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A Generalized Bayesian Instrumental Variable Approach Under Student T Distributed Errors With Application
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Book Synopsis A Generalized Bayesian Instrumental Variable Approach Under Student T-Distributed Errors with Application by : Matthew Jude Salois
Download or read book A Generalized Bayesian Instrumental Variable Approach Under Student T-Distributed Errors with Application written by Matthew Jude Salois and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis is given of an instrumental variable model that allows for t-distributed errors in both the structural equation and the instrument equation. Specifically, the approach for dealing with t-distributed errors is extended to the Bayesian instrumental variable estimator by modelling the variance for each error using a Gamma distributed hierarchical prior. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm for the heteroskedastic case. An example using data illustrates the approach and shows that ignoring the presence of error terms with heavy tails in the instrument equation when it exists may lead to biased estimates.
Book Synopsis Advances in Swarm Intelligence by : Ying Tan
Download or read book Advances in Swarm Intelligence written by Ying Tan and published by Springer Nature. This book was released on 2021-07-07 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.
Download or read book NBS Special Publication written by and published by . This book was released on 1970 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem by : Timothy G. Conley
Download or read book A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem written by Timothy G. Conley and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable equations errors. Our implementation of the Dirichlet process prior uses a normal distribution as a base model. It can therefore be interpreted as modeling the unknown joint distribution with a mixture of normal distributions with a variable number of mixture components. We demonstrate that this procedure is both feasible and sensible using actual and simulated data. Sampling experiments compare inferences from the non-parametric Bayesian procedure with those based on procedures from the recent literature on weak instrument asymptotics. When errors are non-normal, our procedure is more efficient than standard Bayesian or classical methods.
Book Synopsis Mathematical Statistics for Applied Econometrics by : Charles B Moss
Download or read book Mathematical Statistics for Applied Econometrics written by Charles B Moss and published by CRC Press. This book was released on 2014-10-16 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introductory Econometrics TextMathematical Statistics for Applied Econometrics covers the basics of statistical inference in support of a subsequent course on classical econometrics. The book shows students how mathematical statistics concepts form the basis of econometric formulations. It also helps them think about statistics as more than a to
Book Synopsis Bayesian Two-Stage Robust Causal Modeling with Instrumental Variables Using Student's T Distributions by : Dingjing Shi
Download or read book Bayesian Two-Stage Robust Causal Modeling with Instrumental Variables Using Student's T Distributions written by Dingjing Shi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In causal inference research, the issue of the treatment endogeneity is commonly addressed using the two-stage least squares (2SLS) modeling with instrumental variables (IVs), where the local average treatment effect (LATE) is the causal effect of interest. Because practical data are usually heavy tailed or contain outliers, using traditional 2SLS modeling based on normality assumptions may result in inefficient or even biased LATE estimate. This study proposes four types of Bayesian two-stage robust causal models with IVs to model normal and nonnormal data, and evaluates the performance of the four types of models with IVs. The Monte Carlo simulation results show that the Bayesian two-stage robust causal modeling produces reliable parameter estimates and model fits. Particularly, in different types of the two-stage robust models with IVs, the models that take outliers into consideration and use Student's t distributions in the second stage to model heavy-tailed data or data containing outliers provide more accurate and efficient LATE estimates and better model fits than other distribution models when data are contaminated. The preferred models are recommended to be adopted in general in the two-stage causal modeling with IVs.
