Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters
نویسندگان
چکیده
منابع مشابه
Bootstrap Methods for Testing Homogeneity of Variances
This paper describes the use of bootstrap and permutation methods for lhe problem of testing homogeneity of variances when means are not assumed equal or known. The melhods are new in this context, and nontrivial, since lhe composite null hypothesis involves nuisance mean parameters. They allow the use of normal-:'theory test statistics such as F = sUs~ without the normality assumption which is...
متن کاملBootstrap Procedures for Testing Homogeneity Hypotheses
Before pooling data on effect sizes (a generic term for parameters of interest in the context of meta-analysis) from different studies, it is important to test for homogeneity of the effect sizes. A well known test for homogeneity is based on Cochran’s chisquare statistic. Our recent investigation showed that when the effect size of interest is a pairwise correlation, Cochran’s homogeneity test...
متن کاملGraph Clustering by Hierarchical Singular Value Decomposition with Selectable Range for Number of Clusters Members
Graphs have so many applications in real world problems. When we deal with huge volume of data, analyzing data is difficult or sometimes impossible. In big data problems, clustering data is a useful tool for data analysis. Singular value decomposition(SVD) is one of the best algorithms for clustering graph but we do not have any choice to select the number of clusters and the number of members ...
متن کاملAssessing model mimicry using the parametric bootstrap
We present a general sampling procedure to quantify model mimicry, defined as the ability of a model to account for data generated by a competing model. This sampling procedure, called the parametric bootstrap cross-fitting method (PBCM; cf. Williams (J. R. Statist. Soc. B 32 (1970) 350; Biometrics 26 (1970) 23)), generates distributions of differences in goodness-of-fit expected under each of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics and Computing
سال: 2015
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-015-9566-5