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Book Synopsis Multiple Hypothesis Testing by : Juliet Popper Shaffer
Download or read book Multiple Hypothesis Testing written by Juliet Popper Shaffer and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple Hypotheses Testing in the Linear Regression Model with Applications to Economics and Finance by : Raimund Alt
Download or read book Multiple Hypotheses Testing in the Linear Regression Model with Applications to Economics and Finance written by Raimund Alt and published by Cuvillier Verlag. This book was released on 2005 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple Hypothesis Testing by : Houston Nash Gilbert
Download or read book Multiple Hypothesis Testing written by Houston Nash Gilbert and published by . This book was released on 2009 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Stanford University. Institute for Mathematical Studies in the Social Sciences Publisher : ISBN 13 : Total Pages :87 pages Book Rating :4.:/5 (123 download)
Book Synopsis Multiple Hypothesis Testing by : Stanford University. Institute for Mathematical Studies in the Social Sciences
Download or read book Multiple Hypothesis Testing written by Stanford University. Institute for Mathematical Studies in the Social Sciences and published by . This book was released on 1982 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiple Comparisons written by Jason Hsu and published by CRC Press. This book was released on 1996-02-01 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method for each problem. Discussions also include the connections with bioequivalence, drug stability, and toxicity studies Real data sets analyzed by computer software packages illustrate the applications presented.
Book Synopsis Multiple Comparisons by : Alan J. Klockars
Download or read book Multiple Comparisons written by Alan J. Klockars and published by SAGE. This book was released on 1986-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes the most important methods used to investigate differences between levels of an independent variable within an experimental design. Readers will learn not only how to conduct multiple comparisons in experimental designs but also how to better understand and evaluate published research. "A highly readable introduction to multiple comparison methods, which demands little from its reader in the way of background other than some familiarity with analysis of variance." --The Statistician
Book Synopsis Multiple Comparison Procedures by : Larry E. Toothaker
Download or read book Multiple Comparison Procedures written by Larry E. Toothaker and published by SAGE. This book was released on 1993 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you conduct research with more than two groups and want to find out if they are significantly different when compared two at a time, then you need Multiple Comparison Procedures. Using examples to illustrate major concepts, this concise volume is your guide to multiple comparisons. Toothaker thoroughly explains such essential issues as planned vs. post-hoc comparisons, stepwise vs. simultaneous test procedures, types of error rate, unequal sample sizes and variances, and interaction tests vs. cell mean tests.
Book Synopsis Generalized Error Control in Multiple Hypothesis Testing by : Wenge Guo
Download or read book Generalized Error Control in Multiple Hypothesis Testing written by Wenge Guo and published by . This book was released on 2007 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple hypothesis testing is concerned with appropriately controlling the rate of false positives when testing a large number of hypotheses simultaneously, while maintaining the power of each test as much as possible. For testing multiple null hypotheses, the classical approach to dealing with the multiplicity problem is to restrict attention to procedures that control the familywise error rate (FWER), the probability of even one false rejection. However, quite often, especially when a large number of hypotheses are simultaneously tested, the notion of FWER turns out to be too stringent, allowing little chance to detect many false null hypotheses. Therefore, researchers have focused in the last decade on defining alternative less stringent error rates and developing methods that control them. The false discovery rate (FDR), the expected proportion of falsely rejected null hypotheses, due to Benjamini and Hochberg (1995), is the first of these alternative error rates that has received considerable attention. Recently, the ideas of controlling the probabilities of falsely rejecting at least k null hypotheses, which is the k-FWER, and the false discovery proportion (FDP) exceeding a certain threshold y have been introduced as alternatives to the FWER and methods controlling these new error rates have been suggested. Very recently, following the idea similar to that of the k-FWER, Sarkar (2006) generalized the FDR to the k-FDR, the expected ratio of k or more false rejections to the total number of rejections, which is a less conservative notion of error rate than the FDR and k-FWER. In this work, we develop multiple testing theory and methods for controlling the new type I error rates. Specifically, it consists of four parts: (1) We develop a new stepdown FDR controlling procedure under no assumption on dependency of the underlying p-values, which has much smaller critical constants than that of the existing Benjamini-Yekutieli stepup procedure; (2) We develop new k-FWER and FDP stepdown procedures under the assumption of independence, which are much more powerful than the existing k-FWER and FDP procedures and show that under certain condition, the k-FWER stepdown procedure is unimprovable; (3) We offer a unified approach for construction of k-FWER controlling procedures by generalizing the closure principle in the context of the FWER to the case of the k-FWER; (4) We develop new Benjamini-Hochberg type k-FDR stepup and stepdown procedures in different settings and apply them to one real microarray data analysis.
