
Sufficient Dimension Reduction by Bing Li
Sufficient dimension reduction was first introduced in the early 90's as a set of graphical and diagnostic tools for regression with many predictors. Over the past two decades or so it has developed into a powerful theory and technique for handling high-dimensional data. This book will introduce the main results and important techniques in this-
Statistical Inference
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Practical Risk Theory for Actuaries
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Analysis of Survival Data
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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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Sequential Analysis
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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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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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Measurement Error in Nonlinear Models
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Predictive Inference
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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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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
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Statistical Methods for Stochastic Differential Equations
"..Sufficient Dimension Reduction: Methods and Applications with R is a thorough overview of the key ideas and a detailed reference for advanced researchers...Professor Li gives careful discussions of the relevant details, rendering the text impressively self-contained. But as one would expect from a book based on graduate course notes, this manuscript is mainly accessible to those with advanced training in theoretical statistics...This book serves as an excellent introduction to the field of sufficient dimension reduction, and the depth of presentation and theoretical rigor are impressive. It would, of course, naturally serve as the basis for a deep graduate course, and provides a substantial foundation for anyone hoping to contribute in this thriving area."
- Daniel J. McDonald, JASA 2020
Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.
| SKU | Unavailable |
| ISBN 13 | 9780367734725 |
| ISBN 10 | 0367734729 |
| Title | Sufficient Dimension Reduction |
| Author | Bing Li |
| Series | Chapman And Hall Crc Monographs On Statistics And Applied Probability |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Routledge |
| Year published | 2020-12-18 |
| Number of pages | 284 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |









































