In this paper, we investigate the estimation and testing problems of partially linear varying-coefficient errors-in-variables (EV) models under additional restricted condition. The restricted estimators of parametric and nonparametric components are established based on modified profile...

A class of linear classification rules, specifically designed for high-dimensional problems, is proposed. The new rules are based on Gaussian factor models and are able to incorporate successfully the information contained in the sample correlations. Asymptotic results, that allow the number of...

Two-level supersaturated designs (SSDs) are designs that examine more than n-1 factors in n runs. Although SSD literature for both construction and analysis is plentiful, the dearth of actual applications suggests that SSDs are still an unproven tool. Whether using forward selection or...

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these "hierarchical time series". They are commonly forecast using either a "bottom-up"...

Analyses of systems that can be represented by functional responses are becoming common in many scientific disciplines. Functional regression trees (FRT) provide a methodology for modelling such systems. Recent work has focused on fitting models where the response variable is a probability...

Bias in parameter estimates can be substantial when heteroscedastic normal mixtures are misspecified as homoscedastic normal mixtures, and vice versa. We show through simulations that the maximum likelihood estimators under the false assumption of equal variances are inconsistent and bias in...

We consider the coverage rate of model-averaged confidence intervals for the treatment means in a factorial experiment, when we use a normal linear model in the analysis. Model-averaging provides a useful compromise between using the full model (containing all main effects and interactions) and...

In semiparametric regression models, penalized splines can be used to describe complex, non-linear relationships between the mean response and covariates. In some applications it is desirable to restrict the shape of the splines so as to enforce properties such as monotonicity or convexity on...

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models...

Surrogate interpolation models for time-consuming computer experiments are being increasingly used in scientific and engineering problems. A new interpolation method, based on Delaunay triangulations and related to inverse distance weighting, is introduced. This method not only provides an...