Regression for Categorical Data

Medientyp: E-Book

Titel: Regression for Categorical Data

Enthält: ch. 1 Introduction1.1 Categorical Data: Examples and Basic Concepts -- 1.1.1 Some Examples -- 1.1.2 Classification of Variables -- Scale Levels: Nominal and Ordinal Variables -- Discrete and Continuous Variables -- 1.2 Organization of This Book -- 1.3 Basic Components of Structured Regression -- 1.3.1 Structured Univariate Regression -- Structuring the Dependent Variable -- Structuring the Influential Term -- Linear Predictor -- Categorical Explanatory Variables -- Additive Predictor -- Tree-Based Methods -- The Link between Covariates and Response1.3.2 Structured Multicategorical Regression -- 1.3.3 Multivariate Regression -- Structuring the Dependent Variables -- Structuring the Influential Term -- 1.3.4 Statistical Modeling -- 1.4 Classical Linear Regression -- 1.4.1 Interpretation and Coding of Covariates -- Quantitative Explanatory Variables -- Binary Explanatory Variables -- Multicategorical Explanatory Variables or Factors -- 1.4.2 Linear Regression in Matrix Notation -- 1.4.3 Estimation -- Least-Squares Estimation -- Maximum Likelihood Estimation -- Properties of Estimates -- 1.4.4 Residuals and Hat Matrix -- Case Deletion as Diagnostic Tool1.4.5 Decomposition of Variance and Coefficient of Determination -- 1.4.6 Testing in Multiple Linear Regression -- Submodels and the Testing of Linear Hypotheses -- 1.5 Exercises -- ch. 2 Binary Regression: The Logit Model -- 2.1 Distribution Models for Binary Responses and Basic Concepts -- 2.1.1 Single Binary Variables -- 2.1.2 The Binomial Distribution -- Odds, Logits, and Odds Ratios -- Comparing Two Groups -- 2.2 Linking Response and Explanatory Variables -- 2.2.1 Deficiencies of Linear Models -- 2.2.2 Modeling Binary Responses -- Binary Responses as Dichotomized Latent VariablesModeling the Common Distribution of a Binary and a Continuous Distribution -- Basic Form of Binary Regression Models -- 2.3 The Logit Model -- 2.3.1 Model Representations -- 2.3.2 Logit Model with Continuous Predictor -- Multivariate Predictor -- 2.3.3 Logit Model with Binary Predictor -- Logit Model with (0-1)-Coding of Covariates -- Logit Model with Effect Coding -- 2.3.4 Logit Model with Categorical Predictor -- Logit Model with (0-1)-Coding -- Logit Model with Effect Coding -- Logit Model with Several Categorical Predictors -- 2.3.5 Logit Model with Linear Predictor -- 2.4 The Origins of the Logistic Function and the Logit Model2.5 Exercises -- ch. 3 Generalized Linear Models -- 3.1 Basic Structure -- 3.2 Generalized Linear Models for Continuous Responses -- 3.2.1 Normal Linear Regression -- 3.2.2 Exponential Distribution -- 3.2.3 Gamma-Distributed Responses -- 3.2.4 Inverse Gaussian Distribution -- 3.3 GLMs for Discrete Responses -- 3.3.1 Models for Binary Data -- 3.3.2 Models for Binomial Data -- 3.3.3 Poisson Model for Count Data -- 3.3.4 Negative Binomial Distribution -- 3.4 Further Concepts -- 3.4.1 Means and Variances -- 3.4.2 Canonical Link -- 3.4.3 Extensions Including Offsets.

Beteiligte: Tutz, Gerhard [Verfasser:in]

Erschienen: Cambridge [u.a.]: Cambridge University Press, 2011

Ausgabe: Online-Ausg.

Umfang: Online-Ressource (1 online resource (574 p.))

