The objective of this article is to present a depth based multivariate control quantile test using statistically equivalent blocks (DSEBS). Given a random sample {x1,…,xm} of Rd-valued random vectors (d≥1) with a distribution function (DF) F, statistically equivalent blocks (SEBS), a...

Standard first order tests have size error that decreases as m−1/2 where m is a measure of sample size. Parametric bootstrap tests use an exact calculation of the P-value, assuming nuisance parameters equal their null maximum likelihood estimates. It is commonly believed that their performance...

A way of combining a hidden Markov model (HMM) and mutual information analysis is proposed to detect possible precursory signals for earthquakes from Global Positioning System (GPS) data. A non-linear filter, which measures the short-term deformation rate ranges, is introduced to extract...

We examine methods appropriate for heavy-tailed longitudinal outcomes with possibly missing data. Generalized estimating equations (GEEs) have been widely used in longitudinal studies when data are not heavy-tailed and, in general, are valid only when data are missing completely at random....

Many nonparametric smoothing procedures consider independent identically distributed stochastic variables. There are also many important nonparametric smoothing applications where the data is more complicated. Survival data or filtered data, defined as following Aalen’s multiplicative hazard...

A new three-parameter exponential-type family of distributions which can be used in modeling survival data, reliability problems and fatigue life studies is introduced. Its failure rate function can be constant, decreasing, increasing, upside-down bathtub or bathtub-shaped depending on its...

The Poisson regression model is the most common framework for modeling count data, but it is constrained by its equidispersion assumption. The hyper-Poisson regression model described in this paper generalizes it and allows for over- and under-dispersion, although, unlike other models with the...

The problem of widely linear (WL) prediction for both WL ARMA models and WL transfer function models on the basis of infinite past information is studied. A recursive algorithm to obtain a suboptimum predictor for WL ARMA systems is first given. Then this algorithm is used to develop another...

A new class of robust estimators for VAR models is introduced. These estimators are an extension to the multivariate case of the MM-estimators based on a bounded innovation propagation AR model. They have a filtering mechanism that avoids the propagation of the effect of one outlier to the...

Available joint confidence sets for the parameters of the Pareto model are not the regions with minimum area. In order to determine the smallest joint confidence region among all those which are based on the minimal sufficient statistic, a computational procedure is proposed which is applicable...