Bayesian Estimation of the Discrepancy with Misspecified Parametric Models

We study a Bayesian model where we have made specific requests about the parameter values to be estimated. The aim is to find the parameter of a parametric family which minimizes a distance to the data generating density and then to estimate the discrepancy using nonparametric methods. We illustrate how coherent updating can proceed given that the standard Bayesian posterior from an unidentifiable model is inappropriate. Our updating is performed using Markov Chain Monte Carlo methods and in particular a novel method for dealing with intractable normalizing constants is required. Illustrations using synthetic data are provided.European Research Council (ERC) through StG "N-BNP" 306406Regione PiemonteMathematic

A reversible allelic partition process and Pitman sampling formula

We introduce a continuous-time Markov chain describing dynamic allelic partitions which extends the branching process construction of the Pitman sampling formula in Pitman (2006) and the birth-and-death process with immigration studied in Karlin and McGregor (1967), in turn related to the celebrated Ewens sampling formula. A biological basis for the scheme is provided in terms of a population of individuals grouped into families, that evolves according to a sequence of births, deaths and immigrations. We investigate the asymptotic behaviour of the chain and show that, as opposed to the birth-and-death process with immigration, this construction maintains in the temporal limit the mutual dependence among the multiplicities. When the death rate exceeds the birth rate, the system is shown to have reversible distribution identified as a mixture of Pitman sampling formulae, with negative binomial mixing distribution on the population size. The population therefore converges to a stationary random configuration, characterised by a finite number of families and individuals.Comment: 17 pages, to appear in ALEA , Latin American Journal of Probability and Mathematical Statistic

Asymptotics for posterior hazards

An important issue in survival analysis is the investigation and the modeling of hazard rates. Within a Bayesian nonparametric framework, a natural and popular approach is to model hazard rates as kernel mixtures with respect to a completely random measure. In this paper we provide a comprehensive analysis of the asymptotic behavior of such models. We investigate consistency of the posterior distribution and derive fixed sample size central limit theorems for both linear and quadratic functionals of the posterior hazard rate. The general results are then specialized to various specific kernels and mixing measures yielding consistency under minimal conditions and neat central limit theorems for the distribution of functionals.Comment: Published in at http://dx.doi.org/10.1214/08-AOS631 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Extracting the mass scale of a second Higgs boson from a deviation in h(125)h(125) couplings

We investigate the correlation between a possible deviation in the discovered Higgs boson $h(125)$ couplings from the Standard Model prediction and the mass scale ($M_{\text{2nd}}$) of the next-to-lightest Higgs boson in models with non-minimal Higgs sectors. In particular, we comprehensively study a class of next-to-minimal Higgs sectors which satisfy the electroweak $\rho$ parameter to be one at tree level. We derive an upper limit on $M_{\text{2nd}}$ by imposing bounds from perturbative unitarity, vacuum stability, triviality and electroweak precision data as functions of the deviation in the $hVV$ ($V=W,Z$) couplings. Furthermore, we discuss the complementarity between these bounds and the current LHC data, e.g., by considering direct searches for additional Higgs bosons and indirect constraints arising from the measured $h(125)$ signal strengths.Comment: 37 pages, 36 figure

The Bernstein-Von Mises Theorem in Semiparametric Competing Risks Models

Semiparametric Bayesian models are nowadays a popular tool in survival analysis. An important area of research concerns the investigation of frequentist properties of these models. In this paper, a Bernstein-von Mises theorem is derived for semiparametric Bayesian models of competing risks data. The cause-specific hazard is taken as the product of the conditional probability of a failure type and the overall hazard rate. We model the conditional probability as a smooth function of time and leave the cumulative overall hazard unspecified. A prior distribution is defined on the joint parameter space, which includes a beta process prior for the cumulative overall hazard. We show that the posterior distribution for any differentiable functional of interest is asymptotically equivalent to the sampling distribution derived from maximum likelihood estimation. A simulation study is provided to illustrate the coverage properties of credible intervals on cumulative incidence functions.Bayesian nonparametrics, Bernstein-von Mises theorem, beta process, competing risks, conditional probability of a failure type, semiparametric inference.