Over the last decade there has been a marked interest in a Laplace distribution and its properties and generalizations, especially in the framework of financial applications. Such an interest has led to a revision and discussion of available goodness-of-fit procedures for a Laplace distribution....

Many problems can be cast as statistical inference on an attributed random graph. Our motivation is change detection in communication graphs. We prove that tests based on a fusion of graph-derived and content-derived metadata can be more powerful than those based on graph or content features...

Sufficient dimension reduction methods, such as the sliced inverse regression one (SIR) and the sliced average variance estimate one (SAVE), usually put restrictions on the regressor: X being elliptical or normal. We propose a new effective method, called the generalized gradient direction...

Estimating normalizing constants is a common and often difficult problem in statistics, and path sampling (PS) is among the most powerful methods that have been put forward to this end. Using an identity that arises in the formulation of PS, we derive expressions for the Kullback-Leibler (KL)...

This study provides a supplemental report of the performance of the confidence interval for direct standardized rates obtained by the approximate bootstrap method (ABC) method. The ABC method was not considered by the Ng et al. (2008) paper which compared different methods of interval...

Supersaturated designs (SSDs) are widely researched because they can greatly reduce the number of experiments. However, analyzing the data from SSDs is not easy as their run size is not large enough to estimate all the main effects. This paper introduces contrast-orthogonality cluster and...

One of the most common problems in applied statistics is to compare two normal population means when the ratio of variances is unknown and not equal to 1. This is the known Behrens-Fisher problem. There are many approaches to the distribution to the t-statistic in the univariate circumstance...

Correlated or clustered failure time data often occur in medical studies, among other fields ([1] and [9]), and sometimes such data arise together with interval censoring (Wang et al., 2006). Furthermore, the failure time of interest may be related to the cluster size. For example, Williamson...

We propose a variable selection procedure for the canonical correlation analysis (CCA) between two sets of principal components. We attempt to create predictive models for selecting such variables by combining principal component analysis (PCA) and CCA, and we refer to them collectively as...

The aim of this paper is to derive diagnostic procedures based on case-deletion model for symmetrical nonlinear regression models, which complements Galea et al. (2005) that developed local influence diagnostics under some perturbation schemes. This class of models includes all symmetric...