Model Selection and Model Averaging by Gerda Claeskens
Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer? Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code.-
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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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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Regression for Categorical Data
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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
'This is a good textbook for a master-level statistical course about model selection' Mathematical Reviews
'… given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make [a] difference.' MAA Reviews
'… the authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection.' Statistics in Society
'… given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make [a] difference.' MAA Reviews
'… the authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection.' Statistics in Society
Gerda Claeskens is Professor in the OR and Business Statistics and Leuven Statistics Research Center at the Catholic University of Leuven, Belgium. Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo.
| SKU | Unavailable |
| ISBN 13 | 9780521852258 |
| ISBN 10 | 0521852250 |
| Title | Model Selection and Model Averaging |
| Author | Gerda Claeskens |
| Series | Cambridge Series In Statistical And Probabilistic Mathematics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Cambridge University Press |
| Year published | 2008-07-28 |
| Number of pages | 332 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |