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Least Squares Regressions With The Bootstrap
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Book Synopsis Applied Regression Analysis and Generalized Linear Models by : John Fox
Download or read book Applied Regression Analysis and Generalized Linear Models written by John Fox and published by SAGE Publications. This book was released on 2015-03-18 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.
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 : 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 Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition by : Bryan F.J. Manly
Download or read book Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition written by Bryan F.J. Manly and published by CRC Press. This book was released on 1997-03-01 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition features new material on on bootstrap confidence intervals and significance testing, and incorporates new developments on the treatments of randomization methods for regression and analysis variation, including descriptions of applications of these methods in spreadsheet programs such as Lotus and other commercial packages. This second edition illustrates the value of modern computer intensive methods in the solution of a wide range of problems, with particular emphasis on biological applications. Examples given in the text include the controversial topic of whether there is periodicity between co-occurrences of species on islands.
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 Least Squares Regressions with the Bootstrap by : Jonas Böhmer
Download or read book Least Squares Regressions with the Bootstrap written by Jonas Böhmer and published by GRIN Verlag. This book was released on 2009-09-08 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diploma Thesis from the year 2009 in the subject Mathematics - Statistics, grade: 1,6, University of Bonn (Statistische Abteilung der Rechts- und Staatswissenschaftlichen Fakultät), course: Diplomarbeit bei Prof.Dr. Alois Kneip, language: English, abstract: The statistical technique called bootstrap is usable with a lot of inferential problems and it is the main topic of this paper. Since the bootstrap provides material for a whole series of books it is essential to pick one special aspect of the bootstrap and investigate it in depth, otherwise the analysis would inevitably become too general. This aspect is the topic of regression. Hence, this paper will introduce the bootstrap and compare the performance of the new inference methods which it provides with some classical methods of judging a regression which were used in the years before the bootstrap. Therefore the remainder of this paper is as follows: First there will be a description of the basic model in which all of the following investigations will be done, chapter two. The next chapter will describe the different regression techniques which try to solve the model. The fourth chapter is going to show the behavior of these regression techniques in large samples, i.e. shows some classical methods of statistical inference. Following chapter five will give an introduction to the bootstrap which will be succeeded by a description of the bootstrap in regression problems, chapter six. The seventh chapter will show how inference is done with the help of the bootstrap. The eighth chapter is going to compare the performances of classical and bootstrap inference in regressions. Before the concluding remarks of chapter ten, there will be a practical application in chapter nine which tries to prove some observations of the preceeding chapters.
Book Synopsis Least Squares Regressions with the Bootstrap by : Jonas Böhmer
Download or read book Least Squares Regressions with the Bootstrap written by Jonas Böhmer and published by GRIN Verlag. This book was released on 2009-09 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diploma Thesis from the year 2009 in the subject Statistics, grade: 1,6, University of Bonn (Statistische Abteilung der Rechts- und Staatswissenschaftlichen Fakult t), course: Diplomarbeit bei Prof.Dr. Alois Kneip, language: English, abstract: The statistical technique called bootstrap is usable with a lot of inferential problems and it is the main topic of this paper. Since the bootstrap provides material for a whole series of books it is essential to pick one special aspect of the bootstrap and investigate it in depth, otherwise the analysis would inevitably become too general. This aspect is the topic of regression. Hence, this paper will introduce the bootstrap and compare the performance of the new inference methods which it provides with some classical methods of judging a regression which were used in the years before the bootstrap. Therefore the remainder of this paper is as follows: First there will be a description of the basic model in which all of the following investigations will be done, chapter two. The next chapter will describe the different regression techniques which try to solve the model. The fourth chapter is going to show the behavior of these regression techniques in large samples, i.e. shows some classical methods of statistical inference. Following chapter five will give an introduction to the bootstrap which will be succeeded by a description of the bootstrap in regression problems, chapter six. The seventh chapter will show how inference is done with the help of the bootstrap. The eighth chapter is going to compare the performances of classical and bootstrap inference in regressions. Before the concluding remarks of chapter ten, there will be a practical application in chapter nine which tries to prove some observations of the preceeding chapters.
Book Synopsis Randomization, Bootstrap and Monte Carlo Methods in Biology by : Bryan F.J. Manly
Download or read book Randomization, Bootstrap and Monte Carlo Methods in Biology written by Bryan F.J. Manly and published by CRC Press. This book was released on 2018-10-03 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications. This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals. Similar to randomization, the book shows how bootstrapping, or resampling, can be used for confidence intervals and tests of significance. It also explores how to use Monte Carlo methods to test hypotheses and construct confidence intervals. New to the Third Edition Updated information on regression and time series analysis, multivariate methods, survival and growth data as well as software for computational statistics References that reflect recent developments in methodology and computing techniques Additional references on new applications of computer-intensive methods in biology Providing comprehensive coverage of computer-intensive applications while also offering data sets online, Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition supplies a solid foundation for the ever-expanding field of statistics and quantitative analysis in biology.
