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Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 20
Book Description
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).
Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 20
Book Description
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).
Author: Shanti Swarup Gupta Publisher: ISBN: Category : Languages : en Pages : 19
Book Description
There are many situations in the analysis of variance where an experimenter would like to make comparisons among (and select the 'best' set) the treatments. In this paper we study the problem where the data are based on a completely randomized block design. It is shown that the subset selection approach is a useful method to make appropriate 'identification' among the hypotheses and the selected subset. We propose an optimal selection procedure which controls the error probabilities when all the parameters (treatments) are equal and which maximize the infimum of the probability of a correct selection over some preference parameter space, simultaneously. Some examples are provided to illustrate the optimal subset selection rule and its interpretation in terms of the 'identified' hypotheses. (Author).
Author: Deng-Yuan Huang Publisher: ISBN: Category : Languages : en Pages : 78
Book Description
The report makes some contributions to the subset selection procedures - both for the fixed sample and the sequential case. Chapter 1 deals with some subset selection procedures for binomial populations in terms of the entropy functions, which is different from the usual selection problem in terms of the success probabilities. In Chapter 2, some fixed sample optimal subset selection procedures are discussed for model I and II problems in the analysis of variance in treatments versus control, and a method for constructing some subset selection procedures is derived. Chapter 3 discusses a method for constructing some sequential subset selection procedures and some optimal sequential subset selection procedure in treatments versus control. An upper bound on the expected sample size for Bechhofer-Kiefer-Sobel sequential selection procedure with indifference zone approach is also derived. (Author).
Author: Jason C. Hsu Publisher: ISBN: Category : Languages : en Pages : 74
Book Description
Ranking and selection procedures, subset selection procedures in particular, are procedures that provide in a realistic manner attractive ways of handling problems that are commonly treated by the 2-action procedure of a global F-test, and the many-action procedure of a typical multiple range test. Consider the usual one-way layout situation in analysis of variance. In this situation, formulating the problem as a selection problem is appropriate. Subset selection procedures are often thought of as screening procedures. If the data indicates several treatments are better than the remaining treatments but no treatment is clearly the best, then perhaps the experimenter ought to retain all of the better treatments for future considerations.
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: 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: Patricia F. Moodie Publisher: CRC Press ISBN: 1439869529 Category : Mathematics Languages : en Pages : 428
Book Description
Applied Regression and ANOVA Using SAS® has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps. Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided. Features: •Statistical concepts presented in words without matrix algebra and calculus •Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics •Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection •Suggestions of alternative approaches when a method’s ideal inference conditions are unreasonable for one’s data This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.
Author: Shanti S. Gupta
Author: Shanti S. Gupta
Author: Annalene Sadie
Author: Shanti Swarup Gupta
Author: Deng-Yuan Huang
Author: Lloyd William Koenig
Author: Jason C. Hsu
Author: Shanti S. Gupta
Author: James N. Arvesen
Author: Fu-Hsiu Chiang
Author: Patricia F. Moodie