Sprache: Englisch

ISBN: 9781139117890; 1283382407; 9781283382403; 9781139123648

RVK-Notation: QH 234 : Regression und Korrelation, Faktoren-, Komponenten-, Diskriminanzanalyse sowie sonstiger Methoden der mehrdimensionalen Analyse. Assoziation, Kontingenz, MOS, Kausalanalyse/Pfadanalyse/LISREL

Schlagwörter: Regressionsanalyse > Kategoriale Daten

Entstehung:

Anmerkungen: Description based upon print version of record

Beschreibung: Cover; Regression for Categorical Data; Title; Copyright; Contents; Preface; Chapter 1 Introduction; 1.1 Categorical Data: Examples and Basic Concepts; 1.1.1 Some Examples; 1.1.2 Classification of Variables; Scale Levels: Nominal and Ordinal Variables; Discrete and Continuous Variables; 1.2 Organization of This Book; 1.3 Basic Components of Structured Regression; 1.3.1 Structured Univariate Regression; Structuring the Dependent Variable; Structuring the Influential Term; Linear Predictor; Categorical Explanatory Variables; Additive Predictor; Tree-Based Methods

The Link between Covariates and Response1.3.2 Structured Multicategorical Regression; 1.3.3 Multivariate Regression; Structuring the Dependent Variables; Structuring the Influential Term; 1.3.4 Statistical Modeling; 1.4 Classical Linear Regression; 1.4.1 Interpretation and Coding of Covariates; Quantitative Explanatory Variables; Binary Explanatory Variables; Multicategorical Explanatory Variables or Factors; 1.4.2 Linear Regression in Matrix Notation; 1.4.3 Estimation; Least-Squares Estimation; Maximum Likelihood Estimation; Properties of Estimates; 1.4.4 Residuals and Hat Matrix

Case Deletion as Diagnostic Tool1.4.5 Decomposition of Variance and Coefficient of Determination; 1.4.6 Testing in Multiple Linear Regression; Submodels and the Testing of Linear Hypotheses; 1.5 Exercises; Chapter 2 Binary Regression: The Logit Model; 2.1 Distribution Models for Binary Responses and Basic Concepts; 2.1.1 Single Binary Variables; 2.1.2 The Binomial Distribution; Odds, Logits, and Odds Ratios; Comparing Two Groups; 2.2 Linking Response and Explanatory Variables; 2.2.1 Deficiencies of Linear Models; 2.2.2 Modeling Binary Responses

Binary Responses as Dichotomized Latent VariablesModeling the Common Distribution of a Binary and a Continuous Distribution; Basic Form of Binary Regression Models; 2.3 The Logit Model; 2.3.1 Model Representations; 2.3.2 Logit Model with Continuous Predictor; Multivariate Predictor; 2.3.3 Logit Model with Binary Predictor; Logit Model with (0-1)-Coding of Covariates; Logit Model with Effect Coding; 2.3.4 Logit Model with Categorical Predictor; Logit Model with (0-1)-Coding; Logit Model with Effect Coding; Logit Model with Several Categorical Predictors; 2.3.5 Logit Model with Linear Predictor

2.4 The Origins of the Logistic Function and the Logit Model2.5 Exercises; Chapter 3 Generalized Linear Models; 3.1 Basic Structure; 3.2 Generalized Linear Models for Continuous Responses; 3.2.1 Normal Linear Regression; 3.2.2 Exponential Distribution; 3.2.3 Gamma-Distributed Responses; 3.2.4 Inverse Gaussian Distribution; 3.3 GLMs for Discrete Responses; 3.3.1 Models for Binary Data; 3.3.2 Models for Binomial Data; 3.3.3 Poisson Model for Count Data; 3.3.4 Negative Binomial Distribution; 3.4 Further Concepts; 3.4.1 Means and Variances; 3.4.2 Canonical Link; 3.4.3 Extensions Including Offsets

3.5 Modeling of Grouped Data

The book treats many recent developments in flexible and high-dimensional regression not normally included in books on categorical data analysis

Regression for Categorical Data - [Online-Ausg.]

Merkliste

Nur in Feld suchen:

Zuletzt gesuchte Begriffe: