The statistical inference based on the ordinary least squares regression is sub-optimal when the distributions are skewed or when the quantity of interest is the upper or lower tail of the distributions. For example, the changes in Total Sharp Scores (TSS), the primary measurements of the...

Data do not always obey the normality assumption, and outliers can have dramatic impacts on the quality of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression...

This paper proves that the law of the iterated logarithm holds for a stationary negatively associated sequence of random variables with finite variance. The proof is based on a Rosenthal type maximal inequality, a Kolmogorov type exponential inequality and Stein's method.

We prove that the directed random walk satisfies the strong law of large numbers if and only if the environment has a finite mean.

We find a necessary and sufficient condition for the weak convergence of the uniform empirical and quantile processes to a Brownian bridge in weighted Lp-distances. Under the same condition, weighted Lp-functionals of the uniform empirical and quantile processes converge in distribution to the...

We propose a circular block resampling procedure to modify Künsch's moving block bootstrap. Our procedure has the special feature that the resampled data are like drawing from the empirical distribution function of dependent observations. No information is lost concerning the nature of...

We establish upper bounds for moduli of continuity of the local times of Gaussian processes with stationary increments and for those of stationary Gaussian processes.