Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Bootstrap Tests Of Nonnested Linear Regression Models
Download Bootstrap Tests Of Nonnested Linear Regression Models full books in PDF, epub, and Kindle. Read online Bootstrap Tests Of Nonnested Linear Regression Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author :Russell Davidson Publisher :Kingston, Ont. : Institute for Economic Research, Queen's University ISBN 13 : Total Pages :24 pages Book Rating :4.:/5 (432 download)
Book Synopsis Bootstrap Tests of Nonnested Linear Regression Models by : Russell Davidson
Download or read book Bootstrap Tests of Nonnested Linear Regression Models written by Russell Davidson and published by Kingston, Ont. : Institute for Economic Research, Queen's University. This book was released on 1997 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fast Double Bootstrap Tests of Nonnested Linear Regression Models by : Russell Davidson
Download or read book Fast Double Bootstrap Tests of Nonnested Linear Regression Models written by Russell Davidson and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bootstrap Test of Nonnested Linear Regression Models by : Russell Davidson
Download or read book Bootstrap Test of Nonnested Linear Regression Models written by Russell Davidson and published by . This book was released on 1997 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bootstrap Tests for Regression Models by : L. Godfrey
Download or read book Bootstrap Tests for Regression Models written by L. Godfrey and published by Springer. This book was released on 2009-07-29 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation-based tests to help perform econometric analyses.
Book Synopsis Bootstrap Methods by : Gerhard Dikta
Download or read book Bootstrap Methods written by Gerhard Dikta and published by Springer Nature. This book was released on 2021-08-10 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
Book Synopsis Non-nested Models and the Likelihood Ratio Statistic by : George Kapetanios
Download or read book Non-nested Models and the Likelihood Ratio Statistic written by George Kapetanios and published by . This book was released on 2003 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bootstrap Methods by : Michael R. Chernick
Download or read book Bootstrap Methods written by Michael R. Chernick and published by John Wiley & Sons. This book was released on 2011-09-23 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.
Book Synopsis Bootstrap Tests for the Error Distribution in Linear and Nonparametric Regression Models by : Natalie Neumeyer
Download or read book Bootstrap Tests for the Error Distribution in Linear and Nonparametric Regression Models written by Natalie Neumeyer and published by . This book was released on 2004 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bootstraptests in Linear Models with Many Regressors by : Patrick Richard
Download or read book Bootstraptests in Linear Models with Many Regressors written by Patrick Richard and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is concerned with bootstrap hypothesis testing in high dimensional linear regression models. Using a theoretical framework recently introduced by Anatolyev (2012), we show that bootstrap F, LR and LM tests are asymptotically valid even when the numbers of estimated parameters and tested restrictions are not asymptotically negligible fractions of the sample size. These results are derived for models with iid error terms, but Monte Carlo evidence suggests that they extend to the wild bootstrap in the presence of heteroskedasticity and to bootstrap methods for heavy tailed data.
Book Synopsis Tests of non-nested regression models : small sample adjustments and monte carlo evidence by : L. G. Godfrey
Download or read book Tests of non-nested regression models : small sample adjustments and monte carlo evidence written by L. G. Godfrey and published by . This book was released on 1985 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bootstrapped Information-theoretic Model Selection with Error Control (BITSEC) by : Michael J. Cullan
Download or read book Bootstrapped Information-theoretic Model Selection with Error Control (BITSEC) written by Michael J. Cullan and published by . This book was released on 2018 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which has made it increasingly popular in the natural and social sciences. Despite their common usage, model selection methods are not driven by a notion of statistical confidence, so their results entail an unknown degree of uncertainty. This paper introduces a general framework which extends notions of Type-I and Type-II error to model selection. A theoretical method for controlling Type-I error using Difference of Goodness of Fit (DGOF) distributions is given, along with a bootstrap approach that approximates the procedure. Results are presented for simulated experiments using normal distributions, random walk models, nested linear regression, and nonnested regression including nonlinear models. Tests are performed using an R package developed by the author which will be made publicly available on journal publication of research results.
Book Synopsis Model Choice in Nonnested Families by : Basilio de Bragança Pereira
Download or read book Model Choice in Nonnested Families written by Basilio de Bragança Pereira and published by Springer. This book was released on 2016-12-30 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
Book Synopsis Tests of Non-nested Linear Regression Models Subject to Linear Restrictions by : M. Hashem Pesaran
Download or read book Tests of Non-nested Linear Regression Models Subject to Linear Restrictions written by M. Hashem Pesaran and published by . This book was released on 1986 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Beyond Multiple Linear Regression by : Paul Roback
Download or read book Beyond Multiple Linear Regression written by Paul Roback and published by CRC Press. This book was released on 2021-01-14 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Book Synopsis Econometric Modeling and Inference by : Jean-Pierre Florens
Download or read book Econometric Modeling and Inference written by Jean-Pierre Florens and published by Cambridge University Press. This book was released on 2007-07-02 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.
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 Applied Nonparametric Econometrics by : Daniel J. Henderson
Download or read book Applied Nonparametric Econometrics written by Daniel J. Henderson and published by Cambridge University Press. This book was released on 2015-01-12 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.
