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This paper presents a Bayesian analysis of linear mixed models for quantile regression using a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We consider several novel Bayesian shrinkage approaches for both fixed and random effects in a linear mixed quantile model using extended L 1 penalties. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference. We also extend the framework to a Bayesian mixed expectile model and develop a Metropolis–Hastings acceptance–rejection (MHAR) algorithm using proposal densities based on iteratively weighted least squares estimation. The proposed approach is then illustrated via both simulated and real data examples. Results indicate that the proposed approach performs very well in comparison to the other approaches. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel constrained Bayesian elastic net approach for linear quantile mixed model shrinkage. A partially collapsed Gibbs sampling algorithm is developed for efficient posterior computation based on a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We demonstrate the proposed method based on simulated data and an experimental dataset from a longitudinal study of age-related macular degeneration trial. Both simulation studies and real data analysis indicate that the proposed constrained Bayesian elastic net approach is competitive with the existing methods under a variety of scenarios, such as presence of a large number of covariates and collinearity. [ABSTRACT FROM AUTHOR]

In this article, we propose several Bayesian model selection procedures, based on the spike and slab priors, to select significant variables while accounting for some restrictions on them. We concentrate on the following methods: Kuo and Mallick, stochastic search variable selection (SSVS), Ishwaran and Rao (NMIG). Bayesian computation is straightforward via simple Gibbs sampling algorithm. The methods are illustrated using simulated data and an application to the blood lead levels data. Results indicate that the proposed approaches perform very well in various situations. [ABSTRACT FROM AUTHOR]

ABSTRACT As a key chiral intermediate of Levetiracetam, ( S)- α-ethyl-2-oxo-1-pyrrolidine acetic acid can be produced by ester hydrolase-catalyzed hydrolysis of ( R, S)- α-ethyl-2-oxo-1-pyrrolidine acetic acid ethyl ester. In this paper, the characteristics and kinetics of such hydrolysis were investigated using ester hydrolase derived from soil bacterium Tsukamurella tyrosinosolvens E105. Effects of different parameters were systematically investigated to reveal the primary factors that determine the performance of the enantioselective hydrolysis. The optimal reaction conditions were found to be as follows: temperature, 30 °C; pH, 7.0; potassium phosphate buffer, 0.5 mol/L; and 9.2 mmol/L of substrate. Under the optimum conditions, a substrate inhibition kinetic model based on Michaelis-Menten was proposed and verified with experimental data. Moreover, the obtained kinetic constants were Km = 6.73 mmol/L, Vmax = 18.9 × 10−3 mmol/L/min and Ksi = 23.7 mmol/L. This model can be used to predict the ester hydrolase-catalyzed hydrolysis of ( R, S)- α-ethyl-2-oxo-1-pyrrolidine acetic acid ethyl ester and will be advantageous to the industrial application of this process. © 2014 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]