Coherence of the posterior predictive p-value based on the posterior odds.

^aIt is well-known that classical p-values sometimes behave incoherently for testing hypotheses in the sense that, when $\Theta_{0} \subset \Theta_{0}{'}$, the support given to $\Theta_{0}$ is greater than or equal to the support given to $\Theta_{0}^{'}$ . This problem is also found for posterior predictive p-values (a Bayesian-motivated alternative to classical p-values). In this paper, it is proved that, under some conditions, the posterior predictive p-value based on the posterior odds is coherent, showing that the choice of a suitable discrepancy variable is crucial

COHERENCE OF THE POSTERIOR PREDICTIVE P-VALUE BASED ON THE POSTERIOR ODDS.

It is well-known that classical p-values sometimes behave incoherently for testing hypotheses in the sense that, when '0 0 T .T , the support given to 0 T is greater than or equal to the support given to '0 T . This problem is also found for posterior predictive p-values (a Bayesian-motivated alternative to classical p-values). In this paper, it is proved that, under some conditions, the posterior predictive p-value based on theposterior odds is coherent, showing that the choice of a suitable discrepancy variable is crucial.

Bayesian inference and data cloning in population projection matrices

Discrete time models are used in Ecology for describing the evolution of an agestructured population. Usually, they are considered from a deterministic viewpoint but, in practice, this is not very realistic. The statistical model we propose in this article is a reasonable model for the case in which the evolution of the population is described by means of a projection matrix. In this statistical model, fertility rates and survival rates are unknown parameters and they are estimated by using a Bayesian approach. Usual Bayesian and data cloning methods (based on Bayesian methodology) are applied to real data from the population of the Steller sea lions located in the Alaska coast since 1978 to 2004. The estimates obtained from these methods show a good behavior when they are compared to the actual value

BAYESIAN ROBUSTNESS OF THE POSTERIOR PREDICTIVE P-VALUE.

In this paper, the Bayesian robustness of the posterior predictive p-value is studied. First of all, it is proved that Lavine's linearization technique can be extended for analyzing this problem. Then, the result is applied to the E-contamination class of prior distributions.

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Bayesian robustness of the posterior predictive p-value.

In this paper, the Bayesian robustness of the posterior predictive p-value is studied. First of all, it is proved that Lavine's linearization technique can be extended for analyzing this problem. Then, the result is applied to the E-contamination class of prior distributions