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Assessing The Fit Of Simulated Multivariate Dynamic Models
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Book Synopsis Assessing the Fit of Simulated Multivariate Dynamic Models by : Eva Ortega
Download or read book Assessing the Fit of Simulated Multivariate Dynamic Models written by Eva Ortega and published by . This book was released on 1998 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Se desarrolla una estructura formal basada en modelos dinámicos multivariables cuyas soluciones se aproximan a través de simulación. y se aplican a modelos de ciclo económico.
Book Synopsis Simulating Data with SAS by : Rick Wicklin
Download or read book Simulating Data with SAS written by Rick Wicklin and published by SAS Institute. This book was released on 2013 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software. This book is part of the SAS Press program.
Book Synopsis Multivariate Multiple-membership Random Effects Models by : Sunyoung Park (Ph. D. in educational psychology)
Download or read book Multivariate Multiple-membership Random Effects Models written by Sunyoung Park (Ph. D. in educational psychology) and published by . This book was released on 2018 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current dissertation, composed of two studies, focused on the models that handle several data structure complexities simultaneously. The first study introduced and evaluated Markov Chain Monte Carlo (MCMC) estimation of the multivariate multiple-membership random effects model (MV-MMREM) for handling multiple-membership data in scenarios with multiple, related outcomes. The second study assessed performance of the deviance information criterion (DIC) for selecting the best-fitting model to help support a researcher's choices about whether to add fixed effects to the MV-MMREM effects model. While a recent study introduced the idea of the MV-MMREM, no research has directly assessed its estimation nor demonstrated its use with real data. Therefore, real multiple-membership dataset were used that includes multiple related outcomes to demonstrate interpretation of MV-MMREM parameters. In addition, a simulation study was conducted to assess estimation of the MV-MMREM under a number of design conditions including the proportion of multiple membership individuals, the number of clusters, the sample size per cluster, the degree of correlation among pairs of outcomes, the true intra-class correlation coefficient, type of missingness, and proportion of missingness. Additionally, the robustness of results were assessed for multivariate multiple-membership data when analyzed using multivariate, hierarchical linear model that ignores the multiple-membership structure (MV-HLM) and when instead using multiple, univariate MMREMs (one for each outcome). The results were assessed using relative parameter bias (RPB), relative standard error bias (RSEB), and coverage rates. In the second study, another real dataset were analyzed with the resulting DIC values compared to demonstrate how it is used to support selection of the best-fitting model. In the associated simulation study, the same set of design conditions as in the first study (but not considered missingness and considered the magnitude of coefficients of predictor variables) were manipulated. The DIC was used to choose between three different conditional MV-MMREMs that differ in their fixed effects parameter specification. We assessed and compared correct model identification rates based on the DIC with inferences and based on the statistical significance of the parameters being added in the set of nested models.
Book Synopsis Handbook of Structural Equation Modeling by : Rick H. Hoyle
Download or read book Handbook of Structural Equation Modeling written by Rick H. Hoyle and published by Guilford Publications. This book was released on 2023-02-17 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--
Download or read book Time Series written by Raquel Prado and published by CRC Press. This book was released on 2021-07-27 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.
Book Synopsis Dynamic Linear Models with R by : Giovanni Petris
Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Book Synopsis Dynamic Modeling of Multivariate Counts by : Volodymyr Serhiyenko
Download or read book Dynamic Modeling of Multivariate Counts written by Volodymyr Serhiyenko and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by : Elias T. Krainski
Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski and published by CRC Press. This book was released on 2018-12-07 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Book Synopsis Modelling and Parameter Estimation of Dynamic Systems by : J.R. Raol
Download or read book Modelling and Parameter Estimation of Dynamic Systems written by J.R. Raol and published by IET. This book was released on 2004-08-13 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.
Book Synopsis Efficient Bayesian Parcor Approaches for Dynamic Modeling of Multivariate Time Series by : Weinjie Zhao
Download or read book Efficient Bayesian Parcor Approaches for Dynamic Modeling of Multivariate Time Series written by Weinjie Zhao and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian lattice filtering and smoothing approach is proposed for fast and accurate modeling and inference in multivariate non-stationary time series. This approach offers computational feasibility and interpretable time-frequency analysis in the multivariate context. The proposed framework allows us to obtain posterior estimates of the time-varying spectral densities of individual time series components, as well as posterior measurements of the time-frequency relationships across multiple components, such as time-varying coherence and partial coherence. The proposed formulation considers multivariate dynamic linear models (MDLMs) on the forward and backward time-varying partial autocorrelation coefficients (TV-VPARCOR). Computationally expensive schemes for posterior inference on the multivariate dynamic PARCOR model are avoided using approximations in the MDLM context. Approximate inference on the corresponding time-varying vector autoregressive (TV-VAR) coefficients is obtained via Whittle's algorithm. A key aspect of the proposed TV-VPARCOR representations is that they are of lower dimension, and therefore more efficient, than TV-VAR representations. The performance of the TV-VPARCOR models is illustrated in simulation studies and in the analysis of multivariate non-stationary temporal data arising in neuroscience and environmental applications. Model performance is evaluated using goodness-of-fit measurements in the time-frequency domain and also by assessing the quality of short-term forecasting.
Book Synopsis Multivariate GARCH and Dynamic Copula Models for Financial Time Series by : Martin Grziska
Download or read book Multivariate GARCH and Dynamic Copula Models for Financial Time Series written by Martin Grziska and published by Pro BUSINESS. This book was released on 2015-02-05 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk measures. Furthermore, the different dependence models are used to analyze the integration of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model. The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no single parametric model dominates all other models for all portfolios and risk measures. The investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less integrated into the world economy than proprietary. Thus, secondary emerging markets are moresuitable to diversify a portfolio consisting of developed equity or bond indices than proprietary.
Book Synopsis Seasonal Outliers in Time Series by : Regina Kaiser
Download or read book Seasonal Outliers in Time Series written by Regina Kaiser and published by . This book was released on 1999 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Modeling written by Bruce Hannon and published by . This book was released on 2012-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer models offer a means of interpreting and analyzing the dynamics of real-world systems ranging from population growth to ozone depletion. Dynamic Modeling introduces an approach to modeling that makes it a more practical, intuitive endeavor. The book enables readers to convert their understanding of a phenomenon to a computer model, and then to run the model and let it yield the inevitable dynamic consequences built into the structure of the model. Dynamic Modeling uses STELLA II software to develop simulation models. Part I provides an introduction to modeling dynamic systems. Part II offers general methods for modeling. Parts III through VIII apply these methods to model real-world phenomena from chemistry, genetics, ecology, economics, and engineering. To develop and execute dynamic simulation models, Dynamic Modeling comes with STELLA II run-time software for Windows-based computers, as well as computer files of sample models used in the book. Dynamic Modeling offers a clear, approachable introduction to the modeling process, and will be of interest in any field where real problems can be illuminated by computer simulation.
Download or read book Time Series written by Raquel Prado and published by CRC Press. This book was released on 2010-05-21 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites. Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.
Book Synopsis An Application of TRAMO-SEATS by : Regina Kaiser
Download or read book An Application of TRAMO-SEATS written by Regina Kaiser and published by . This book was released on 2000 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Longitudinal Multivariate Psychology by : Emilio Ferrer
Download or read book Longitudinal Multivariate Psychology written by Emilio Ferrer and published by Routledge. This book was released on 2018-10-09 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes. The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.
