterization of metabolites in pleural effusion formed during murine cerebral malaria. The study indicates that the origin of PE metabolites in murine CM may be the serum. The loss of the components like glucose, glutamine and creatine into the PE may worsen the situation of patients, in conjunction with the enhanced
Development of multivariate NTCP models for radiation-induced hypothyroidism: a comparative analysis
International Nuclear Information System (INIS)
Cella, Laura; Liuzzi, Raffaele; Conson, Manuel; D’Avino, Vittoria; Salvatore, Marco; Pacelli, Roberto
2012-01-01
Hypothyroidism is a frequent late side effect of radiation therapy of the cervical region. Purpose of this work is to develop multivariate normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RHT) and to compare them with already existing NTCP models for RHT. Fifty-three patients treated with sequential chemo-radiotherapy for Hodgkin’s lymphoma (HL) were retrospectively reviewed for RHT events. Clinical information along with thyroid gland dose distribution parameters were collected and their correlation to RHT was analyzed by Spearman’s rank correlation coefficient (Rs). Multivariate logistic regression method using resampling methods (bootstrapping) was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). Models were tested against external published data on RHT and compared with other published NTCP models. If we express the thyroid volume exceeding X Gy as a percentage (V x (%)), a two-variable NTCP model including V 30 (%) and gender resulted to be the optimal predictive model for RHT (Rs = 0.615, p < 0.001. AUC = 0.87). Conversely, if absolute thyroid volume exceeding X Gy (V x (cc)) was analyzed, an NTCP model based on 3 variables including V 30 (cc), thyroid gland volume and gender was selected as the most predictive model (Rs = 0.630, p < 0.001. AUC = 0.85). The three-variable model performs better when tested on an external cohort characterized by large inter-individuals variation in thyroid volumes (AUC = 0.914, 95% CI 0.760–0.984). A comparable performance was found between our model and that proposed in the literature based on thyroid gland mean dose and volume (p = 0.264). The absolute volume of thyroid gland exceeding 30 Gy in combination with thyroid gland volume and gender provide an NTCP model for RHT with improved prediction capability not only within our patient population but also in an
Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.
2012-01-01
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
Linear models for multivariate, time series, and spatial data
Christensen, Ronald
1991-01-01
This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understand...
Individual loss reserving with the Multivariate Skew Normal model
Pigeon, M.; Antonio, K.; Denuit, M.
2011-01-01
In general insurance, the evaluation of future cash ows and solvency capital has become increasingly important. To assist in this process, the present paper proposes an individual discrete-time loss re- serving model describing the occurrence, the reporting delay, the timeto the first payment, and
Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model
Directory of Open Access Journals (Sweden)
Erasmo Cadenas
2016-02-01
Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.
Ultracentrifuge separative power modeling with multivariate regression using covariance matrix
International Nuclear Information System (INIS)
Migliavacca, Elder
2004-01-01
In this work, the least-squares methodology with covariance matrix is applied to determine a data curve fitting to obtain a performance function for the separative power δU of a ultracentrifuge as a function of variables that are experimentally controlled. The experimental data refer to 460 experiments on the ultracentrifugation process for uranium isotope separation. The experimental uncertainties related with these independent variables are considered in the calculation of the experimental separative power values, determining an experimental data input covariance matrix. The process variables, which significantly influence the δU values are chosen in order to give information on the ultracentrifuge behaviour when submitted to several levels of feed flow rate F, cut θ and product line pressure P p . After the model goodness-of-fit validation, a residual analysis is carried out to verify the assumed basis concerning its randomness and independence and mainly the existence of residual heteroscedasticity with any explained regression model variable. The surface curves are made relating the separative power with the control variables F, θ and P p to compare the fitted model with the experimental data and finally to calculate their optimized values. (author)
Multivariate Models for Prediction of Human Skin Sensitization ...
One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens TM assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches , logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine
Hengartner, M P; Heekeren, K; Dvorsky, D; Walitza, S; Rössler, W; Theodoridou, A
2017-09-01
The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23). Altogether n=24 persons developed manifest psychosis within three years and according to Kaplan-Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, Pthinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
Multivariate Models for Prediction of Human Skin Sensitization Hazard
Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole
2016-01-01
One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324
DEFF Research Database (Denmark)
Rombouts, Jeroen V.K.; Stentoft, Lars; Violante, Francesco
innovation for a Laplace innovation assumption improves the pricing in a smaller way. Apart from investigating directly the value of model sophistication in terms of dollar losses, we also use the model condence set approach to statistically infer the set of models that delivers the best pricing performance.......We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differer...
Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors
DEFF Research Database (Denmark)
Halbleib, Roxana; Voev, Valeri
2011-01-01
This paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariancematrices, the model generates......, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches....
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Ter¨asvirta (2005) by including...... another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition......, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. The model is applied to a selection of world stock indices, and it is found that time is an important factor affecting...
Tracking the business cycle of the Euro area: A multivariate model-based band-pass filter
Azevedo, J.M.; Koopman, S.J.; Rua, A.
2006-01-01
This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties.
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.
Ford, Jon J; Richards BPhysio, Matt C; Surkitt BPhysio, Luke D; Chan BPhysio, Alexander Yp; Slater, Sarah L; Taylor, Nicholas F; Hahne, Andrew J
2018-05-28
To identify predictors for back pain, leg pain and activity limitation in patients with early persistent low back disorders. Prospective inception cohort study; Setting: primary care private physiotherapy clinics in Melbourne, Australia. 300 adults aged 18-65 years with low back and/or referred leg pain of ≥6-weeks and ≤6-months duration. Not applicable. Numerical rating scales for back pain and leg pain as well as the Oswestry Disability Scale. Prognostic factors included sociodemographics, treatment related factors, subjective/physical examination, subgrouping factors and standardized questionnaires. Univariate analysis followed by generalized estimating equations were used to develop a multivariate prognostic model for back pain, leg pain and activity limitation. Fifty-eight prognostic factors progressed to the multivariate stage where 15 showed significant (pduration, high multifidus tone, clinically determined inflammation, higher back and leg pain severity, lower lifting capacity, lower work capacity and higher pain drawing percentage coverage). The preliminary model identifying predictors of low back disorders explained up to 37% of the variance in outcome. This study evaluated a comprehensive range of prognostic factors reflective of both the biomedical and psychosocial domains of low back disorders. The preliminary multivariate model requires further validation before being considered for clinical use. Copyright © 2018. Published by Elsevier Inc.
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. © The Author(s) 2016.
Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input
Addo, Peter Martey
2014-01-01
This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.
A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series
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…
Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative
Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.
2017-03-01
Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were 0.60.
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.
Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune
This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Lunde, Asger; Voev, Valeri
We introduce a multivariate GARCH model that utilizes and models realized measures of volatility and covolatility. The realized measures extract information contained in high-frequency data that is particularly beneficial during periods with variation in volatility and covolatility. Applying the ...
Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins
DEFF Research Database (Denmark)
Pang, Z; Zhang, D; Li, S
2010-01-01
AIMS/HYPOTHESIS: The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese...
Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome. Chave
2014-01-01
We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...
Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes
Vettori, Sabrina; Huser, Raphaë l; Genton, Marc G.
2018-01-01
Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested $\\alpha$-stable random factors. The hierarchical structure facilitates Bayesian inference and offers a convenient and interpretable characterization. We fit this nested multivariate max-stable model to the maxima of air pollution concentrations and temperatures recorded at a number of sites in the Los Angeles area, showing that the proposed model succeeds in capturing their complex tail dependence structure.
Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes
Vettori, Sabrina
2018-03-18
Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested $\\\\alpha$-stable random factors. The hierarchical structure facilitates Bayesian inference and offers a convenient and interpretable characterization. We fit this nested multivariate max-stable model to the maxima of air pollution concentrations and temperatures recorded at a number of sites in the Los Angeles area, showing that the proposed model succeeds in capturing their complex tail dependence structure.
Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei
2017-09-25
It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).
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.
Simulation research on multivariable fuzzy model predictive control of nuclear power plant
International Nuclear Information System (INIS)
Su Jie
2012-01-01
To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)
Söderström, Karin; Nilsson, Per; Laurell, Göran; Zackrisson, Björn; Jäghagen, Eva Levring
2017-02-01
To establish predictive models for late objective aspiration and late patient-reported choking based on dose-volume parameters and baseline patient and treatment characteristics, for patients with head and neck cancer undergoing definitive radiotherapy (RT). The impact of electively treated volume on late aspiration was also investigated. This prospective cohort is a subsample of 124 survivors from the ARTSCAN study. Late aspiration was identified with videofluoroscopy, at a minimum of 25months after the start of RT. Patient-reported choking was analysed at 12 and 60months post RT using the EORTC Quality of Life Module for Head and Neck Cancer 35. Univariable and multivariable analyses were performed to describe the association between clinical factors and dose-volume descriptors for organs at risk (OARs) and late dysphagia. Aspiration was found in 47% of the eligible patients. Mean dose to the middle pharyngeal constrictor (MPC), neck dissection post RT and age at randomisation in ARTSCAN were associated to late aspiration. Mean dose to the superior pharyngeal constrictor (SPC) and swallowing complaints at baseline were associated to patient reported choking at both time-points. Three separate risk groups for late aspiration, and two risk groups for late patient-reported choking were identified based on number of risk factors. The size of the electively treated volume could be used as a surrogate for individual OARs predicting late aspiration. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Shangli Zhang
2009-01-01
Full Text Available By using the methods of linear algebra and matrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set. In the classes of homogeneous and general linear estimators, the necessary and suffcient conditions that the estimators of regression coeffcient function are admissible are established.
DEFF Research Database (Denmark)
Nielsen, Jan; Parner, Erik
2010-01-01
In this paper, we model multivariate time-to-event data by composite likelihood of pairwise frailty likelihoods and marginal hazards using natural cubic splines. Both right- and interval-censored data are considered. The suggested approach is applied on two types of family studies using the gamma...
Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity
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…
The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples
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…
Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.
Molenaar, Peter C. M.; Nesselroade, John R.
2001-01-01
Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…
Molenaar, P.C.M.
1987-01-01
Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic
Rotation in the dynamic factor modeling of multivariate stationary time series.
Molenaar, P.C.M.; Nesselroade, J.R.
2001-01-01
A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.
Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.
Kreibich, Heidi; Botto, Anna; Merz, Bruno; Schröter, Kai
2017-04-01
Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study. © 2016 Society for Risk Analysis.
The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China
Directory of Open Access Journals (Sweden)
Ling-Ling Pei
2018-03-01
Full Text Available The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N model based on the nonlinear least square (NLS method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N and the NLS-based TNGM (1, N models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC, and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly.
Duan, L L; Szczesniak, R D; Wang, X
2017-11-01
Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization.
Duan, L. L.; Szczesniak, R. D.; Wang, X.
2018-01-01
Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization. PMID:29576735
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
Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin
2013-10-15
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 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 is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme 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). Because 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 because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.
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
Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling
Directory of Open Access Journals (Sweden)
Serge Gale
2017-07-01
Full Text Available A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input–output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t + 1 from known input reference up till and including time (t. A new partial least squares regression (PLSR based method, nominal PLSR with interactions was developed and used to handle, unmodelled non-linearities. The performance of the new method is compared with least squares (LS. Different cross-validation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.
Wahid, A.; Putra, I. G. E. P.
2018-03-01
Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.
Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy
Energy Technology Data Exchange (ETDEWEB)
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.
Clinical professional governance for detailed clinical models.
Goossen, William; Goossen-Baremans, Anneke
2013-01-01
This chapter describes the need for Detailed Clinical Models for contemporary Electronic Health Systems, data exchange and data reuse. It starts with an explanation of the components related to Detailed Clinical Models with a brief summary of knowledge representation, including terminologies representing clinic relevant "things" in the real world, and information models that abstract these in order to let computers process data about these things. Next, Detailed Clinical Models are defined and their purpose is described. It builds on existing developments around the world and accumulates in current work to create a technical specification at the level of the International Standards Organization. The core components of properly expressed Detailed Clinical Models are illustrated, including clinical knowledge and context, data element specification, code bindings to terminologies and meta-information about authors, versioning among others. Detailed Clinical Models to date are heavily based on user requirements and specify the conceptual and logical levels of modelling. It is not precise enough for specific implementations, which requires an additional step. However, this allows Detailed Clinical Models to serve as specifications for many different kinds of implementations. Examples of Detailed Clinical Models are presented both in text and in Unified Modelling Language. Detailed Clinical Models can be positioned in health information architectures, where they serve at the most detailed granular level. The chapter ends with examples of projects that create and deploy Detailed Clinical Models. All have in common that they can often reuse materials from earlier projects, and that strict governance of these models is essential to use them safely in health care information and communication technology. Clinical validation is one point of such governance, and model testing another. The Plan Do Check Act cycle can be applied for governance of Detailed Clinical Models
Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price
Directory of Open Access Journals (Sweden)
Kaijian He
2016-04-01
Full Text Available Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.
Lehermeier, Christina; Schön, Chris-Carolin; de Los Campos, Gustavo
2015-09-01
Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to "correct" for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP. Copyright © 2015 by the Genetics Society of America.
A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.
French, Dustin D; Werner, Dennis C; Campbell, Robert R; Powell-Cope, Gail M; Nelson, Audrey L; Rubenstein, Laurence Z; Bulat, Tatjana; Spehar, Andrea M
2007-02-01
The purpose of this study was to develop a multivariate fall risk assessment model beyond the current fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). Retrospective, clustered secondary data analysis. National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). The study population consisted of 6577 national VHA nursing home residents who had an annual assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission assessment within a 1-year look-back period. A dichotomous multivariate model of nursing home residents coded with a fall on selected fall risk characteristics from the MDS, estimated with general estimation equations (GEE). There were 17 170 assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a faller relative to the omitted "dependent" category of activities of daily living (ADL) ranged from OR = 1.35 for "limited" ADL category up to OR = 1.57 for "extensive-2" ADL (P canes, walkers, or crutches, or the use of wheelchairs increases the odds of being a faller (OR = 1.17, P falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate fall risk assessment model.
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.
Multivariable modeling of pressure vessel and piping J-R data
International Nuclear Information System (INIS)
Eason, E.D.; Wright, J.E.; Nelson, E.E.
1991-05-01
Multivariable models were developed for predicting J-R curves from available data, such as material chemistry, radiation exposure, temperature, and Charpy V-notch energy. The present work involved collection of public test data, application of advanced pattern recognition tools, and calibration of improved multivariable models. Separate models were fitted for different material groups, including RPV welds, Linde 80 welds, RPV base metals, piping welds, piping base metals, and the combined database. Three different types of models were developed, involving different combinations of variables that might be available for applications: a Charpy model, a preirradiation Charpy model, and a copper-fluence model. In general, the best results were obtained with the preirradiation Charpy model. The copper-fluence model is recommended only if Charpy data are unavailable, and then only for Linde 80 welds. Relatively good fits were obtained, capable of predicting the values of J for pressure vessel steels to with a standard deviation of 13--18% over the range of test data. The models were qualified for predictive purposes by demonstrating their ability to predict validation data not used for fitting. 20 refs., 45 figs., 16 tabs
Estimation of Seismic Wavelets Based on the Multivariate Scale Mixture of Gaussians Model
Directory of Open Access Journals (Sweden)
Jing-Huai Gao
2009-12-01
Full Text Available This paper proposes a new method for estimating seismic wavelets. Suppose a seismic wavelet can be modeled by a formula with three free parameters (scale, frequency and phase. We can transform the estimation of the wavelet into determining these three parameters. The phase of the wavelet is estimated by constant-phase rotation to the seismic signal, while the other two parameters are obtained by the Higher-order Statistics (HOS (fourth-order cumulant matching method. In order to derive the estimator of the Higher-order Statistics (HOS, the multivariate scale mixture of Gaussians (MSMG model is applied to formulating the multivariate joint probability density function (PDF of the seismic signal. By this way, we can represent HOS as a polynomial function of second-order statistics to improve the anti-noise performance and accuracy. In addition, the proposed method can work well for short time series.
Contributions to Estimation and Testing Block Covariance Structures in Multivariate Normal Models
Liang, Yuli
2015-01-01
This thesis concerns inference problems in balanced random effects models with a so-called block circular Toeplitz covariance structure. This class of covariance structures describes the dependency of some specific multivariate two-level data when both compound symmetry and circular symmetry appear simultaneously. We derive two covariance structures under two different invariance restrictions. The obtained covariance structures reflect both circularity and exchangeability present in the data....
2016-09-23
Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177...Relationship between Team Performance and Joint Attention with Longitudinal Multivariate Mixed Models 5a. CONTRACT NUMBER FA8650-14-D-6501-0009 5b...Annual Meeting, 19-23 September 2016. 14. Previous research indicates that measures of joint attention provide unique insight into team cognition
International Nuclear Information System (INIS)
Rupšys, P.
2015-01-01
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
Energy Technology Data Exchange (ETDEWEB)
Rupšys, P. [Aleksandras Stulginskis University, Studenų g. 11, Akademija, Kaunas district, LT – 53361 Lithuania (Lithuania)
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.
Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series
DEFF Research Database (Denmark)
Catania, Leopoldo; Di Mari, Roberto
2018-01-01
We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...
A multivariable model for predicting the frictional behaviour and hydration of the human skin.
Veijgen, N K; van der Heide, E; Masen, M A
2013-08-01
The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These
Directory of Open Access Journals (Sweden)
J. C. Ochoa-Rivera
2002-01-01
Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..
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…
Forecasting of municipal solid waste quantity in a developing country using multivariate grey models
International Nuclear Information System (INIS)
Intharathirat, Rotchana; Abdul Salam, P.; Kumar, S.; Untong, Akarapong
2015-01-01
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
Forecasting of municipal solid waste quantity in a developing country using multivariate grey models
Energy Technology Data Exchange (ETDEWEB)
Intharathirat, Rotchana, E-mail: rotchana.in@gmail.com [Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, KlongLuang, Pathumthani 12120 (Thailand); Abdul Salam, P., E-mail: salam@ait.ac.th [Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, KlongLuang, Pathumthani 12120 (Thailand); Kumar, S., E-mail: kumar@ait.ac.th [Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, KlongLuang, Pathumthani 12120 (Thailand); Untong, Akarapong, E-mail: akarapong_un@hotmail.com [School of Tourism Development, Maejo University, Chiangmai (Thailand)
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.
A trust region approach with multivariate Padé model for optimal circuit design
Abdel-Malek, Hany L.; Ebid, Shaimaa E. K.; Mohamed, Ahmed S. A.
2017-11-01
Since the optimization process requires a significant number of consecutive function evaluations, it is recommended to replace the function by an easily evaluated approximation model during the optimization process. The model suggested in this article is based on a multivariate Padé approximation. This model is constructed using data points of ?, where ? is the number of parameters. The model is updated over a sequence of trust regions. This model avoids the slow convergence of linear models of ? and has features of quadratic models that need interpolation data points of ?. The proposed approach is tested by applying it to several benchmark problems. Yield optimization using such a direct method is applied to some practical circuit examples. Minimax solution leads to a suitable initial point to carry out the yield optimization process. The yield is optimized by the proposed derivative-free method for active and passive filter examples.
Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.
2018-05-01
Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.
Reduced Multivariate Polynomial Model for Manufacturing Costs Estimation of Piping Elements
Directory of Open Access Journals (Sweden)
Nibaldo Rodriguez
2013-01-01
Full Text Available This paper discusses the development and evaluation of an estimation model of manufacturing costs of piping elements through the application of a Reduced Multivariate Polynomial (RMP. The model allows obtaining accurate estimations, even when enough and adequate information is not available. This situation typically occurs in the early stages of the design process of industrial products. The experimental evaluations show that the approach is capable, with a low complexity, of reducing uncertainties and to predict costs with significant precision. Comparisons with a neural network showed also that the RMP performs better considering a set of classical performance measures with the corresponding lower complexity and higher accuracy.
Energy Technology Data Exchange (ETDEWEB)
Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)
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.
Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A
2012-03-15
To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. 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. 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. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright Â© 2012 Elsevier Inc. All rights reserved.
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.
International Nuclear Information System (INIS)
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t
2012-01-01
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.
International Nuclear Information System (INIS)
Weathers, J.B.; Luck, R.; Weathers, J.W.
2009-01-01
The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.
Energy Technology Data Exchange (ETDEWEB)
Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com
2009-11-15
The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.
Parameter Estimation in Probit Model for Multivariate Multinomial Response Using SMLE
Directory of Open Access Journals (Sweden)
Jaka Nugraha
2012-02-01
Full Text Available In the research field of transportation, market research and politics, often involving the response of the multinomial multivariate observations. In this paper, we discused a modeling of multivariate multinomial responses using probit model. The estimated parameters were calculated using Maximum Likelihood Estimations (MLE based on the GHK simulation. method known as Simulated Maximum Likelihood Estimations (SMLE. Likelihood function on the Probit model contains probability values that must be resolved by simulation. By using the GHK simulation algorithm, the estimator equation has been obtained for the parameters in the model Probit Keywords : Probit Model, Newton-Raphson Iteration, GHK simulator, MLE, simulated log-likelihood
Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian
2017-01-01
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.
Exploring the potential of multivariate depth-damage and rainfall-damage models
DEFF Research Database (Denmark)
van Ootegem, Luc; van Herck, K.; Creten, T.
2018-01-01
In Europe, floods are among the natural catastrophes that cause the largest economic damage. This article explores the potential of two distinct types of multivariate flood damage models: ‘depth-damage’ models and ‘rainfall-damage’ models. We use survey data of 346 Flemish households that were...... victim of pluvial floods complemented with rainfall data from both rain gauges and weather radars. In the econometrical analysis, a Tobit estimation technique is used to deal with the issue of zero damage observations. The results show that in the ‘depth-damage’ models flood depth has a significant...... impact on the damage. In the ‘rainfall-damage’ models there is a significant impact of rainfall accumulation on the damage when using the gauge rainfall data as predictor, but not when using the radar rainfall data. Finally, non-hazard indicators are found to be important for explaining pluvial flood...
Matos, Larissa A.; Bandyopadhyay, Dipankar; Castro, Luis M.; Lachos, Victor H.
