This paper studies Bayesian predictive densities based on different priors and frequentist plug-in type predictive densities when the predicted variables are dependent on the observations. Average Kullback-Leibler divergence to the true predictive density is used to measure the performance of different inference procedures. The notion of second-order KL dominance is introduced, and an explicit ...

We describe an extension of our empirical Bayes approach to magnetoencephalography/electroencephalography (MEG/EEG) source reconstruction that covers both evoked and induced responses. The estimation scheme is based on classical covariance component estimation using restricted maximum likelihood (ReML). We have focused previously on the estimation of spatial covariance components under simple a...

Summary In this research, an intrinsically nonlinear split-plot design model (INSPDM) is formulated and studied. It was by fitting a Johnson–Schumacher (JS) function to the mean function. The fitted parameters are estimated using generalized least squares (EGLS) technique based on Gauss–Newton procedure with Taylor series expansion, minimizing objective of model. variance components for whole p...

AIM Glucagon-like peptide-1 can reduce both postprandial plasma glucose (PG) and chylomicron (CM) levels in patients with type 2 diabetes. However, there have been no reports regarding the relationship between the postprandial metabolism of PG and CM. METHODS Patients with type 2 diabetes who were admitted for glycemic control were randomized to insulin alone (Ins; n=16) or insulin plus vilda...

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense SNPheritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report th...

This paper establishes asymptotic results for the maximum likelihood and restricted (REML) estimators of parameters in nested error regression model clustered data when both number independent clusters cluster sizes (the observations each cluster) go to infinity. Under very mild conditions, are shown be asymptotically normal with an elegantly structured covariance matrix. There no restrictions ...

Lognormal linear models are widely used in applications, and many times it is of interest to predict the response variable at the original scale for a new set of covariate values. In this paper we consider the problem of efficient estimation of the conditional mean of the response variable at the original scale for lognormal linear models. Several existing estimators are reviewed, including the...

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the inf...