Enduring Effects of Women's Early Employment Experiences on Child-spacing: The Canadian Evidence

This study examines the effects of early work experiences on child-spacing among Canadian women, with data from the 1984 Family History Survey conducted by Statistics Canada. The analyses, based on life-table and proportional hazards models, show that longer and less interrupted early work experiences are associated with longer birth intervals, and that these effects tend to persist throughout the childbearing years. The study further shows that these effects are greater on the third birth interval than on the second, and that they are more pronounced among highly educated than among less educated women.     

Variable selection in quantile regression via Gibbs sampling

Due to computational challenges and non-availability of conjugate prior distributions, Bayesian variable selection in quantile regression models is often a difficult task. In this paper, we address these two issues for quantile regression models. In particular, we develop an informative stochastic search variable selection (ISSVS) for quantile regression models that introduces an informative prior distribution. We adopt prior structures which incorporate historical data into the current data by quantifying them with a suitable prior distribution on the model parameters. This allows ISSVS to search more efficiently in the model space and choose the more likely models. In addition, a Gibbs sampler is derived to facilitate the computation of the posterior probabilities. A major advantage of ISSVS is that it avoids instability in the posterior estimates for the Gibbs sampler as well as convergence problems that may arise from choosing vague priors. Finally, the proposed methods are illustrated with both simulation and real data.     

Harnessing SD and CSR within Corporate Self‐regulation of Weak Economies— A Meta‐regulation Approach

The semantic of the terms “sustainable development” and “corporate social responsibility” have changed over time to a point where these concepts have become two interrelated processes for ensuring the far‐reaching development of society. Their convergence has given dimension to the environmental and corporate regulation mechanisms in strong economies. This article deals with the question of how the ethos of this convergence could be incorporated into the self‐regulation of businesses in weak economies where nonlegal drivers are either inadequate or inefficient. It proposes that the policies for this incorporation should be based on the precepts of meta‐regulation that have the potential to hold force majeure, economic incentives, and assistance‐related strategies to reach an objective from the perspective of weak economies.     

Bayesian quantile regression for ordinal longitudinal data

Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.     

Model selection in quantile regression models

Lasso methods are regularisation and shrinkage methods widely used for subset selection and estimation in regression problems. From a Bayesian perspective, the Lasso-type estimate can be viewed as a Bayesian posterior mode when specifying independent Laplace prior distributions for the coefficients of independent variables [32 T. Park, G. Casella, The Bayesian Lasso, J. Amer. Statist. Assoc. 103 (2008), pp. 681–686. doi: 10.1198/016214508000000337[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. A scale mixture of normal priors can also provide an adaptive regularisation method and represents an alternative model to the Bayesian Lasso-type model. In this paper, we assign a normal prior with mean zero and unknown variance for each quantile coefficient of independent variable. Then, a simple Markov Chain Monte Carlo-based computation technique is developed for quantile regression (QReg) models, including continuous, binary and left-censored outcomes. Based on the proposed prior, we propose a criterion for model selection in QReg models. The proposed criterion can be applied to classical least-squares, classical QReg, classical Tobit QReg and many others. For example, the proposed criterion can be applied to rq(), lm() and crq() which is available in an R package called Brq. Through simulation studies and analysis of a prostate cancer data set, we assess the performance of the proposed methods. The simulation studies and the prostate cancer data set analysis confirm that our methods perform well, compared with other approaches.     

Pharmacotherapy for obesity in menopausal women

Weight gain, during and after the menopause is common. Contributing factors include ethnicity, reduced physical activity, reduced lean mass, reduced resting metabolic rate and treatment with certain drugs, e.g. steroids, insulin, glitazones. Excess body weight increases the risk of medical conditions including type 2 diabetes, hypertension, osteoarthritis, certain cancers and is associated with increased mortality. This review examines pharmacological approaches to promote weight loss. Pharmacological therapy should be considered as an adjunct to diet and lifestyle changes. The licensed drugs orlistat, sibutramine and rimonabant are discussed. Obesity increases the risk of type 2 diabetes. Thus, the effects of metformin and exenatide are examined.     

Bayesian bridge quantile regression

Regularization methods for simultaneous variable selection and coefficient estimation have been shown to be effective in quantile regression in improving the prediction accuracy. In this article, we propose the Bayesian bridge for variable selection and coefficient estimation in quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a scale mixture of uniform representation of the Bayesian bridge prior. This is the first work to discuss regularized quantile regression with the bridge penalty. Both simulated and real data examples show that the proposed method often outperforms quantile regression without regularization, lasso quantile regression, and Bayesian lasso quantile regression.     

Detecting outliers in GARCH(p,q) models

This article is concerned with the outliers in GARCH models. An iterative procedure is given for testing the presence of any type of the four common outliers. Since the distribution of test statistic cannot be obtained analytically, its distributional behavior is investigated via a simulation study. The simulation study is based on estimation of residuals standard deviation (σ_ν), which are obtained using two methods, median absolute deviation method (MAD), and omit-one method. The proposed procedure is employed for testing the presence of outliers in weekly light oil price Indexes of Iran during 1997 to 2010.