background: few investigations have focused on the patients with lupus nephritis (ln) and neuropsychiatric systemic lupus erythematosus (npsle). this study was aimed to investigate the clinical features, mortality, and the predictors for mortality of this group of patients. patients and methods: medical records were retrospectively reviewed in sun yat-sen memorial hospital from 1996 to 2012. da...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random regression model fits best to weight data of pigs. Two random regression models that described w...

Background & Aims: Health beliefs play an important role in encouraging people to engage in behaviors related to health promotion. Thus, the purpose of this study is to evaluate the role of health beliefs in predicting barriersto cervical cancerscreening among women in Bandar Abbas, Iran. Methods: Using convenience and random sampling, this cross-sectional study was conducted on 681 married wom...

Multivariate regression model is a natural generalization of the classical univariate regression model for fitting multiple responses. In this paper, we propose a highdimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency structure among the multiple responses. The proposed method...