Klik op een omslag om naar Google Boeken te gaan.
Bezig met laden... Subset Selection in Regression, Second Editondoor Alan Miller
Geen Bezig met laden...
Meld je aan bij LibraryThing om erachter te komen of je dit boek goed zult vinden. Op dit moment geen Discussie gesprekken over dit boek. geen besprekingen | voeg een bespreking toe
Onderdeel van de uitgeversreeks(en)
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 methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic 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 presentedMore 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. Geen bibliotheekbeschrijvingen gevonden. |
Actuele discussiesGeen
Google Books — Bezig met laden... GenresDewey Decimale Classificatie (DDC)519.536Natural sciences and mathematics Mathematics Applied Mathematics, Probabilities Statistical MathematicsLC-classificatieWaarderingGemiddelde:
Ben jij dit?Word een LibraryThing Auteur. |
1. Objectives
2. Least-squartes computations
3. Finding subsets which fit well
4. Hypothesis testing
5. When to stop?
6. Estimation of regression coefficients
7. Bayesian methods
8. Conclusions and some recommendations
Chapter 8 is a particularly good summary. I would buy this book if it wasn't so expensive; I secured my copy via interlibrary loan through my public library. ( )