Rank-based two-sample tests for paired data with missing values

Tests for mean vectors with two-step monotone missing data for the k-sample problem

We continue our recent work on the problem of testing the equality of two normal mean vectors when the data have two-step monotone pattern missing observations. This paper extends the two-sample problem in our previous paper to the k-sample problem. Under the assumption that the population covariance matrices are equal, we obtain the likelihood ratio test statistic for testing the hypothesis H0...

Symmetrized Approximate Score Rank Tests for the Two-sample Case

Graph-based Tests for Two-sample Comparisons of Categorical Data

We study the problem of two-sample comparison with categorical data when the contingency table is sparsely populated. In modern applications, the number of categories is often comparable to the sample size, causing existing methods to have low power. When the number of categories is large, there is often underlying structure on the sample space that can be exploited. We propose a general non-pa...

Linear Rank Tests for Two-Sample Fuzzy Data: A P-Value Approach

The linear rank test for two-sample data, which is a popular non-parametric statistical test, is extended to the case when the available data are imprecise rather than crisp. To do this, using some elements of credibility theory, we suggest a ranking method among imprecise observations and apply it to extend the usual concept of p-value leading a degree to accept or reject the null hypothesis o...

Anova for Longitudinal Data with Missing Values

We carry out an ANOVA analysis to compare multiple treatment effects for longitudinal studies with missing values. The treatment effects are modeled semiparametrically via a partially linear regression which is flexible in quantifying the time effects of treatments. The empirical likelihood is employed to formulate nonparametric ANOVA tests for treatment effects with respect to covariates and t...