2015-01-01
In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyse these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails. Motivated by this, Matos et al. (2013b) recently proposed an exact EM-type algorithm for LMEC/NLMEC models using a multivariate Student’s-t distribution, with closed-form expressions at the E-step. In this paper, we develop influence diagnostics for LMEC/NLMEC models using the multivariate Student’s-t density, based on the conditional expectation of the complete data log-likelihood. This partially eliminates the complexity associated with the approach of Cook (1977, 1986) for censored mixed-effects models. The new methodology is illustrated via an application to a longitudinal HIV dataset. In addition, a simulation study explores the accuracy of the proposed measures in detecting possible influential observations for heavy-tailed censored data under different perturbation and censoring schemes. PMID:26190871
Energy consumption and economic growth in New Zealand: Results of trivariate and multivariate models
International Nuclear Information System (INIS)
Bartleet, Matthew; Gounder, Rukmani
2010-01-01
This study examines the energy consumption-growth nexus in New Zealand. Causal linkages between energy and macroeconomic variables are investigated using trivariate demand-side and multivariate production models. Long run and short run relationships are estimated for the period 1960-2004. The estimated results of demand model reveal a long run relationship between energy consumption, real GDP and energy prices. The short run results indicate that real GDP Granger-causes energy consumption without feedback, consistent with the proposition that energy demand is a derived demand. Energy prices are found to be significant for energy consumption outcomes. Production model results indicate a long run relationship between real GDP, energy consumption and employment. The Granger-causality is found from real GDP to energy consumption, providing additional evidence to support the neoclassical proposition that energy consumption in New Zealand is fundamentally driven by economic activities. Inclusion of capital in the multivariate production model shows short run causality from capital to energy consumption. Also, changes in real GDP and employment have significant predictive power for changes in real capital.
Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel
2014-05-20
A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.
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
Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition
Directory of Open Access Journals (Sweden)
Md. Rabiul Islam
2012-01-01
Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.
International Nuclear Information System (INIS)
Cella, Laura; D’Avino, Vittoria; Liuzzi, Raffaele; Conson, Manuel; Doria, Francesca; Faiella, Adriana; Loffredo, Filomena; Salvatore, Marco; Pacelli, Roberto
2013-01-01
The risk of radio-induced gastrointestinal (GI) complications is affected by several factors other than the dose to the rectum such as patient characteristics, hormonal or antihypertensive therapy, and acute rectal toxicity. Purpose of this work is to study clinical and dosimetric parameters impacting on late GI toxicity after prostate external beam radiotherapy (RT) and to establish multivariate normal tissue complication probability (NTCP) model for radiation-induced GI complications. A total of 57 men who had undergone definitive RT for prostate cancer were evaluated for GI events classified using the RTOG/EORTC scoring system. Their median age was 73 years (range 53–85). The patients were assessed for GI toxicity before, during, and periodically after RT completion. Several clinical variables along with rectum dose-volume parameters (Vx) were collected and their correlation to GI toxicity was analyzed by Spearman’s rank correlation coefficient (Rs). Multivariate logistic regression method using resampling techniques was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). At a median follow-up of 30 months, 37% (21/57) patients developed G1-2 acute GI events while 33% (19/57) were diagnosed with G1-2 late GI events. An NTCP model for late mild/moderate GI toxicity based on three variables including V65 (OR = 1.03), antihypertensive and/or anticoagulant (AH/AC) drugs (OR = 0.24), and acute GI toxicity (OR = 4.3) was selected as the most predictive model (Rs = 0.47, p < 0.001; AUC = 0.79). This three-variable model outperforms the logistic model based on V65 only (Rs = 0.28, p < 0.001; AUC = 0.69). We propose a logistic NTCP model for late GI toxicity considering not only rectal irradiation dose but also clinical patient-specific factors. Accordingly, the risk of G1-2 late GI increases as V65 increases, it is higher for patients experiencing
Yue, Chen; Chen, Shaojie; Sair, Haris I; Airan, Raag; Caffo, Brian S
2015-09-01
Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model
Arumugam, S.; Libera, D.
2017-12-01
Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. 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 compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.
Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks
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.
Czech Academy of Sciences Publication Activity Database
Čech, František; Baruník, Jozef
2017-01-01
Roč. 36, č. 1 (2017), s. 181-206 ISSN 0277-6693 R&D Projects: GA ČR GA13-32263S Institutional support: RVO:67985556 Keywords : Multivariate volatility * realized covariance * portfolio optimisation Subject RIV: AH - Economic s OBOR OECD: Economic Theory Impact factor: 0.747, year: 2016 http://library.utia.cas.cz/separaty/2017/E/barunik-0478479.pdf
Modeling multivariate time series on manifolds with skew radial basis functions.
Jamshidi, Arta A; Kirby, Michael J
2011-01-01
We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.
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.
International Nuclear Information System (INIS)
Morishima, N.
1996-01-01
The multivariate autoregressive (MAR) modeling of a vector noise process is discussed in terms of the estimation of dominant noise sources in BWRs. The discussion is based on a physical approach: a transfer function model on BWR core dynamics is utilized in developing a noise model; a set of input-output relations between three system variables and twelve different noise sources is obtained. By the least-square fitting of a theoretical PSD on neutron noise to an experimental one, four kinds of dominant noise sources are selected. It is shown that some of dominant noise sources consist of two or more different noise sources and have the spectral properties of being coloured and correlated with each other. By diagonalizing the PSD matrix for dominant noise sources, we may obtain an MAR expression for a vector noise process as a response to the diagonal elements(i.e. residual noises) being white and mutually-independent. (Author)
Warton, David I; Thibaut, Loïc; Wang, Yi Alice
2017-01-01
Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.
Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M
2015-06-01
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations
Postma, Erik; Siitari, Heli; Schwabl, Hubert; Richner, Heinz; Tschirren, Barbara
2014-03-01
Egg components are important mediators of prenatal maternal effects in birds and other oviparous species. Because different egg components can have opposite effects on offspring phenotype, selection is expected to favour their mutual adjustment, resulting in a significant covariation between egg components within and/or among clutches. Here we tested for such correlations between maternally derived yolk immunoglobulins and yolk androgens in great tit (Parus major) eggs using a multivariate mixed-model approach. We found no association between yolk immunoglobulins and yolk androgens within clutches, indicating that within clutches the two egg components are deposited independently. Across clutches, however, there was a significant negative relationship between yolk immunoglobulins and yolk androgens, suggesting that selection has co-adjusted their deposition. Furthermore, an experimental manipulation of ectoparasite load affected patterns of covariance among egg components. Yolk immunoglobulins are known to play an important role in nestling immune defence shortly after hatching, whereas yolk androgens, although having growth-enhancing effects under many environmental conditions, can be immunosuppressive. We therefore speculate that variation in the risk of parasitism may play an important role in shaping optimal egg composition and may lead to the observed pattern of yolk immunoglobulin and yolk androgen deposition across clutches. More generally, our case study exemplifies how multivariate mixed-model methodology presents a flexible tool to not only quantify, but also test patterns of (co)variation across different organisational levels and environments, allowing for powerful hypothesis testing in ecophysiology.
Sang, Huiyan
2011-12-01
This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.
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
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
Energy Technology Data Exchange (ETDEWEB)
Nilsson, Aasa; Persson, Fredrik; Andersson, Magnus
2009-07-15
IVL, together with Emerson Process Management, has developed a decision support system (DSS) based on multivariate statistical process models. The system was implemented at Nynas AB's refinery in order to provide real-time TBP curves and to enable the operator to optimise the process with regards to product quality and energy consumption. The project resulted in the following proven benefits at the industrial reference site, Nynas Refinery in Gothenburg: - Increased yield with up to 14 % (relative terms) for the most valuable product - Decreased energy consumption of 8 %. Validation of model predictions compared to the laboratory analysis showed that the prediction error lay within 1 deg C throughout the whole test period
Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.
2016-10-01
The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.
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. © 2015 WILEY PERIODICALS, INC.
Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang
2014-10-01
Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.
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. Copyright © 2014 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Zhifeng Zhong
2017-01-01
Full Text Available Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.
Directory of Open Access Journals (Sweden)
Angela Koutsokera
2017-07-01
Full Text Available BackgroundChronic lung allograft dysfunction and its main phenotypes, bronchiolitis obliterans syndrome (BOS and restrictive allograft syndrome (RAS, are major causes of mortality after lung transplantation (LT. RAS and early-onset BOS, developing within 3 years after LT, are associated with particularly inferior clinical outcomes. Prediction models for early-onset BOS and RAS have not been previously described.MethodsLT recipients of the French and Swiss transplant cohorts were eligible for inclusion in the SysCLAD cohort if they were alive with at least 2 years of follow-up but less than 3 years, or if they died or were retransplanted at any time less than 3 years. These patients were assessed for early-onset BOS, RAS, or stable allograft function by an adjudication committee. Baseline characteristics, data on surgery, immunosuppression, and year-1 follow-up were collected. Prediction models for BOS and RAS were developed using multivariate logistic regression and multivariate multinomial analysis.ResultsAmong patients fulfilling the eligibility criteria, we identified 149 stable, 51 BOS, and 30 RAS subjects. The best prediction model for early-onset BOS and RAS included the underlying diagnosis, induction treatment, immunosuppression, and year-1 class II donor-specific antibodies (DSAs. Within this model, class II DSAs were associated with BOS and RAS, whereas pre-LT diagnoses of interstitial lung disease and chronic obstructive pulmonary disease were associated with RAS.ConclusionAlthough these findings need further validation, results indicate that specific baseline and year-1 parameters may serve as predictors of BOS or RAS by 3 years post-LT. Their identification may allow intervention or guide risk stratification, aiming for an individualized patient management approach.
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
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.
Dynamics analysis of a boiling water reactor based on multivariable autoregressive modeling
International Nuclear Information System (INIS)
Oguma, Ritsuo; Matsubara, Kunihiko
1980-01-01
The establishment of the highly reliable mathematical model for the dynamic characteristics of a reactor is indispensable for the achievement of safe operation in reactor plants. The authors have tried to model the dynamic characteristics of a reactor based on the identification technique, taking the JPDR (Japan Power Demonstration Reactor) as the object, as one of the technical studies for diagnosing BWR anomaly, and employed the multivariable autoregressive modeling (MAR method) as one of the useful methods for forwarding the analysis. In this paper, the outline of the system analysis by MAR modeling is explained, and the identification experiments and their analysis results performed in the phase 4 of the power increase test of the JPDR are described. The authors evaluated the results of identification based on only reactor noises, making reference to the results of identification in the case of exciting the system by applying artificial irregular disturbance, in order to clarify the extent in which the modeling is possible by reactor noises only. However, some difficulties were encountered. The largest problem is the one concerning the separation and identification of the noise sources exciting the variables from the dynamic characteristics among the variables. If the effective technique can be obtained to this problem, the approach by the identification technique based on the probability model might be a powerful tool in the field of reactor noise analysis and the development of diagnosis technics. (Wakatsuki, Y.)
Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K
2017-01-01
The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.
Inference of reactive transport model parameters using a Bayesian multivariate approach
Carniato, Luca; Schoups, Gerrit; van de Giesen, Nick
2014-08-01
Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least squares with weight estimation (WLS(we)) where weights are estimated from the data together with the parameters. In this study, we formulate the parameter estimation task as a multivariate Bayesian inference problem. The WLS and WLS(we) methods are special cases in this framework, corresponding to specific prior assumptions about the residual covariance matrix. The Bayesian perspective allows for generalizations to cases where residual correlation is important and for efficient inference by analytically integrating out the variances (weights) and selected covariances from the joint posterior. Specifically, the WLS and WLS(we) methods are compared to a multivariate (MV) approach that accounts for specific residual correlations without the need for explicit estimation of the error parameters. When applied to inference of reactive transport model parameters from column-scale data on dissolved species concentrations, the following results were obtained: (1) accounting for residual correlation between species provides more accurate parameter estimation for high residual correlation levels whereas its influence for predictive uncertainty is negligible, (2) integrating out the (co)variances leads to an efficient estimation of the full joint posterior with a reduced computational effort compared to the WLS(we) method, and (3) in the presence of model structural errors, none of the methods is able to identify the correct parameter values.
Energy Technology Data Exchange (ETDEWEB)
Joensson, S. [Man-Technology-Environment Research Centre, Department of Natural Sciences, Orebro University, 701 82 Orebro (Sweden)], E-mail: sofie.jonsson@nat.oru.se; Eriksson, L.A. [Department of Natural Sciences and Orebro Life Science Center, Orebro University, 701 82 Orebro (Sweden); Bavel, B. van [Man-Technology-Environment Research Centre, Department of Natural Sciences, Orebro University, 701 82 Orebro (Sweden)
2008-07-28
A multivariate model to characterise nitroaromatics and related compounds based on molecular descriptors was calculated. Descriptors were collected from literature and through empirical, semi-empirical and density functional theory-based calculations. Principal components were used to describe the distribution of the compounds in a multidimensional space. Four components described 76% of the variation in the dataset. PC1 separated the compounds due to molecular weight, PC2 separated the different isomers, PC3 arranged the compounds according to different functional groups such as nitrobenzoic acids, nitrobenzenes, nitrotoluenes and nitroesters and PC4 differentiated the compounds containing chlorine from other compounds. Quantitative structure-property relationship models were calculated using partial least squares (PLS) projection to latent structures to predict gas chromatographic (GC) retention times and the distribution between the water phase and air using solid-phase microextraction (SPME). GC retention time was found to be dependent on the presence of polar amine groups, electronic descriptors including highest occupied molecular orbital, dipole moments and the melting point. The model of GC retention time was good, but the precision was not precise enough for practical use. An important environmental parameter was measured using SPME, the distribution between headspace (air) and the water phase. This parameter was mainly dependent on Henry's law constant, vapour pressure, log P, content of hydroxyl groups and atmospheric OH rate constant. The predictive capacity of the model substantially improved when recalculating a model using these five descriptors only.
Directory of Open Access Journals (Sweden)
Yoonsu Shin
2016-01-01
Full Text Available In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs and related network parameters (NPs using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.
BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods
Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.
2017-12-01
Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made
Roldán, J. B.; Miranda, E.; González-Cordero, G.; García-Fernández, P.; Romero-Zaliz, R.; González-Rodelas, P.; Aguilera, A. M.; González, M. B.; Jiménez-Molinos, F.
2018-01-01
A multivariate analysis of the parameters that characterize the reset process in Resistive Random Access Memory (RRAM) has been performed. The different correlations obtained can help to shed light on the current components that contribute in the Low Resistance State (LRS) of the technology considered. In addition, a screening method for the Quantum Point Contact (QPC) current component is presented. For this purpose, the second derivative of the current has been obtained using a novel numerical method which allows determining the QPC model parameters. Once the procedure is completed, a whole Resistive Switching (RS) series of thousands of curves is studied by means of a genetic algorithm. The extracted QPC parameter distributions are characterized in depth to get information about the filamentary pathways associated with LRS in the low voltage conduction regime.
Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.
2017-06-01
The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.
Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery
Directory of Open Access Journals (Sweden)
Fan Liang
2013-02-01
Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, O.; Hoogenboom, J.E.; Dam, H. van
1988-01-01
Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality.
International Nuclear Information System (INIS)
Ciftcioglu, O.
1988-01-01
Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality. (author)
Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D
2018-05-18
Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.
Cannon, Alex J.
2018-01-01
Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin
Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.
Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to
Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.
2017-01-01
Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to
Application of Multivariate Modeling for Radiation Injury Assessment: A Proof of Concept
Directory of Open Access Journals (Sweden)
David L. Bolduc
2014-01-01
Full Text Available 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.
Multivariate Cholesky models of human female fertility patterns in the NLSY.
Rodgers, Joseph Lee; Bard, David E; Miller, Warren B
2007-03-01
Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.
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.
International Nuclear Information System (INIS)
Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.
2009-01-01
In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)
Multivariate poisson lognormal modeling of crashes by type and severity on rural two lane highways.
Wang, Kai; Ivan, John N; Ravishanker, Nalini; Jackson, Eric
2017-02-01
In an effort to improve traffic safety, there has been considerable interest in estimating crash prediction models and identifying factors contributing to crashes. To account for crash frequency variations among crash types and severities, crash prediction models have been estimated by type and severity. The univariate crash count models have been used by researchers to estimate crashes by crash type or severity, in which the crash counts by type or severity are assumed to be independent of one another and modelled separately. When considering crash types and severities simultaneously, this may neglect the potential correlations between crash counts due to the presence of shared unobserved factors across crash types or severities for a specific roadway intersection or segment, and might lead to biased parameter estimation and reduce model accuracy. The focus on this study is to estimate crashes by both crash type and crash severity using the Integrated Nested Laplace Approximation (INLA) Multivariate Poisson Lognormal (MVPLN) model, and identify the different effects of contributing factors on different crash type and severity counts on rural two-lane highways. The INLA MVPLN model can simultaneously model crash counts by crash type and crash severity by accounting for the potential correlations among them and significantly decreases the computational time compared with a fully Bayesian fitting of the MVPLN model using Markov Chain Monte Carlo (MCMC) method. This paper describes estimation of MVPLN models for three-way stop controlled (3ST) intersections, four-way stop controlled (4ST) intersections, four-way signalized (4SG) intersections, and roadway segments on rural two-lane highways. Annual Average Daily traffic (AADT) and variables describing roadway conditions (including presence of lighting, presence of left-turn/right-turn lane, lane width and shoulder width) were used as predictors. A Univariate Poisson Lognormal (UPLN) was estimated by crash type and
Sediment fingerprinting experiments to test the sensitivity of multivariate mixing models
Gaspar, Leticia; Blake, Will; Smith, Hugh; Navas, Ana
2014-05-01
Sediment fingerprinting techniques provide insight into the dynamics of sediment transfer processes and support for catchment management decisions. As questions being asked of fingerprinting datasets become increasingly complex, validation of model output and sensitivity tests are increasingly important. This study adopts an experimental approach to explore the validity and sensitivity of mixing model outputs for materials with contrasting geochemical and particle size composition. The experiments reported here focused on (i) the sensitivity of model output to different fingerprint selection procedures and (ii) the influence of source material particle size distributions on model output. Five soils with significantly different geochemistry, soil organic matter and particle size distributions were selected as experimental source materials. A total of twelve sediment mixtures were prepared in the laboratory by combining different quantified proportions of the Kruskal-Wallis test, Discriminant Function Analysis (DFA), Principal Component Analysis (PCA), or correlation matrix). Summary results for the use of the mixing model with the different sets of fingerprint properties for the twelve mixed soils were reasonably consistent with the initial mixing percentages initially known. Given the experimental nature of the work and dry mixing of materials, geochemical conservative behavior was assumed for all elements, even for those that might be disregarded in aquatic systems (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
Zubia-Olaskoaga, Felix; Maraví-Poma, Enrique; Urreta-Barallobre, Iratxe; Ramírez-Puerta, María-Rosario; Mourelo-Fariña, Mónica; Marcos-Neira, María-Pilar; García-García, Miguel Ángel
2018-03-01
Development and validation of a multivariate prediction model for patients with acute pancreatitis (AP) admitted in Intensive Care Units (ICU). A prospective multicenter observational study, in 1 year period, in 46 international ICUs (EPAMI study). adults admitted to an ICU with AP and at least one organ failure. Development of a multivariate prediction model, using the worst data of the stay in ICU, based in multivariate analysis, simple imputation in a development cohort. The model was validated in another cohort. 374 patients were included (mortality of 28.9%). Variables with statistical significance in multivariate analysis were age, no alcoholic and no biliary etiology, development of shock, development of respiratory failure, need of continuous renal replacement therapy, and intra-abdominal pressure. The model created with these variables presented an AUC of ROC curve of 0.90 (CI 95% 0.81-0.94) in the validation cohort. We developed a multivariable prediction model, and AP cases could be classified as low mortality risk (between 2 and 9.5 points, mortality of 1.35%), moderate mortality risk (between 10 and 12.5 points, 28.92% of mortality), and high mortality risk (13 points of more, mortality of 88.37%). Our model presented better AUC of ROC curve than APACHE II (0.91 vs 0.80) and SOFA in the first 24 h (0.91 vs 0.79). We developed and validated a multivariate prediction model, which can be applied in any moment of the stay in ICU, with better discriminatory power than APACHE II and SOFA in the first 24 h. Copyright © 2018 IAP and EPC. Published by Elsevier B.V. All rights reserved.
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...
Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt
2017-03-01
Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All
International Nuclear Information System (INIS)
Schaaf, Arjen van der; Xu Chengjian; Luijk, Peter van; Veld, Aart A. van’t; Langendijk, Johannes A.; Schilstra, Cornelis
2012-01-01
Purpose: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. Materials and methods: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. Results: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small
Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A
2018-02-12
We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.
Noise source analysis of nuclear ship Mutsu plant using multivariate autoregressive model
International Nuclear Information System (INIS)
Hayashi, K.; Shimazaki, J.; Shinohara, Y.
1996-01-01
The present study is concerned with the noise sources in N.S. Mutsu reactor plant. The noise experiments on the Mutsu plant were performed in order to investigate the plant dynamics and the effect of sea condition and and ship motion on the plant. The reactor noise signals as well as the ship motion signals were analyzed by a multivariable autoregressive (MAR) modeling method to clarify the noise sources in the reactor plant. It was confirmed from the analysis results that most of the plant variables were affected mainly by a horizontal component of the ship motion, that is the sway, through vibrations of the plant structures. Furthermore, the effect of ship motion on the reactor power was evaluated through the analysis of wave components extracted by a geometrical transform method. It was concluded that the amplitude of the reactor power oscillation was about 0.15% in normal sea condition, which was small enough for safe operation of the reactor plant. (authors)
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.
Directory of Open Access Journals (Sweden)
Abdolreza Yazdani-Chamzini
2017-12-01
Full Text Available Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1 artificial intelligence, (2 statistical methods, and (3 analytical methods. In this paper, the multivariate regression (MVR method, which is one of the most popular linear models, and the artificial neural network (ANN method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.
Roidl, Ernst; Siebert, Felix Wilhelm; Oehl, Michael; Höger, Rainer
2013-12-01
Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior. © 2013.
Multivariate analysis with LISREL
Jöreskog, Karl G; Y Wallentin, Fan
2016-01-01
This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.
