Bayesian Non And Semi Parametric Methods And Applications
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Bayesian Non And Semi Parametric Methods And Applications
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Author : Peter Rossi
language : en
Publisher: Princeton University Press
Release Date : 2014-04-27
Bayesian Non And Semi Parametric Methods And Applications written by Peter Rossi and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-27 with Business & Economics categories.
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Bayesian Non And Semi Parametric Methods And Applications
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Author : Peter Rossi
language : en
Publisher: Princeton University Press
Release Date : 2014-04-27
Bayesian Non And Semi Parametric Methods And Applications written by Peter Rossi and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-27 with Business & Economics categories.
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Practical Nonparametric And Semiparametric Bayesian Statistics
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Author : Dipak D. Dey
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Practical Nonparametric And Semiparametric Bayesian Statistics written by Dipak D. Dey and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Handbook Of Missing Data Methodology
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Author : Geert Molenberghs
language : en
Publisher: CRC Press
Release Date : 2014-11-06
Handbook Of Missing Data Methodology written by Geert Molenberghs and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-06 with Mathematics categories.
Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.
Statistical Paradigms
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Author : Ashis SenGupta
language : en
Publisher: World Scientific
Release Date : 2014-10-03
Statistical Paradigms written by Ashis SenGupta and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-03 with Mathematics categories.
This volume consists of a collection of research articles on classical and emerging Statistical Paradigms — parametric, non-parametric and semi-parametric, frequentist and Bayesian — encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple Testing Procedures. The areas in emerging applications include: Bioinformatics, Factorial Experiments and Linear Models, Hotspot Geoinformatics and Reliability. Contents:Reviews:Weak Paradoxes and Paradigms (Jayanta K Ghosh)Nonparametrics in Modern Interdisciplinary Research: Some Perspectives and Prospectives (Pranab K Sen)Parametric:Bounds on Distributions Involving Partial, Marginal and Conditional Information: The Consequences of Incomplete Prior Specification (Barry C Arnold)Stepdown Procedures Controlling a Generalized False Discovery Rate (Wenge Guo and Sanat K Sarkar)On Confidence Intervals for Expected Response in 2n Factorial Experiments with Exponentially Distributed Response Variables (H V Kulkarni and S C Patil)Predictive Influence of Variables in a Linear Regression Model when the Moment Matrix is Singular (Md Nurul Haque Mollah and S K Bhattacharjee)New Wrapped Distributions — Goodness of Fit (A V Dattatreya Rao, I Ramabhadra Sarma and S V S Girija)Semi-Parametric:Non-Stationary Samples and Meta-Distribution (Dominique Guégan)MDL Model Selection Criterion for Mixed Models with an Application to Spline Smoothing (Antti Liski and Erkki P Liski)Digital Governance and Hotspot Geoinformatics with Continuous Fractional Response (G P Patil, S W Joshi and R E Koli)Bayesian Curve Registration of Functional Data (Z Zhong, A Majumdar and R L Eubank)Non-Parametric & Probability:Nonparametric Estimation in a One-Way Error Component Model: A Monte Carlo Analysis (Daniel J Henderson and Aman Ullah)GERT Analysis of Consecutive-k Systems: An Overview (Kanwar Sen, Manju Agarwal and Pooja Mohan)Moment Bounds for Strong-Mixing Processes with Applications (Ratan Dasgupta) Readership: Researchers, professionals and advanced students working on Bayesian and frequentist approaches to statistical modeling and on interfaces for both theory and applications. Key Features:A scholarly and motivating review of non-parametric methods by P K Sen, winner of the Wilks Medal in 2010Discussion of paradoxes of the frequentist and Bayesian paradigms, related counterexamples, and their implicationsStands out in terms of the width and depthKeywords:Bayesian Inference;Design of Experiments;Econometrics;Hotspot Geoinformatics;Linear Models and Regression Analysis;Multiple Testing Procedures;Probability Distributions for Linear and Directional Data;Reliability
Semiparametric Regression
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Author : David Ruppert
language : en
Publisher: Cambridge University Press
Release Date : 2003-07-14
Semiparametric Regression written by David Ruppert and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-14 with Mathematics categories.
Even experts on semiparametric regression should find something new here.
A Bayesian Approach To Additive Semi Parametric Regression
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Author : Chi-ming Wong
language : en
Publisher:
Release Date : 1993
A Bayesian Approach To Additive Semi Parametric Regression written by Chi-ming Wong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Bayesian statistical decision theory categories.
Topics In Identification Limited Dependent Variables Partial Observability Experimentation And Flexible Modeling
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Author : Ivan Jeliazkov
language : en
Publisher: Emerald Group Publishing
Release Date : 2019-10-18
Topics In Identification Limited Dependent Variables Partial Observability Experimentation And Flexible Modeling written by Ivan Jeliazkov and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-18 with Business & Economics categories.
Volume 40B of Advances in Econometrics examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.
Advanced Methods For Modeling Markets
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Author : Peter S. H. Leeflang
language : en
Publisher: Springer
Release Date : 2017-08-29
Advanced Methods For Modeling Markets written by Peter S. H. Leeflang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-29 with Business & Economics categories.
This volume presents advanced techniques to modeling markets, with a wide spectrum of topics, including advanced individual demand models, time series analysis, state space models, spatial models, structural models, mediation, models that specify competition and diffusion models. It is intended as a follow-on and companion to Modeling Markets (2015), in which the authors presented the basics of modeling markets along the classical steps of the model building process: specification, data collection, estimation, validation and implementation. This volume builds on the concepts presented in Modeling Markets with an emphasis on advanced methods that are used to specify, estimate and validate marketing models, including structural equation models, partial least squares, mixture models, and hidden Markov models, as well as generalized methods of moments, Bayesian analysis, non/semi-parametric estimation and endogeneity issues. Specific attention is given to big data. The market environment is changing rapidly and constantly. Models that provide information about the sensitivity of market behavior to marketing activities such as advertising, pricing, promotions and distribution are now routinely used by managers for the identification of changes in marketing programs that can improve brand performance. In today’s environment of information overload, the challenge is to make sense of the data that is being provided globally, in real time, from thousands of sources. Although marketing models are now widely accepted, the quality of the marketing decisions is critically dependent upon the quality of the models on which those decisions are based. This volume provides an authoritative and comprehensive review, with each chapter including: · an introduction to the method/methodology · a numerical example/application in marketing · references to other marketing applications · suggestions about software. Featuring contributions from top authors in the field, this volume will explore current and future aspects of modeling markets, providing relevant and timely research and techniques to scientists, researchers, students, academics and practitioners in marketing, management and economics.
Applied Bayesian Semiparametric Methods With Special Application To The Accelerated Failure Time Model And To Hierarchical Models For Screening
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Author : Timothy Edward Hanson
language : en
Publisher:
Release Date : 2000
Applied Bayesian Semiparametric Methods With Special Application To The Accelerated Failure Time Model And To Hierarchical Models For Screening written by Timothy Edward Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.