Joint Variable Selection for Fixed and Random Effects in Linear Mixed‐Effects Models

Summary It is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed‐effects (LME) model. Typical approaches perform selection separately on each of the fixed and random effect components. However, changing the structure of one set of effects can lead to different choices of variables for the other set of effects. We propose simultaneous selection of the fixed and random factors in an LME model using a modified Cholesky decomposition. Our method is based on a penalized joint log likelihood with an adaptive penalty for the selection and estimation of both the fixed and random effects. It performs model selection by allowing fixed effects or standard deviations of random effects to be exactly zero. A constrained expectation–maximization algorithm is then used to obtain the final estimates. It is further shown that the proposed penalized estimator enjoys the Oracle property, in that, asymptotically it performs as well as if the true model was known beforehand. We demonstrate the performance of our method based on a simulation study and a real data example.     

Noncrossing quantile regression curve estimation

Since quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedure. Asymptotic properties of the estimator are equivalent to the typical approach under standard conditions, and the proposed estimator reduces to the classical one if there is no crossing. The performance of the constrained estimator has shown significant improvement by adding smoothing and stability across the quantile levels.     

A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data

Summary Identifying homogeneous groups of individuals is an important problem in population genetics. Recently, several methods have been proposed that exploit spatial information to improve clustering algorithms. In this article, we develop a Bayesian clustering algorithm based on the Dirichlet process prior that uses both genetic and spatial information to classify individuals into homogeneous clusters for further study. We study the performance of our method using a simulation study and use our model to cluster wolverines in Western Montana using microsatellite data.     

Erratum to: Rumen-protected conjugated linoleic acid supplementation to dairy cows in late pregnancy and early lactation: effects on milk composition,milk yield,blood metabolites and gene expression in liver

Background  

Conjugated linoleic acid (CLA) is a collective term for isomers of octadecadienoic acid with conjugated double-bond system. Thus, it was the objective to investigate whether milk composition and metabolic key parameters are affected by adding CLA to the diet of dairy cows in the first four weeks of lactation.     

PTC and PROP behave differently in tests of discrimination from their solvents

Better discrimination was possible between phenylthiocarbamide (PTC) solutions and the pure solvent when the solvent was a tasteless low- concentration NaCl solution to which the subject had adapted than when the solvent was purified water. The reverse was true for 6-n- propylthiouracil (PROP). The differences in discrimination for PROP and PTC in the different solvents were caused by differences in the intensity and persistence of aftertastes, rather than a more intense perception of the PTC and PROP tastes per se. This has consequences for traditional approaches to measuring taste sensitivity, as well as indicating that PTC and PROP are not necessarily equivalent indicators of 'taster' versus 'non-taster' status.