Structural Equations with Latent Variables
Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is a case study work with illuminating examples taken from across the wide spectrum of ordinal categorical applications. 1984 (0 471-89055-3) 287 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch This book provides the practicing statistician and econometrician with new tools for assessing the quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are either unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data and help to identify the variables involved in each; and pinpoint the estimated coefficients that are potentially most adversely affected. The primary emphasis of these contributions is on diagnostics, but suggestions for remedial action are given and illustrated. 1980 (0 471-05856-4) 292 pp. Applied Regression Analysis Second Edition Norman Draper and Harry Smith Featuring a significant expansion of material reflecting recent advances, here is a complete and up-to-date introduction to the fundamentals of regression analysis, focusing on understanding the latest concepts and applications of these methods. The authors thoroughly explore the fitting and checking of both linear and nonlinear regression models, using small or large data sets and pocket or high-speed computing equipment. Features added to this Second Edition include the practical implications of linear regression; the Durbin-Watson test for serial correlation; families of transformations; inverse, ridge, latent root and robust regression; and nonlinear growth models. Includes many new exercises and worked examples. 1981 (0 471-02995-5) 709 pp.
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Model Notation Covariances and Path Analysis
Causality and Causal Models
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alternative assume assumption asymptotic covariance matrix asymptotic variance baseline causal Chapter chi-square estimate chi-square test coefficient of determination column condition consistent estimator converges correlation matrix covariance structure distribution disturbance elements endogenous equal error variances errors of measurement example exogenous variables explanatory variables FGLS Figure fit measures fitting function free parameters groups hypothesis identified illustrate implied covariance matrix income indirect effects influence instance Joreskog kurtosis latent variable model leads LISREL means measurement error measurement model method ML estimates model fit multinormal observed variables occupational prestige parameter estimates path analysis path diagram political democracy procedures provides pseudo-isolation random variables relation reliability researchers residuals restrictions sample covariance matrix scale shows specific squared standard deviation standard errors standardized coefficients starting values structural equation models Table tion total effects uncorrelated union sentiment validity vector xx and x2 zero