Bayesian variable selection in quantile regression models is often a difficult task due to the computational challenges and non-availability of conjugate prior distributions. These challenges are rarely addressed via either penalized likelihood function or stochastic search variable selection....

This paper investigates the use of different priors to improve the inflation forecasting performance of BVAR models with Litterman’s prior. A Quasi-Bayesian method, with several different priors, is applied to a VAR model of simulated data as well as to the Australian economy from 1978:Q2 to...

Recently the patient centered medical home (PCMH) model has become a popular team based approach focused on delivering more streamlined care to patients. In current practices of medical homes, a clinical based prediction frame is recommended because it can help match the portfolio capacity of...

In a high-dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting...

To alleviate the computational burden of making the relevant estimation algorithms stable for nonlinear and semiparametric regression models with, particularly, high-dimensional data, a transformation-based method combining sufficient dimension reduction approach is proposed. To this end,...

The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation....

For the first time, we obtain a general formula for the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$n^{-2}$$</EquationSource> </InlineEquation> asymptotic covariance matrix of the bias-corrected maximum likelihood estimators of the linear parameters in generalized linear models, where <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$n$$</EquationSource> </InlineEquation> is the sample size. The usefulness of the formula is illustrated in order to...</equationsource></inlineequation></equationsource></inlineequation>

In a series of recent articles, Karlson, Holm, and Breen (Breen, Karlson, and Holm, 2011, http://papers.ssrn.com/sol3/papers.cfm?abstractid=1730065; Karlson and Holm, 2011, Research in Stratification and Social Mobility 29: 221– 237; Karlson, Holm, and Breen, 2010, http://www.yale.edu/ciqle/Breen...

In a generalized linear model of binary data, we consider models based on a general link function including a logistic regression model and a probit model as special cases. For testing the null hypothesis H0 that the considered model is correct, we consider a family of ϕ-divergence...

We present a joint copula-based model for insurance claims and sizes. It uses bivariate copulae to accommodate for the dependence between these quantities. We derive the general distribution of the policy loss without the restrictive assumption of independence. We illustrate that this...