In this paper, we propose interior-point algorithms for <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$P_* (\kappa )$$</EquationSource> </InlineEquation>-linear complementarity problem based on a new class of kernel functions. New search directions and proximity measures are defined based on these functions. We show that if a strictly feasible starting point is available,...</equationsource></inlineequation>

Motivated by Chaudhuri's work (1996) on unconditional geometric quantiles, we explore the asymptotic properties of sample geometric conditional quantiles, defined through kernel functions, in high dimensional spaces. We establish a Bahadur type linear representation for the geometric conditional...

Under the condition that the observations, which come from a high-dimensional population (<I>X,Y</I>), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric <I>M</I>-estimator for the unknown...</i></i>

A global seismic hazard assessment was conducted using the probabilistic approach in conjunction with a modified means of evaluating the seismicity parameters. The earthquake occurrence rate function was formulated for area source cells from recent instrumental earthquake catalogs. For the...

We extend the KVB approach of Kiefer, Vogelsang, and Bunzel (2000, Econometrica) and Kiefer and Vogelsang (2002b, Econometric Theory) to construct a class of robust tests for over-identifying restrictions in the context of GMM. The proposed test does not require consistent estimation of the...

We propose new over-identifying restriction (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. The proposed tests do not require consistent estimation of the asymptotic covariance matrix and hence avoid choosing the bandwidth in nonparametric kernel...

Motivated by Chaudhuri's work (1996) on unconditional geometric quantiles, we explore the asymptotic properties of sample geometric conditional quantiles, defined through kernel functions, in high dimensional spaces. We establish a Bahadur type linear representation for the geometric conditional...

In many situations, we want to verify the existence of a relationship between multivariate time series. Here, we propose a semiparametric approach for testing the independence between two infinite order vector autoregressive (VAR()) series which is an extension of Hong's (1996a) univariate...

The problem of estimating an unknown density function has been widely studied. In this paper we present a convolution estimator for the density of the responses in a nonlinear regression model. The rate of convergence for the variance of the convolution estimator is of order 1/n. This is faster...