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Author : C. P. Quesenberry
Publisher :
ISBN 13 :
Total Pages : 46 pages
Book Rating : 4.:/5 (884 download)
Download or read book Analyzing Uniform Residuals for Misspecification of a Linear Regression Model written by C. P. Quesenberry and published by . This book was released on 1982 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : A. P. Rainosek
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (227 download)
Download or read book Linear Models with Uniformly Distributed Residuals written by A. P. Rainosek and published by . This book was released on 1968 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we consider regression analysis assuming uniformly distributed residuals for the general-linear-hypothesis model of full rank Y = X beta + e where Y is a n x 1 observable random vector, X is a n x k matrix of known values with rank = k where k = or
Author : Douglas C. Montgomery
Publisher : John Wiley & Sons
ISBN 13 : 1118548507
Total Pages : 112 pages
Book Rating : 4.1/5 (185 download)
Download or read book Solutions Manual to accompany Introduction to Linear Regression Analysis written by Douglas C. Montgomery and published by John Wiley & Sons. This book was released on 2013-04-23 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.
Author : Christopher Bingham
Publisher :
ISBN 13 :
Total Pages : 14 pages
Book Rating : 4.:/5 (258 download)
Download or read book Some Identities Useful in the Analysis of Residuals from Linear Regression written by Christopher Bingham and published by . This book was released on 1977 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Alvin C. Rencher
Publisher : John Wiley & Sons
ISBN 13 : 0470192607
Total Pages : 690 pages
Book Rating : 4.4/5 (71 download)
Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Author : Stella Ingileela
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783659389757
Total Pages : 136 pages
Book Rating : 4.3/5 (897 download)
Download or read book On Some Aspects Of Residual Analysis In Regression Models written by Stella Ingileela and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book an attempt has been made by proposing some inferential techniques for linear regression models by using various types of residuals other than OLS residuals.Chapter I contains the general introduction about the concept of residuals in regression analysis. Various types of residuals and their properties have been described in chapter II. The literature on residual analysis and regression diagnostics have been reviewed in chapter III. In particular, it is discussed about examination of residual plots diagnostics for identifying unusual data points, detection of influential subsets, until applications of residuals. Chapter IV contains the proposed methods under is the contributory one in which outliers are detected; Leverage and influential points are identified; Goldfeld-Quandt test statistics computed to know the heteroscodastocity; Chow test statistic is computed to know the structural change and Durbin-Waston test statistic is competed to know the autocorrelation using different types of residuals.
Author : William H. DuMouchel
Publisher :
ISBN 13 :
Total Pages : 27 pages
Book Rating : 4.:/5 (393 download)
Download or read book Using Sample Survey Weights to Test for Misspecification in a Linear Regression Model written by William H. DuMouchel and published by . This book was released on 1976 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Xin Yan
Publisher : World Scientific
ISBN 13 : 9812834109
Total Pages : 349 pages
Book Rating : 4.8/5 (128 download)
Download or read book Linear Regression Analysis written by Xin Yan and published by World Scientific. This book was released on 2009 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the techniques described in the book. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject area." --Book Jacket.
Author : Alan Agresti
Publisher : John Wiley & Sons
ISBN 13 : 1118730038
Total Pages : 471 pages
Book Rating : 4.1/5 (187 download)
Download or read book Foundations of Linear and Generalized Linear Models written by Alan Agresti and published by John Wiley & Sons. This book was released on 2015-02-23 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
Author : Richard A. Berk
Publisher : SAGE
ISBN 13 : 0761929045
Total Pages : 281 pages
Book Rating : 4.7/5 (619 download)
Download or read book Regression Analysis written by Richard A. Berk and published by SAGE. This book was released on 2004 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An interesting and lively text, rich in practical wisdom, written for people who do empirical work in the social sciences and their graduate students." --David A. Freedman, Professor of Statistics, University of California, Berkeley
Author : Mei-Yu Lee
Publisher :
ISBN 13 :
Total Pages : 22 pages
Book Rating : 4.:/5 (13 download)
Download or read book Computer Simulates the Effect of Internal Restriction on Residuals in Linear Regression Model with First-Order Autoregressive Procedures written by Mei-Yu Lee and published by . This book was released on 2014 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper demonstrates the impact of particular factors - such as a non-normal error distribution, constraints of the residuals, sample size, the multi-collinear values of independent variables and the autocorrelation coefficient - on the distributions of errors and residuals. This explains how residuals increasingly tend to a normal distribution with increased linear constraints on residuals from the linear regression analysis method. Furthermore, reduced linear requirements cause the shape of the error distribution to be more clearly shown on the residuals. We find that if the errors follow a normal distribution, then the residuals do as well. However, if the errors follow a U-quadratic distribution, then the residuals have a mixture of the error distribution and a normal distribution due to the interaction of linear requirements and sample size. Thus, increasing the constraints on the residual from more independent variables causes the residuals to follow a normal distribution, leading to a poor estimator in the case where errors have a non-normal distribution. Only when the sample size is large enough to eliminate the effects of these linear requirements and multi-collinearity can the residuals be viewed as an estimator of the errors.
