Predictive Inference by Seymour Geisser
The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.-
Statistical Inference
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Practical Risk Theory for Actuaries
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Analysis of Survival Data
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Statistics in the 21st Century
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Generalized Linear Models with Random Effects
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An Introduction to the Bootstrap
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Queues
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Transformation and Weighting in Regression
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Asymptotic Analysis of Mixed Effects Models
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Statistics for Long-Memory Processes
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Analysis of Infectious Disease Data
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ROC Curves for Continuous Data
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Missing Data in Longitudinal Studies
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Analyzing and Modeling Rank Data
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Stochastic Geometry
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Semimartingales and their Statistical Inference
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Accelerated Life Models
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Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
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Quasi-Least Squares Regression
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Large Covariance and Autocovariance Matrices
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Design and Analysis of Cross-Over Trials
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Analysis of Variance for Functional Data
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Pareto Distributions
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Analysis of Incomplete Multivariate Data
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Simultaneous Inference in Regression
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Gaussian Markov Random Fields
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Sufficient Dimension Reduction
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Markov Models & Optimization
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Multidimensional Scaling
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Biplots
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Analog Est Methods Econometric
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Subjective Probability Models for Lifetimes
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Smoothing Splines
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Bayesian Inference for Partially Identified Models
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Maximum Likelihood Estimation for Sample Surveys
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Mean Field Simulation for Monte Carlo Integration
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Robust Nonparametric Statistical Methods
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Sequential Analysis
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The Statistical Analysis of Multivariate Failure Time Data
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Measuring Statistical Evidence Using Relative Belief
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Sequential Change Detection and Hypothesis Testing
"..this monograph is a very welcome attempt to shift back the main emphasis of statistics from parametric estimation and testing to prediction which, as noted by the author, was originally the earliest and most prealent form of statistical inference...I am sure all statisticians and students of statistics with an open mind will enjoy reading it and, hopefully will appreciate the beauty and usefulness of a coherent predictive view of their subject." -Mathematical Reviews "Predictive Inference: An Introduction is rich both in the coverage of topics and in applications...The monograph is addressed to statisticians and research workers who are intrested in the predictive approach. Its major contribution is likely to be as a resource for persons interested in trying predictive inference in some application." -Journal of the ASA
Geisser\, Seymour
| SKU | Unavailable |
| ISBN 13 | 9780412034718 |
| ISBN 10 | 0412034719 |
| Title | Predictive Inference |
| Author | Seymour Geisser |
| Series | Chapman And Hall Crc Monographs On Statistics And Applied Probability |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer |
| Year published | 1993-06-01 |
| Number of pages | 276 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |