Journal of Biometrics & Biostatistics

Guo introduced an adaptive Bonferroni procedure and he proved that his adaptive Bonferroni procedure controls the familywise error rate under a conditional dependence model. However, how to choose the tuning parameter λ to control the familywise error rate in the procedure under positive dependence is not clear in his paper. In this paper, we suggest that λ = α . Simulation studies are provided.

The daily number of hospital admission due to respiratory diseases can have a great variability. This variability could be explained by different factors as year seasons, temperature, pollution levels among many others [1,2]. In this paper, we have been using non-homogeneous Poisson processes with different intensity functions under the Bayesian paradigm and using standard existing MCMC (Markov Chain Monte Carlo) methods to simulate samples for the joint posterior distribution of interest. An application is given considering the daily number of hospital admission in Ribeirao Preto, Brazil in the period ranging from January 01, 1998 to December 31, 2007. The proposed model showed a good fit for the seasonality of the disease with simple interpretation in the framework of epidemiology.

This work discusses the statistical and mathematical concepts and principles applicable to in vivo 109Cd K X Ray Fluorescence measurements (109Cd KXRF) of lead in human bone. The primary aim of this paper is to examine divergent views about the quantitative methods applied to estimate in vivo bone lead concentrations and their uncertainty when using the 109Cd KXRF technique. The emphasis is on the potential effect of covariance between quantities of interest on the result of an in vivo measurement of bone lead by 109Cd KXRF, a question which has received little attention in view of the long standing problem of why the average measurement uncertainty is less than the standard deviation of repeated measurements.