Econometric applications of kernel estimators are proliferating, suggesting the need for convenient variance estimates and conditions for asymptotic normality. This paper develops a general "delta method" variance estimator for functionals of kernel estimators. Also, regularity conditions for asymptotic normality are given, along with a guide to verifying them for particular estimators. The gen...

We consider probability measure estimation in a nonparametric model using a leastsquares approach under the Prohorov metric framework. We summarize the computational methods and their convergence results that were developed by our group over the past two decades. New results are presented on the bias and the variance due to the approximation and the pointwise asymptotic normality of the approxi...

In this paper, we consider a number of technical problems associated with identification of linear systems using quantized and intermittent information. More specifically, we study asymptotic properties for identification of such systems, including strong consistency, asymptotic normality and asymptotic efficiency, and determine their relationship with quantization errors and the packet losses....

We study the consistency and asymptotic normality of the LS estimator of a function h(θ) of the parameters θ in a nonlinear regression model with observations yi = η(xi, θ) + εi, i = 1, 2 . . . and independent errors εi. Optimum experimental design for the estimation of h(θ) frequently yields singular information matrices, which corresponds to the situation considered here. The difficulties cau...

In this paper we study the asymptotic normality of the normalized partial sum of a Hilbert-space valued strictly stationary random field satisfying the interlaced ρ-mixing condition.