Author : Jun Li
Publisher :
ISBN 13 : 9781124293363
Total Pages : 298 pages
Book Rating : 4.2/5 (933 download)
Download or read book Residuals for the General Linear Model with Patterned Covariance Matrices written by Jun Li and published by . This book was released on 2010 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gelman et al. (2003) state that, "Checking the model is crucial to statistical analysis". Residuals have long been used to perform model assessment. However, properties of residuals for normal linear models of correlated observations have not been assessed. In this study, various studentized residuals are examined in terms of their ability to produce approximate standard normal distributions under correct model specification. This assessment includes both frequentist and Bayesian modeling approaches. Not only is there interest in assessing the properties of such residuals, but there is also interest in obtaining suitable diagnostic plots and tests to check violations of model assumptions when a fitted model is misspecified. This includes the ability of the residuals to identify misspecification of the covariance structure. Residuals that have been recommended in the literature are considered as well as new residuals based upon replicated observations. Particular emphasis is given to models with patterned covariance matrices in order to examine the effects of covariance matrix complexity and the number of subjects on the properties of the proposed residuals and diagnostics.
Author : Rebekka Topp
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (112 download)
Download or read book Regression and Residual Analysis in Linear Models with Interval Censored Data written by Rebekka Topp and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Chris M. Alaouze
Publisher :
ISBN 13 :
Total Pages : 32 pages
Book Rating : 4.3/5 ( download)
Download or read book Systematic Patterns in the Residual Plots of Short-panel Regression Models written by Chris M. Alaouze and published by . This book was released on 1990 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : David O'Sullivan
Publisher : John Wiley & Sons
ISBN 13 : 1119023874
Total Pages : 432 pages
Book Rating : 4.1/5 (19 download)
Download or read book Geographic Information Analysis written by David O'Sullivan and published by John Wiley & Sons. This book was released on 2014-07-30 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clear, up-to-date coverage of methods for analyzing geographicalinformation in a GIS context Geographic Information Analysis, Second Edition is fullyupdated to keep pace with the most recent developments of spatialanalysis in a geographic information systems (GIS) environment.Still focusing on the universal aspects of this science, thisrevised edition includes new coverage on geovisualization andmapping as well as recent developments using local statistics. Building on the fundamentals, this book explores such keyconcepts as spatial processes, point patterns, and autocorrelationin area data, as well as in continuous fields. Also addressed aremethods for combining maps and performing computationally intensiveanalysis. New chapters tackle mapping, geovisualization, and localstatistics, including the Moran Scatterplot and GeographicallyWeighted Regression (GWR). An appendix provides a primer on linearalgebra using matrices. Complete with chapter objectives, summaries, "thoughtexercises," explanatory diagrams, and a chapter-by-chapterbibliography, Geographic Information Analysis is a practicalbook for students, as well as a valuable resource for researchersand professionals in the industry.
Author : Raymond Jozef Gerardus Maria Florax
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (249 download)
Download or read book Misspecification in Linear Spatial Regression Models written by Raymond Jozef Gerardus Maria Florax and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : C. Ramesh Reddy
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783659503467
Total Pages : 108 pages
Book Rating : 4.5/5 (34 download)
Download or read book Residuals and Their Applications in Econometrics written by C. Ramesh Reddy and published by LAP Lambert Academic Publishing. This book was released on 2014-01 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the Present Book Chapter-I is an introductory one. Chapter - II deals the various types of residuals discussed in the literature for the linear regression analysis. Different types of residuals such as OLS, BLUS, Recursive, Internally and Externally studentized, predicted, and weighted residuals have been explained with their properties. Chapter - III presents some new applications of residuals in linear regression models under the problem of heteroscedasticity.Chapter - IV proposes some criteria for testing the equality between sets of regression coefficients in two linear models under two different specifications of error variance using studentized residuals. Chapter - V depicts the conclusion of the present research study. Various selected references regarding present study have been documented under a separate title "BIBLIOGRAPHY."
