A fundamental issue of applying a copula method in applications is how to choose an appropriate copula function for a given problem. In this article we address this issue by proposing a new copula selection approach via penalized likelihood plus a shrinkage operator. The proposed method selects...

Due to nonstationary (nearly integrated or integrated) regressors and the embedded endogeneity, a linear predictive regression model produces biased coefficient estimates, which consequentially leads to the conventional t-test to over-reject the misspecification test. In this paper, our aim is...

Let {Xj: j [greater-or-equal, slanted] 1} be a real-valued stationary process. Recursive kernel estimators of the joint probability density functions, and of conditional probability density functions of Xj, given past behavior, are considered. Their strong consistency, along with rates, are...

In this paper, we consider the instrumental variable estimation (the two-stage least squares estimator and the limited information maximum likelihood estimator) using weak instruments in a repeated measurements or a panel data model. We show that independently repeated cross-sectional data can...

In some long term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common marginal distribution function having a density, and the nonparametric estimation of density and hazard rate under random censorship is of our...

Consider a long term study, where a series of possibly censored failure times is observed. Suppose the failure times have a common marginal distribution functionF, but they exhibit a mode of dependence characterized by positive or negative association. Under suitable regularity conditions, it is...

In some long term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common marginal distribution function, and inferences about it are of interest to us. The main result of this paper is that, under certain regularity...

In this article, we investigate a robust version of local linear regression smoothers for stationary time series sequence by using robust-type local polynomial techniques. Under some regularity conditions, we establish the weak and strong consistency as well as the asymptotic normality of the...