subset selection in regression

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Subset Selection In Regression

Author : Alan Miller
ISBN : 9781420035933
Genre : Mathematics
File Size : 57. 4 MB
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Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition: A separate chapter on Bayesian methods Complete revision of the chapter on estimation A major example from the field of near infrared spectroscopy More emphasis on cross-validation Greater focus on bootstrapping Stochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presented More examples Subset Selection in Regression, Second Edition remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model that fits only slightly better than others. The presentation is clear, concise, and belongs on the shelf of anyone researching, using, or teaching subset selecting techniques.

Optimal Subset Selection

Author : David Boyce
ISBN : 9783642463112
Genre : Mathematics
File Size : 38. 5 MB
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In the course of one's research, the expediency of meeting contractual and other externally imposed deadlines too often seems to take priority over what may be more significant research findings in the longer run. Such is the case with this volume which, despite our best intentions, has been put aside time and again since 1971 in favor of what seemed to be more urgent matters. Despite this delay, to our knowledge the principal research results and documentation presented here have not been superseded by other publications. The background of this endeavor may be of some historical interest, especially to those who agree that research is not a straightforward, mechanistic process whose outcome or even direction is known in ad vance. In the process of this brief recounting, we would like to express our gratitude to those individuals and organizations who facilitated and supported our efforts. We were introduced to the Beale, Kendall and Mann algorithm, the source of all our efforts, quite by chance. Professor Britton Harris suggested to me in April 1967 that I might like to attend a CEIR half-day seminar on optimal regression being given by Professor M. G. Kendall in Washington. D. C. I agreed that the topic seemed interesting and went along. Had it not been for Harris' suggestion and financial support, this work almost certainly would have never begun.

Optimal Subset Selection Multiple Regression Interdependence And Optimal Network Algorithms

Author : David E. Boyce
ISBN : UOM:39015000964620
Genre : Mathematics
File Size : 68. 50 MB
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Subset Selection In Regression Using Robust Versions Of Mallows S Cp

Author : Jingna Xia
ISBN : OCLC:54380591
Genre : Least squares
File Size : 32. 65 MB
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Applied Logistic Regression

Author : David W. Hosmer, Jr.
ISBN : 9780471654025
Genre : Mathematics
File Size : 61. 46 MB
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The Elements Of Statistical Learning

Author : Trevor Hastie
ISBN : 0387848584
Genre : Computers
File Size : 30. 94 MB
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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

Mathematische Methoden F R Digitalrechner

Author : Anthony Ralston
ISBN : OCLC:9977910
Genre : Computer science
File Size : 61. 20 MB
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Bmdp Statistical Software

Author : Wilfrid Joseph Dixon
ISBN : 0520044088
Genre : Biometry
File Size : 34. 62 MB
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Design Of Experiments

Author : Santner
ISBN : 0824772741
Genre : Technology & Engineering
File Size : 87. 23 MB
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Multiple comparisons; Selection and ranking; Estimation and testing.

Artificial Neural Networks Icann 2006

Author : Stefanos Kollias
ISBN : 9783540386254
Genre : Computers
File Size : 64. 28 MB
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The two-volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006. The set presents 208 revised full papers, carefully reviewed and selected from 475 submissions. This first volume presents 103 papers, organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, hybrid architectures, and more.

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