Antic, Darko; Milic, Natasa; Nikolovski, Srdjan; Todorovic, Milena; Bila, Jelena; Djurdjevic, Predrag; Andjelic, Bosko; Djurasinovic, Vladislava; Sretenovic, Aleksandra; Vukovic, Vojin; Jelicic, Jelena; Hayman, Suzanne; Mihaljevic, Biljana
2016-10-01
Lymphoma patients are at increased risk of thromboembolic events but thromboprophylaxis in these patients is largely underused. We sought to develop and validate a simple model, based on individual clinical and laboratory patient characteristics that would designate lymphoma patients at risk for thromboembolic event. The study population included 1,820 lymphoma patients who were treated in the Lymphoma Departments at the Clinics of Hematology, Clinical Center of Serbia and Clinical Center Kragujevac. The model was developed using data from a derivation cohort (n = 1,236), and further assessed in the validation cohort (n = 584). Sixty-five patients (5.3%) in the derivation cohort and 34 (5.8%) patients in the validation cohort developed thromboembolic events. The variables independently associated with risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m(2) , reduced mobility, extranodal localization, development of neutropenia and hemoglobin level 3). For patients classified at risk (intermediate and high-risk scores), the model produced negative predictive value of 98.5%, positive predictive value of 25.1%, sensitivity of 75.4%, and specificity of 87.5%. A high-risk score had positive predictive value of 65.2%. The diagnostic performance measures retained similar values in the validation cohort. Developed prognostic Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis in cancer patients. Am. J. Hematol. 91:1014-1019, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Heddam, Salim; Kisi, Ozgur
2018-04-01
In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5T) are applied for modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality variables data from three stations operated by the United States Geological Survey (USGS) were used for developing the three models. The water quality data selected consisted of daily measured of water temperature (TE, °C), pH (std. unit), specific conductance (SC, μS/cm) and discharge (DI cfs), are used as inputs to the LSSVM, MARS and M5T models. The three models were applied for each station separately and compared to each other. According to the results obtained, it was found that: (i) the DO concentration could be successfully estimated using the three models and (ii) the best model among all others differs from one station to another.
Dong, Yijun
The research about measuring the risk of a bond portfolio and the portfolio optimization was relatively rare previously, because the risk factors of bond portfolios are not very volatile. However, this condition has changed recently. The 2008 financial crisis brought high volatility to the risk factors and the related bond securities, even if the highly rated U.S. treasury bonds. Moreover, the risk factors of bond portfolios show properties of fat-tailness and asymmetry like risk factors of equity portfolios. Therefore, we need to use advanced techniques to measure and manage risk of bond portfolios. In our paper, we first apply autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model with multivariate normal tempered stable (MNTS) distribution innovations to predict risk factors of U.S. treasury bonds and statistically demonstrate that MNTS distribution has the ability to capture the properties of risk factors based on the goodness-of-fit tests. Then based on empirical evidence, we find that the VaR and AVaR estimated by assuming normal tempered stable distribution are more realistic and reliable than those estimated by assuming normal distribution, especially for the financial crisis period. Finally, we use the mean-risk portfolio optimization to minimize portfolios' potential risks. The empirical study indicates that the optimized bond portfolios have better risk-adjusted performances than the benchmark portfolios for some periods. Moreover, the optimized bond portfolios obtained by assuming normal tempered stable distribution have improved performances in comparison to the optimized bond portfolios obtained by assuming normal distribution.
Multi-variable optimization of PEMFC cathodes using an agglomerate model
Energy Technology Data Exchange (ETDEWEB)
Secanell, M.; Suleman, A.; Djilali, N. [Institute for Integrated Energy Systems and Department Mechanical Engineering, University of Victoria, PO Box 3055 STN CSC, Victoria, BC (Canada); Karan, K. [Queen' s-RMC Fuel Cell Research Centre and Department Chemical Engineering, Queen' s University, Kingston, Ont. (Canada)
2007-06-30
A comprehensive numerical framework for cathode electrode design is presented and applied to predict the catalyst layer and the gas diffusion layer parameters that lead to an optimal electrode performance at different operating conditions. The design and optimization framework couples an agglomerate cathode catalyst layer model to a numerical gradient-based optimization algorithm. The set of optimal parameters is obtained by solving a multi-variable optimization problem. The parameters are the catalyst layer platinum loading, platinum to carbon ratio, amount of electrolyte in the agglomerate and the gas diffusion layer porosity. The results show that the optimal catalyst layer composition and gas diffusion layer porosity depend on operating conditions. At low current densities, performance is mainly improved by increasing platinum loading to values above 1 mg cm{sup -2}, moderate values of electrolyte volume fraction, 0.5, and low porosity, 0.1. At higher current densities, performance is improved by reducing the platinum loading to values below 0.35 mg cm{sup -2} and increasing both electrolyte volume fraction, 0.55, and porosity 0.32. The underlying improvements due to the optimized compositions are analyzed in terms of the spatial distribution of the various overpotentials, and the effect of the agglomerate structure parameters (radius and electrolyte film) are investigated. The paper closes with a discussion of the optimized composition obtained in this study in the context of available experimental data. The analysis suggests that reducing the solid phase volume fraction inside the catalyst layer might lead to improved electrode performance. (author)
Wingbermühle, Roel W; van Trijffel, Emiel; Nelissen, Paul M; Koes, Bart; Verhagen, Arianne P
2018-01-01
Which multivariable prognostic model(s) for recovery in people with neck pain can be used in primary care? Systematic review of studies evaluating multivariable prognostic models. People with non-specific neck pain presenting at primary care. Baseline characteristics of the participants. Recovery measured as pain reduction, reduced disability, or perceived recovery at short-term and long-term follow-up. Fifty-three publications were included, of which 46 were derivation studies, four were validation studies, and three concerned combined studies. The derivation studies presented 99 multivariate models, all of which were at high risk of bias. Three externally validated models generated usable models in low risk of bias studies. One predicted recovery in non-specific neck pain, while two concerned participants with whiplash-associated disorders (WAD). Discriminative ability of the non-specific neck pain model was area under the curve (AUC) 0.65 (95% CI 0.59 to 0.71). For the first WAD model, discriminative ability was AUC 0.85 (95% CI 0.79 to 0.91). For the second WAD model, specificity was 99% (95% CI 93 to 100) and sensitivity was 44% (95% CI 23 to 65) for prediction of non-recovery, and 86% (95% CI 73 to 94) and 55% (95% CI 41 to 69) for prediction of recovery, respectively. Initial Neck Disability Index scores and age were identified as consistent prognostic factors in these three models. Three externally validated models were found to be usable and to have low risk of bias, of which two showed acceptable discriminative properties for predicting recovery in people with neck pain. These three models need further validation and evaluation of their clinical impact before their broad clinical use can be advocated. PROSPERO CRD42016042204. [Wingbermühle RW, van Trijffel E, Nelissen PM, Koes B, Verhagen AP (2018) Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: a systematic review
Multivariate Hybrid Modelling of Future Wave-Storms at the Northwestern Black Sea
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Jue Lin-Ye
2018-02-01
Full Text Available The characterization of future wave-storms and their relationship to large-scale climate can provide useful information for environmental or urban planning at coastal areas. A hybrid methodology (process-based and statistical was used to characterize the extreme wave-climate at the northwestern Black Sea. The Simulating WAve Nearshore spectral wave-model was employed to produce wave-climate projections, forced with wind-fields projections for two climate change scenarios: Representative Concentration Pathways (RCPs 4.5 and 8.5. A non-stationary multivariate statistical model was built, considering significant wave-height and peak-wave-period at the peak of the wave-storm, as well as storm total energy and storm-duration. The climate indices of the North Atlantic Oscillation, East Atlantic Pattern, and Scandinavian Pattern have been used as covariates to link to storminess, wave-storm threshold, and wave-storm components in the statistical model. The results show that, first, under both RCP scenarios, the mean values of significant wave-height and peak-wave-period at the peak of the wave-storm remain fairly constant over the 21st century. Second, the mean value of storm total energy is more markedly increasing in the RCP4.5 scenario than in the RCP8.5 scenario. Third, the mean value of storm-duration is increasing in the RCP4.5 scenario, as opposed to the constant trend in the RCP8.5 scenario. The variance of each wave-storm component increases when the corresponding mean value increases under both RCP scenarios. During the 21st century, the East Atlantic Pattern and changes in its pattern have a special influence on wave-storm conditions. Apart from the individual characteristics of each wave-storm component, wave-storms with both extreme energy and duration can be expected in the 21st century. The dependence between all the wave-storm components is moderate, but grows with time and, in general, the severe emission scenario of RCP8.5 presents
Koopman, S.J.; Creal, D.D.
2010-01-01
We develop a flexible business cycle indicator that accounts for potential time variation in macroeconomic variables. The coincident economic indicator is based on a multivariate trend cycle decomposition model and is constructed from a moderate set of US macroeconomic time series. In particular, we
Detailed clinical models: a review.
Goossen, William; Goossen-Baremans, Anneke; van der Zel, Michael
2010-12-01
Due to the increasing use of electronic patient records and other health care information technology, we see an increase in requests to utilize these data. A highly level of standardization is required during the gathering of these data in the clinical context in order to use it for analyses. Detailed Clinical Models (DCM) have been created toward this purpose and several initiatives have been implemented in various parts of the world to create standardized models. This paper presents a review of DCM. Two types of analyses are presented; one comparing DCM against health care information architectures and a second bottom up approach from concept analysis to representation. In addition core parts of the draft ISO standard 13972 on DCM are used such as clinician involvement, data element specification, modeling, meta information, and repository and governance. SIX INITIATIVES WERE SELECTED: Intermountain Healthcare, 13606/OpenEHR Archetypes, Clinical Templates, Clinical Contents Models, Health Level 7 templates, and Dutch Detailed Clinical Models. Each model selected was reviewed for their overall development, involvement of clinicians, use of data types, code bindings, expressing semantics, modeling, meta information, use of repository and governance. Using both a top down and bottom up approach to comparison reveals many commonalties and differences between initiatives. Important differences include the use of or lack of a reference model and expressiveness of models. Applying clinical data element standards facilitates the use of conceptual DCM models in different technical representations.
The Dirichet-Multinomial model for multivariate randomized response data and small samples
Avetisyan, Marianna; Fox, Gerardus J.A.
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
Hulled wheats are largely untapped genetic resources with >10,000 years of genetic memory and diversity that can be used for wheat quality improvement, development of healthy products, and adaptation to climate change. Multivariate diversity was assessed in the diploid Triticum monococcum L. var mon...
Hayn, Dieter; Kreiner, Karl; Ebner, Hubert; Kastner, Peter; Breznik, Nada; Rzepka, Angelika; Hofmann, Axel; Gombotz, Hans; Schreier, Günter
2017-06-14
Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated. It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns. 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another. Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2. We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.
Multivariable prediction model for suspected giant cell arteritis: development and validation
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Ing EB
2017-11-01
Full Text Available Edsel B Ing,1 Gabriela Lahaie Luna,2 Andrew Toren,3 Royce Ing,4 John J Chen,5 Nitika Arora,6 Nurhan Torun,7 Otana A Jakpor,8 J Alexander Fraser,9 Felix J Tyndel,10 Arun NE Sundaram,10 Xinyang Liu,11 Cindy TY Lam,1 Vivek Patel,12 Ezekiel Weis,13 David Jordan,14 Steven Gilberg,14 Christian Pagnoux,15 Martin ten Hove21Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto, 2Department of Ophthalmology, Queen’s University, Kingston, ON, 3Department of Ophthalmology, University of Laval, Quebec, QC, 4Toronto Eyelid, Strabismus and Orbit Surgery Clinic, Toronto, ON, Canada; 5Mayo Clinic, Department of Ophthalmology and Neurology, 6Mayo Clinic, Department of Ophthalmology, Rochester, MN, 7Department of Surgery, Division of Ophthalmology, Harvard Medical School, Boston, MA, 8Harvard Medical School, Boston, MA, USA; 9Department of Clinical Neurological Sciences and Ophthalmology, Western University, London, 10Department of Medicine, University of Toronto Medical School, Toronto, ON, Canada; 11Department of Medicine, Fudan University Shanghai Medical College, Shanghai, People’s Republic of China; 12Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 13Departments of Ophthalmology, Universities of Alberta and Calgary, Edmonton and Calgary, AB, 14Department of Ophthalmology, University of Ottawa, Ottawa, ON, 15Vasculitis Clinic, Mount Sinai Hospital, Toronto, ON, CanadaPurpose: To develop and validate a diagnostic prediction model for patients with suspected giant cell arteritis (GCA.Methods: A retrospective review of records of consecutive adult patients undergoing temporal artery biopsy (TABx for suspected GCA was conducted at seven university centers. The pathologic diagnosis was considered the final diagnosis. The predictor variables were age, gender, new onset headache, clinical temporal artery abnormality, jaw claudication, ischemic vision loss (VL, diplopia
Role Modeling for Clinical Educators.
Ettinger, Ellen Richter
1991-01-01
To become better role models, higher educators in institutions of clinical education should be conscious of the behaviors they demonstrate and the broad range of activities and attitudes that students observe and emulate, including clinical competence, professional demeanor, doctor-patient interactions, ethical values, and social consciousness.…
2017-09-01
application of statistical inference. Even when human forecasters leverage their professional experience, which is often gained through long periods of... application throughout statistics and Bayesian data analysis. The multivariate form of 2( , ) (e.g., Figure 12) is similarly analytically...data (i.e., no systematic manipulations with analytical functions), it is common in the statistical literature to apply mathematical transformations
R.W. Wingbermühle (Roel); E. van Trijffel (Emiel); Nelissen, P.M. (Paul M.); B.W. Koes (Bart); A.P. Verhagen (Arianne)
2017-01-01
markdownabstractQuestion: Which multivariable prognostic model(s) for recovery in people with neck pain can be used in primary care? Design: Systematic review of studies evaluating multivariable prognostic models. Participants: People with non-specific neck pain presenting at primary care.
International Nuclear Information System (INIS)
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.
Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena
2007-11-05
A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.
Continuous multivariate exponential extension
International Nuclear Information System (INIS)
Block, H.W.
1975-01-01
The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE
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.
International Nuclear Information System (INIS)
Soto, R; Wu, Ch. H; Bubela, A M
1999-01-01
This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in west Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (infill drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), oilfield intelligence (01), as an engineering tool to improve the characterization of oil and gas reservoirs. 01 integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial water flooding, number of wells for primary recovery, number of infill wells over the initial water flooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery
Frantzidis, Christos A; Gilou, Sotiria; Billis, Antonis; Karagianni, Maria; Bratsas, Charalampos D; Bamidis, Panagiotis
2016-03-01
Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.
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Abdelfattah M. Selim
2018-03-01
Full Text Available Aim: The present cross-sectional study was conducted to determine the seroprevalence and potential risk factors associated with Bovine viral diarrhea virus (BVDV disease in cattle and buffaloes in Egypt, to model the potential risk factors associated with the disease using logistic regression (LR models, and to fit the best predictive model for the current data. Materials and Methods: A total of 740 blood samples were collected within November 2012-March 2013 from animals aged between 6 months and 3 years. The potential risk factors studied were species, age, sex, and herd location. All serum samples were examined with indirect ELIZA test for antibody detection. Data were analyzed with different statistical approaches such as Chi-square test, odds ratios (OR, univariable, and multivariable LR models. Results: Results revealed a non-significant association between being seropositive with BVDV and all risk factors, except for species of animal. Seroprevalence percentages were 40% and 23% for cattle and buffaloes, respectively. OR for all categories were close to one with the highest OR for cattle relative to buffaloes, which was 2.237. Likelihood ratio tests showed a significant drop of the -2LL from univariable LR to multivariable LR models. Conclusion: There was an evidence of high seroprevalence of BVDV among cattle as compared with buffaloes with the possibility of infection in different age groups of animals. In addition, multivariable LR model was proved to provide more information for association and prediction purposes relative to univariable LR models and Chi-square tests if we have more than one predictor.
Fuchs, Julia; Cermak, Jan; Andersen, Hendrik
2017-04-01
This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.
Heggeseth, Brianna C; Jewell, Nicholas P
2013-07-20
Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.
Sang, Huiyan; Jun, Mikyoung; Huang, Jianhua Z.
2011-01-01
This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models
Directory of Open Access Journals (Sweden)
Luis Cláudio Lemos Correia
Full Text Available Abstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD based on variables assessed at admission to the coronary care unit (CCU due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%, while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84 in the derivation sample and 0.86 (95%CI = 0.79 - 0.93 in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively. Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002 and validation (p = 0.039 samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001 and validation (p < 0.0015 samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain.
Herrero Olaizola, Juan; Rodríguez Díaz, Francisco Javier; Musitu Ochoa, Gonzalo
2014-01-01
The literature has rarely paid attention to the differential influence of intergroup contact on subtle and blatant prejudice. In this study, we hypothesized that the influence of intergroup contact on subtle prejudice will be smaller than its influence on blatant prejudice. This hypothesis was tested with data from a cross-sectional design on 1,655 school-aged native Spanish adolescents. Prejudice was measured with a shortened version of the Meertens and Pettigrew scale of blatant and subtle prejudice adapted to Spanish adolescent population. Results from multivariate multilevel analyses for correlated outcome variables supported the hypothesis. Students tended to score higher on the subtle prejudice scale; contact with the outgroup was statistically related both to levels of blatant and subtle prejudice; and, the negative relationship of contact with the outgroup and prejudice is greater for blatant prejudice as compared to subtle prejudice. Overall, results provide statistical evidence supporting the greater resistance to change of subtle forms of prejudice.
Voss, Jesse S; Iqbal, Seher; Jenkins, Sarah M; Henry, Michael R; Clayton, Amy C; Jett, James R; Kipp, Benjamin R; Halling, Kevin C; Maldonado, Fabien
2014-01-01
Studies have shown that fluorescence in situ hybridization (FISH) testing increases lung cancer detection on cytology specimens in peripheral nodules. The goal of this study was to determine whether a predictive model using clinical features and routine cytology with FISH results could predict lung malignancy after a nondiagnostic bronchoscopic evaluation. Patients with an indeterminate peripheral lung nodule that had a nondiagnostic bronchoscopic evaluation were included in this study (N = 220). FISH was performed on residual bronchial brushing cytology specimens diagnosed as negative (n = 195), atypical (n = 16), or suspicious (n = 9). FISH results included hypertetrasomy (n = 30) and negative (n = 190). Primary study end points included lung cancer status along with time to diagnosis of lung cancer or date of last clinical follow-up. Hazard ratios (HRs) were calculated using Cox proportional hazards regression model analyses, and P values < .05 were considered statistically significant. The mean age of the 220 patients was 66.7 years (range, 35-91), and most (58%) were men. Most patients (79%) were current or former smokers with a mean pack year history of 43.2 years (median, 40; range, 1-200). After multivariate analysis, hypertetrasomy FISH (HR = 2.96, P < .001), pack years (HR = 1.03 per pack year up to 50, P = .001), age (HR = 1.04 per year, P = .02), atypical or suspicious cytology (HR = 2.02, P = .04), and nodule spiculation (HR = 2.36, P = .003) were independent predictors of malignancy over time and were used to create a prediction model (C-statistic = 0.78). These results suggest that this multivariate model including test results and clinical features may be useful following a nondiagnostic bronchoscopic examination. © 2013.
International Nuclear Information System (INIS)
Gloeckler, O.; Upadhyaya, B.R.
1987-01-01
Multivariate noise analysis of power reactor operating signals is useful for plant diagnostics, for isolating process and sensor anomalies, and for automated plant monitoring. In order to develop a reliable procedure, the previously established techniques for empirical modeling of fluctuation signals in power reactors have been improved. Application of the complete algorithm to operational data from the Loss-of-Fluid-Test (LOFT) Reactor showed that earlier conjectures (based on physical modeling) regarding the perturbation sources in a Pressurized Water Reactor (PWR) affecting coolant temperature and neutron power fluctuations can be systematically explained. This advanced methodology has important implication regarding plant diagnostics, and system or sensor anomaly isolation. 6 refs., 24 figs
Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S
2017-02-01
The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P information. Copyright © 2016. Published by Elsevier Inc.
Directory of Open Access Journals (Sweden)
Marder Luciano
2006-01-01
Full Text Available In the present work multivariate regression models were developed for the quantitative analysis of ternary systems using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS to determine the concentration in weight of calcium carbonate, magnesium carbonate and magnesium oxide. Nineteen spectra of standard samples previously defined in ternary diagram by mixture design were prepared and mid-infrared diffuse reflectance spectra were recorded. The partial least squares (PLS regression method was applied to the model. The spectra set was preprocessed by either mean-centered and variance-scaled (model 2 or mean-centered only (model 1. The results based on the prediction performance of the external validation set expressed by RMSEP (root mean square error of prediction demonstrated that it is possible to develop good models to simultaneously determine calcium carbonate, magnesium carbonate and magnesium oxide content in powdered samples that can be used in the study of the thermal decomposition of dolomite rocks.
Verdam, M.G.E.; Oort, F.J.
2014-01-01
Highlights: - Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data. - A method for the investigation of measurement bias with Kronecker product restricted models. - Application of these methods to health-related quality of life data
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. Copyright © 2013 Elsevier Ltd. All rights reserved.
Saha, Ashirbani; Harowicz, Michael R; Wang, Weiyao; Mazurowski, Maciej A
2018-05-01
To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores. A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients. The set of patients was divided into a training set and a test set. Using the training set we developed two machine learning-based models to discriminate (1) high ODX scores from intermediate and low ODX scores, and (2) high and intermediate ODX scores from low ODX scores. The performance of these models was evaluated on the independent test set. High against low and intermediate ODX scores were predicted by the multivariate model with AUC 0.77 (95% CI 0.56-0.98, p replacement of ODX with imaging alone.
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Yang Yu
2013-01-01
Full Text Available Based on a brief review on current harmonics generation mechanism for grid-connected inverter under distorted grid voltage, the harmonic disturbances and uncertain items are immersed into the original state-space differential equation of grid-connected inverter. A new algorithm of global current harmonic rejection based on nonlinear backstepping control with multivariable internal model principle is proposed for grid-connected inverter with exogenous disturbances and uncertainties. A type of multivariable internal model for a class of nonlinear harmonic disturbances is constructed. Based on application of backstepping control law of the nominal system, a multivariable adaptive state feedback controller combined with multivariable internal model and adaptive control law is designed to guarantee the closed-loop system globally uniformly bounded, which is proved by a constructed Lyapunov function. The presented algorithm extends rejection of nonlinear single-input systems to multivariable globally defined normal form, the correctness and effectiveness of which are verified by the simulation results.
Energy Technology Data Exchange (ETDEWEB)
Harris, Candace [Florida Agriculture & Mechanic Univ.; Profeta, Luisa [Alakai Defense Systems, Inc.; Akpovo, Codjo [Florida Agriculture & Mechanic Univ.; Stowe, Ashley [Y-12 National Security Complex, Oak Ridge, TN (United States); Johnson, Lewis [Florida Agriculture & Mechanic Univ.
2017-10-09
The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.
Multivariate statistical process control of batch processes based on three-way models
Louwerse, D. J.; Smilde, A. K.
2000-01-01
The theory of batch MSPC control charts is extended and improved control charts an developed. Unfold-PCA, PARAFAC and Tucker3 models are discussed and used as a basis for these charts. The results of the different models are compared and the performance of the control charts based on these models is
Collins, G. S.; Reitsma, J. B.; Altman, D. G.; Moons, K. G. M.
2015-01-01
Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming
Collins, Gary S.; Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G. M.
2015-01-01
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present ( diagnostic models) or that a specific event will occur in the future ( prognostic models), to inform their decision making. However, the overwhelming
Liu, Siwei; Molenaar, Peter C M
2014-12-01
This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.
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
Performance of multivariable traffic model that allows estimating Throughput mean values
Directory of Open Access Journals (Sweden)
Cesar Hernández
2013-01-01
Full Text Available El presente trabajo de investigación tiene por objetivo desarrollar un modelo multivariable de tráfico para una red de datos Wi-Fi que permita estimar el valor medio de throughput; para lograr lo anterior se procedió a capturar los datos correspondientes con el software WireShark de una red inalámbrica Ad Hoc compuesta por ocho host, diseñada e implementada para tal fin. A continuación se estimaron los modelos multivariados más convenientes de acuerdo a las características del tráfico capturado y posteriormente se evaluaron los resultados obtenidos a partir del software STATA, determinando las variables explicativas más significativas dentro del modelo y su nivel desempeño. Los resultados arrojados por este proyecto de investigación demuestran la autosimilaridad presente en el tráfico capturado de la red Wi-Fi, además, se muestran en diferentes tablas los coeficientes de los modelos y sus respectivos niveles de significancia. Finalmente se desarrolló un modelo multivariado de cuatro variables explicativas a partir de la metodología de mínimos cuadrados ordinarios con un error porcentual del 22,16. Como conclusión, el modelo multivariado de tráfico desarrollado permite realizar un análisis de los valores medios del throughput con suficientes niveles de confiabilidad, sin embargo, no realiza una buena predicción de los valores de tráfico para datos que estén fuera del conjunto seleccionado para su estimación.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Vasconcelos, A G; Almeida, R M; Nobre, F F
2001-08-01
This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.
Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M
2015-10-01
To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Wenlei Bai
2017-12-01
Full Text Available The deterministic methods generally used to solve DC optimal power flow (OPF do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.
DEFF Research Database (Denmark)
Baadsgaard, Mikkel; Nielsen, Jan Nygaard; Madsen, Henrik
2000-01-01
An econometric analysis of continuous-timemodels of the term structure of interest rates is presented. A panel of coupon bond prices with different maturities is used to estimate the embedded parameters of a continuous-discrete state space model of unobserved state variables: the spot interest rate...... noise term should account for model errors. A nonlinear filtering method is used to compute estimates of the state variables, and the model parameters are estimated by a quasimaximum likelihood method provided that some assumptions are imposed on the model residuals. Both Monte Carlo simulation results...
On the explaining-away phenomenon in multivariate latent variable models.
van Rijn, Peter; Rijmen, Frank
2015-02-01
Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.
International Nuclear Information System (INIS)
Batlle, C.; Barquin, J.
2004-01-01
This paper presents a fuel prices scenario generator in the frame of a simulation tool developed to support risk analysis in a competitive electricity environment. The tool feeds different erogenous risk factors to a wholesale electricity market model to perform a statistical analysis of the results. As the different fuel series that are studied, such as the oil or gas ones, present stochastic volatility and strong correlation among them, a multivariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) model has been designed in order to allow the generation of future fuel prices paths. The model makes use of a decomposition method to simplify the consideration of the multidimensional conditional covariance. An example of its application with real data is also presented. (author)
Anacleto, Osvaldo; Queen, Catriona; Albers, Casper J.
Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for
DEFF Research Database (Denmark)
Petersen, Nanna; Stocks, S.; Gernaey, Krist
2008-01-01
fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield...... in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining theological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (mu(app)), yield stress (tau......(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as mu...
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 bet...
Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio
2017-04-01
Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.
Binder, Harald; Sauerbrei, Willi; Royston, Patrick
2013-06-15
In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2) = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.
Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models
International Nuclear Information System (INIS)
Lamboni, Matieyendou; Monod, Herve; Makowski, David
2011-01-01
Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 ) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.
Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models
Energy Technology Data Exchange (ETDEWEB)
Lamboni, Matieyendou [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Monod, Herve, E-mail: herve.monod@jouy.inra.f [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Makowski, David [INRA, UMR Agronomie INRA/AgroParisTech (UMR 211), BP 01, F78850 Thiverval-Grignon (France)
2011-04-15
Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.
Multivariate power-law models for streamflow prediction in the Mekong Basin
Directory of Open Access Journals (Sweden)
Guillaume Lacombe
2014-11-01
New hydrological insights for the region: A combination of 3–6 explanatory variables – chosen among annual rainfall, drainage area, perimeter, elevation, slope, drainage density and latitude – is sufficient to predict a range of flow metrics with a prediction R-squared ranging from 84 to 95%. The inclusion of forest or paddy percentage coverage as an additional explanatory variable led to slight improvements in the predictive power of some of the low-flow models (lowest prediction R-squared = 89%. A physical interpretation of the model structure was possible for most of the resulting relationships. Compared to regional regression models developed in other parts of the world, this new set of equations performs reasonably well.
Samavati, Vahid; D-jomeh, Zahra Emam
2013-11-06
Optimization for retention and partition coefficient of ethyl acetate in emulsion model systems was investigated using response surface methodology in this paper. The effects of emulsion model ingredients, tragacanth gum (TG) (0.5-1 wt%), whey protein isolate (WPI) (2-4 wt%) and oleic acid (5-10%, v/v) on retention and partition coefficient of ethyl acetate were studied using a five-level three-factor central composite rotatable design (CCRD). Results showed that the regression models generated adequately explained the data variation and significantly represented the actual relationships between the independent and response parameters. The results showed that the highest retention (97.20±0.51%) and lowest partition coefficient (4.51±0.13%) of ethyl acetate were reached at the TG concentration 1 wt%, WPI concentration 4 wt% and oleic acid volume fraction 10% (v/v). Copyright © 2013 Elsevier Ltd. All rights reserved.
Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context
DEFF Research Database (Denmark)
Bouwman, Aniek C; Valente, Bruno D; Janss, Luc L G
2014-01-01
Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models...... are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select...
Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models
Directory of Open Access Journals (Sweden)
Gerdesmeier Dieter
2017-12-01
Full Text Available Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR-models are among the most popular tools.
Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu
2012-10-01
Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.
Directory of Open Access Journals (Sweden)
Zeren Fatma
2010-01-01
Full Text Available This paper tries to examine the long run relationships between the aggregate consumer prices and some cost-based components for the Turkish economy. Based on a simple economic model of the macro-scaled price formation, multivariate cointegration techniques have been applied to test whether the real data support the a priori model construction. The results reveal that all of the factors, related to the price determination, have a positive impact on the consumer prices as expected. We find that the most significant component contributing to the price setting is the nominal exchange rate depreciation. We also cannot reject the linear homogeneity of the sum of all the price data as to the domestic inflation. The paper concludes that the Turkish consumer prices have in fact a strong cost-push component that contributes to the aggregate pricing.
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...
Weijs, Teus J; Seesing, Maarten F J; van Rossum, Peter S N; Koëter, Marijn; van der Sluis, Pieter C; Luyer, Misha D P; Ruurda, Jelle P; Nieuwenhuijzen, Grard A P; van Hillegersberg, Richard
BACKGROUND: Pneumonia is an important complication following esophagectomy; however, a wide range of pneumonia incidence is reported. The lack of one generally accepted definition prevents valid inter-study comparisons. We aimed to simplify and validate an existing scoring model to define pneumonia
Directory of Open Access Journals (Sweden)
Tao Gao
2014-01-01
Full Text Available Extreme precipitation is likely to be one of the most severe meteorological disasters in China; however, studies on the physical factors affecting precipitation extremes and corresponding prediction models are not accurately available. From a new point of view, the sensible heat flux (SHF and latent heat flux (LHF, which have significant impacts on summer extreme rainfall in Yangtze River basin (YRB, have been quantified and then selections of the impact factors are conducted. Firstly, a regional extreme precipitation index was applied to determine Regions of Significant Correlation (RSC by analyzing spatial distribution of correlation coefficients between this index and SHF, LHF, and sea surface temperature (SST on global ocean scale; then the time series of SHF, LHF, and SST in RSCs during 1967–2010 were selected. Furthermore, other factors that significantly affect variations in precipitation extremes over YRB were also selected. The methods of multiple stepwise regression and leave-one-out cross-validation (LOOCV were utilized to analyze and test influencing factors and statistical prediction model. The correlation coefficient between observed regional extreme index and model simulation result is 0.85, with significant level at 99%. This suggested that the forecast skill was acceptable although many aspects of the prediction model should be improved.
A multivariable model for predicting the frictional behaviour and hydration of the human skin
Veijgen, N.K.; van der Heide, Emile; Masen, Marc Arthur
2013-01-01
Background The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is
A multivariable model for predicting the frictional behaviour and hydration of the human skin
Veijgen, N.K.; Heide, E. van der; Masen, M.A.
2013-01-01
Background: The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is
Inference of reactive transport model parameters using a Bayesian multivariate approach
Carniato, L.; Schoups, G.H.W.; Van de Giesen, N.C.
2014-01-01
Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least
A multivariate dynamic systems model for psychotherapy with more than one client
DEFF Research Database (Denmark)
Butner, Jonathan E.; Deits-Lebehn, Carlene; Crenshaw, Alexander O.
2017-01-01
cross-lagged panel models can be extended to psychotherapeutic encounters involving 3 people and used to test processes that exist between dyadic subsets of the larger group as well as the group as one cohesive unit. Three hundred seventy-nine talk turns of fundamental frequency from a couple therapy...... of the regression coefficients from the 3 dyadic cross-lagged panel models suggests that interdependence exists at the level of the whole group (i.e., therapist–husband–wife) rather than between pairs of individuals within the group (e.g., husband–wife). Further, an interaction involving husband’s RSA suggested...... that interdependence involving the husband ceased when the husband displayed greater regulatory effort. This combination of statistical methods allows for clearly distinguishing between dyadic therapeutic processes and group-level therapeutic processes....
International Nuclear Information System (INIS)
Carvajal Escobar Yesid; Munoz, Flor Matilde
2007-01-01
The project this centred in the revision of the state of the art of the ocean-atmospheric phenomena that you affect the Colombian hydrology especially The Phenomenon Enos that causes a socioeconomic impact of first order in our country, it has not been sufficiently studied; therefore it is important to approach the thematic one, including the variable macroclimates associated to the Enos in the analyses of water planning. The analyses include revision of statistical techniques of analysis of consistency of hydrological data with the objective of conforming a database of monthly flow of the river reliable and homogeneous Cauca. Statistical methods are used (Analysis of data multivariante) specifically The analysis of principal components to involve them in the development of models of prediction of flows monthly means in the river Cauca involving the Lineal focus as they are the model autoregressive AR, ARX and Armax and the focus non lineal Net Artificial Network.
Advanced Multivariate Inversion Techniques for High Resolution 3D Geophysical Modeling
2011-09-01
2005). We implemented a method to increase the usefulness of gravity data by filtering the Bouguer anomaly map. Though commonly applied 40 km 30 35...remove the long-wavelength components from the Bouguer gravity map we follow Tessema and Antoine (2004), who use an upward continuation method and...inversion of group velocities and gravity. (a) Top: Group velocities from a representative cell in the model. Bottom: Filtered Bouguer anomalies. (b
Petersen, Nanna; Stocks, Stuart; Gernaey, Krist V
2008-05-01
The main purpose of this article is to demonstrate that principal component analysis (PCA) and partial least squares regression (PLSR) can be used to extract information from particle size distribution data and predict rheological properties. Samples from commercially relevant Aspergillus oryzae fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield stress (tau(y)), consistency index (K), and flow behavior index (n)) resulted in a large standard deviation of the parameter estimates. The flow behavior index was not found to be correlated with any of the other measured variables and previous studies have suggested a constant value of the flow behavior index in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining rheological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (micro(app)), yield stress (tau(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as micro(app). Validation on an independent test set yielded a root mean square error of 1.21 Pa for tau(y), 0.209 Pa s(n) for K, and 0.0288 Pa s for micro(app), corresponding to R(2) = 0.95, R(2) = 0.94, and R(2) = 0.95 respectively. Copyright 2007 Wiley Periodicals, Inc.
Cheng, Wen; Gill, Gurdiljot Singh; Sakrani, Taha; Dasu, Mohan; Zhou, Jiao
2017-11-01
Motorcycle crashes constitute a very high proportion of the overall motor vehicle fatalities in the United States, and many studies have examined the influential factors under various conditions. However, research on the impact of weather conditions on the motorcycle crash severity is not well documented. In this study, we examined the impact of weather conditions on motorcycle crash injuries at four different severity levels using San Francisco motorcycle crash injury data. Five models were developed using Full Bayesian formulation accounting for different correlations commonly seen in crash data and then compared for fitness and performance. Results indicate that the models with serial and severity variations of parameters had superior fit, and the capability of accurate crash prediction. The inferences from the parameter estimates from the five models were: an increase in the air temperature reduced the possibility of a fatal crash but had a reverse impact on crashes of other severity levels; humidity in air was not observed to have a predictable or strong impact on crashes; the occurrence of rainfall decreased the possibility of crashes for all severity levels. Transportation agencies might benefit from the research results to improve road safety by providing motorcyclists with information regarding the risk of certain crash severity levels for special weather conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Chengfeng Zhou
2015-01-01
Full Text Available 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.
Romeo, L.; Rose, K.; Bauer, J. R.; Dick, D.; Nelson, J.; Bunn, A.; Buenau, K. E.; Coleman, A. M.
2016-02-01
Increased offshore oil exploration and production emphasizes the need for environmental, social, and economic impact models that require big data from disparate sources to conduct thorough multi-scale analyses. The National Energy Technology Laboratory's Cumulative Spatial Impact Layers (CSILs) and Spatially Weighted Impact Model (SWIM) are user-driven flexible suites of GIS-based tools that can efficiently process, integrate, visualize, and analyze a wide variety of big datasets that are acquired to better to understand potential impacts for oil spill prevention and response readiness needs. These tools provide solutions to address a range of stakeholder questions and aid in prioritization decisions needed when responding to oil spills. This is particularly true when highlighting ecologically sensitive areas and spatially analyzing which species may be at risk. Model outputs provide unique geospatial visualizations of potential impacts and informational reports based on user preferences. The spatio-temporal capabilities of these tools can be leveraged to a number of anthropogenic and natural disasters enabling decision-makers to be better informed to potential impacts and response needs.
Update on Multi-Variable Parametric Cost Models for Ground and Space Telescopes
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.
Towards a Multi-Variable Parametric Cost Model for Ground and Space Telescopes
Stahl, H. Philip; Henrichs, Todd
2016-01-01
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 hypothesizes a single model, based on published models and engineering intuition, for both ground and space telescopes: OTA Cost approximately (X) D(exp (1.75 +/- 0.05)) lambda(exp(-0.5 +/- 0.25) T(exp -0.25) e (exp (-0.04)Y). Specific findings include: space telescopes cost 50X to 100X more ground telescopes; diameter is the most important CER; cost is reduced by approximately 50% every 20 years (presumably because of technology advance and process improvements); and, for space telescopes, cost associated with wavelength performance is balanced by cost associated with operating temperature. Finally, duplication only reduces cost for the manufacture of identical systems (i.e. multiple aperture sparse arrays or interferometers). And, while duplication does reduce the cost of manufacturing the mirrors of segmented primary mirror, this cost savings does not appear to manifest itself in the final primary mirror assembly (presumably because the structure for a segmented mirror is more complicated than for a monolithic mirror).
Energy Technology Data Exchange (ETDEWEB)
Bortolet, P.
1998-12-11
During these last two decades, the growing awareness of the contribution of the automobile to the degradation of the environment has forces different figures from the transportation world to put automobiles under more and more severe controls. Fuzzy logic is a technique which allows for the taking into account of experts knowledge; the most recent research work has moreover shown interest in associating fuzzy logic with algorithmic control techniques (adaptive control, robust control...). Our research work can be broken down into three distinct parts: a theoretical approach concerning the methods of fuzzy modeling permitting one to achieve models of the type Takagi-Sugeno and to use them in the synthesis of controls; the work of physical modeling of a four-stroke direct injection gas motor in collaboration with the development teams from Siemens Automotive SA; the simulated application of fuzzy modeling techniques and of fuzzy control developed on a theoretical level to a four-stroke direct injection gas motor. (author) 105 refs.
Directory of Open Access Journals (Sweden)
Soyoung Park
2017-07-01
Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.
Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M
2015-01-13
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 The Authors.
Energy Technology Data Exchange (ETDEWEB)
Bonne, François; Bonnay, Patrick [INAC, SBT, UMR-E 9004 CEA/UJF-Grenoble, 17 rue des Martyrs, 38054 Grenoble (France); Alamir, Mazen [Gipsa-Lab, Control Systems Department, CNRS-University of Grenoble, 11, rue des Mathématiques, BP 46, 38402 Saint Martin d' Hères (France); Bradu, Benjamin [CERN, CH-1211 Genève 23 (Switzerland)
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.
Development of a Mathematical Model for Multivariate Process by Balanced Six Sigma
Directory of Open Access Journals (Sweden)
Díaz-Castellanos Elizabeth Eugenia
2015-07-01
Full Text Available The Six Sigma methodology is widely used in business to improve quality, increase productivity and lower costs, impacting on business improvement. However, today the challenge is to use those tools for improvements that will have a direct impact on the differentiation of value, which requires the alignment of Six Sigma with the competitive strategies of the organization.Hence the importance of a strategic management system to measure, analyze, improve and control corporate performance, while setting out responsibilities of leadership and commitment. The specific purpose of this research is to provide a mathematical model through the alignment of strategic objectives (Balanced Scorecard and tools for productivity improvement (Six Sigma for processes with multiple answers, which is sufficiently robust so that it can serve as basis for application in manufacturing and thus effectively link strategy performance and customer satisfaction. Specifically we worked with a case study: Córdoba, Ver. The model proposes that is the strategy, performance and customer satisfaction are aligned, the organization will benefit from the intense relationship between process performance and strategic initiatives. These changes can be measured by productivity and process metrics such as cycle time, production rates, production efficiency and percentage of reprocessing, among others.
A multivariate quadrature based moment method for LES based modeling of supersonic combustion
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
DEFF Research Database (Denmark)
Christiansen, Dorte M.; Hansen, Maj
2015-01-01
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...... 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. Conclusion: The findings suggest...
Accounting for sex differences in PTSD: A multi-variable mediation model.
Christiansen, Dorte M; Hansen, Maj
2015-01-01
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. 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. 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. 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. 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 more prevalent in females, such as depression
Accounting for sex differences in PTSD: A multi-variable mediation model
Directory of Open Access Journals (Sweden)
Dorte M. Christiansen
2015-01-01
Full Text Available 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
DEFF Research Database (Denmark)
Skjærbæk, P. S.; Nielsen, Søren R. K.; Kirkegaard, Poul Henning
1997-01-01
in the comparison. The data investigated are sampled from a laboratory model of a plane 6-storey, 2-bay RC-frame. The laboratory model is excited at the top storey where two different types of excitation where considered. In the first case the structure was excited in the first mode and in the second case......The scope of the paper is to apply multi-variate time-domain models for identification of eginfrequencies and mode shapes of a time- invariant model test Reinforced Concrete (RC) frame from measured decays. The frequencies and mode shapes of interest are the two lowest ones since they are normally...
DEFF Research Database (Denmark)
Skjærbæk, P. S.; Nielsen, Søren R. K.; Kirkegaard, Poul Henning
in the comparison. The data investigated are sampled from a laboratory model of a plane 6-storey, 2-bay RC-frame. The laboratory model is excited at the top storey where two different types of excitation where considered. In the first case the structure was excited in the first mode and in the second case......The scope of the paper is to apply multi-variate time-domain models for identification of eginfrequencies and mode shapes of a time- invariant model test Reinforced Concrete (RC) frame from measured decays. The frequencies and mode shapes of interest are the two lowest ones since they are normally...
Directory of Open Access Journals (Sweden)
Peng Nai
2016-03-01
Full Text Available A great number of immigration populations resident permanently in Yunnan Border Area of China. To some extent, these people belong to refugees or immigrants in accordance with International Rules, which significantly features the social diversity of this area. However, this kind of social diversity always impairs the social order. Therefore, there will be a positive influence to the local society governance by a research on local immigration integration. This essay hereby attempts to acquire the data of the living situation of these border area immigration and refugees. The analysis of the social integration of refugees and immigration in Yunnan border area in China will be deployed through the modeling of multivariable linear regression based on these data in order to propose some more achievable resolutions.
Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong
2017-12-01
Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.
