In this paper we propose a stationary nonlinear dynamic functional coefficient panel data models with fixed effects and develops semiparametric estimation procedure using series approximation. Convergence rate and asymptotic distribution of the proposed series estimators are derived in which...

In this paper, we provide the almost-sure convergence and the asymptotic normality of a smooth version of the Robbins–Monro algorithm for the quantile estimation. A Monte Carlo simulation study shows that our proposed method works well within the framework of a data stream.

The robust estimation for Poisson autoregressive models is studied. As a robust estimator, a minimum density power divergence estimator (MDPDE) is considered. It is shown that under regularity conditions, the MDPDE is strongly consistent and asymptotically normal. Simulation results are provided...

This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses the...

A changepoint in a time series is a time of change in the marginal distribution, autocovariance, or any other distributional structure of the series. Examples include mean level shifts and volatility (variance) changes. Climate data, for example, is replete with mean shift changepoints,...

In this paper, we propose a locally linear estimation of a regression discontinuity model. The proposed estimator is applicable to evaluation of the effectiveness of the program treatment, and it improves upon the existing literature by providing not just the treatment effect at discontinuity...

In this paper, we consider the statistical inference for the partially liner varying coefficient model with measurement error in the nonparametric part when some prior information about the parametric part is available. The prior information is expressed in the form of exact linear restrictions....

We construct and investigate a (1−α)-upper prediction bound for a future observation of a cyclic Poisson process using past data. A normal based confidence interval for our upper prediction bound is established. A comparison of the new prediction bound with a simpler nonparametric prediction...

In the present paper, we propose a Palm likelihood approach as a general estimating principle for stationary point processes in <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\mathbf{R}^d$$</EquationSource> </InlineEquation> for which the density of the second-order factorial moment measure is available in closed form or in an integral representation. Examples of such...</equationsource></inlineequation>

In this paper, we consider a scale adjusted-type distance-based classifier for high-dimensional data. We first give such a classifier that can ensure high accuracy in misclassification rates for two-class classification. We show that the classifier is not only consistent but also asymptotically...