Book Synopsis Practical Spreadsheet Modeling Using @Risk by : Dale Lehman
Download or read book Practical Spreadsheet Modeling Using @Risk written by Dale Lehman and published by CRC Press. This book was released on 2019-11-11 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Spreadsheet Modeling Using @Risk provides a guide of how to construct applied decision analysis models in spreadsheets. The focus is on the use of Monte Carlo simulation to provide quantitative assessment of uncertainties and key risk drivers. The book presents numerous examples based on real data and relevant practical decisions in a variety of settings, including health care, transportation, finance, natural resources, technology, manufacturing, retail, and sports and entertainment. All examples involve decision problems where uncertainties make simulation modeling useful to obtain decision insights and explore alternative choices. Good spreadsheet modeling practices are highlighted. The book is suitable for graduate students or advanced undergraduates in business, public policy, health care administration, or any field amenable to simulation modeling of decision problems. The book is also useful for applied practitioners seeking to build or enhance their spreadsheet modeling skills. Features Step-by-step examples of spreadsheet modeling and risk analysis in a variety of fields Description of probabilistic methods, their theoretical foundations, and their practical application in a spreadsheet environment Extensive example models and exercises based on real data and relevant decision problems Comprehensive use of the @Risk software for simulation analysis, including a free one-year educational software license
Book Synopsis Regression and Time Series Model Selection by : Allan D. R. McQuarrie
Download or read book Regression and Time Series Model Selection written by Allan D. R. McQuarrie and published by World Scientific. This book was released on 1998 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.
Book Synopsis A Gentle Introduction to Stata, Second Edition by : Alan C. Acock
Download or read book A Gentle Introduction to Stata, Second Edition written by Alan C. Acock and published by Stata Press. This book was released on 2008-09-03 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: "A Gentle Introduction to Stata, Second Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will not only be able to use Stata well but also learn new aspects of Stata easily. Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the portion of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that should help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book. Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material. The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward-referencing. Rather than splitting topics by their Stata implementation, Acock chose to arrange the topics as they would be in a basic statistics textbook; graphics and postestimation are woven into the material in a natural fashion. Real datasets, such as the General Social Surveys from 2002 and 2006, are used throughout the book. The focus of the book is especially helpful for those in psychology and the social sciences, because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. The second edition of the book has been updated to reflect new features in Stata 10 and includes a new chapter on the use of factor analysis to develop valid, reliable scale measures."--Publisher's website.
Download or read book Risk Analysis written by David Vose and published by John Wiley & Sons. This book was released on 2008-04-28 with total page 754 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk Analysis concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modeling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems.
Download or read book Comprehensive Chemometrics written by and published by Elsevier. This book was released on 2009-03-09 with total page 2880 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed to serve as the first point of reference on the subject, Comprehensive Chemometrics presents an integrated summary of the present state of chemical and biochemical data analysis and manipulation. The work covers all major areas ranging from statistics to data acquisition, analysis, and applications. This major reference work provides broad-ranging, validated summaries of the major topics in chemometrics—with chapter introductions and advanced reviews for each area. The level of material is appropriate for graduate students as well as active researchers seeking a ready reference on obtaining and analyzing scientific data. Features the contributions of leading experts from 21 countries, under the guidance of the Editors-in-Chief and a team of specialist Section Editors: L. Buydens; D. Coomans; P. Van Espen; A. De Juan; J.H. Kalivas; B.K. Lavine; R. Leardi; R. Phan-Tan-Luu; L.A. Sarabia; and J. Trygg Examines the merits and limitations of each technique through practical examples and extensive visuals: 368 tables and more than 1,300 illustrations (750 in full color) Integrates coverage of chemical and biological methods, allowing readers to consider and test a range of techniques Consists of 2,200 pages and more than 90 review articles, making it the most comprehensive work of its kind Offers print and online purchase options, the latter of which delivers flexibility, accessibility, and usability through the search tools and other productivity-enhancing features of ScienceDirect
Book Synopsis An Introduction to Bartlett Correction and Bias Reduction by : Gauss M. Cordeiro
Download or read book An Introduction to Bartlett Correction and Bias Reduction written by Gauss M. Cordeiro and published by Springer Science & Business Media. This book was released on 2014-05-08 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique and discuss concrete applications to several important statistical models.
Book Synopsis Dividend Policy by : George Frankfurter
Download or read book Dividend Policy written by George Frankfurter and published by Elsevier. This book was released on 2003-06-24 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dividend Policy provides a comprehensive study of dividend policy. It explores the puzzle presented by dividends: irrational and subject to fashion, yet popular and desirable, they remain a priority among managers, even while perceived as largely symbolic. After exploring the history of dividend payments, from the emergence of the modern corporation to current perspectives, it traces the evolution of academic models on dividend policy. Here the authors review models of symmetric and asymmetric information before analyzing academia's accomplishments in solving the dividend puzzle. Related subjects, such as valuation and wealth distribution, round out the authors' presentation about new ways to think about one of the most intriguing subjects in financial economics. The book is recommended for professors and students in departments of finance and business, corporate finance staff, and financial regulators. The only comprehensive study of dividend policy Covers the historical evolution of dividends and academic research on dividend policy Presents new ways of thinking about dividends and dividend policy
Book Synopsis Modern Statistics for the Social and Behavioral Sciences by : Rand Wilcox
Download or read book Modern Statistics for the Social and Behavioral Sciences written by Rand Wilcox and published by CRC Press. This book was released on 2011-08-05 with total page 862 pages. Available in PDF, EPUB and Kindle. Book excerpt: In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.
Book Synopsis Learning Statistics Using R by : Randall E. Schumacker
Download or read book Learning Statistics Using R written by Randall E. Schumacker and published by SAGE Publications. This book was released on 2014-01-28 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Randall E. Schumacker’s comprehensive book describes in detail the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Learning Statistics Using R covers data input from vectors, arrays, matrices and data frames, as well as the input of data sets from SPSS, SAS, STATA and other software packages. Schumacker’s text provides the freedom to effectively calculate, manipulate, and graphically display data, using R, on different computer operating systems without the expense of commercial software. Learning Statistics Using R places statistics within the framework of conducting research, where statistical research hypotheses can be directly addressed. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers′ understanding of statistics through statistical computing and modeling.
