Tests for deletion

It has been suggested in the ecological literature that species may be excluded (or "deleted") from an environment because they do not differ sufficiently from other species in the environment. We develop tests of various deletion hypotheses based on the assumption of a random distribution of species sizes. The results provide information on the behavior of quantities of interest to ecologists studying this phenomenon, namely contiguous ratios, and allow us to gauge the extent of deletion required before we can be confident of detecting it. The results indicate that this random-effects approach leads to tests which have low power for ecological applications but may be more useful in fields which permit larger sample sizes.     

Low Efficiency of Short-Term Tests in the Assessment of the Potential Mutagenic Hazard of Chemical Compounds to Mammals

A new method of the efficiency assessment of testing mutagenicity chemical pollutants is proposed. The method is based on the selective information criterion and allows one to compare the prognostic significance of results obtained in both individual tests and test batteries. The efficiency of mutagen detection in mammals was estimated in Ames' test, the in vivo test for cytogenetic abnormalities in rodent bone-marrow cells, and the battery combining both these tests. The level of evidence for mutagenicity was determined for chemicals analyzed in these tests. Based on information obtained during the trials, a low efficiency of the analyzed tests and their battery was inferred.     

Tests for Genetic Differentiation

When testing for genetic differentiation the joint null hypothesis that there is no allele frequency difference at any locus is of interest. Common approaches to test this hypothesis are based on the summation of χ² statistics over loci and on the Bonferroni correction, respectively. Here, we also consider the Simes adjustment and a recently proposed truncated product method (TPM) to combine P‐values. The summation and the TPM (using a relatively large truncation point) are powerful when there are differences in many or all loci. The Simes adjustment, however, is powerful when there are differences regarding one or a few loci only. As a compromise between the different approaches we introduce a combination between the Simes adjustment and the TPM, i.e. the joint null hypothesis is rejected if at least one of the two methods, Simes and TPM, is significant at the α/2‐level. Simulation results indicate that this combination is a robust procedure with high power over the different types of alternatives.     

Mixture Hazard Models for Lifetime Data

We propose a general family of mixture hazard models to analyze lifetime data associated with bathtub and multimodal hazard functions. With this model we have a great flexibility for fitting lifetime data. Its version with covariates has the proportional hazard and the accelerated failure time models as special cases. A Bayesian analysis is presented for the model using informative priors, using sampling‐based approaches to perform the Bayesian computations. A real example with a medical data illustrates the methodology.