Directory of Open Access Journals (Sweden)
Manuel Sousa Gabrie
2014-09-01
Full Text Available This study analyzed market risk of an international investment portfolio by means of a new methodological proposal based on Value-at- Risk, using the covariance matrix of multivariate GARCH-type models and the extreme value theory to realize if an international diversification strategy minimizes market risk, and to determine if the VaR methodology adequately captures market risk, by applying Backtesting tests. To this end, we considered twelve international stock indexes, accounting for about 62% of the world stock market capitalization, and chose the period from the Dot-Com crisis to the current global financial crisis. Results show that the proposed methodology is a good alternative to accommodate the high market turbulence and can be considered as an adequate portfolio risk management instrument.
Hasyim, M.; Prastyo, D. D.
2018-03-01
Survival analysis performs relationship between independent variables and survival time as dependent variable. In fact, not all survival data can be recorded completely by any reasons. In such situation, the data is called censored data. Moreover, several model for survival analysis requires assumptions. One of the approaches in survival analysis is nonparametric that gives more relax assumption. In this research, the nonparametric approach that is employed is Multivariate Regression Adaptive Spline (MARS). This study is aimed to measure the performance of private university’s lecturer. The survival time in this study is duration needed by lecturer to obtain their professional certificate. The results show that research activities is a significant factor along with developing courses material, good publication in international or national journal, and activities in research collaboration.
Tien, Hai Minh; Le, Kien Anh; Le, Phung Thi Kim
2017-09-01
Bio hydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, high energy efficiency and non-polluting nature resource. In this work, the experiments have been carried out to indicate the possibility of generating bio hydrogen as well as identifying effective factors and the optimum conditions from cassava starch. Experimental design was used to investigate the effect of operating temperature (37-43 °C), pH (6-7), and inoculums ratio (6-10 %) to the yield hydrogen production, the COD reduction and the ratio of volume of hydrogen production to COD reduction. The statistical analysis of the experiment indicated that the significant effects for the fermentation yield were the main effect of temperature, pH and inoculums ratio. The interaction effects between them seem not significant. The central composite design showed that the polynomial regression models were in good agreement with the experimental results. This result will be applied to enhance the process of cassava starch processing wastewater treatment.
Directory of Open Access Journals (Sweden)
Kehinde Anthony Mogaji
2016-07-01
Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.
Directory of Open Access Journals (Sweden)
S. Sparnocchia
Full Text Available Multivariate vertical Empirical Orthogonal Functions (EOF are calculated for the entire Mediterranean Sea both from observations and model simulations, in order to find the optimal number of vertical modes to represent the upper thermocline vertical structure. For the first time, we show that the large-scale Mediterranean thermohaline vertical structure can be represented by a limited number of vertical multivariate EOFs, and that the "optimal set" can be selected on the basis of general principles. In particular, the EOFs are calculated for the combined temperature and salinity statistics, dividing the Mediterranean Sea into 9 regions and grouping the data seasonally. The criterion used to establish whether a reduced set of EOFs is optimal is based on the analysis of the root mean square residual error between the original data and the profiles reconstructed by the reduced set of EOFs. It was found that the number of EOFs needed to capture the variability contained in the original data changes with geographical region and seasons. In particular, winter data require a smaller number of modes (4–8, depending on the region than the other seasons (8–9 in summer. Moreover, western Mediterranean regions require more modes than the eastern Mediterranean ones, but this result may depend on the data scarcity in the latter regions. The EOFs computed from the in situ data set are compared to those calculated using data obtained from a model simulation. The main results of this exercise are that the two groups of modes are not strictly comparable but their ability to reproduce observations is the same. Thus, they may be thought of as equivalent sets of basis functions, upon which to project the thermohaline variability of the basin.
Key words. Oceanography: general (water masses – Oceanography: physical (hydrography; instruments and techniques
Directory of Open Access Journals (Sweden)
S. Sparnocchia
2003-01-01
Full Text Available Multivariate vertical Empirical Orthogonal Functions (EOF are calculated for the entire Mediterranean Sea both from observations and model simulations, in order to find the optimal number of vertical modes to represent the upper thermocline vertical structure. For the first time, we show that the large-scale Mediterranean thermohaline vertical structure can be represented by a limited number of vertical multivariate EOFs, and that the "optimal set" can be selected on the basis of general principles. In particular, the EOFs are calculated for the combined temperature and salinity statistics, dividing the Mediterranean Sea into 9 regions and grouping the data seasonally. The criterion used to establish whether a reduced set of EOFs is optimal is based on the analysis of the root mean square residual error between the original data and the profiles reconstructed by the reduced set of EOFs. It was found that the number of EOFs needed to capture the variability contained in the original data changes with geographical region and seasons. In particular, winter data require a smaller number of modes (4–8, depending on the region than the other seasons (8–9 in summer. Moreover, western Mediterranean regions require more modes than the eastern Mediterranean ones, but this result may depend on the data scarcity in the latter regions. The EOFs computed from the in situ data set are compared to those calculated using data obtained from a model simulation. The main results of this exercise are that the two groups of modes are not strictly comparable but their ability to reproduce observations is the same. Thus, they may be thought of as equivalent sets of basis functions, upon which to project the thermohaline variability of the basin. Key words. Oceanography: general (water masses – Oceanography: physical (hydrography; instruments and techniques
Applied multivariate statistical analysis
Härdle, Wolfgang Karl
2015-01-01
Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...
Sasakura, D; Nakayama, K; Sakamoto, T; Chikuma, T
2015-05-01
The use of transmission near infrared spectroscopy (TNIRS) is of particular interest in the pharmaceutical industry. This is because TNIRS does not require sample preparation and can analyze several tens of tablet samples in an hour. It has the capability to measure all relevant information from a tablet, while still on the production line. However, TNIRS has a narrow spectrum range and overtone vibrations often overlap. To perform content uniformity testing in tablets by TNIRS, various properties in the tableting process need to be analyzed by a multivariate prediction model, such as a Partial Least Square Regression modeling. One issue is that typical approaches require several hundred reference samples to act as the basis of the method rather than a strategically designed method. This means that many batches are needed to prepare the reference samples; this requires time and is not cost effective. Our group investigated the concentration dependence of the calibration model with a strategic design. Consequently, we developed a more effective approach to the TNIRS calibration model than the existing methodology.
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.
International Nuclear Information System (INIS)
Verloh, N.; Haimerl, M.; Stroszczynski, C.; Fellner, C.; Wiggermann, P.; Zeman, F.; Teufel, A.; Lang, S.
2015-01-01
The purpose of this study was to identify clinical factors influencing Gd-EOB-DTPA liver uptake in patients with healthy liver parenchyma. A total of 124 patients underwent contrast-enhanced MRI with a hepatocyte-specific contrast agent at 3T. T1-weighted volume interpolated breath-hold examination (VIBE) sequences with fat suppression were acquired before and 20 minutes after contrast injection. The relative enhancement (RE) between plain and contrast-enhanced signal intensity was calculated. Simple and multiple linear regression analyses were performed to evaluate clinical factors influencing the relative enhancement. Patients were subdivided into three groups according to their relative liver enhancement (HRE, RE ≥ 100 %; MRE, 100 % > RE > 50 %; NRE, RE ≤ 50 %) and were analyzed according to the relevant risk factors. Simple regression analyses revealed patient age, transaminases (AST, ALT, GGT), liver, spleen and delta-liver volume (the difference between the volumetrically measured liver volume and the estimated liver volume based on body weight) as significant factors influencing relative enhancement. In the multiple analysis the transaminase AST, spleen and delta liver volume remained significant factors influencing relative enhancement. Delta liver volume showed a significant difference between all analyzed groups. Liver enhancement in the hepatobiliary phase depends on a variety of factors. Body weight-adapted administration of Gd-EOB-DTPA may lead to inadequate liver enhancement after 20 minutes especially when the actual liver volume differs from the expected volume.
Directory of Open Access Journals (Sweden)
Alexander Vladimirovich Kirillov
2015-12-01
Full Text Available The international integration of the Russian economy is connected to the need of the realization of the competitive advantages of the geopolitical position of Russia, the industrial potential of regions, the logistic infrastructure of transport corridors. This article discusses the design model of the supply chain (distribution network based on the multivariate analysis and the methodology of the substantiation of its configuration based on the cost factors and the level of the logistics infrastructure development. For solving the problem of placing one or more logistics centers in the service area, a two-stage algorithm is used. At the first stage, the decisions on the reasonability of the choice of one or another version of the development are made with А. В. Кириллов, В. Е. Целин 345 ЭКОНОМИКА РЕГИОНА №4 (2015 the use of the “Make or Buy” standard model. The criterion of decision making is the guaranteed overcoming of the threshold of “indifference” taking into account the statistical characteristics of costs for options of “buy” and “make” depending on the volume of consumption of goods or services. At the second stage, the Ardalan’s heuristic method is used for the evaluation of the choice of placing one or more logistics centers in the service area. The model parameters are based on the assessment of the development prospects of the region and its investment potential (existence and composition of employment, production, natural resources, financial and consumer opportunities, institutional, innovation, infrastructure capacity. Furthermore, such criteria as a regional financial appeal, professionally trained specialists, the competitive advantages of the promoted company and others are analyzed. An additional criterion is the development of the priority matrix, which considers such factors as difficulties of customs registration and certification, a level of regional transport
Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila
2015-11-01
Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage
International Nuclear Information System (INIS)
Bwo-Nung Huang; National Chia-Yi University; Hwang, M.J.; Hsiao-Ping Peng
2005-01-01
This paper applies the multivariate threshold model to investigate the impacts of an oil price change and its volatility on economic activities (changes in industrial production and real stock returns). The statistical test on the existence of a threshold effect indicates that a threshold value does exist. Using monthly data of the US, Canada, and Japan during the period from 1970 to 2002, we conclude: (i) the optimal threshold level seems to vary according to how an economy depends on imported oil and the attitude towards adopting energy-saving technology; (ii) an oil price change or its volatility has a limited impact on the economies if the change is below the threshold levels; (iii) if the change is above threshold levels, it appears that the change in oil price better explains macroeconomic variables than the volatility of the oil price; and (iv) if the change is above threshold levels, a change in oil price or its volatility explains the model better than the real interest rate. (author)
Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung
2016-01-01
A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.
Cottam, Austin; Billing, Josiah; Cottam, Daniel; Billing, Peter; Cottam, Samuel; Zaveri, Hinali; Surve, Amit
2017-08-01
Despite being the most common surgery in the United States, little is known about predicting weight loss success and failure with sleeve gastrectomy (SG). Papers that have been published are inconclusive. We decided to use multivariate analysis from 2 practices to design a model to predict weight loss outcomes using data widely available to any surgical practice at 3 months to determine weight loss outcomes at 1 year. Two private practices in the United States. A retrospective review of 613 patients from 2 bariatric institutions were included in this study. Co-morbidities and other preoperative characteristics were gathered, and %EWL was calculated for 1, 3, and 12 months. Excess weight loss (%EWL)failure. Multiple variate analysis was used to find factors that affect %EWL at 12 months. Preoperative sleep apnea, preoperative diabetes, %EWL at 1 month, and %EWL at 3 months all affect %EWL at 1 year. The positive predictive value and negative predictive value of our model was 72% and 91%, respectively. Sensitivity and specificity were 71% and 91%, respectively. One-year results of the SG can be predicted by diabetes, sleep apnea, and weight loss velocity at 3 months postoperatively. This can help surgeons direct surgical or medical interventions for patients at 3 months rather than at 1 year or beyond. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Trine Krogh-Madsen
2017-12-01
Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.
Multivariate pattern dependence.
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Stefano Anzellotti
2017-11-01
Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.
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Yolanda K Mueller
2016-04-01
Full Text Available Access to laboratory diagnosis can be a challenge for individuals suspected of Buruli Ulcer (BU. Our objective was to develop a clinical score to assist clinicians working in resource-limited settings for BU diagnosis.Between 2011 and 2013, individuals presenting at Akonolinga District Hospital, Cameroon, were enrolled consecutively. Clinical data were collected prospectively. Based on a latent class model using laboratory test results (ZN, PCR, culture, patients were categorized into high, or low BU likelihood. Variables associated with a high BU likelihood in a multivariate logistic model were included in the Buruli score. Score cut-offs were chosen based on calculated predictive values. Of 325 patients with an ulcerative lesion, 51 (15.7% had a high BU likelihood. The variables identified for the Buruli score were: characteristic smell (+3 points, yellow color (+2, female gender (+2, undermining (+1, green color (+1, lesion hyposensitivity (+1, pain at rest (-1, size >5cm (-1, locoregional adenopathy (-2, age above 20 up to 40 years (-3, or above 40 (-5. This score had AUC of 0.86 (95%CI 0.82-0.89, indicating good discrimination between infected and non-infected individuals. The cut-off to reasonably exclude BU was set at scores <0 (NPV 96.5%; 95%CI 93.0-98.6. The treatment threshold was set at a cut-off ≥4 (PPV 69.0%; 95%CI 49.2-84.7. Patients with intermediate BU probability needed to be tested by PCR.We developed a decisional algorithm based on a clinical score assessing BU probability. The Buruli score still requires further validation before it can be recommended for wide use.
Finto Antony; Laurence R. Schimleck; Alex Clark; Richard F. Daniels
2012-01-01
Specific gravity (SG) and moisture content (MC) both have a strong influence on the quantity and quality of wood fiber. We proposed a multivariate mixed model system to model the two properties simultaneously. Disk SG and MC at different height levels were measured from 3 trees in 135 stands across the natural range of loblolly pine and the stand level values were used...
Verdam, Mathilde G. E.; Oort, Frans J.
2014-01-01
Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data. A method for the investigation of measurement bias with Kronecker product restricted models. Application of these methods to health-related quality of life data from bone
Transient multivariable sensor evaluation
Energy Technology Data Exchange (ETDEWEB)
Vilim, Richard B.; Heifetz, Alexander
2017-02-21
A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.
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Sepedeh Gholizadeh
2016-07-01
Full Text Available Background:Obesity and hypertension are the most important non-communicable diseases thatin many studies, the prevalence and their risk factors have been performedin each geographic region univariately.Study of factors affecting both obesity and hypertension may have an important role which to be adrressed in this study. Materials &Methods:This cross-sectional study was conducted on 1000 men aged 20-70 living in Bushehr province. Blood pressure was measured three times and the average of them was considered as one of the response variables. Hypertension was defined as systolic blood pressure ≥140 (and-or diastolic blood pressure ≥90 and obesity was defined as body mass index ≥25. Data was analyzed by using multilevel, multivariate logistic regression model by MlwiNsoftware. Results:Intra class correlations in cluster level obtained 33% for high blood pressure and 37% for obesity, so two level model was fitted to data. The prevalence of obesity and hypertension obtained 43.6% (0.95%CI; 40.6-46.5, 29.4% (0.95%CI; 26.6-32.1 respectively. Age, gender, smoking, hyperlipidemia, diabetes, fruit and vegetable consumption and physical activity were the factors affecting blood pressure (p≤0.05. Age, gender, hyperlipidemia, diabetes, fruit and vegetable consumption, physical activity and place of residence are effective on obesity (p≤0.05. Conclusion: The multilevel models with considering levels distribution provide more precise estimates. As regards obesity and hypertension are the major risk factors for cardiovascular disease, by knowing the high-risk groups we can d careful planning to prevention of non-communicable diseases and promotion of society health.
Yip, Wai Lam; Gausemel, Ingvil; Sande, Sverre Arne; Dyrstad, Knut
2012-11-01
Accurate determination of residual moisture content of a freeze-dried (FD) pharmaceutical product is critical for prediction of its quality. Near-infrared (NIR) spectroscopy is a fast and non-invasive method routinely used for quantification of moisture. However, several physicochemical properties of the FD product may interfere with absorption bands related to the water content. A commonly used stabilizer and bulking agent in FD known for variation in physicochemical properties, is mannitol. To minimize this physicochemical interference, different approaches for multivariate correlation between NIR spectra of a FD product containing mannitol and the corresponding moisture content measured by Karl Fischer (KF) titration have been investigated. A novel method, MIPCR (Main and Interactions of Individual Principal Components Regression), was found to have significantly increased predictive ability of moisture content compared to a traditional PLS approach. The philosophy behind the MIPCR is that the interference from a variety of particle and morphology attributes has interactive effects on the water related absorption bands. The transformation of original wavelength variables to orthogonal scores gives a new set of variables (scores) without covariance structure, and the possibility of inclusion of interaction terms in the further modeling. The residual moisture content of the FD product investigated is in the range from 0.7% to 2.6%. The mean errors of cross validated prediction of models developed in the investigated NIR regions were reduced from a range of 24.1-27.6% for traditional PLS method to 15.7-20.5% for the MIPCR method. Improved model quality by application of MIPCR, without the need for inclusion of a large number of calibration samples, might increase the use of NIR in early phase product development, where availability of calibration samples is often limited. Copyright © 2012 Elsevier B.V. All rights reserved.
Ye, Xiao-hua; Xu, Ya; Zhou, Shu-dong; Gao, Yan-hui; Li, Yan-fen
2011-09-01
To analyze the awareness on health among high school students and its influencing factors in Guangdong. Multi-stage sampling and questionnaire "2009 health awareness survey of the Chinese citizens" developed by our Department of Health, were used. Data were analyzed by multivariate multilevel model under MLwinN 2.19 software. The mean scores on knowledge and ideas, behaviors and related skills among 1606 high school students of Guangdong province, were 69.08 ± 14.81, 60.05 ± 16.85 and 74.99 ± 21.17 respectively. Three items on health showed that they all related to each other and relations between grades (0.972, 0.715 and 0.855) were greater than the individuals (0.565, 0.426 and 0.438). Factors as students from outside the Pearl River Delta region or from the rural areas, being male, at general secondary schools, at grade one, with poor academic performance and more pocket money etc., had lower levels on those related information of health.
Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun
2017-01-01
Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection. PMID:28678864
Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale
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H. Kreibich
2016-05-01
Full Text Available 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.
Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale
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.
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.
Validating EHR clinical models using ontology patterns.
Martínez-Costa, Catalina; Schulz, Stefan
2017-12-01
Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit. This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models. SHACL, a W3C constraint language for the validation of RDF graphs, builds on the concept of "Shape", a description of data in terms of expected cardinalities, datatypes and other restrictions. SHACL, as opposed to OWL, subscribes to the Closed World Assumption (CWA) and is therefore more suitable for the validation of clinical models. We have demonstrated the feasibility of the approach by manually describing the correspondences between six CIMI clinical models represented in RDF and two SHACL ontology design patterns. Using a Java-based SHACL implementation, we found at least eleven modeling and binding errors within these CIMI models. This demonstrates the usefulness of ontology design patterns not only as a modeling tool but also as a tool for validation. Copyright © 2017 Elsevier Inc. All rights reserved.
Multivariate strategies in functional magnetic resonance imaging
DEFF Research Database (Denmark)
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....
Crispin, Alexander; Strahwald, Brigitte; Cheney, Catherine; Mansmann, Ulrich
2018-06-04
Quality control, benchmarking, and pay for performance (P4P) require valid indicators and statistical models allowing adjustment for differences in risk profiles of the patient populations of the respective institutions. Using hospital remuneration data for measuring quality and modelling patient risks has been criticized by clinicians. Here we explore the potential of prediction models for 30- and 90-day mortality after colorectal cancer surgery based on routine data. Full census of a major statutory health insurer. Surgical departments throughout the Federal Republic of Germany. 4283 and 4124 insurants with major surgery for treatment of colorectal cancer during 2013 and 2014, respectively. Age, sex, primary and secondary diagnoses as well as tumor locations as recorded in the hospital remuneration data according to §301 SGB V. 30- and 90-day mortality. Elixhauser comorbidities, Charlson conditions, and Charlson scores were generated from the ICD-10 diagnoses. Multivariable prediction models were developed using a penalized logistic regression approach (logistic ridge regression) in a derivation set (patients treated in 2013). Calibration and discrimination of the models were assessed in an internal validation sample (patients treated in 2014) using calibration curves, Brier scores, receiver operating characteristic curves (ROC curves) and the areas under the ROC curves (AUC). 30- and 90-day mortality rates in the learning-sample were 5.7 and 8.4%, respectively. The corresponding values in the validation sample were 5.9% and once more 8.4%. Models based on Elixhauser comorbidities exhibited the highest discriminatory power with AUC values of 0.804 (95% CI: 0.776 -0.832) and 0.805 (95% CI: 0.782-0.828) for 30- and 90-day mortality. The Brier scores for these models were 0.050 (95% CI: 0.044-0.056) and 0.067 (95% CI: 0.060-0.074) and similar to the models based on Charlson conditions. Regardless of the model, low predicted probabilities were well calibrated, while
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Deslandes Emmanuelle
2010-07-01
Full Text Available Abstract Background Joint modeling of longitudinal and survival data has been increasingly considered in clinical trials, notably in cancer and AIDS. In critically ill patients admitted to an intensive care unit (ICU, such models also appear to be of interest in the investigation of the effect of treatment on severity scores due to the likely association between the longitudinal score and the dropout process, either caused by deaths or live discharges from the ICU. However, in this competing risk setting, only cause-specific hazard sub-models for the multiple failure types data have been used. Methods We propose a joint model that consists of a linear mixed effects submodel for the longitudinal outcome, and a proportional subdistribution hazards submodel for the competing risks survival data, linked together by latent random effects. We use Markov chain Monte Carlo technique of Gibbs sampling to estimate the joint posterior distribution of the unknown parameters of the model. The proposed method is studied and compared to joint model with cause-specific hazards submodel in simulations and applied to a data set that consisted of repeated measurements of severity score and time of discharge and death for 1,401 ICU patients. Results Time by treatment interaction was observed on the evolution of the mean SOFA score when ignoring potentially informative dropouts due to ICU deaths and live discharges from the ICU. In contrast, this was no longer significant when modeling the cause-specific hazards of informative dropouts. Such a time by treatment interaction persisted together with an evidence of treatment effect on the hazard of death when modeling dropout processes through the use of the Fine and Gray model for sub-distribution hazards. Conclusions In the joint modeling of competing risks with longitudinal response, differences in the handling of competing risk outcomes appear to translate into the estimated difference in treatment effect on the
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Ericson Thorild
2009-11-01
Full Text Available Abstract Background Dental caries is a chronic disease with plaque bacteria, diet and saliva modifying disease activity. Here we have used the PLS method to evaluate a multiplicity of such biological variables (n = 88 for ability to predict caries in a cross-sectional (baseline caries and prospective (2-year caries development setting. Methods Multivariate PLS modelling was used to associate the many biological variables with caries recorded in thirty 14-year-old children by measuring the numbers of incipient and manifest caries lesions at all surfaces. Results A wide but shallow gliding scale of one fifth caries promoting or protecting, and four fifths non-influential, variables occurred. The influential markers behaved in the order of plaque bacteria > diet > saliva, with previously known plaque bacteria/diet markers and a set of new protective diet markers. A differential variable patterning appeared for new versus progressing lesions. The influential biological multimarkers (n = 18 predicted baseline caries better (ROC area 0.96 than five markers (0.92 and a single lactobacilli marker (0.7 with sensitivity/specificity of 1.87, 1.78 and 1.13 at 1/3 of the subjects diagnosed sick, respectively. Moreover, biological multimarkers (n = 18 explained 2-year caries increment slightly better than reported before but predicted it poorly (ROC area 0.76. By contrast, multimarkers based on previous caries predicted alone (ROC area 0.88, or together with biological multimarkers (0.94, increment well with a sensitivity/specificity of 1.74 at 1/3 of the subjects diagnosed sick. Conclusion Multimarkers behave better than single-to-five markers but future multimarker strategies will require systematic searches for improved saliva and plaque bacteria markers.
