The adaptive lasso can consistently identify the true model in regression model. However, the adaptive lasso cannot account for lag effects, which are essential for a time series model. Consequently, the adaptive lasso can not reflect certain properties of a time series model. To improve the...

Matrix conic optimization induced by spectral norm (MOSN) has found important applications in many fields. This paper focus on the optimality conditions and perturbation analysis of the MOSN problem. The Karush–Kuhn–Tucker (KKT) conditions of the MOSN problem can be reformulated as a...

The construction of a set of simultaneous confidence intervals for any finite number of contrasts of p generally correlated normal means is considered. It is shown that the simultaneous confidence level can be expressed as a (p−2)-dimensional integral for a general p≥3. This expression...

The varying-coefficient single-index models (VCSIMs) form a class of very flexible and general dimension reduction models, which contain many important regression models such as partially linear models, pure single-index models, partially linear single-index models, varying-coefficient...

This paper builds on recent research that focuses on regression modeling of continuous bounded data, such as proportions measured on a continuous scale. Specifically, it deals with beta regression models with mixed effects from a Bayesian approach. We use a suitable parameterization of the beta...

Assessment of interaction in unreplicated two-factor experiments is a challenging problem that has received considerable attention in the literature. A model is proposed in which the levels of one factor belong in two or more groups. Within each group the effects of the two factors are additive...

The kernel method is a nonparametric procedure used to estimate densities with support in R. When nonnegative data are modeled, the classical kernel density estimator presents a bias problem in the neighborhood of zero. Several methods have been developed to reduce this bias, which include the...

The empirical variogram is a standard tool in the investigation and modelling of spatial covariance. However, its properties can be difficult to identify and exploit in the context of exploring the characteristics of individual datasets. This is particularly true when seeking to move beyond...

The classical Hill estimator of a positive extreme value index (EVI) can be regarded as the logarithm of the geometric mean, or equivalently the logarithm of the mean of order p=0, of a set of adequate statistics. A simple generalisation of the Hill estimator is now proposed, considering a more...

This paper proposes a new minimum distance methodology for the estimation of ARFIMA processes with Gaussian and non-Gaussian errors. The main advantage of this method is that it allows for a computationally efficient estimation when the long-memory parameter is in the interval d∈(−12,12)....