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Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 14
Book Description
In the past decade a number of methods have been developed for selecting the 'best' or at least a 'good' subset of variables in regression analysis. For various reasons, one may be interested in selecting a random size subset excluding all inferior independent variables. The authors are interested in deriving a selection procedure to the goal. Some results on the efficiency of the procedure are also discussed.
Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 14
Book Description
In the past decade a number of methods have been developed for selecting the 'best' or at least a 'good' subset of variables in regression analysis. For various reasons, one may be interested in selecting a random size subset excluding all inferior independent variables. The authors are interested in deriving a selection procedure to the goal. Some results on the efficiency of the procedure are also discussed.
Author: Alan Miller Publisher: CRC Press ISBN: 1420035932 Category : Mathematics Languages : en Pages : 258
Book Description
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 ha
Author: James N. Arvesen Publisher: ISBN: Category : Languages : en Pages : 19
Book Description
The paper obtains a subset selection procedure for correlated variances. Emphasis is placed on the asymptotic case. An application to selecting the best set of independent variables in a regression problem is given. (Author).
Author: Max Kuhn Publisher: CRC Press ISBN: 1351609467 Category : Business & Economics Languages : en Pages : 266
Book Description
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 13
Book Description
This paper deals with selection of an optimal subset of variables in a linear regression model. Based on the criterion of expected residual mean squares, we reject inferior regression models. The derivation of the rule is different from those of the earlier papers in that here we use the simultaneous tests of a family of hypotheses. Using real data, an example is provided to illustrate the application of the proposed procedure. (Author).
Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 16
Book Description
This paper deals with subset selection rules based on ranks in the pooled sample. The procedures satisfy the P-condition and also locally maximize the probability of a correct selection. An application to a problem in regression analysis is provided. (Author).
Author: Alan Miller Publisher: Chapman and Hall/CRC ISBN: 9781584881711 Category : Mathematics Languages : en Pages : 256
Book Description
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.
Author: Tong-An Hsu Publisher: ISBN: Category : Languages : en Pages : 15
Book Description
In the past decade a number of fixed sampling methods have been developed for selecting the 'best' or at least a 'good' subset of variable in regression analysis. We are interested in deriving a sequential selection procedure to select a subset of a random size including all good regression equations. Tables for an example are given at the end of this paper. (Author).
Author: Agency for Health Care Research and Quality (U.S.) Publisher: Government Printing Office ISBN: 1587634236 Category : Medical Languages : en Pages : 236
Book Description
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Author: Shanti S. Gupta
Author: Shanti S. Gupta
Author: Alan Miller
Author: George P McCabe (Jr)
Author: James N. Arvesen
Author: Max Kuhn
Author: Shanti S. Gupta
Author: Shanti S. Gupta
Author: Alan Miller
Author: Tong-An Hsu
Author: Agency for Health Care Research and Quality (U.S.)