Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class of closed skew-normal distributions. Some basic...

How to sufficiently use the structure information behind the data is still a challenging issue. In this paper, a local linear–additive estimation and its relevant version are proposed to automatically capture the additive information for general multiple nonparametric regressions. Our method...

We establish sufficient conditions for the asymptotic normality of kernel density estimators applied to causal linear random fields, by m-dependent approximation. Our conditions on the coefficients of linear random fields are weaker than the known results, although our assumption on the...

Measures of association are suggested between two random vectors. The measures are copula-based and therefore invariant with respect to the univariate marginal distributions. The measures are able to capture positive as well as negative association. In case the random vectors are just random...

A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When...

The multivariate two-sample testing problem has been well investigated in the literature, and several parametric and nonparametric methods are available for it. However, most of these two-sample tests perform poorly for high dimensional data, and many of them are not applicable when the...

Each stationary process can be biunivoquely associated with a random measure, through the Fourier transform. Consequently, every particularity of a process in the temporal domain has its corresponding one in the frequency domain. We propose to study the characteristics of the random measure when...

We consider a marginal mixture cure model for clustered survival data in which there may be a cure fraction and the survival times may be correlated. We propose a generalized estimating equation approach by incorporating working correlation matrices into an EM algorithm to estimate the...

This paper considers the class of p-dimensional elliptic distributions (p≥1) satisfying the consistency property (Kano, 1994)  [23] and within this general framework presents a two-stage nonparametric estimator for the Lebesgue density based on Gaussian mixture sieves. Under the on-line...

Our goal is to predict a scalar value or a group membership from the discretized observation of curves with sharp local features that might vary both vertically and horizontally. To this aim, we propose to combine the use of the nonparametric functional regression estimator developed by Ferraty...