Linear Mixed-Effects Models Using R (Record no. 95027)

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fixed length control field 05323nam a22004695i 4500
001 - CONTROL NUMBER
control field 978-1-4614-3900-4
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220082813.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 130217s2013 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461439004
-- 978-1-4614-3900-4
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-1-4614-3900-4
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA276-280
072 #7 - SUBJECT CATEGORY CODE
Subject category code PBT
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT029000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Gałecki, Andrzej.
Relator term author.
245 10 - TITLE STATEMENT
Title Linear Mixed-Effects Models Using R
Medium [electronic resource] :
Remainder of title A Step-by-Step Approach /
Statement of responsibility, etc by Andrzej Gałecki, Tomasz Burzykowski.
264 #1 -
-- New York, NY :
-- Springer New York :
-- Imprint: Springer,
-- 2013.
300 ## - PHYSICAL DESCRIPTION
Extent XXXII, 542 p. 67 illus.
Other physical details online resource.
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-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
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-- rdacarrier
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-- text file
-- PDF
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490 1# - SERIES STATEMENT
Series statement Springer Texts in Statistics,
International Standard Serial Number 1431-875X
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction -- Linear Models for Independent Observations -- Linear Fixed-effects Models for Correlated Data -- Linear Mixed-effects Models.
520 ## - SUMMARY, ETC.
Summary, etc Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text. Andrzej Gałecki is a Research Professor in the Division of Geriatric Medicine, Department of Internal Medicine, and Institute of Gerontology at the University of Michigan Medical School, and is Research Scientist in the Department of Biostatistics at the University of Michigan School of Public Health. He earned his M.Sc. in applied mathematics (1977) from the Technical University of Warsaw, Poland, and an M.D. (1981) from the Medical University of Warsaw. In 1985 he earned a Ph.D. in epidemiology from the Institute of Mother and Child Care in Warsaw (Poland). He is a member of the Editorial Board of the Open Journal of Applied Sciences. Since 1990, Dr. Galecki has collaborated with researchers in gerontology and geriatrics. His research interests lie in the development and application of statistical methods for analyzing correlated and over- dispersed data. He developed the SAS macro NLMEM for nonlinear mixed-effects models, specified as a solution to ordinary differential equations. He also proposed a general class of variance-covariance structures for the analysis of multiple continuous dependent variables measured over time. This methodology is considered to be one of first approaches to joint models for longitudinal data.   Tomasz Burzykowski is Professor of Biostatistics and Bioinformatics at Hasselt University (Belgium) and Vice-President of Research at the International Drug Development Institute (IDDI) in Louvain-la-Neuve (Belgium). He received the M.Sc. degree in applied mathematics (1990) from Warsaw University, and the M.Sc. (1991) and Ph.D. (2001) degrees from Hasselt University. He has held guest professorships at the Karolinska Institute (Sweden), the Medical University of Bialystok (Poland), and the Technical University of Warsaw (Poland). He serves as Associate Editor of Biometrics. Dr. Burzykowski published methodological work on survival analysis, meta-analyses of clinical trials, validation of surrogate endpoints, analysis of gene expression data, and modelling of peptide-centric mass-spectrometry data. He is also a co-author of numerous papers applying statistical methods to clinical data in different disease areas.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical Theory and Methods.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics, general.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics and Computing/Statistics Programs.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Burzykowski, Tomasz.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9781461438991
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Springer Texts in Statistics,
-- 1431-875X
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4614-3900-4
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-- ZDB-2-SMA

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