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This article considers Bayesian variable selection problems for binary responses via stochastic search variable selection and Bayesian Lasso. To avoid matrix inversion in the corresponding Markov chain Monte Carlo implementations, the componentwise Gibbs sampler (CGS) idea is adopted. Moreover, we also propose automatic hyperparameter tuning rules for the proposed approaches. Simulation studies and a real example are used to demonstrate the performances of the proposed approaches. These results show that CGS approaches do not only have good performances in variable selection but also have the lower batch mean standard error values than those of original methods, especially for large number of covariates. [ABSTRACT FROM PUBLISHER]

For left-truncated and right-censored data, the technique proposed by Brookmeyer and Crowley (1982) is extended to construct a point-wise confidence interval for median residual lifetime. This procedure is computationally simpler than the score type confidence interval in Jeong et al. (2008) and empirical likelihood ratio confidence interval in Zhou and Jeong (2011). Further, transformations of the estimator are applied to improve the approximation to the asymptotic distribution for small sample sizes. A simulation study is conducted to investigate the accuracy of these confidence intervals and the implementation of these confidence intervals to two real datasets is illustrated. [ABSTRACT FROM PUBLISHER]

It is well known that the logrank test is locally most powerful under proportional hazards model for right censored data when two groups have same censoring distributions. For a given data set, however, one seldom knows what the exact alternative is. Therefore, Lee (1) constructed versatile tests by combining four weighted logrank (WLR) tests with various weight functions detecting proportional, early, late and middle hazards differences, respectively. However, Pepe and Fleming (2) had pointed out that WLR tests are based on ranks, and these tests might not be sensitive to the magnitude of the difference in survival times against a specific alternative. Hence, they constructed weighted Kaplan-Meier (WKM) tests that are more sensitive than the logrank test under various alternatives. Therefore, another type of versatile tests based on the simultaneous use of WLR and WKM tests is developed in this paper and the performances of the proposed tests and Lee's tests are compared by simulation. None of the tests investigated in this paper is uniformly better than the others. Lee's maximum test is more robust for detecting various alternatives; however, for computation simplicity, the linear combination of WLR and WKM tests is recommended to apply in practice and its overall performance is better than Lee's linear combination test. [ABSTRACT FROM AUTHOR]