The paper discusses an approach based on the multivariate Delta method for approximating the distribution of posterior probabilities in finite mixture models. It can be used for developing distributions of many other characteristics involving posterior probabilities such as the entropy of fuzzy...

Life history data arising in clusters with pre-specified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The...

We present a new method for estimating the frontier of a multidimensional sample. The estimator is based on a kernel regression on high order moments. It is assumed that the order of the moments goes to infinity while the bandwidth of the kernel goes to zero. The consistency of the estimator is...

This paper addresses the problem of reconstructing a low-rank signal matrix observed with additive Gaussian noise. We first establish that, under mild assumptions, one can restrict attention to orthogonally equivariant reconstruction methods, which act only on the singular values of the observed...

The concept of generalized order statistics is a distribution theoretical set-up, which contains a variety of models for ordered data as particular cases, such as common order statistics, sequential order statistics, progressively type-II censored order statistics, record values, kth record...

We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and error term can be contemporaneously correlated. The asymptotic properties of the Nadaraya–Watson estimator are already examined in the literature. In this paper, we consider nonparametric least...

In this paper, we study the statistical properties of the method of regularization with radial basis functions in the context of linear inverse problems. Radial basis function regularization is widely used in machine learning because of its demonstrated effectiveness in numerous applications and...

Robustness and efficiency of the residual scale estimators in the regression model is important for robust inference. We introduce the class of robust generalized M-scale estimators for the regression model, derive their influence function and gross-error sensitivity, and study their maxbias...

The problem of hypothesis testing concerning the mean vector for high dimensional data has been investigated by many authors. They have proposed several test criteria and obtained their asymptotic distributions, under somewhat restrictive conditions, when both the sample size and the dimension...

Most of the research on small area estimation has focused on unconditional mean squared error (MSE) estimation under an assumed small area model. Datta et al. (2011) [3] studied conditional MSE estimation of a small area mean under a basic area-level model, conditional on the area-specific...