The empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty of EBLUP. To obtain a second-order unbiased estimator of the MSE, the...

In this paper, we consider a flexible class of semiparametric varying-coefficient mean residual lifetime (MRL) models that depended on an exposure variable where some effects may be functions of the exposure variables and some may be constants. We develop three-step estimation procedures to...

Case-cohort designs provide a cost effective way to conduct epidemiological follow-up studies in which event times are the outcome variables. This paper develops a quantile regression approach to the analysis of case-cohort data. Quantile regression is a highly useful tool to delineate...

Maximum entropy models, motivated by applications in neuron science, are natural generalizations of the β-model to weighted graphs. Similar to the β-model, each vertex in maximum entropy models is assigned a potential parameter, and the degree sequence is the natural sufficient statistic....

Functional mixed-effects models are very useful in analyzing data. In this paper, we consider a functional mixed-effects model, where the observations are the real functions, and derive the maximum likelihood estimators of the functional parameters and variance components. The properties of the...

We consider a noisy observed vector y=x+u∈Rn. The unobserved vector x is a solution of a non-invertible linear system Ax=v, where v is a forcing term. A unique solution of the system is obtained by considering additional constraint on the vector x. This constraint is defined by a triple...

We study a new approach to simultaneous variable selection and estimation via random-effect models. Introducing random effects as the solution of a regularization problem is a flexible paradigm and accommodates likelihood interpretation for variable selection. This approach leads to a new type...

We report a matrix expression for the covariance matrix of MLEs of factor loadings in factor analysis. We then derive the analytical formula for covariance matrix of the covariance estimators of MLEs of factor loadings by obtaining the matrix of partial derivatives, which maps the differential...

Differential entropy and log determinant of the covariance matrix of a multivariate Gaussian distribution have many applications in coding, communications, signal processing and statistical inference. In this paper we consider in the high-dimensional setting optimal estimation of the...

We introduce the concept of an extremely negatively dependent (END) sequence of random variables with a given common marginal distribution. An END sequence has a partial sum which, subtracted by its mean, does not diverge as the number of random variables goes to infinity. We show that an END...