Uniform TitleMultiple testing methods in dependent cases
NameXu, Minya (author), Sackrowitz, Harold (internal member), Cohen, Arthur (internal member), Zhang, Cun-Hui (internal member), Sarkar, Sanat (outside member), Rutgers University, Graduate School - New Brunswick,
SubjectStatistics and Biostatistics,
DescriptionThe most popular multiple testing procedures are stepwise procedures based on P-values for individual test statistics. Included among these are the false discovery rate (FDR) controlling procedures of Benjamini-Hochberg(1995)
and their offsprings. For many models including the case where model variables are multivariate normal, dependent and alternatives are two sided, these stepwise procedures lack an intuitive convexity property which is also needed for admissibility. Here we present two new stepwise methods that do in fact have the convexity property. Furthermore unlike the method using P-values based on marginal distributions, the new methods take dependency into account in all stages. Still further the new methodology is computationally feasible. Applications are detailed for models such as testing for change points of variances
and testing treatments against control of variances.
NoteIncludes bibliographical references (p. 66-67).
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.