Download or read book Subset Selection Procedures for Regression Analysis written by Shanti S. Gupta and published by . This book was released on 1975 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Subset Selection in Regression written by Alan Miller and published by CRC Press. This book was released on 2002-04-15 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book A Subset Selection Procedure for Regression Variables written by George P McCabe (Jr) and published by . This book was released on 1973 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given a regression model with p independent variables, several methods are available for selecting a subset of size t
Download or read book Feature Engineering and Selection written by Max Kuhn and published by CRC Press. This book was released on 2019-07-25 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Subset Selection Problems for Variances with Applications to Regression Analysis written by James N. Arvesen and published by . This book was released on 1972 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Subset Selection in Regression written by Alan J. Miller and published by Springer. This book was released on 2013-08-22 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nearly all statistical packages, and many scientific computing libraries, contain facilities for the empirical choice of a model given a set of data and many variables or alternative models from which to select. There is an abundance of advice on how to perform the mechanics of choosing a model, much of which can only be described as folklore and some of wh ich is quite contradictory. There is a dearth of respectable theory, or even of trustworthy advice, such as recommendations based upon adequate simulations. This mono graph collects together what is known, and presents some new material on estimation. This relates almost entirely to multiple linear regression. The same problems apply to nonlinear regression, such as to the fitting of logistic regressions, to the fitting of autoregressive moving average models, or to any situation in which the same data are to be used both to choose a model and to fit it. This monograph is not a cookbook of recommendations on how to carry out stepwise regression; anyone searching for such advice in its pages will be very disappointed. I hope that it will disturb many readers and awaken them to the dangers in using automatie packages which pick a model and then use least squares to estimate regression coefficients using the same data. My own awareness of these problems was brought horne to me dramatically when fitting models for the prediction of meteorological variables such as temperature or rainfall.
Download or read book Selection Procedures for Optimal Subsets of Regression Variables written by Shanti S. Gupta and published by . This book was released on 1983 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Locally Optimal Subset Selection Procedures Based on Ranks written by Shanti S. Gupta and published by . This book was released on 1977 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Optimal Subset Selection written by David Boyce and published by Springer Science & Business Media. This book was released on 2013-03-08 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Some sequential Selection Procedures for good regression models written by Tong-An Hsu and published by . This book was released on 1981 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Subset Selection Procedures for the Binomial Models Based on Some Priors and Some Applications written by Shanti Swarup Gupta and published by . This book was released on 1990 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Selection Procedures in Multivariate Models written by Shanti S. Gupta and published by . This book was released on 1986 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selection and ranking problems have been studied over the last thirty years, generally under one of two formulations: Bechhofer's indifference zone approach and Gupta's subset selection approach. This paper deals with subset selection. Subset selection procedures in multivariate models are briefly reviewed. These include: (1) Procedures for selecting the best component in a multivariate normal population in terms of the component means as well as the component variances; (2) Procedures for selecting the best from several multivariate normal populations in terms of the Mahalanobis distance, the generalized variance, and the multiple correlation coefficient; (3) Procedures (fixed sample size as well as inverse sampling) for selecting the most (least) probable cell in a multinominal distribution; (4) Procedures for selecting the best from several multinomial populations in terms of the Shannon entropy function; and (5) Procedures for choosing the best subset of the predictor variables in a linear regression model. (Author).
Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)
Download or read book An Adaptive Model Selection Procedure for All subsets Regression written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Subset Selection Procedures in Analysis of Variance written by Shanti S. Gupta and published by . This book was released on 1975 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In most of the practical situations to which the analysis of variance tests are applied, they do not supply the information that the experimenter aims at. If, for example, the hypothesis is rejected in actual application of the F-test, the resulting conclusion that the true means (theta sub 1), (theta sub 2), ..., (theta sub k) are not all equal, would by itself usually the insufficient to satisfy the experimenter. In fact his problems would begin at this stage. He may desire to select the 'best' population or a subset of the 'best' populations; he may like to rank the populations in order of 'bestness' or he may like to draw some other inferences about the parameters of interest to him. The authors interest lies in selecting a non-empty subset of the k populations containing the 'best' population as ranked in terms of (theta sub i's).
Download or read book Some Results in the theory of subset selection procedures written by Hwa-Ming Yang and published by . This book was released on 1980 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selection and ranking (ordering) problems in statistical inference arise mainly because the classical tests of homogeneity are often inadequate in certain situations where the experimenter is interested in comparing k (> or = 2) populations, treatments or processes with the goal of selecting one or more worth-while (good) population. Chapter I of this thesis considers the problem of selecting a subset containing all populations that are better than a control under the assumptions of an ordering prior. Here, by an ordering prior we mean that there exists a known simple or partial order relationship among the unknown parameters of the treatments (excluding the control). Three new selection procedures are proposed and studied. These procedures do meet the usual requirement that the probability of a correct selection is greater than or equal to a pre-determined number P*. Two of the three procedures use the isotonic regression over the sample means of the k treatments with respect to the given ordering prior. Tables which are necessary to carry out the selection procedure with isotonic approach for the selection of unknown means of normal populations and gamma populations are given. Monte Carlo comparisons on the performance of several procedures for the normal or gamma means problem were carried out in several selected cases. The results of this study seem to indicate that the procedures based on isotonic estimators always have superior prformance, especially, when there are more than one bad population (in comparison with the control).
Download or read book Selecting Procedures for Optimal Subset of Regression Variables written by Shanti S. Gupta and published by . This book was released on 1982 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently 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, we may be interested in including only a subset, say, of size r
