Quasi Least Squares Regression

Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of ...

Author: Justine Shults

Publisher: CRC Press

ISBN: 9781420099942

Category: Mathematics

Page: 221

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Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a d

Quasi Least Squares Regression

... and explanation without intent to infringe. Shults, Justine. Quasi-least squares regression / Justine Shults, Joseph M. Hilbe. pages cm. -- (Chapman & Hall/CRC monographs on statistics & applied probability) “A CRC title.

Author: Justine Shults

Publisher: CRC Press

ISBN: 9781420099935

Category: Mathematics

Page: 221

View: 382

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Drawing on the authors’ substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression—a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitudinal data, familial data, and data with multiple sources of correlation. In some settings, QLS also allows for improved analysis with an unstructured correlation matrix. Special focus is given to goodness-of-fit analysis as well as new strategies for selecting the appropriate working correlation structure for QLS and GEE. A chapter on longitudinal binary data tackles recent issues raised in the statistical literature regarding the appropriateness of semi-parametric methods, such as GEE and QLS, for the analysis of binary data; this chapter includes a comparison with the first-order Markov maximum-likelihood (MARK1ML) approach for binary data. Examples throughout the book demonstrate each topic of discussion. In particular, a fully worked out example leads readers from model building and interpretation to the planning stages for a future study (including sample size calculations). The code provided enables readers to replicate many of the examples in Stata, often with corresponding R, SAS, or MATLAB® code offered in the text or on the book’s website.

Analysis of Longitudinal Data with Example

Analysis of serially correlated data using quasilikelihood squares. Biometrics, 54:1622–1630, 1998. J. Shults, and J. M. Hilbe. Quasi-Least Squares Regression. Chapman & Hall/CRC Monographs on Statistics & Applied Probability.

Author: You-Gan Wang

Publisher: CRC Press

ISBN: 9781498764629

Category: Mathematics

Page: 248

View: 732

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Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code

Analysis of Longitudinal Data with Example

Analysis of serially correlated data using quasi-likelihood squares. Biometrics, 54: 1622–1630, 1998. J. Shults, and J. M. Hilbe. Quasi-Least Squares Regression. Chapman & Hall/CRC Monographs on Statistics & Applied Probability.

Author: You-Gan Wang

Publisher: CRC Press

ISBN: 9781351649674

Category: Mathematics

Page: 248

View: 231

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Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code

Transformation and Weighting in Regression

The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression.

Author: Raymond J. Carroll

Publisher: Routledge

ISBN: 9781351407267

Category: Mathematics

Page: 264

View: 324

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This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression.

Statistical Modelling in Biostatistics and Bioinformatics

LASSO penalised likelihood in high-dimensional contingency tables. In D. Conesa, A. Forte, ... On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. ... New York: Chapman & Hall/CRC.

Author: Gilbert MacKenzie

Publisher: Springer Science & Business Media

ISBN: 9783319045795

Category: Mathematics

Page: 244

View: 297

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This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.

Local Polynomial Modelling and Its Applications

Monographs on Statistics and Applied Probability 66 Jianqing Fan ... Estimating regression parameters using linear rank tests for censored data. ... Asymmetric least squares regression estimation: a nonparametric approach. J. Nonpar.

Author: Jianqing Fan

Publisher: Routledge

ISBN: 9781351434805

Category: Mathematics

Page: 360

View: 538

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Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.

Learning Statistics Using R

The analysis of crossclassified data: Independence, quasi-independence, and interactions in contingency tables ... An extended table of chi-square for two degrees of freedom, for use in combining probabilities from independent samples.

Author: Randall E. Schumacker

Publisher: SAGE Publications

ISBN: 9781483324777

Category: Social Science

Page: 648

View: 861

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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. Lecturers - contact your local SAGE representative to discuss your course needs or to request an inspection copy. 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.