Book Synopsis An Author and Permuted Title Index to Selected Statistical Journals by : Brian L. Joiner
Download or read book An Author and Permuted Title Index to Selected Statistical Journals written by Brian L. Joiner and published by . This book was released on 1970 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: All articles, notes, queries, corrigenda, and obituaries appearing in the following journals during the indicated years are indexed: Annals of mathematical statistics, 1961-1969; Biometrics, 1965-1969#3; Biometrics, 1951-1969; Journal of the American Statistical Association, 1956-1969; Journal of the Royal Statistical Society, Series B, 1954-1969,#2; South African statistical journal, 1967-1969,#2; Technometrics, 1959-1969.--p.iv.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Book Synopsis Bayesian Approaches for Instrumental Variable Analysis with Censored Time-to-event Outcome by : Xuyang Lu
Download or read book Bayesian Approaches for Instrumental Variable Analysis with Censored Time-to-event Outcome written by Xuyang Lu and published by . This book was released on 2014 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: The method of instrumental variable (IV) analysis has been widely used in economics, epidemiology, and other fields to estimate the causal effects of intermediate covariates on outcomes, in the presence of unobserved confounders and/or measurement errors in covariates. Consistent estimation of the effect has been developed when the outcome is continuous, while methods for binary outcome produce inconsistent estimation. In this dissertation, we examine two IV methods in the literature for binary outcome and show the bias in parameter estimate by a simulation study. The identifiability problem of IV analysis with binary outcome is discussed. Moreover, IV methods for time-to-event outcome with censored data remain underdeveloped. We propose two Bayesian approaches for IV analysis with censored time-to-event outcome by using a two-stage linear model: One is a parametric Bayesian model with normal and non-normal elliptically contoured error distributions, and the other is a semiparametric Bayesian model with Dirichlet process mixtures for the random errors, in order to relax the parametric assumptions and address heterogeneous clustering problems. Markov Chain Monte Carlo sampling methods are developed for both parametric and semiparametric Bayesian models to estimate the endogenous parameter. Performance of our methods is examined by simulation studies. Both methods largely reduce bias in estimation and greatly improve coverage probability of the endogenous parameter, compared to the regular method where the unobserved confounders and/or measurement errors are ignored. We illustrate our methods on the Women's Health Initiative Observational Study and the Atherosclerosis Risk in Communities Study.
Book Synopsis Bayesian Analysis of Instrumental Variable Models by :
Download or read book Bayesian Analysis of Instrumental Variable Models written by and published by . This book was released on 2012 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Guide to Econometrics by : Peter Kennedy
Download or read book A Guide to Econometrics written by Peter Kennedy and published by John Wiley & Sons. This book was released on 2008-02-19 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dieses etwas andere Lehrbuch bietet keine vorgefertigten Rezepte und Problemlösungen, sondern eine kritische Diskussion ökonometrischer Modelle und Methoden: voller überraschender Fragen, skeptisch, humorvoll und anwendungsorientiert. Sein Erfolg gibt ihm Recht.
Book Synopsis Bayesian Inference by : Javier Prieto Tejedor
Download or read book Bayesian Inference written by Javier Prieto Tejedor and published by BoD – Books on Demand. This book was released on 2017-11-02 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.
Book Synopsis Contemporary Bayesian Econometrics and Statistics by : John Geweke
Download or read book Contemporary Bayesian Econometrics and Statistics written by John Geweke and published by John Wiley & Sons. This book was released on 2005-10-03 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including: * Linear models and policy choices * Modeling with latent variables and missing data * Time series models and prediction * Comparison and evaluation of models The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets. This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.
Book Synopsis Applied Linear Statistical Models by : Michael H. Kutner
Download or read book Applied Linear Statistical Models written by Michael H. Kutner and published by McGraw-Hill/Irwin. This book was released on 2005 with total page 1396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
Book Synopsis Bayesian Spectrum Analysis and Parameter Estimation by : G. Larry Bretthorst
Download or read book Bayesian Spectrum Analysis and Parameter Estimation written by G. Larry Bretthorst and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is essentially an extensive revision of my Ph.D. dissertation, [1J. It 1S primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material. Any person with the equivalent of the mathematics background required for the graduate level study of physics should be able to follow the material contained in this book, though not without eIfort. From the time the dissertation was written until now (approximately one year) our understanding of the parameter estimation problem has changed extensively. We have tried to incorporate what we have learned into this book. I am indebted to a number of people who have aided me in preparing this docu ment: Dr. C. Ray Smith, Steve Finney, Juana Sunchez, Matthew Self, and Dr. Pat Gibbons who acted as readers and editors. In addition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.
Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train
Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
