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Abstract We study the mutual information estimation for mixed-pair random variables. One random variable is discrete and the other one is continuous. We develop a kernel method to estimate the mutual information between the two random variables. The estimates enjoy a central limit theorem under some regular conditions on the distributions. The theoretical results are demonstrated by simulation study. [ABSTRACT FROM AUTHOR]

Abstract: This article establishes the connection between quadratic discrimination and model selection criterion in the ARMA framework. We show that analyzing model selection in ARMA time series models as a quadratic discrimination problem provides a unifying approach for deriving model selection criteria. [Copyright &y& Elsevier]

Abstract: In this paper, we study the kernel conditional quantile estimation for censored data and a uniform strong convergence rate of the resulting estimator is established. The rate obtained here is the same as that for uncensored case. [Copyright &y& Elsevier]

This note considers a new class of nonparametric estimators for nonlinear time-series models based on kernel smoothers. Various new results are given for two popular nonlinear time-series models and compared with the results of Thavaneswaran and Peiris (Statist. Probab. Lett. 28 (1996) 227). [Copyright &y& Elsevier]