The prediction of breeding values depends on the reliable estimation of variance components. This complex task leads to nonlinear minimization problems that have to be solved by numerical algorithms. In order to evaluate the reliability of these algorithms benchmark problems have to be...

The Pareto distribution is an important distribution in statistics, which has been widely used in economics to model the distribution of incomes. Separate interval estimations for parameters of the Pareto distribution have been well established in the literature. For a type-II right censored...

In this paper, we consider statistical inference for linear regression models when neither the response nor the predictors can be directly observed, but are measured with errors in a multiplicative fashion and distorted as single index models of observable confounding variables. We propose a...

Presence-only data occur in a classification, which consist of a sample of observations from the presence class and a large number of background observations with unknown presence/absence. Since absence data are generally unavailable, conventional semi-supervised learning approaches are no...

To understand the nature and cause of natural climate variability, it is important to possess an accurate estimate of past climate forcings. Direct measurements that are reliable only exist for the past few decades. Therefore knowledge of prior variations has to be established based on indirect...

The aim of this paper is to introduce generalized symmetric linear models (GSLMs) in the same sense of generalized linear models (GLMs), in which a link function is defined to establish a relationship between the mean values of symmetric distributions and linear predictors. The class of...

In biological and medical research, continuous, strictly positive, right-skewed data, possibly with heterogeneous variances, are common, and can be described by log-normal distributions. In experiments involving multiple treatments in a one-way layout, various sets of multiple comparisons among...

In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows’ Cp type criteria may be...

Deposition of silicon dioxide (SiO2) is a critical step of integrated circuit manufacturing; hence it is monitored during the manufacturing process at a grid of points defined on the wafer area. Since collecting thickness measurements is expensive, it is a compelling issue to investigate how a...

Poisson regression is important in the analysis of longitudinal count data. However, the variance of responses is often much greater than the sample mean in practice, contradicting the Poisson model. To solve this overdispersion problem, negative binomial regression model was introduced by...