Univariate And Multivariate General Linear Models - Kim Kevin; Timm Neil | Libro Chapman And Hall/Crc 10/2006 - HOEPLI.it


home libri books ebook dvd e film top ten sconti 0 Carrello


Torna Indietro

kim kevin; timm neil - univariate and multivariate general linear models

Univariate and Multivariate General Linear Models Theory and Applications with SAS, Second Edition

;




Disponibilità: Normalmente disponibile in 10 giorni


PREZZO
97,98 €
NICEPRICE
93,08 €
SCONTO
5%



Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.


Pagabile anche con App18 Bonus Cultura e Carta Docenti


Facebook Twitter Aggiungi commento


Spese Gratis

Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 10/2006
Edizione: 1° edizione





Note Editore

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences.

With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models.

New to the Second Edition
  • Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure
  • Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments
  • Expanded material on missing data to include multiple imputation and the EM algorithm
  • Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures

    A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.




  • Sommario

    PREFACE

    OVERVIEW OF THE GENERAL LINEAR MODEL
    Introduction
    General Linear Model
    Restricted General Linear Model
    Multivariate Normal Distribution
    Elementary Properties of Normal Random Variables
    Hypothesis Testing
    Generating Multivariate Normal Data
    Assessing Univariate Normality
    Assessing Multivariate Normality with Chi-Square Plots
    Using SAS INSIGHT
    Three-Dimensional Plots

    UNRESTRICTED GENERAL LINEAR MODELS
    Introduction
    Linear Models without Restrictions
    Hypothesis Testing
    Simultaneous Inference
    Multiple Linear Regression
    Linear Mixed Models
    One-Way Analysis of Variance
    Multiple Linear Regression: Calibration
    Two-Way Nested Designs
    Intraclass Covariance Models

    RESTRICTED GENERAL LINEAR MODELS
    Introduction
    Estimation and Hypothesis Testing
    Two-Way Factorial Design without Interaction
    Latin Square Designs
    Repeated Measures Designs
    Analysis of Covariance

    WEIGHTED GENERAL LINEAR MODELS
    Introduction
    Estimation and Hypothesis Testing
    OLSE versus FGLS
    General Linear Mixed Model Continued
    Maximum Likelihood Estimation and Fisher's Information Matrix
    WLSE for data Data Heteroscedasticity
    WLSE for Correlated Errors
    FGLS for Categorical Data

    MULTIVARIATE GENERAL LINEAR MODELS
    Introduction
    Developing the Model
    Estimation Theory and Hypothesis Testing
    Multivariate Regression
    Classical and Normal Multivariate Linear Regression Models
    Jointly Multivariate Normal Regression Model
    Multivariate Mixed Models and the Analysis of Repeated Measurements
    Extended Linear Hypotheses
    Multivariate Regression: Calibration and Prediction
    Multivariate Regression: Influential Observations
    Nonorthogonal MANOVA Designs
    MANCOVA Designs
    Stepdown Analysis
    Repeated Measures Analysis
    Extended Linear Hypotheses

    DOUBLY MULTIVARIATE LINEAR MODEL
    Introduction
    Classical Model Development
    Responsewise Model Development
    The Multivariate Mixed Model
    Double Multivariate and Mixed Models

    RESTRICTED MGLM AND GROWTH CURVE MODEL
    Introduction
    Restricted Multivariate General Linear Model
    The GMANOVA Model
    Canonical Form of the GMANOVA Model
    Restricted Nonorthogonal Three-Factor Factorial MANOVA
    Restricted Intraclass Covariance Design
    Growth Curve Analysis
    Multiple Response Growth Curves
    Single Growth Curve

    SUR MODEL AND RESTRICTED GMANOVA MODEL
    Introduction
    MANOVA-GMANOVA Model
    Tests of Fit
    Sum of Profiles and CGMANOVA Models
    SUR Model
    Restricted GMANOVA Model
    GMANOVA-SUR: One Population
    GMANOVA-SUR: Several Populations
    SUR Model
    Two-Period Crossover Design with Changing Covariates
    Repeated Measurements with Changing Covariates
    MANOVA-GMANOVA Model
    CGMANOVA Model

    SIMULTANEOUS INFERENCE USING FINITE INTERSECTION TESTS
    Introduction
    Finite Intersection Tests
    Finite Intersection Tests of Univariate Means
    Finite Intersection Tests for Linear Models
    Comparison of Some Tests of Univariate Means with the FIT Procedure
    Analysis of Means Analysis
    Simultaneous Test Procedures for Mean Vectors
    Finite Intersection Test of Mean Vectors
    Finite Intersection Test of Mean Vectors with Covariates
    Summary
    Univariate: One-Way ANOVA
    Multivariate: One-Way MANOVA
    Multivariate: One-Way MANCOVA

    COMPUTING POWER FOR UNIVARIATE AND MULTIVARIATE GLM
    Introduction
    Power for Univariate GLMs
    Estimating Power, Sample Size, and Effect Size for the GLM
    Power and Sample Size Based on Interval Estimation
    Calculating Power and Sample Size for Some Mixed Models
    Power for Multivariate GLMs
    Power and Effect Size Analysis for Univariate GLMs
    Power and Sample Size Based on Interval Estimation
    Power Analysis for Multivariate GLMs

    TWO-LEVEL HIERARCHICAL LINEAR MODELS
    Introduction
    Two-Level Hierarchical Linear Models
    Random Coefficient Model: One Population
    Random Coefficient Model: Several Populations
    Mixed Model Repeated Measures
    Mixed Model Repeated Measures with Changing Covariates
    Application: Two-Level Hierarchical Linear Models

    INCOMPLETE REPEATED MEASUREMENT DATA
    Introduction
    Missing Mechanisms
    FGLS Procedure
    ML Procedure
    Imputations
    Repeated Measures Analysis
    Repeated Measures with Changing Covariates
    Random Coefficient Model
    Growth Curve Analysis

    STRUCTURAL EQUATION MODELING
    Introduction
    Model Notation
    Estimation
    Model Fit in Practice
    Model Modification
    Summary
    Path Analysis
    Confirmatory Factor Analysis
    General SEM

    REFERENCES
    AUTHOR INDEX
    SUBJECT INDEX







    Altre Informazioni

    ISBN:

    9781584886341

    Condizione: Nuovo
    Collana: Statistics: A Series of Textbooks and Monographs
    Dimensioni: 9 x 6 in Ø 2.00 lb
    Formato: Copertina rigida
    Illustration Notes:7 b/w images and 53 tables
    Pagine Arabe: 549






    Utilizziamo i cookie di profilazione, anche di terze parti, per migliorare la navigazione, per fornire servizi e proporti pubblicità in linea con le tue preferenze. Se vuoi saperne di più o negare il consenso a tutti o ad alcuni cookie clicca qui. Chiudendo questo banner o proseguendo nella navigazione acconsenti all’uso dei cookie.

    X