Tests for Equality of Several Binomial Populations Against an Order Restricted Alternative and Model Selection for One-Dimensional Multinomials

The problem of testing the equality of several binomial population against an order restricted alternative and model selection for one-dimensional multinomials is studied. Test procedures are proposed. The asymptotic distribution of the test statistics are obtained. Comparisons are made with other test statistics including the likelihood ratio test for stochastic ordering. Also alternatives which does not depend on the distribution of test statistic is proposed.     

Comments on Improving estimates of the basic reproductive ratio: using both the mean and the dispersal of transition times

Comments on Heffernan, J. M., Wahl, L. M. (2006). Improving estimates of the basic reproductive ratio: Using both the mean and the dispersal of transition times. Theoretical Population Biology, 70, 135-145.     

A note on the estimation of selective values from recaptures of marked animals when selection pressures remain constant over time

An equation is given for the estimation of selective values from data obtained by mark-recapture experiments, assuming that selective pressures remain constant while the experiments are carried out. The equation does not have an explicit solution but can readily be solved using a trial-and-error method. The use of the equation is illustrated on some data reported byKettlewell et al. (1969) from an experiment involving typica and edda morphs of the moth Amathes glareosa. It is found that the edda morph apparently had a selective advantage of about 12% per day compared to the typica morph and that this is significantly different from zero. Using another methodKettlewell concluded that the selective advantage of the edda morph was only 7% and that this was not statistically significant.     

Improving estimates of the basic reproductive ratio: using both the mean and the dispersal of transition times

In both within-host and epidemiological models of pathogen dynamics, the basic reproductive ratio, R(0), is a powerful tool for gauging the risk associated with an emerging pathogen, or for estimating the magnitude of required control measures. Techniques for estimating R(0), either from incidence data or in-host clinical measures, often rely on estimates of mean transition times, that is, the mean time before recovery, death or quarantine occurs. In many cases, however, either data or intuition may provide additional information about the dispersal of these transition times about the mean, even if the precise form of the underlying probability distribution remains unknown. For example, we may know that recovery typically occurs within a few days of the mean recovery time. In this paper we elucidate common situations in which R(0) is sensitive to the dispersal of transition times about their respective means. We then provide simple correction factors that may be applied to improve estimates of R(0) when not only the mean but also the standard deviation of transition times out of the infectious state can be estimated.     

A "Long Indel" model for evolutionary sequence alignment

We present a new probabilistic model of sequence evolution, allowing indels of arbitrary length, and give sequence alignment algorithms for our model. Previously implemented evolutionary models have allowed (at most) single-residue indels or have introduced artifacts such as the existence of indivisible "fragments." We compare our algorithm to these previous methods by applying it to the structural homology dataset HOMSTRAD, evaluating the accuracy of (1) alignments and (2) evolutionary time estimates. With our method, it is possible (for the first time) to integrate probabilistic sequence alignment, with reliability indicators and arbitrary gap penalties, in the same framework as phylogenetic reconstruction. Our alignment algorithm requires that we evaluate the likelihood of any specific path of mutation events in a continuous-time Markov model, with the event times integrated out. To this effect, we introduce a "trajectory likelihood" algorithm (Appendix A). We anticipate that this algorithm will be useful in more general contexts, such as Markov Chain Monte Carlo simulations.     

A Model for Censored Survival Data When There is an Intervening Event

A model is discussed for incorporating information from a time-dependent covariable (an intervening event) and covariables independent of time into the analysis of survival data. In the model, it is assumed that individuals are potentially subject to two paths to failure, one including the intervening event and the other not. Additional assumptions are that failure times associated with the two paths are independent and that the time to failure subsequent to the intervening event is dependent on the intervening event time. Allowing the underlying hazard rates for the model to follow a WEIBULL form, use of the model and methods for fitting and hypothesis testing are illustrated by application to a follow-up study involving industrial workers where disability retirement was the intervening event. Extensions of the model to accommodate grouped survival data are presented.     

Accuracy and Power of the Likelihood Ratio Test for Comparing Evolutionary Rates Among Genes

Sequences for multiple protein-coding genes are now commonly available from several, often closely related species. These data sets offer intriguing opportunities to test hypotheses regarding whether different types of genes evolve under different selective pressures. Although maximum likelihood (ML) models of codon substitution that are suitable for such analyses have been developed, little is known about the statistical properties of these tests. We use a previously developed fixed-sites model and computer simulations to examine the accuracy and power of the likelihood ratio test (LRT) in comparing the nonsynonymous-to-synonymous substitution rate ratio (=dN/dS) between two genes. Our results show that the LRT applied to fixed-sites models may be inaccurate in some cases when setting significance thresholds using a ² approximation. Instead, we use a parametric bootstrap to describe the distribution of the LRT statistic for fixed-sites models and examine the power of the test as a function of sampling variables and properties of the genes under study. We find that the power of the test is high (>80%) even when sampling few taxa (e.g., six species) if sequences are sufficiently diverged and the test is largely unaffected by the tree topology used to simulate data. Our simulations show fixed-sites models are suitable for comparing substitution parameters among genes evolving under even strong evolutionary constraint ( 0.05), although relative rate differences of 25% or less may be difficult to detect.Reviewing Editor: Dr. Rosmus Nielsen