For regression analysis of interval-censored failure time data, Zhang et al. (2005) [40] proposed an estimating equation approach to fit linear transformation models. In this paper, we develop two empirical likelihood (EL) inference approaches for the regression parameters based on the...

We consider a general mixed linear model ℳ without any rank assumptions to the covariance matrix and without any restrictions on the correlation between the random effects vector and the random errors vector. We get the representations of best linear unbiased estimators (BLUEs)/ best linear...

In this paper, we present both integral and infinite series expressions of μqp≡E[(x′Ax)p/(x′Bx)q] when x∼N(μ,In), where p, q are nonnegative real numbers, A is a symmetric matrix, and B is a positive semi-definite matrix. We also present efficient numerical methods for computing μqp...

One of the most essential topics in robust statistics is the robust estimation of location and covariance. Many popular robust (location and scatter) estimators such as Fast-MCD, MVE, and MZE require at least a convex distribution of the underlying data. In the case of non-convex data...

Given two univariate distributions F and G, the hypothesis that F=G can be tested against the alternative that FG using a precedence test statistic, or more generally the procentile–procentile plot of G against F. In this paper we propose a d-dimensional generalization of the p–p plot that...

In this paper we define a kernel estimator of the conditional density for a left-truncated and right-censored model based on the generalized product-limit estimator of the conditional distributed function. Under the observations with multivariate covariates form a stationary α-mixing sequence,...

We consider parametric hypotheses testing for multidimensional ergodic diffusion processes observed at discrete time. We propose a family of test statistics related to the so called ϕ-divergence measures. It is proved that the test statistics in this family are all asymptotically distribution...

Consider the semiparametric regression model yi=xiTβ+g(ti)+εi for i=1,…,n, where xi∈Rp are the random design vectors, ti are the constant sequences on [0,1], β∈Rp is an unknown vector of the slop parameter, g is an unknown real-valued function defined on the closed interval [0,1], and...

Nonparametric density estimators on RK may fail to be consistent when the sample size n does not grow fast enough relative to reduction in smoothing. For example a Gaussian kernel estimator with bandwidths proportional to some sequence hn is not consistent if nhnK fails to diverge to infinity....

Many parameters and positive-definiteness are two major obstacles in estimating and modeling a correlation matrix for longitudinal data. In addition, when longitudinal data is incomplete, incorrectly modeling the correlation matrix often results in bias in estimating mean regression parameters....