In this paper, we apply the empirical likelihood method to heteroscedastic partially linear errors-in-variables model. For the cases of known and unknown error variances, the two different empirical log-likelihood ratios for the parameter of interest are constructed. If the error variances are...

In this paper, we construct a nonparametric regression quantile estimator by using the local linear fitting for left-truncated data, and establish the Bahadur-type representation and asymptotic normality of the proposed estimator when the observations form a stationary α-mixing sequence....

Summary In this paper, we discuss the global L 2 error of the nonlinear wavelet estimators of the density function in the Besov space B s pq , when the survival times form a stationary α-mixing sequence, and prove that the nonlinear wavelet estimators can achieve the optimal rate of...

In this paper we study the strong and weak convergence with rates for the estimators of the conditional distribution function as well as conditional cumulative hazard rate function for a left truncated and right censored model. It is assumed that the lifetime observations with multivariate...

This article is concerned with the estimating problem of heteroscedastic partially linear errors-in-variables models. We derive the asymptotic normality for estimators of the slope parameter and the nonparametric component in the case of known error variance with stationary <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\alpha $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="italic">α</mi> </math> </EquationSource>...</equationsource></equationsource></inlineequation>

Let {X <Subscript> n </Subscript>,n≥1} be a strictly stationary sequence of negatively associated random variables with the marginal probability density function f(x), the recursive kernel estimate of f(x) is defined by [InlineMediaObject not available: see fulltext.] where h <Subscript> n </Subscript> is a sequence of positive bandwidths...</subscript></subscript>