Multidimensional medians induced from depth functions as the generalizations of the univariate median have been proposed and studied. Like their univariate counterpart, they usually possess the desirable properties including affine equivariance, high breakdown point robustness, etc. Furthermore,...

The importance of dimension reduction has been increasing according to the growth of the size of available data in many fields. An appropriate dimension reduction method of raw data helps to reduce computational time and to expose the intrinsic structure of complex data. Sliced inverse...

We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full...

Composite endpoints are increasingly used in clinical trials, particularly in the field of cardiology. Thereby, the overall impact of the therapeutic intervention is captured by including several events of interest in a single variable. To demonstrate the significance of an overall clinical...

Independent Component Analysis (ICA) is closely related to Principal Component Analysis (PCA) and factor analysis. Whereas ICA finds a set of source data that are mutually independent, PCA finds a set of data that are mutually uncorrelated. The assumption that data from different physical...

In practice, when both survival and quantitative outcomes are of interest, we encounter outcomes of mixed type: a censored outcome and a quantitative outcome. Joint modeling of the survival and quantitative outcomes rather than analyzing the outcomes separately has become a method of choice for...

Small area estimation is studied under a spatio-temporal Fay–Herriot model. Model fitting based on restricted maximum likelihood is described and empirical best linear unbiased predictors are derived under the model. A parametric bootstrap procedure is proposed for the estimation of the mean...

The Weibull family is widely used to model failure data, or lifetime data, although the classical two-parameter Weibull distribution is limited to positive data and monotone failure rate. The parameters of the Weibull model are commonly obtained by maximum likelihood estimation; however, it is...

The Bayesian estimators for the unknown parameters of the bivariate Marshall–Olkin exponential distribution under noninformative priors have been considered and several reference priors have been derived. A class of priors is found by matching the coverage probability of one-side Bayesian...

A new non-randomized response (NRR) model (called a variant of the parallel model) is proposed. The survey design and corresponding statistical inferences including likelihood-based methods, Bayesian methods and bootstrap methods are provided. Theoretical and numerical comparisons showed that...