Download or read book Str-Was written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays in Multiple Comparison Testing by : Elliot Williams
Download or read book Essays in Multiple Comparison Testing written by Elliot Williams and published by . This book was released on 2003 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple Hypothesis Testing Procedures with Applications to Epidemiologic Studies by : Conghui Qu
Download or read book Multiple Hypothesis Testing Procedures with Applications to Epidemiologic Studies written by Conghui Qu and published by . This book was released on 2009 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epidemiologic and genetic studies often involve the testing of a large number of hypotheses with test statistics that are potentially dependent. In this project, we investigate multiple testing procedures to control the family-wise error rate and false discovery rate. We consider several classic and novel multiple hypothesis testing procedures. Furthermore, we compare the results of the procedures which take advantage of the dependent structure among test statistics to those of the procedures which do not. The data we used is from a case-control study of non-Hodgkin Lymphoma.
Book Synopsis Multiple Hypothesis Testing of Discrete Data by : Stefanie R. Austin
Download or read book Multiple Hypothesis Testing of Discrete Data written by Stefanie R. Austin and published by . This book was released on 2014 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Resampling-based Methods for Multiple Hypothesis Testing by : Jennifer A. Schumi
Download or read book Resampling-based Methods for Multiple Hypothesis Testing written by Jennifer A. Schumi and published by . This book was released on 2006 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Large-scale Multiple Hypothesis Testing with Complex Data Structure by : Xiaoyu Dai
Download or read book Large-scale Multiple Hypothesis Testing with Complex Data Structure written by Xiaoyu Dai and published by . This book was released on 2018 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, motivated by a variety of applications in medicine, bioinformatics, genomics, brain imaging, etc., a growing amount of statistical research has been devoted to large-scale multiple testing, where thousands or even greater numbers of tests are conducted simultaneously. However, due to the complexity of real data sets, the assumptions of many existing multiple testing procedures, e.g. that tests are independent and have continuous null distributions of p-values, may not hold. This poses limitations in their performances such as low detection power and inflated false discovery rate (FDR). In this dissertation, we study how to better proceed the multiple testing problems under complex data structures. In Chapter 2, we study the multiple testing with discrete test statistics. In Chapter 3, we study the discrete multiple testing with prior ordering information incorporated. In Chapter 4, we study the multiple testing under complex dependency structure. We propose novel procedures under each scenario, based on the marginal critical functions (MCFs) of randomized tests, the conditional random field (CRF) or the deep neural network (DNN). The theoretical properties of our procedures are carefully studied, and their performances are evaluated through various simulations and real applications with the analysis of genetic data from next-generation sequencing (NGS) experiments.
Book Synopsis Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing by : Joseph P. Romano
Download or read book Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing written by Joseph P. Romano and published by . This book was released on 2003 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A New Approach to False Discovery Rates and Multiple Hypothesis Testing by : John D. Storey
Download or read book A New Approach to False Discovery Rates and Multiple Hypothesis Testing written by John D. Storey and published by . This book was released on 2001 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem by : Tejas Desai
Download or read book A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem written by Tejas Desai and published by Springer Science & Business Media. This book was released on 2013-02-26 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: In statistics, the Behrens–Fisher problem is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples. In his 1935 paper, Fisher outlined an approach to the Behrens-Fisher problem. Since high-speed computers were not available in Fisher’s time, this approach was not implementable and was soon forgotten. Fortunately, now that high-speed computers are available, this approach can easily be implemented using just a desktop or a laptop computer. Furthermore, Fisher’s approach was proposed for univariate samples. But this approach can also be generalized to the multivariate case. In this monograph, we present the solution to the afore-mentioned multivariate generalization of the Behrens-Fisher problem. We start out by presenting a test of multivariate normality, proceed to test(s) of equality of covariance matrices, and end with our solution to the multivariate Behrens-Fisher problem. All methods proposed in this monograph will be include both the randomly-incomplete-data case as well as the complete-data case. Moreover, all methods considered in this monograph will be tested using both simulations and examples.