
Regression for Categorical Data by Gerhard Tutz
This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.-
Data Analysis and Graphics Using R
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Measure Theory and Filtering
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Essentials of Statistical Inference
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Markov Chains
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Applied Asymptotics
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A User's Guide to Measure Theoretic Probability
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Probability
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High-Dimensional Probability
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Asymptotic Statistics
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Statistical Models
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Model Selection and Model Averaging
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Bootstrap Methods and their Application
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Random Graph Dynamics
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Modern Statistical Methods and Theory
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A Course in Interacting Particle Systems
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Networks
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The Coordinate-Free Approach to Linear Models
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The Estimation and Tracking of Frequency
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Semiparametric Regression
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Statistical Mechanics of Disordered Systems
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Random Networks for Communication
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Statistical Analysis of Stochastic Processes in Time
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Numerical Methods of Statistics
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Symmetry Studies
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Nonparametric Estimation under Shape Constraints
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Elements of Distribution Theory
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From Finite Sample to Asymptotic Methods in Statistics
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Bayesian Methods
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Design of Comparative Experiments
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Analysis of Multivariate and High-Dimensional Data
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Mathematical Foundations of Infinite-Dimensional Statistical Models
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Probability on Trees and Networks
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Spectral Analysis for Univariate Time Series
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Predictive Statistics
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Long-Range Dependence and Self-Similarity
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Statistical Hypothesis Testing in Context: Volume 52
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Saddlepoint Approximations with Applications
"Regression for Categorical Data is a well-written and nicely organized bookIt focuses on the regression analysis of categorical data, including both binary and count data, and introduced up-to-date developments in the field." Xia Wang, Mathematical Reviews
Dr Gerhard Tutz is a Professor of Mathematics in the Department of Statistics at Ludwig-Maximilians University, Munich. He is formerly a Professor at the Technical University Berlin. He is the author or co-author of nine books and more than 100 papers.
| SKU | Unavailable |
| ISBN 13 | 9781107009653 |
| ISBN 10 | 1107009650 |
| Title | Regression for Categorical Data |
| Author | Gerhard Tutz |
| Series | Cambridge Series In Statistical And Probabilistic Mathematics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Cambridge University Press |
| Year published | 2011-11-21 |
| Number of pages | 572 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |




