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Shuntaro Okazaki
Full Text Available 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
Model for investigating the benefits of clinical supervision in psychiatric nursing
DEFF Research Database (Denmark)
Gonge, Henrik; Buus, Niels
2011-01-01
with the effectiveness of clinical supervision, as measured by the Manchester Clinical Supervision Scale (MCSS). Furthermore, MCSS scores were associated with benefits, such as increased job satisfaction, vitality, rational coping and less stress, emotional exhaustion, and depersonalization. Multivariate analyses......The objective of this study was to test a model for analysing the possible benefits of clinical supervision. The model suggested a pathway from participation to effectiveness to benefits of clinical supervision, and included possible influences of individual and workplace factors. The study sample...
Energy Technology Data Exchange (ETDEWEB)
Bardon, B
1995-01-31
Rapid Thermal Processing (RTP) technology is a delicate field to the control engineer. Its compatibility to single-wafer processing is well suited for performing thermal steps in the state-of-the-art integrated circuit (IC) manufacturing. Control of the wafer temperature during the processing is essential. The main problem in the scalar (SISO) approach is due to the steady-state temperature uniformity. A solution to this problem is to vary the spatial energy flux distribution radiating to the wafer. To achieve this requirement, one approach is the use a multivariable (MIMO) control law to manipulate independently the different lamps banks. Thermal process are highly non linear and distributed in nature. Besides, these non-linearities implies process dynamics variations. In this work, after physically describing our process about a reference value of the power and temperature, we present an off-line identification procedure (in the aim of devising a linear multivariable model) using input/output data for different reference values from real experiences and multi-variable least square algorithm. Afterwards, particular attention is devoted to the structure parameter determination of the linear model. Based on the linear model, a multivariable PID controller is designed. The controller coupled with the least mean square identification algorithm is tested under real conditions. The performances of the MIMO adaptive controller is also evaluated in tracking as well as in regulation. (author) refs.
An Exact Confidence Region in Multivariate Calibration
Mathew, Thomas; Kasala, Subramanyam
1994-01-01
In the multivariate calibration problem using a multivariate linear model, an exact confidence region is constructed. It is shown that the region is always nonempty and is invariant under nonsingular transformations.
A primer of multivariate statistics
Harris, Richard J
2014-01-01
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why
Senn, Theresa E.; Scott-Sheldon, Lori A. J.; Vanable, Peter A.; Carey, Michael P.
2011-01-01
Background The Information-Motivation-Behavioral Skills (IMB) model often guides sexual risk reduction programs even though no studies have examined covariation in the theory’s constructs in a dynamic fashion with longitudinal data. Purpose Using new developments in latent growth modeling, we explore how changes in information, motivation, and behavioral skills over 9 months relate to changes in condom use among STD clinic patients. Methods Participants (N = 1281, 50% female, 66% African American) completed measures of IMB constructs at three time points. We used parallel process latent growth modeling to examine associations among intercepts and slopes of IMB constructs. Results Initial levels of motivation, behavioral skills, and condom use were all positively associated, with behavioral skills partially mediating associations between motivation and condom use. Changes over time in behavioral skills positively related to changes in condom use. Conclusions Results support the key role of behavioral skills in sexual risk reduction, suggesting these skills should be targeted in HIV prevention interventions. PMID:21638196
van de Mheen, Lidewij; Schuit, Ewoud; Lim, Arianne C; Porath, Martina M; Papatsonis, Dimitri; Erwich, Jan J; van Eyck, Jim; van Oirschot, Charlotte M; Hummel, Piet; Duvekot, Johannes J; Hasaart, Tom H M; Groenwold, Rolf H H; Moons, Karl G M; de Groot, Christianne J M; Bruinse, Hein W; van Pampus, Maria G; Mol, Ben W J
2014-04-01
To develop a multivariable prognostic model for the risk of preterm delivery in women with multiple pregnancy that includes cervical length measurement at 16 to 21 weeks' gestation and other variables. We used data from a previous randomized trial. We assessed the association between maternal and pregnancy characteristics including cervical length measurement at 16 to 21 weeks' gestation and time to delivery using multivariable Cox regression modelling. Performance of the final model was assessed for the outcomes of preterm and very preterm delivery using calibration and discrimination measures. We studied 507 women, of whom 270 (53%) delivered models for preterm and very preterm delivery had a c-index of 0.68 (95% CI 0.63 to 0.72) and 0.68 (95% CI 0.62 to 0.75), respectively, and showed good calibration. In women with a multiple pregnancy, the risk of preterm delivery can be assessed with a multivariable model incorporating cervical length and other predictors.
Introduction to multivariate discrimination
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
Introduction to multivariate discrimination
International Nuclear Information System (INIS)
Kegl, B.
2013-01-01
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 hyper-parameter 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
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Wopken, Kim; Bijl, Hendrik P.; Schaaf, Arjen van der; Laan, Hans Paul van der; Chouvalova, Olga; Steenbakkers, Roel J.H.M.; Doornaert, Patricia; Slotman, Ben J.; Oosting, Sjoukje F.; Christianen, Miranda E.M.C.; Laan, Bernard F.A.M. van der; Roodenburg, Jan L.N.; René Leemans, C.; Verdonck-de Leeuw, Irma M.; Langendijk, Johannes A.
2014-01-01
Background and purpose: Curative radiotherapy/chemo-radiotherapy for head and neck cancer (HNC) may result in severe acute and late side effects, including tube feeding dependence. The purpose of this prospective cohort study was to develop a multivariable normal tissue complication probability (NTCP) model for tube feeding dependence 6 months (TUBE M6 ) after definitive radiotherapy, radiotherapy plus cetuximab or concurrent chemoradiation based on pre-treatment and treatment characteristics. Materials and methods: The study included 355 patients with HNC. TUBE M6 was scored prospectively in a standard follow-up program. To design the prediction model, the penalized learning method LASSO was used, with TUBE M6 as the endpoint. Results: The prevalence of TUBE M6 was 10.7%. The multivariable model with the best performance consisted of the variables: advanced T-stage, moderate to severe weight loss at baseline, accelerated radiotherapy, chemoradiation, radiotherapy plus cetuximab, the mean dose to the superior and inferior pharyngeal constrictor muscle, to the contralateral parotid gland and to the cricopharyngeal muscle. Conclusions: We developed a multivariable NTCP model for TUBE M6 to identify patients at risk for tube feeding dependence. The dosimetric variables can be used to optimize radiotherapy treatment planning aiming at prevention of tube feeding dependence and to estimate the benefit of new radiation technologies
Directory of Open Access Journals (Sweden)
Cláudio Roberto Rosário
2012-07-01
Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.
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James R. Moeller
2006-01-01
Full Text Available In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI, the general linear model (GLM is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1 verify activation of neural machinery we already understand and (2 discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.
Drew Creal; Siem Jan Koopman; Eric Zivot
2008-01-01
In this paper we investigate whether the dynamic properties of the U.S. business cycle have changed in the last fifty years. For this purpose we develop a flexible business cycle indicator that is constructed from a moderate set of macroeconomic time series. The coincident economic indicator is based on a multivariate trend-cycle decomposition model that accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an unobserved components ...
Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea
2017-11-01
Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Control Multivariable por Desacoplo
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Fernando Morilla
2013-01-01
results obtained by the authors after several years of research giving priority to the problem generalization and practical issues like easiness of implementation and utilization of PID controllers as elementary blocks. This combination of interests makes difficult to obtain perfect decoupling in all cases; although it is possible to achieve an important interaction reduction at the basic level of the control pyramid in such a way that other control systems at higher hierarchical levels benefit of this fact. This article summarizes the main aspects of decoupling control and presents its application to two illustrative examples: an experimental quadruple tank process and a 4×4 model of a heat, ventilation and air conditioning system. Palabras clave: Control de procesos, Control multivariable, Control por desacoplo, Control PID, Keywords: Process control, multivariable control, decoupling control, PID control
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.
Wound care clinical pathway: a conceptual model.
Barr, J E; Cuzzell, J
1996-08-01
A clinical pathway is a written sequence of clinical processes or events that guides a patient with a defined problem toward an expected outcome. Clinical pathways are tools to assist with the cost-effective management of clinical outcomes related to specific problems or disease processes. The primary obstacles to developing clinical pathways for wound care are the chronic natures of some wounds and the many variables that can delay healing. The pathway introduced in this article was modeled upon the three phases of tissue repair: inflammatory, proliferative, and maturation. This physiology-based model allows clinicians to identify and monitor outcomes based on observable and measurable clinical parameters. The pathway design, which also includes educational and behavioral outcomes, allows the clinician to individualize the expected timeframe for outcome achievement based on individual patient criteria and expert judgement. Integral to the pathway are the "4P's" which help standardize the clinical processes by wound type: Protocols, Policies, Procedures, and Patient education tools. Four categories into which variances are categorized based on the cause of the deviation from the norm are patient, process/system, practitioner, and planning/discharge. Additional research is warranted to support the value of this clinical pathway in the clinical arena.
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
Verdam, Mathilde G. E.; Oort, Frans J.
2014-01-01
Highlights Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data. A method for the investigation of measurement bias with Kronecker product restricted models. Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions. The use of curves to facilitate substantive interpretation of apparent measurement bias. Assessment of change in common factor means, after accounting for apparent measurement bias. Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks. PMID:25295016
Verdam, Mathilde G E; Oort, Frans J
2014-01-01
Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data.A method for the investigation of measurement bias with Kronecker product restricted models.Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions.The use of curves to facilitate substantive interpretation of apparent measurement bias.Assessment of change in common factor means, after accounting for apparent measurement bias.Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks.
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Verloh, N.; Haimerl, M.; Stroszczynski, C.; Fellner, C.; Wiggermann, P. [University Hospital Regensburg (Germany). Dept. of Radiology; Zeman, F. [University Hospital Regensburg (Germany). Center for Clinical Trials; Teufel, A. [University Hospital Regensburg (Germany). Dept. of Gastroenterology; Lang, S. [University Hospital Regensburg (Germany). Dept. of Surgery
2015-01-15
The purpose of this study was to identify clinical factors influencing Gd-EOB-DTPA liver uptake in patients with healthy liver parenchyma. A total of 124 patients underwent contrast-enhanced MRI with a hepatocyte-specific contrast agent at 3T. T1-weighted volume interpolated breath-hold examination (VIBE) sequences with fat suppression were acquired before and 20 minutes after contrast injection. The relative enhancement (RE) between plain and contrast-enhanced signal intensity was calculated. Simple and multiple linear regression analyses were performed to evaluate clinical factors influencing the relative enhancement. Patients were subdivided into three groups according to their relative liver enhancement (HRE, RE ≥ 100 %; MRE, 100 % > RE > 50 %; NRE, RE ≤ 50 %) and were analyzed according to the relevant risk factors. Simple regression analyses revealed patient age, transaminases (AST, ALT, GGT), liver, spleen and delta-liver volume (the difference between the volumetrically measured liver volume and the estimated liver volume based on body weight) as significant factors influencing relative enhancement. In the multiple analysis the transaminase AST, spleen and delta liver volume remained significant factors influencing relative enhancement. Delta liver volume showed a significant difference between all analyzed groups. Liver enhancement in the hepatobiliary phase depends on a variety of factors. Body weight-adapted administration of Gd-EOB-DTPA may lead to inadequate liver enhancement after 20 minutes especially when the actual liver volume differs from the expected volume.
International Nuclear Information System (INIS)
Schoenwiese, C.D.
1990-01-01
Based on univariate correction and coherence analyses, including techniques moving in time, and taking account of the physical basis of the relationships, a simple multivariate concept is presented which correlates observational climatic time series simultaneously with solar, volcanic, ENSO (El Nino/Souther Oscillation) and anthropogenic greenhouse-gas forcing. The climatic elements considered are air temperature (near the ground and stratosphere), sea surface temperature, sea level and precipitation, and cover at least the period 1881-1980 (stratospheric temperature only since 1960). The climate signal assessments which may be hypothetically attributed to the observed CO 2 or equivalent CO 2 (implying additional greenhouse gases) increase are compared with those resulting from GCM experiments. In case of the Northern hemisphere air temperature these comparisons are performed not only in respect to hemispheric and global means, but also in respect to the regional and seasonal patterns. Autocorrelations and phase shifts of the climate response to natural and anthropogenic forcing complicate the statistical assessments
J Olive, David
2017-01-01
This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
Boersen, Nathan; Carvajal, M Teresa; Morris, Kenneth R; Peck, Garnet E; Pinal, Rodolfo
2015-01-01
While previous research has demonstrated roller compaction operating parameters strongly influence the properties of the final product, a greater emphasis might be placed on the raw material attributes of the formulation. There were two main objectives to this study. First, to assess the effects of different process variables on the properties of the obtained ribbons and downstream granules produced from the rolled compacted ribbons. Second, was to establish if models obtained with formulations of one active pharmaceutical ingredient (API) could predict the properties of similar formulations in terms of the excipients used, but with a different API. Tolmetin and acetaminophen, chosen for their different compaction properties, were roller compacted on Fitzpatrick roller compactor using the same formulation. Models created using tolmetin and tested using acetaminophen. The physical properties of the blends, ribbon, granule and tablet were characterized. Multivariate analysis using partial least squares was used to analyze all data. Multivariate models showed that the operating parameters and raw material attributes were essential in the prediction of ribbon porosity and post-milled particle size. The post compacted ribbon and granule attributes also significantly contributed to the prediction of the tablet tensile strength. Models derived using tolmetin could reasonably predict the ribbon porosity of a second API. After further processing, the post-milled ribbon and granules properties, rather than the physical attributes of the formulation were needed to predict downstream tablet properties. An understanding of the percolation threshold of the formulation significantly improved the predictive ability of the models.
Molenaar, P.C.M.; Nesselroade, J.R.
1998-01-01
The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM),
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Seayed Jaber Yousefi
2012-04-01
Full Text Available The study area is located in southeastern Iran, about 110 km southwest of Kerman. Geologically, the area is composed of ophiolitic rocks, volcanic rocks, intrusive bodies and sedimentary rocks. Vein mineralization within andesite, andesitic basalt, andesitic tuffs occurred along the Chahar Gonbad fault. Sulfide mineralization in the ore deposit has taken place as dissemination, veins and veinlets in which pyrite and chalcopyrite are the most important ores. In this area, argillic, phyllic and propylitic alteration types are observed. Such elements as Au, Bi, Cu, S and Se are more enriched than others and the enrichment factors for these elements in comparison with background concentration are 321, 503, 393, 703 and 208, and with respect to Clark concentration are 401, 222, 532, 101 and 156, respectively. According to multivariate analysis, three major mineralization phases are recognized in the deposit. During the first phase, hydrothermal calcite veins are enriched in As, Cd, Pb, Zn and Ca, the second phase is manifested by the enrichment of sulfide veins in Cu, Au, Ag, Bi, Fe and S and the third phase mineralization includes Ni, Mn, Se and Sb as an intermediate level between the two previous phases.
Role modeling excellence in clinical nursing practice.
Perry, R N Beth
2009-01-01
Role modeling excellence in clinical nursing practice is the focus of this paper. The phenomenological research study reported involved a group of 8 nurses identified by their colleagues as exemplary. The major theme revealed in this study was that these exemplary nurses were also excellent role models in the clinical setting. This paper details approaches used by these nurses that made them excellent role models. Specifically, the themes of attending to the little things, making connections, maintaining a light-hearted attitude, modeling, and affirming others are presented. These themes are discussed within the framework of Watson [Watson, J., 1989. Human caring and suffering: a subjective model for health services. In: Watson, J., Taylor, R. (Eds.), They Shall Not Hurt: Human Suffering and Human Caring. Colorado University, Boulder, CO] "transpersonal caring" and [Bandura, A., 1997. Social Learning Theory. Prentice Hall, Englewood Cliffs, NJ] "Social Learning Theory." Particular emphasis in the discussion is on how positive role modeling by exemplary practitioners can contribute to the education of clinical nurses in the practice setting.
Random effects models in clinical research
Cleophas, T. J.; Zwinderman, A. H.
2008-01-01
BACKGROUND: In clinical trials a fixed effects research model assumes that the patients selected for a specific treatment have the same true quantitative effect and that the differences observed are residual error. If, however, we have reasons to believe that certain patients respond differently
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.
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.
Applied multivariate statistics with R
Zelterman, Daniel
2015-01-01
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...
Study on the Method of Grass Yield Model in the Source Region of Three Rivers with Multivariate Data
International Nuclear Information System (INIS)
You, Haoyan; Luo, Chengfeng; Liu, Zhengjun; Wang, Jiao
2014-01-01
This paper uses remote sensing and GIS technology to analyse the Source Region of Three Rivers (SRTR) to establish a grass yield estimation model during 2010 with remote sensing data, meteorological data, grassland type data and ground measured data. Analysis of the correlation between ground measured data, vegetation index based HJ-1A/B satellite data, meteorological data and grassland type data were used to establish the grass yield model. The grass yield model was studied by several statistical methods, such as multiple linear regression and Geographically Weighted Regression (GWR). The model's precision was validated. Finally, the best model to estimate the grass yield of Maduo County in SRTR was contrasted with the TM degraded grassland interpretation image of Maduo County from 2009. The result shows that: (1) Comparing with the multiple linear regression model, the GWR model gave a much better fitting result with the quality of fit increasing significantly from less than 0.3 to more than 0.8; (2) The most sensitive factors affecting the grass yield in SRTR were precipitation from May to August and drought index from May to August. From calculation of the five vegetation indices, MSAVI fitted the best; (3) The Maduo County grass yield estimated by the optimal model was consistent with the TM degraded grassland interpretation image, the spatial distribution of grass yield in Maduo County for 2010 showed a ''high south and low north'' pattern
Methods of Multivariate Analysis
Rencher, Alvin C
2012-01-01
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit
Triangular model integrating clinical teaching and assessment.
Abdelaziz, Adel; Koshak, Emad
2014-01-01
Structuring clinical teaching is a challenge facing medical education curriculum designers. A variety of instructional methods on different domains of learning are indicated to accommodate different learning styles. Conventional methods of clinical teaching, like training in ambulatory care settings, are prone to the factor of coincidence in having varieties of patient presentations. Accordingly, alternative methods of instruction are indicated to compensate for the deficiencies of these conventional methods. This paper presents an initiative that can be used to design a checklist as a blueprint to guide appropriate selection and implementation of teaching/learning and assessment methods in each of the educational courses and modules based on educational objectives. Three categories of instructional methods were identified, and within each a variety of methods were included. These categories are classroom-type settings, health services-based settings, and community service-based settings. Such categories have framed our triangular model of clinical teaching and assessment.
Modelling and Decision Support of Clinical Pathways
Gabriel, Roland; Lux, Thomas
The German health care market is under a rapid rate of change, forcing especially hospitals to provide high-quality services at low costs. Appropriate measures for more effective and efficient service provision are process orientation and decision support by information technology of clinical pathway of a patient. The essential requirements are adequate modelling of clinical pathways as well as usage of adequate systems, which are capable of assisting the complete path of a patient within a hospital, and preferably also outside of it, in a digital way. To fulfil these specifications the authors present a suitable concept, which meets the challenges of well-structured clinical pathways as well as rather poorly structured diagnostic and therapeutic decisions, by interplay of process-oriented and knowledge-based hospital information systems.
Directory of Open Access Journals (Sweden)
Abdelaleem Eglal A
2012-04-01
Full Text Available Abstract Background Metronidazole (MET and Diloxanide Furoate (DF, act as antiprotozoal drugs, in their ternary mixtures with Mebeverine HCl (MEH, an effective antispasmodic drug. This work concerns with the development and validation of two simple, specific and cost effective methods mainly for simultaneous determination of the proposed ternary mixture. In addition, the developed multivariate calibration model has been updated to determine Metronidazole benzoate (METB in its binary mixture with DF in Dimetrol® suspension. Results Method (I is the mean centering of ratio spectra spectrophotometric method (MCR that depends on using the mean centered ratio spectra in two successive steps that eliminates the derivative steps and therefore the signal to noise ratio is enhanced. The developed MCR method has been successfully applied for determination of MET, DF and MEH in different laboratory prepared mixtures and in tablets. Method (II is the partial least square (PLS multivariate calibration method that has been optimized for determination of MET, DF and MEH in Dimetrol ® tablets and by updating the developed model, it has been successfully used for prediction of binary mixtures of DF and Metronidazole Benzoate ester (METB in Dimetrol ® suspension with good accuracy and precision without reconstruction of the calibration set. Conclusion The developed methods have been validated; accuracy, precision and specificity were found to be within the acceptable limits. Moreover results obtained by the suggested methods showed no significant difference when compared with those obtained by reported methods. Graphical Abstract
DEFF Research Database (Denmark)
Olsen, Jørgen; Gerds, Thomas A; Seidelin, Jakob B
2009-01-01
Background: Endoscopically obtained mucosal biopsies play an important role in the differential diagnosis between ulcerative colitis (UC) and Crohn's disease (CD), but in some cases where neither macroscopic nor microscopic signs of inflammation are present the biopsies provide only inconclusive...... biopsies from 78 patients were included. A diagnostic model was derived with the random forest method based on 71 biopsies from 60 patients. The model-internal out-of-bag performance measure yielded perfect classification. Furthermore, the model was validated in independent 18 noninflamed biopsies from 18...... of random forest modeling of genome-wide gene expression data for distinguishing quiescent and active UC colonic mucosa versus control and CD colonic mucosa.(Inflamm Bowel Dis 2009)....
