The nonparametric covariance estimation of a stationary Gaussian field X observed on a lattice is investigated. To tackle this issue, a neighborhood selection procedure has been recently introduced. This procedure amounts to selecting a neighborhood by a penalization method and estimating the...

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive...

The objective of the research presented in this paper is to shed light into the benefits of multi-dimensional wavelet-based methodology applied to NMR biomolecular data analysis. Specifically, the emphasis is on noise reduction for enhanced component identification in multi-dimensional mixture...

We consider point and interval estimation for risk ratios based on two independent samples of binomial data subject to false positive misclassification. For such data it is well known that the model is unidentifiable. We consider incorporating training data obtained by using a double-sampling...

We discuss robust designs for generalized linear models with protection for possible departures from the usual model assumptions. Besides possible inaccuracy in an assumed linear predictor, both problems of overdispersion and misspecification in link function are addressed. For logistic and...

Tang and Tian (2009) derived formulas for the development of confidence bounds for the risk difference under inverse sampling. Unfortunately, the last formula in the appendix for the inverse of the expected Fisher Information matrix I-1 contains an error which leads-if used-to incorrect...

Mis-specification of the covariance structure in longitudinal data can result in loss of regression estimation efficiency and in misleading influence diagnostics. Therefore, a rule-of-thumb, even one that is rough, for detecting covariance mis-specification would prove valuable to data analysts....

Time series of counts are commonly observed in real-world applications. The integer-valued ARCH(p) models are able to describe integer-valued processes and offer the potential to be widely applied in practice in future. This paper develops an asymptotic theory for (partial) autocorrelations of...

In this paper, we consider a semiparametric partially linear regression model where there are missing data in the response. We propose robust Fisher-consistent estimators for the regression parameter, for the regression function and for the marginal location parameter of the response variable. A...