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  • Produktbild: Applied Regression Analysis and Generalized Linear Models
  • Produktbild: Applied Regression Analysis and Generalized Linear Models

Applied Regression Analysis and Generalized Linear Models

Fr. 279.00

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.04.2015

Verlag

Sage Publications

Seitenzahl

816

Maße (L/B/H)

26/18.3/4.8 cm

Gewicht

1690 g

Auflage

3. überarbeitete Auflage

Sprache

Englisch

ISBN

978-1-4522-0566-3

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.04.2015

Verlag

Sage Publications

Seitenzahl

816

Maße (L/B/H)

26/18.3/4.8 cm

Gewicht

1690 g

Auflage

3. überarbeitete Auflage

Sprache

Englisch

ISBN

978-1-4522-0566-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Applied Regression Analysis and Generalized Linear Models
  • Produktbild: Applied Regression Analysis and Generalized Linear Models
  • Preface
    About the Author
    1. Statistical Models and Social Science
    1.1 Statistical Models and Social Reality
    1.2 Observation and Experiment
    1.3 Populations and Samples
    I. DATA CRAFT
    2. What Is Regression Analysis?
    2.1 Preliminaries
    2.2 Naive Nonparametric Regression
    2.3 Local Averaging
    3. Examining Data
    3.1 Univariate Displays
    3.2 Plotting Bivariate Data
    3.3 Plotting Multivariate Data
    4. Transforming Data
    4.1 The Family of Powers and Roots
    4.2 Transforming Skewness
    4.3 Transforming Nonlinearity
    4.4 Transforming Nonconstant Spread
    4.5 Transforming Proportions
    4.6 Estimating Transformations as Parameters*
    II. LINEAR MODELS AND LEAST SQUARES
    5. Linear Least-Squares Regression
    5.1 Simple Regression
    5.2 Multiple Regression
    6. Statistical Inference for Regression
    6.1 Simple Regression
    6.2 Multiple Regression
    6.3 Empirical Versus Structural Relations
    6.4 Measurement Error in Explanatory Variables*
    7. Dummy-Variable Regression
    7.1 A Dichotomous Factor
    7.2 Polytomous Factors
    7.3 Modeling Interactions
    8. Analysis of Variance
    8.1 One-Way Analysis of Variance
    8.2 Two-Way Analysis of Variance
    8.3 Higher-Way Analysis of Variance
    8.4 Analysis of Covariance
    8.5 Linear Contrasts of Means
    9. Statistical Theory for Linear Models*
    9.1 Linear Models in Matrix Form
    9.2 Least-Squares Fit
    9.3 Properties of the Least-Squares Estimator
    9.4 Statistical Inference for Linear Models
    9.5 Multivariate Linear Models
    9.6 Random Regressors
    9.7 Specification Error
    9.8 Instrumental Variables and Two-Stage Least Squares
    10. The Vector Geometry of Linear Models*
    10.1 Simple Regression
    10.2 Multiple Regression
    10.3 Estimating the Error Variance
    10.4 Analysis-of-Variance Models
    III. LINEAR-MODEL DIAGNOSTICS
    11. Unusual and Influential Data
    11.1 Outliers, Leverage, and Influence
    11.2 Assessing Leverage: Hat-Values
    11.3 Detecting Outliers: Studentized Residuals
    11.4 Measuring Influence
    11.5 Numerical Cutoffs for Diagnostic Statistics
    11.6 Joint Influence
    11.7 Should Unusual Data Be Discarded?
    11.8 Some Statistical Details*
    12. Non-Normality, Nonconstant Error Variance, Nonlinearity
    12.1 Non-Normally Distributed Errors
    12.2 Nonconstant Error Variance
    12.3 Nonlinearity
    12.4 Discrete Data
    12.5 Maximum-Likelihood Methods*
    12.6 Structural Dimension
    13. Collinearity and Its Purported Remedies
    13.1 Detecting Collinearity
    13.2 Coping With Collinearity: No Quick Fix
    IV. GENERALIZED LINEAR MODELS
    14. Logit and Probit Models for Categorical Response Variables
    14.1 Models for Dichotomous Data
    14.2 Models for Polytomous Data
    14.3 Discrete Explanatory Variables and Contingency Tables
    15. Generalized Linear Models
    15.1 The Structure of Generalized Linear Models
    15.2 Generalized Linear Models for Counts
    15.3 Statistical Theory for Generalized Linear Models*
    15.4 Diagnostics for Generalized Linear Models
    15.5 Analyzing Data From Complex Sample Surveys
    V. EXTENDING LINEAR AND GENERALIZED LINEAR MODELS
    16. Time-Series Regression and Generalized Leasr Squares*
    16.1 Generalized Least-Squares Estimation
    16.2 Serially Correlated Errors
    16.3 GLS Estimation With Autocorrelated Errors
    16.4 Correcting OLS Inference for Autocorrelated Errors
    16.5 Diagnosing Serially Correlated Errors
    16.6 Concluding Remarks
    17. Nonlinear Regression
    17.1 Polynomial Regression
    17.2 Piece-wise Polynomials and Regression Splines
    17.3 Transformable Nonlinearity
    17.4 Nonlinear Least Squares*
    18. Nonparametric Regression
    18.1 Nonparametric Simple Regression: Scatterplot Smoothing
    18.2 Nonparametric Multiple Regression
    18.3 Generalized Nonparametric Regression
    19. Robust Regression*
    19.1 M Estimation
    19.2 Bounded-Influence Regression
    19.3 Quantile Regression
    19.4 Robust Estimation of Generalized Linear Models
    19.5 Concluding Remarks
    20. Missing Data in Regression Models
    20.1 Missing Data Basics
    20.2 Traditional Approaches to Missing Data
    20.3 Maximum-Likelihood Estimation for Data Missing at Random*
    20.4 Bayesian Multiple Imputation
    20.5 Selection Bias and Censoring
    21. Bootstrapping Regression Models
    21.1 Bootstrapping Basics
    21.2 Bootstrap Confidence Intervals
    21.3 Bootstrapping Regression Models
    21.4 Bootstrap Hypothesis Tests*
    21.5 Bootstrapping Complex Sampling Designs
    21.6 Concluding Remarks
    22. Model Selection, Averaging, and Validation
    22.1 Model Selection
    22.2 Model Averaging*
    22.3 Model Validation
    VI. MIXED-EFFECT MODELS
    23. Linear Mixed-Effects Models for Hierarchical and Longitudinal Data
    23.1 Hierarchical and Longitudinal Data
    23.2 The Linear Mixed-Effects Model
    23.3 Modeling Hierarchical Data
    23.4 Modeling Longitudinal Data
    23.5 Wald Tests for Fixed Effects
    23.6 Likelihood-Ratio Tests of Variance and Covariance Components
    23.7 Centering Explanatory Variables, Contextual Effects, and Fixed-Effects Models
    23.8 BLUPs
    23.9 Statistical Details*
    24. Generalized Linear and Nonlinear Mixed-Effects Models
    24.1 Generalized Linear Mixed Models
    24.2 Nonlinear Mixed Models
    Appendix A
    References
    Author Index
    Subject Index
    Data Set Index