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Smith-Moritz Andreia M
2011-08-01
Full Text Available Abstract We outline a high throughput procedure that improves outlier detection in cell wall screens using FT-NIR spectroscopy of plant leaves. The improvement relies on generating a calibration set from a subset of a mutant population by taking advantage of the Mahalanobis distance outlier scheme to construct a monosaccharide range predictive model using PLS regression. This model was then used to identify specific monosaccharide outliers from the mutant population.
Multivariate Time Series Search
National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...
Explaining clinical behaviors using multiple theoretical models
Eccles, Martin P; Grimshaw, Jeremy M; MacLennan, Graeme; Bonetti, Debbie; Glidewell, Liz; Pitts, Nigel B; Steen, Nick; Thomas, Ruth; Walker, Anne; Johnston, Marie
2012-01-01
Abstract Background In the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of...
Schoch, Anna; Blöthe, Jan Henrik; Hoffmann, Thomas; Schrott, Lothar
2018-02-01
There is a notable discrepancy between detailed sediment budget studies in small headwater catchments ( 103 km2) in higher order catchments applying modeling and/or remote sensing based approaches for major sediment storage delineation. To bridge the gap between these scales, we compiled an inventory of sediment and bedrock coverage from field mapping, remote sensing analysis and published data for five key sites in the Upper Rhone Basin (Val d'Illiez, Val de la Liène, Turtmanntal, Lötschental, Goms; 360.3 km2, equivalent to 6.7% of the Upper Rhone Basin). This inventory was used as training and testing data for the classification of sediment and bedrock cover. From a digital elevation model (2 × 2 m ground resolution) and Landsat imagery we derived 22 parameters characterizing local morphometry, topography and position, contributing area, and climatic and biotic factors on different spatial scales, which were used as inputs for different statistical models (logistic regression, principal component logistic regression, generalized additive model). Best prediction results with an excellent performance (mean AUROC: 0.8721 ± 0.0012) and both a high spatial and non-spatial transferability were achieved applying a generalized additive model. Since the model has a high thematic consistency, the independent input variables chosen based on their geomorphic relevance are suitable to model the spatial distribution of sediment. Our high-resolution classification shows that 53.5 ± 21.7% of the Upper Rhone Basin are covered with sediment. These are by no means evenly distributed: small headwaters (analysis.
Development of a clinical decision model for thyroid nodules
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Eberhardt John
2009-08-01
Full Text Available Abstract Background Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people has a palpable thyroid nodule, however the majority (>95% of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80% of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery. Methods Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US, electrical impedance scanning (EIS and fine needle aspiration cytology (FNA prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records and a test set (10% of records. A receiver-operating-characteristics (ROC curve of these predictions and area under the curve (AUC were calculated to determine model robustness for predicting malignancy in thyroid nodules. Results Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of
Directory of Open Access Journals (Sweden)
X. Fang
2013-04-01
Full Text Available 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
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
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Roozbeh Hasanzadeh Nafari
2016-07-01
Full Text Available Flood is a frequent natural hazard that has significant financial consequences for Australia. In Australia, physical losses caused by floods are commonly estimated by stage-damage functions. These methods usually consider only the depth of the water and the type of buildings at risk. However, flood damage is a complicated process, and it is dependent on a variety of factors which are rarely taken into account. This study explores the interaction, importance, and influence of water depth, flow velocity, water contamination, precautionary measures, emergency measures, flood experience, floor area, building value, building quality, and socioeconomic status. The study uses tree-based models (regression trees and bagging decision trees and a dataset collected from 2012 to 2013 flood events in Queensland, which includes information on structural damages, impact parameters, and resistance variables. The tree-based approaches show water depth, floor area, precautionary measures, building value, and building quality to be important damage-influencing parameters. Furthermore, the performance of the tree-based models is validated and contrasted with the outcomes of a multi-parameter loss function (FLFArs from Australia. The tree-based models are shown to be more accurate than the stage-damage function. Consequently, considering more parameters and taking advantage of tree-based models is recommended. The outcome is important for improving established Australian flood loss models and assisting decision-makers and insurance companies dealing with flood risk assessment.
DEFF Research Database (Denmark)
Stygar, Anna Helena; Krogh, Mogens Agerbo; Kristensen, Troels
2017-01-01
Evolutionary operations is a method to exploit the association of often small changes in process variables, planned during systematic experimentation and occurring during the normal production flow, to production characteristics to find a way to alter the production process to be more efficient....... The objective of this study was to construct a tool to assess the intervention effect on milk production in an evolutionary operations setup. The method used for this purpose was a dynamic linear model (DLM) with Kalman filtering. The DLM consisted of parameters describing milk yield in a herd, individual cows...... bulk tank records. The presented model proved to be a flexible and dynamic tool, and it was successfully applied for systematic experimentation in dairy herds. The model can serve as a decision support tool for on-farm process optimization exploiting planned changes in process variables...
Zhang, Jun; Yao, Duoxi; Su, Yue
2018-02-01
Under the current situation of energy demand, coal is still one of the major energy sources in China for a certain period of time, so the task of coal mine safety production remains arduous. In order to identify the water source of the mine accurately, this article takes the example from Renlou and Tongting coal mines in the northern Anhui mining area. A total of 7 conventional water chemical indexes were selected, including Ca2+, Mg2+, Na++K+, Cl-, SO4 2-, HCO3 - and TDS, to establish a multivariate matrix model for the source identifying inrush water. The results show that the model is simple and is rarely limited by the quantity of water samples, and the recognition effect is ideal, which can be applied to the control and treatment for water inrush.
Kandelbauer, A; Kessler, W; Kessler, R W
2008-03-01
The laccase-catalysed transformation of indigo carmine (IC) with and without a redox active mediator was studied using online UV-visible spectroscopy. Deconvolution of the mixture spectra obtained during the reaction was performed on a model-free basis using multivariate curve resolution (MCR). Thereby, the time courses of educts, products, and reaction intermediates involved in the transformation were reconstructed without prior mechanistic assumptions. Furthermore, the spectral signature of a reactive intermediate which could not have been detected by a classical hard-modelling approach was extracted from the chemometric analysis. The findings suggest that the combined use of UV-visible spectroscopy and MCR may lead to unexpectedly deep mechanistic evidence otherwise buried in the experimental data. Thus, although rather an unspecific method, UV-visible spectroscopy can prove useful in the monitoring of chemical reactions when combined with MCR. This offers a wide range of chemists a cheap and readily available, highly sensitive tool for chemical reaction online monitoring.
Westerhuis, J.A; Coenegracht, P.M J; Lerk, C.F
1997-01-01
The process of tablet manufacturing with granulation is described as a two-step process. The first step comprises wet granulation of the powder mixture, and in the second step the granules are compressed into tablets. For the modelling of the pharmaceutical process of wet granulation and tableting,
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Riches, S.F.; Payne, G.S.; Morgan, V.A.; DeSouza, N.M. [Royal Marsden NHS Foundation Trust and Institute of Cancer Research, CRUK and EPSRC Cancer Imaging Centre, Sutton, Surrey (United Kingdom); Dearnaley, D. [Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Department of Urology and Department of Academic Radiotherapy, Sutton, Surrey (United Kingdom); Morgan, S. [The Ottawa Hospital Cancer Centre and the University of Ottawa, Division of Radiation Oncology, Ottawa, Ontario (Canada); Partridge, M. [The Institute of Cancer Research, Section of Radiotherapy and Imaging, Sutton, Surrey (United Kingdom); University of Oxford, The Gray Institute for Radiation Oncology and Biology, Oxford (United Kingdom); Livni, N. [Royal Marsden NHS Foundation Trust Chelsea, Department of Histopathology, London (United Kingdom); Ogden, C. [Royal Marsden NHS Foundation Trust Chelsea, Department of Urology, London (United Kingdom)
2015-05-01
The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T{sub 2}-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T{sub 2,} Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K{sup trans},K{sub ep},V{sub e}), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. (orig.)
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. Copyright © 2011 Elsevier B.V. All rights reserved.
Voorhoeve, R.J.; van der Maas, A.; Oomen, T.A.J.
2018-01-01
Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF
Bolck, A.; Ni, H.; Lopatka, M.
2015-01-01
Likelihood ratio (LR) models are moving into the forefront of forensic evidence evaluation as these methods are adopted by a diverse range of application areas in forensic science. We examine the fundamentally different results that can be achieved when feature- and score-based methodologies are
Coaching Model + Clinical Playbook = Transformative Learning.
Fletcher, Katherine A; Meyer, Mary
2016-01-01
Health care employers demand that workers be skilled in clinical reasoning, able to work within complex interprofessional teams to provide safe, quality patient-centered care in a complex evolving system. To this end, there have been calls for radical transformation of nursing education including the development of a baccalaureate generalist nurse. Based on recommendations from the American Association of Colleges of Nursing, faculty concluded that clinical education must change moving beyond direct patient care by applying the concepts associated with designer, manager, and coordinator of care and being a member of a profession. To accomplish this, the faculty utilized a system of focused learning assignments (FLAs) that present transformative learning opportunities that expose students to "disorienting dilemmas," alternative perspectives, and repeated opportunities to reflect and challenge their own beliefs. The FLAs collected in a "Playbook" were scaffolded to build the student's competencies over the course of the clinical experience. The FLAs were centered on the 6 Quality and Safety Education for Nurses competencies, with 2 additional concepts of professionalism and systems-based practice. The FLAs were competency-based exercises that students performed when not assigned to direct patient care or had free clinical time. Each FLA had a lesson plan that allowed the student and faculty member to see the competency addressed by the lesson, resources, time on task, student instructions, guide for reflection, grading rubric, and recommendations for clinical instructor. The major advantages of the model included (a) consistent implementation of structured learning experiences by a diverse teaching staff using a coaching model of instruction; (b) more systematic approach to present learning activities that build upon each other; (c) increased time for faculty to interact with students providing direct patient care; (d) guaranteed capture of selected transformative
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
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Farjam, R; Pramanik, P; Srinivasan, A; Chapman, C; Tsien, C; Lawrence, T; Cao, Y [University of Michigan, Ann Arbor, MI (United States)
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
International Nuclear Information System (INIS)
Farjam, R; Pramanik, P; Srinivasan, A; Chapman, C; Tsien, C; Lawrence, T; Cao, Y
2014-01-01
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
Directory of Open Access Journals (Sweden)
Wong J
2018-04-01
Full Text Available Jenna Wong, Michal Abrahamowicz, David L Buckeridge, Robyn Tamblyn Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada Objective: Physicians commonly prescribe antidepressants for indications other than depression that are not evidence-based and need further evaluation. However, lack of routinely documented treatment indications for medications in administrative and medical databases creates a major barrier to evaluating antidepressant use for indications besides depression. Thus, the aim of this study was to derive a model to predict when primary care physicians prescribe antidepressants for indications other than depression and to identify important determinants of this prescribing practice. Methods: Prediction study using antidepressant prescriptions from January 2003–December 2012 in an indication-based electronic prescribing system in Quebec, Canada. Patients were linked to demographic files, medical billings data, and hospital discharge summary data to create over 370 candidate predictors. The final prediction model was derived on a random 75% sample of the data using 3-fold cross-validation integrated within a score-based forward stepwise selection procedure. The performance of the final model was assessed in the remaining 25% of the data. Results: Among 73,576 antidepressant prescriptions, 32,405 (44.0% were written for indications other than depression. Among 40 predictors in the final model, the most important covariates included the molecule name, the patient’s education level, the physician’s workload, the prescribed dose, and diagnostic codes for plausible indications recorded in the past year. The final model had good discrimination (concordance (c statistic 0.815; 95% CI, 0.787–0.847 and good calibration (ratio of observed to expected events 0.986; 95% CI, 0.842–1.136. Conclusion: In the absence of documented treatment indications, researchers may be able to use
Houle, Timothy T; Turner, Dana P; Golding, Adrienne N; Porter, John A H; Martin, Vincent T; Penzien, Donald B; Tegeler, Charles H
2017-07-01
To develop and validate a prediction model that forecasts future migraine attacks for an individual headache sufferer. Many headache patients and physicians believe that precipitants of headache can be identified and avoided or managed to reduce the frequency of headache attacks. Of the numerous candidate triggers, perceived stress has received considerable attention for its association with the onset of headache in episodic and chronic headache sufferers. However, no evidence is available to support forecasting headache attacks within individuals using any of the candidate headache triggers. This longitudinal cohort with forecasting model development study enrolled 100 participants with episodic migraine with or without aura, and N = 95 contributed 4626 days of electronic diary data and were included in the analysis. Individual headache forecasts were derived from current headache state and current levels of stress using several aspects of the Daily Stress Inventory, a measure of daily hassles that is completed at the end of each day. The primary outcome measure was the presence/absence of any headache attack (head pain > 0 on a numerical rating scale of 0-10) over the next 24 h period. After removing missing data (n = 431 days), participants in the study experienced a headache attack on 1613/4195 (38.5%) days. A generalized linear mixed-effects forecast model using either the frequency of stressful events or the perceived intensity of these events fit the data well. This simple forecasting model possessed promising predictive utility with an AUC of 0.73 (95% CI 0.71-0.75) in the training sample and an AUC of 0.65 (95% CI 0.6-0.67) in a leave-one-out validation sample. This forecasting model had a Brier score of 0.202 and possessed good calibration between forecasted probabilities and observed frequencies but had only low levels of resolution (ie, sharpness). This study demonstrates that future headache attacks can be forecasted for a diverse group of
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Mattson, Earl [Idaho National Lab. (INL), Idaho Falls, ID (United States); Smith, Robert [Idaho National Lab. (INL), Idaho Falls, ID (United States); Fujita, Yoshiko [Idaho National Lab. (INL), Idaho Falls, ID (United States); McLing, Travis [Idaho National Lab. (INL), Idaho Falls, ID (United States); Neupane, Ghanashyam [Idaho National Lab. (INL), Idaho Falls, ID (United States); Palmer, Carl [Idaho National Lab. (INL), Idaho Falls, ID (United States); Reed, David [Idaho National Lab. (INL), Idaho Falls, ID (United States); Thompson, Vicki [Idaho National Lab. (INL), Idaho Falls, ID (United States)
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.
Directory of Open Access Journals (Sweden)
Pedro Beschoren da Costa
Full Text Available 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.
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Paulino José García Nieto
2016-05-01
Full Text Available Remaining useful life (RUL estimation is considered as one of the most central points in the prognostics and health management (PHM. The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS, which have been successfully adopted for regression problems, with the artificial bee colony (ABC technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.
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
El-Assmy, Ahmed; El-Nahas, Ahmed R; Abo-Elghar, Mohamed E; Eraky, Ibrahim; El-Kenawy, Mahmoud R; Sheir, Khaled Z
2006-03-23
The first-line management of renal stones between 20-30 mm remains controversial. The Extracorporeal Shock Wave Lithotripsy (ESWL) stone-free rates for such patient groups vary widely. The purpose of this study was to define factors that have a significant impact on the stone-free rate after ESWL in such controversial groups. Between January 1990 and January 2004, 594 patients with renal stones 20-30 mm in length underwent ESWL monotherapy. Stone surface area was measured for all stones. The results of treatment were evaluated after 3 months of follow-up. The stone-free rate was correlated with stone and patient characteristics using the Chi-square test; factors found to be significant were further analyzed using multivariate analysis. Repeat ESWL was needed in 56.9% of cases. Post-ESWL complications occurred in 5% of cases and post-ESWL secondary procedures were required in 5.9%. At 3-month follow-up, the overall stone-free rate was 77.2%. Using the Chi-square test, stone surface area, location, number, radiological renal picture, and congenital renal anomalies had a significant impact on the stone-free rate. Multivariate analysis excluded radiological renal picture from the logistic regression model while other factors maintained their statistically significant effect on success rate, indicating that they were independent predictors. A regression analysis model was designed to estimate the probability of stone-free status after ESWL. The sensitivity of the model was 97.4%, the specificity 90%, and the overall accuracy 95.6%. Stone surface area, location, number, and congenital renal anomalies are prognostic predictors determining stone clearance after ESWL of renal calculi of 20-30 mm. High probability of stone clearance is obtained with single stone ESWL in such controversial groups and can define patients who would need other treatment modality.
Radiation repair models for clinical application.
Dale, Roger G
2018-02-28
A number of newly emerging clinical techniques involve non-conventional patterns of radiation delivery which require an appreciation of the role played by radiation repair phenomena. This review outlines the main models of radiation repair, focussing on those which are of greatest clinical usefulness and which may be incorporated into biologically effective dose assessments. The need to account for the apparent "slowing-down" of repair rates observed in some normal tissues is also examined, along with a comparison of the relative merits of the formulations which can be used to account for such phenomena. Jack Fowler brought valuable insight to the understanding of radiation repair processes and this article includes reference to his important contributions in this area.
Riley, P.; Richardson, I. G.
2012-01-01
In-situ measurements of interplanetary coronal mass ejections (ICMEs) display a wide range of properties. A distinct subset, "magnetic clouds" (MCs), are readily identifiable by a smooth rotation in an enhanced magnetic field, together with an unusually low solar wind proton temperature. In this study, we analyze Ulysses spacecraft measurements to systematically investigate five possible explanations for why some ICMEs are observed to be MCs and others are not: i) An observational selection effect; that is, all ICMEs do in fact contain MCs, but the trajectory of the spacecraft through the ICME determines whether the MC is actually encountered; ii) interactions of an erupting flux rope (PR) with itself or between neighboring FRs, which produce complex structures in which the coherent magnetic structure has been destroyed; iii) an evolutionary process, such as relaxation to a low plasma-beta state that leads to the formation of an MC; iv) the existence of two (or more) intrinsic initiation mechanisms, some of which produce MCs and some that do not; or v) MCs are just an easily identifiable limit in an otherwise corntinuous spectrum of structures. We apply quantitative statistical models to assess these ideas. In particular, we use the Akaike information criterion (AIC) to rank the candidate models and a Gaussian mixture model (GMM) to uncover any intrinsic clustering of the data. Using a logistic regression, we find that plasma-beta, CME width, and the ratio O(sup 7) / O(sup 6) are the most significant predictor variables for the presence of an MC. Moreover, the propensity for an event to be identified as an MC decreases with heliocentric distance. These results tend to refute ideas ii) and iii). GMM clustering analysis further identifies three distinct groups of ICMEs; two of which match (at the 86% level) with events independently identified as MCs, and a third that matches with non-MCs (68 % overlap), Thus, idea v) is not supported. Choosing between ideas i) and
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Hegerty Scott W.
2015-11-01
Full Text Available Recent commodity price declines have added to worldwide macroeconomic risk, which has had serious effects on both commodity exporters and manufacturers that use oil and raw materials. These effects have been keenly felt in Central and Eastern Europe—particularly in Russia, but also in European Union member states. This study tests for spillovers among commodity-price and macroeconomic volatility by applying a VAR(1-MGARCH model to monthly time series for eight CEE countries. Overall, we find that oil prices do indeed have effects throughout the region, as do spillovers among exchange rates, inflation, interest rates, and output, but that they differ from country to country—particularly when different degrees of transition and integration are considered. While oil prices have a limited impact on the currencies of Russia and Ukraine, they do make a much larger contribution to the two countries’ macroeconomic volatility than do spillovers among the other macroeconomic variables.
Multivariate Birkhoff interpolation
Lorentz, Rudolph A
1992-01-01
The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...
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.
Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi
2015-03-15
Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate
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Maths Halstensen
2017-01-01
Full Text Available Liquid speciation is important for reliable process design and optimization of gas-liquid absorption process. Liquid-phase speciation methods are currently available, although they involve tedious and time-consuming laboratory work. Raman spectroscopy is well suited for in situ monitoring of aqueous chemical reactions. Here, we report on the development of a method for speciation of the CO2-NH3-H2O equilibrium using Raman spectroscopy and PLS-R modeling. The quantification methodology presented here offers a novel approach to provide rapid and reliable predictions of the carbon distribution of the CO2-NH3-H2O system, which may be used for process control and optimization. Validation of the reported speciation method which is based on independent, known, NH3-CO2-H2O solutions shows estimated prediction uncertainties for carbonate, bicarbonate, and carbamate of 6.45 mmol/kg H2O, 34.39 mmol/kg H2O, and 100.9 mmol/kg H2O, respectively.
Meléndez, E; Ortiz, M C; Sarabia, L A; Íñiguez, M; Puras, P
2013-01-25
The ripeness of grapes at the harvest time is one of the most important parameters for obtaining high quality red wines. Traditionally the decision of harvesting is to be taken only after analysing sugar concentration, titratable acidity and pH of the grape juice (technological maturity). However, these parameters only provide information about the pulp ripeness and overlook the real degree of skins and seeds maturities (phenolic maturity). Both maturities, technological and phenolic, are not simultaneously reached, on the contrary they tend to separate depending on several factors: grape variety, cultivar, adverse weather conditions, soil, water availability and cultural practices. Besides, this divergence is increasing as a consequence of the climate change (larger quantities of CO(2), less rain, and higher temperatures). 247 samples collected in vineyards representative of the qualified designation of origin Rioja from 2007 to 2011 have been analysed. Samples contain the four grape varieties usual in the elaboration of Rioja wines ('tempranillo', 'garnacha', 'mazuelo' and 'graciano'). The present study is the first systematic investigation on the maturity of grapes that includes the organoleptic evaluation of the degree of grapes maturity (sugars/acidity maturity, aromatic maturity of the pulp, aromatic maturity of the skins and tannins maturity) together with the values of the physicochemical parameters (probable alcohol degree, total acidity, pH, malic acid, K, total index polyphenolics, anthocyans, absorbances at 420, 520 and 620 nm, colour index and tartaric acid) determined over the same samples. A varimax rotation of the latent variables of a PLS model between the physicochemical variables and the mean of four sensory variables allows identifying both maturities. Besides, the position of the samples in the first plane defines the effect that the different factors exert on both phenolic and technological maturities. Copyright © 2012 Elsevier B.V. All
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Kolsoom Mohammad Moradi
2017-02-01
Full Text Available Background: Bromide is found naturally in groundwater and surface water. The rapid growth of industrial activities, drainage of surface runoff, and use of methyl bromide in pesticides has increased bromide discharge to the environment. Disinfection of water-containing bromide causes the creation of additional products of organo-halogenated that are considered cancer-causing agents. In this study, the effect and optimization of factors in removal of this ion was evaluated by using the nano-photocatalytic UV/ZnO process. Methods: This analytical study was conducted in a batch system by the phenol-red method. The test design was performed through the analysis model of multi-factor variance with 99 subjects, while the main, interactive, and reciprocal effects of variables, such as reaction time, catalyst concentration, bromide concentration, and pH at different levels of each factor, were analysed by using SPSS version 16. Results: The main, interactive, and reciprocal effects of factors were significant in three different levels with P < 0.001, and the optimal level of the factors reaction time, catalyst concentration, bromide concentration, and pH were 120 minutes, 0.5, 0.1, and 7 mg/L, respectively, by using the Schaffer test. The highest removal efficiency of 95% was obtained at least 91.56 and a maximum of 94.76% was obtained under optimal conditions of all factors. Conclusion: The results show that by optimization of factors, this process can be effectively used to remove bromide from aquatic environments.
Triangular model integrating clinical teaching and assessment
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Abdelaziz A
2014-03-01
Full Text Available Adel Abdelaziz,1,2 Emad Koshak3 1Medical Education Development Unit, Faculty of Medicine, Al Baha University, Al Baha, Saudi Arabia; 2Medical Education Department, Faculty of Medicine, Suez Canal University, Egypt; 3Dean and Internal Medicine Department, Faculty of Medicine, Al Baha University, Al Baha, Saudi Arabia Abstract: Structuring clinical teaching is a challenge facing medical education curriculum designers. A variety of instructional methods on different domains of learning are indicated to accommodate different learning styles. Conventional methods of clinical teaching, like training in ambulatory care settings, are prone to the factor of coincidence in having varieties of patient presentations. Accordingly, alternative methods of instruction are indicated to compensate for the deficiencies of these conventional methods. This paper presents an initiative that can be used to design a checklist as a blueprint to guide appropriate selection and implementation of teaching/learning and assessment methods in each of the educational courses and modules based on educational objectives. Three categories of instructional methods were identified, and within each a variety of methods were included. These categories are classroom-type settings, health services-based settings, and community service-based settings. Such categories have framed our triangular model of clinical teaching and assessment. Keywords: curriculum development, teaching, learning, assessment, apprenticeship, community-based settings, health service-based settings
Multivariate process monitoring of EAFs
Energy Technology Data Exchange (ETDEWEB)
Sandberg, E.; Lennox, B.; Marjanovic, O.; Smith, K.
2005-06-01
Improved knowledge of the effect of scrap grades on the electric steelmaking process and optimised scrap loading practices increase the potential for process automation. As part of an ongoing programme, process data from four Scandinavian EAFs have been analysed, using the multivariate process monitoring approach, to develop predictive models for end point conditions such as chemical composition, yield and energy consumption. The models developed generally predict final Cr, Ni and Mo and tramp element contents well, but electrical energy consumption, yield and content of oxidisable and impurity elements (C, Si, Mn, P, S) are at present more difficult to predict. Potential scrap management applications of the prediction models are also presented. (author)
International Nuclear Information System (INIS)
Tencate, Alister J.; Kalivas, John H.; White, Alexander J.
2016-01-01
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
Energy Technology Data Exchange (ETDEWEB)
Tencate, Alister J. [Department of Chemistry, Idaho State University, Pocatello, ID 83209 (United States); Kalivas, John H., E-mail: kalijohn@isu.edu [Department of Chemistry, Idaho State University, Pocatello, ID 83209 (United States); White, Alexander J. [Department of Physics and Optical Engineering, Rose-Hulman Institute of Technology, Terre Huate, IN 47803 (United States)
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
A MULTIVARIATE WEIBULL DISTRIBUTION
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Cheng Lee
2010-07-01
Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...
DEFF Research Database (Denmark)
Hansen, Michael Adsetts Edberg
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
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Liu Yufeng
2011-01-01
Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA
[Establishment of A Clinical Prediction Model of Prolonged Air Leak after Anatomic Lung Resection].
Wu, Xianning; Xu, Shibin; Ke, Li; Fan, Jun; Wang, Jun; Xie, Mingran; Jiang, Xianliang; Xu, Meiqing
2017-12-20
Prolonged air leak (PAL) after anatomic lung resection is a common and challenging complication in thoracic surgery. No available clinical prediction model of PAL has been established in China. The aim of this study was to construct a model to identify patients at increased risk of PAL by using preoperative factors exclusively. We retrospectively reviewed clinical data and PAL occurrence of patients after anatomic lung resection, in department of thoracic surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, from January 2016 to October 2016. 359 patients were in group A, clinical data including age, body mass index (BMI), gender, smoking history, surgical methods, pulmonary function index, pleural adhesion, pathologic diagnosis, side and site of resected lung were analyzed. By using univariate and multivariate analysis, we found the independent predictors of PAL after anatomic lung resection and subsequently established a clinical prediction model. Then, another 112 patients (group B), who underwent anatomic lung resection in different time by different team, were chosen to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curve was constructed using the prediction model. Multivariate Logistic regression analysis was used to identify six clinical characteristics [BMI, gender, smoking history, forced expiratory volume in one second to forced vital capacity ratio (FEV1%), pleural adhesion, site of resection] as independent predictors of PAL after anatomic lung resection. The area under the ROC curve for our model was 0.886 (95%CI: 0.835-0.937). The best predictive P value was 0.299 with sensitivity of 78.5% and specificity of 93.2%. Our prediction model could accurately identify occurrence risk of PAL in patients after anatomic lung resection, which might allow for more effective use of intraoperative prophylactic strategies. .
Belay, T K; Dagnachew, B S; Kowalski, Z M; Ådnøy, T
2017-08-01
Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross
Baker, Valerie L; Luke, Barbara; Brown, Morton B; Alvero, Ruben; Frattarelli, John L; Usadi, Rebecca; Grainger, David A; Armstrong, Alicia Y
2010-09-01
To evaluate factors predictive of clinical pregnancy and of pregnancy loss from assisted reproductive technology (ART) using data from the Society for Assisted Reproductive Technology database for 2004-2006. Retrospective cohort. Clinic-based data. The study population included 225,889 fresh embryo transfer cycles using autologous oocytes and partner semen. None. Clinical intrauterine gestation (presence of gestational sac) and live birth (>or=22 weeks gestation and >or=300 g birth weight). Increasing maternal age was significantly associated with a reduced odds of conception and increased fetal loss until 19 weeks gestation, but not with later pregnancy loss. Intracytoplasmic sperm injection (ICSI), assisted hatching, and increasing number of embryos transferred had significant positive effects on the odds of conception and pregnancy continuation through the first trimester, but did not affect the risk of later loss. Blacks, Asians, and Hispanics had significantly lower odds of clinical pregnancy compared with whites. Also compared with whites, Hispanics and Asians had a significantly greater risk of pregnancy loss in the second and third trimesters, and blacks had a significantly greater risk of pregnancy loss in all trimesters. Certain demographic and ART treatment parameters influenced chance of conception and early pregnancy loss, whereas black race and Hispanic ethnicity were also significantly associated with late pregnancy loss in ART-conceived pregnancies. Copyright (c) 2010 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
Wiktelius, Daniel; Ahlinder, Linnea; Larsson, Andreas; Höjer Holmgren, Karin; Norlin, Rikard; Andersson, Per Ola
2018-08-15
Collecting data under field conditions for forensic investigations of chemical warfare agents calls for the use of portable instruments. In this study, a set of aged, crude preparations of sulfur mustard were characterized spectroscopically without any sample preparation using handheld Raman and portable IR instruments. The spectral data was used to construct Random Forest multivariate models for the attribution of test set samples to the synthetic method used for their production. Colored and fluorescent samples were included in the study, which made Raman spectroscopy challenging although fluorescence was diminished by using an excitation wavelength of 1064 nm. The predictive power of models constructed with IR or Raman data alone, as well as with combined data was investigated. Both techniques gave useful data for attribution. Model performance was enhanced when Raman and IR spectra were combined, allowing correct classification of 19/23 (83%) of test set spectra. The results demonstrate that data obtained with spectroscopy instruments amenable for field deployment can be useful in forensic studies of chemical warfare agents. Copyright © 2018 Elsevier B.V. All rights reserved.
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Ahmed El-Assmy
2006-01-01
Full Text Available The first-line management of renal stones between 20—30 mm remains controversial. The Extracorporeal Shock Wave Lithotripsy (ESWL stone-free rates for such patient groups vary widely. The purpose of this study was to define factors that have a significant impact on the stone-free rate after ESWL in such controversial groups. Between January 1990 and January 2004, 594 patients with renal stones 20—30 mm in length underwent ESWL monotherapy. Stone surface area was measured for all stones. The results of treatment were evaluated after 3 months of follow-up. The stone-free rate was correlated with stone and patient characteristics using the Chi-square test; factors found to be significant were further analyzed using multivariate analysis.Repeat ESWL was needed in 56.9% of cases. Post-ESWL complications occurred in 5% of cases and post-ESWL secondary procedures were required in 5.9%. At 3-month follow-up, the overall stone-free rate was 77.2%. Using the Chi-square test, stone surface area, location, number, radiological renal picture, and congenital renal anomalies had a significant impact on the stone-free rate. Multivariate analysis excluded radiological renal picture from the logistic regression model while other factors maintained their statistically significant effect on success rate, indicating that they were independent predictors. A regression analysis model was designed to estimate the probability of stone-free status after ESWL. The sensitivity of the model was 97.4%, the specificity 90%, and the overall accuracy 95.6%.Stone surface area, location, number, and congenital renal anomalies are prognostic predictors determining stone clearance after ESWL of renal calculi of 20—30 mm. High probability of stone clearance is obtained with single stone ≤400 mm2 located in renal pelvis with no congenital anomalies. Our regression model can predict the probability of the success of ESWL in such controversial groups and can define patients who
Ontology modeling for generation of clinical pathways
Directory of Open Access Journals (Sweden)
Jasmine Tehrani
2012-12-01
Full Text Available Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the
Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M
2015-07-01
Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.
Energy Technology Data Exchange (ETDEWEB)
Chauvin, Benoit, E-mail: benoit.chauvin@u-psud.fr [Univ. Paris-Sud, EA 4041, IFR 141, Faculte de Pharmacie, F-92296 Chatenay-Malabry (France); Institut Curie, UMR 176 CNRS, Centre Universitaire, Univ Paris-Sud, F-91405 Orsay (France); Kasselouri, Athena; Chaminade, Pierre; Quiameso, Rita [Univ. Paris-Sud, EA 4041, IFR 141, Faculte de Pharmacie, F-92296 Chatenay-Malabry (France); Nicolis, Ioannis [Laboratoire de Biomathematiques et Informatique, Departement de Sante publique et biostatistiques et EA 4466, Faculte de Pharmacie, Universite Paris Descartes, 4, avenue de l' Observatoire, 75270 Paris cedex 06 (France); Maillard, Philippe [Institut Curie, UMR 176 CNRS, Centre Universitaire, Univ Paris-Sud, F-91405 Orsay (France); Prognon, Patrice [Univ. Paris-Sud, EA 4041, IFR 141, Faculte de Pharmacie, F-92296 Chatenay-Malabry (France)
2011-10-31
Highlights: {yields} Diprotonation of 17 meso-tetraphenylporphyrin derivatives. {yields} MCR-ALS resolution of multi-component mixtures. {yields} Determination of stepwise protonation constants. {yields} Prediction of protonation constants from ET-State indices. - Abstract: Tetrapyrrole rings possess four nitrogen atoms, two of which act as Broendsted 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{sub 3}TPP{sup +}) in solution resemble those of diprotonated species (H{sub 4}TPP{sup 2+}), mainly differing by a slight blue-shift of bands. Overlap of H{sub 3}TPP{sup +} and H{sub 4}TPP{sup 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
Skopina, Maria; Protasov, Vladimir
2016-01-01
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...
Multivariate calculus and geometry
Dineen, Seán
2014-01-01
Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.
Intelligent multivariate process supervision
International Nuclear Information System (INIS)
Visuri, Pertti.
1986-01-01
This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)
Multivariate rational data fitting
Cuyt, Annie; Verdonk, Brigitte
1992-12-01
Sections 1 and 2 discuss the advantages of an object-oriented implementation combined with higher floating-point arithmetic, of the algorithms available for multivariate data fitting using rational functions. Section 1 will in particular explain what we mean by "higher arithmetic". Section 2 will concentrate on the concepts of "object orientation". In sections 3 and 4 we shall describe the generality of the data structure that can be dealt with: due to some new results virtually every data set is acceptable right now, with possible coalescence of coordinates or points. In order to solve the multivariate rational interpolation problem the data sets are fed to different algorithms depending on the structure of the interpolation points in then-variate space.
Kennedy, Curtis E; Turley, James P
2011-10-24
Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9
Cobin, P. F.; Oommen, T.; Gierke, J. S.
2013-12-01
The Lake Atitlán watershed is home to approximately 200,000 people and is located in the western highlands of Guatemala. Steep slopes, highly susceptible to landslides during the rainy season, characterize the region. Typically these landslides occur during high-intensity precipitation events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. Different datasets of landslide and non-landslide points across the watershed were used to compare model success at a small scale and regional scale. This study used data from multiple attributes: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The open source software Weka was used for the data mining. Several attribute selection methods were applied to the data to predetermine the potential landslide causative influence. Different multivariate algorithms were then evaluated for their ability to predict landslide occurrence. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The attribute combinations of the most successful models were compared to the attribute evaluator results. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points for the regions selected in the watershed. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.
Callén, M S; López, J M; Mastral, A M
2010-08-15
The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R(2)=0.817, PRESS/SSY=0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q(CV)(2)=0.813, PRESS/SSY=0.187) and with the maximal external prediction for the 2001-2002 campaign (Q(ext)(2)=0.679 and PRESS/SSY=0.321) versus the 2001-2004 campaign (Q(ext)(2)=0.551, PRESS/SSY=0.449). Copyright 2010 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Callen, M.S.; Lopez, J.M.; Mastral, A.M.
2010-01-01
The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R 2 = 0.817, PRESS/SSY = 0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q CV 2 =0.813, PRESS/SSY = 0.187) and with the maximal external prediction for the 2001-2002 campaign (Q ext 2 =0.679 and PRESS/SSY = 0.321) versus the 2001-2004 campaign (Q ext 2 =0.551, PRESS/SSY = 0.449).
Clinical psychology of religion. A training model
Uden, M.H.F. van; Pieper, J.Z.T.
2003-01-01
In this paper we will show you a part of a course "Clinical Psychology of Religion" that has been developed in the Netherlands for introducing mental health professionals in the field of clinical psychology of religion. Clinical psychology of religion applies insights from general psychology of
Directory of Open Access Journals (Sweden)
De Pedro, Emiliano
2009-07-01
Full Text Available Multivariate Classification models to classify real farm conditions Iberian pigs, according to the feeding regime were developed by using fatty acids composition or NIR spectral data of liquid fat samples. A total of 121 subcutaneous fat samples were taken from Iberian pigs carcasses belonging to 5 batches reared under different feeding systems. Once the liquid sample was extracted from each subcutaneous fat sample, it was determined the percentage of 11 fatty acids (C14:0, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C20:1. At the same time, Near Infrared (NIR spectrum of each liquid sample was obtained. Linear Discriminant Analysis (LDA was considered as pattern recognition method to develop the multivariate models. Classification errors of the LDA models generated by using NIR spectral data were 0.0% and 1.7% for the model generated by using fatty acids composition. Results confirm the possibility to discriminate Iberian pig liquid samples from animals reared under different feeding regimes on real farm conditions by using NIR spectral data or fatty acids composition. Classification error obtained using models generated from NIR spectral data were lower than those obtained in models based on fatty acids composition.Se han desarrollado modelos multivariantes, generados a partir de la composición en ácidos grasos o datos espectrales NIR, para clasificar según el régimen alimenticio cerdos Ibéricos producidos bajo condiciones no experimentales. Se han empleado 121 muestras de grasa líquida procedentes de grasa subcutánea de canales de cerdos Ibéricos pertenecientes a 5 partidas con regímenes alimenticios diferentes. A dichas muestras líquidas se les determinó el contenido en 11 ácidos grasos (C14:0, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C20:1 y se obtuvo su espectro NIR. Los modelos de clasificación multivariantes se desarrollaron mediante Análisis Discriminante Lineal. Dichos
Directory of Open Access Journals (Sweden)
Gregor M Hoerzer
2010-05-01
Full Text Available Processing and storage of sensory information is based on the interaction between different neural populations rather than the isolated activity of single neurons. In order to characterize the dynamic interaction and transient cooperation of sub-circuits within a neural network, multivariate autoregressive (MVAR models have proven to be an important analysis tool. In this study, we apply directed functional coupling based on MVAR models and describe the temporal and spatial changes of functional coupling between simultaneously recorded local field potentials (LFP in extrastriate area V4 during visual memory. Specifically, we compare the strength and directional relations of coupling based on Generalized Partial Directed Coherence (GDPC measures while two rhesus monkeys perform a visual short-term memory task. In both monkeys we find increases in theta power during the memory period that are accompanied by changes in directed coupling. These interactions are most prominent in the low frequency range encompassing the theta band (3-12~Hz and, more importantly, are asymmetric between pairs of recording sites. Furthermore, we find that the degree of interaction decreases as a function of distance between electrode positions, suggesting that these interactions are a predominantly local phenomenon. Taken together, our results show that directed coupling measures based on MVAR models are able to provide important insights into the spatial and temporal formation of local functionally coupled ensembles during visual memory in V4. Moreover, our findings suggest that visual memory is accompanied not only by a temporary increase of oscillatory activity in the theta band, but by a direction-dependent change in theta coupling, which ultimately represents a change in functional connectivity within the neural circuit.
Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C
2008-01-01
As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.
Bangdiwala, S I; Anzola-Pérez, E
1990-03-01
Injuries and accidents are acknowledged as leading causes of morbidity and mortality among children and adolescents in the developing countries of the world. The Pan American Health Organization sponsored a collaborative study in four selected countries in Latin America to study the extent of the problem as well as to examine the potential risk factors associated with selected non-fatal injuries in the countries. The study subjects were injured children and adolescents (0-19 years of age) presenting at the study hospitals in chosen urban centres, as well as injured that were surveyed in households in the catchment areas of the hospitals. Study methods and descriptive frequency results were presented earlier. In this paper, log-linear multivariate regression models are used to examine the potentiating effects within country of several measured variables on specific types of injuries. The significance of risk factors varied between countries; however, some general patterns emerged. Falls were more likely in younger children, and occurred at home. The main risk factor for home accidents was the age of the child. The education of the head of the household was an important risk factor for the type of injury suffered. The likelihood of traffic accident injury varied with time of day and day of the week, but also was more likely in higher educated households. The results found are consistent with those found in other studies in the developed world and suggest specific areas of concern for health planners to address.
Directory of Open Access Journals (Sweden)
Junxiu Ma
2017-01-01
Full Text Available 3,5-Diamino-1,2,4-triazole (DAT became a significant energetic materials intermediate, and the study of its reaction mechanism has fundamental significance in chemistry. The aim of this study is to investigate the ability of online attenuated total reflection infrared (ATR-IR spectroscopy combined with the novel approach of hybrid hard- and soft-modelling multivariate curve resolution-alternating least squares (HS-MCR analysis to monitor and detect changes in structural properties of compound during 3,5-diamino-1,2,4-triazole (DAT synthesis processes. The subspace comparison method (SCM was used to obtain the principal components number, and then the pure IR spectra of each substance were obtained by independent component analysis (ICA and HS-MCR. The extent of rotation ambiguity was estimated from the band boundaries of feasible solutions calculated using the MCR-BANDS procedure. There were five principal components including two intermediates in the process in the results. The reaction rate constants of DAT formation reaction were also obtained by HS-MCR. HS-MCR was used to analyze spectroscopy data in chemical synthesis process, which not only increase the information domain but also reduce the ambiguities of the obtained results. This study provides the theoretical basis for the optimization of synthesis process and technology of energetic materials and provides a strong technical support of research and development of energy material with extraordinary damage effects.
Ma, Junxiu; Qi, Juan; Gao, Xinyu; Yan, Chunhua; Zhang, Tianlong; Tang, Hongsheng; Li, Hua
2017-01-01
3,5-Diamino-1,2,4-triazole (DAT) became a significant energetic materials intermediate, and the study of its reaction mechanism has fundamental significance in chemistry. The aim of this study is to investigate the ability of online attenuated total reflection infrared (ATR-IR) spectroscopy combined with the novel approach of hybrid hard- and soft-modelling multivariate curve resolution-alternating least squares (HS-MCR) analysis to monitor and detect changes in structural properties of compound during 3,5-diamino-1,2,4-triazole (DAT) synthesis processes. The subspace comparison method (SCM) was used to obtain the principal components number, and then the pure IR spectra of each substance were obtained by independent component analysis (ICA) and HS-MCR. The extent of rotation ambiguity was estimated from the band boundaries of feasible solutions calculated using the MCR-BANDS procedure. There were five principal components including two intermediates in the process in the results. The reaction rate constants of DAT formation reaction were also obtained by HS-MCR. HS-MCR was used to analyze spectroscopy data in chemical synthesis process, which not only increase the information domain but also reduce the ambiguities of the obtained results. This study provides the theoretical basis for the optimization of synthesis process and technology of energetic materials and provides a strong technical support of research and development of energy material with extraordinary damage effects.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...
Likelihood estimators for multivariate extremes
Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.
2015-01-01
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Likelihood estimators for multivariate extremes
Huser, Raphaël
2015-11-17
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.