A Bayes method for a monotone hazard rate via S-paths

A class of random hazard rates, which is defined as a mixture of an indicator kernel convolved with a completely random measure, is of interest. We provide an explicit characterization of the posterior distribution of this mixture hazard rate model via a finite mixture of S-paths. A closed and tractable Bayes estimator for the hazard rate is derived to be a finite sum over S-paths. The path characterization or the estimator is proved to be a Rao--Blackwellization of an existing partition characterization or partition-sum estimator. This accentuates the importance of S-paths in Bayesian modeling of monotone hazard rates. An efficient Markov chain Monte Carlo (MCMC) method is proposed to approximate this class of estimates. It is shown that S-path characterization also exists in modeling with covariates by a proportional hazard model, and the proposed algorithm again applies. Numerical results of the method are given to demonstrate its practicality and effectiveness.Comment: Published at http://dx.doi.org/10.1214/009053606000000047 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Risk Assessment for Developmental Toxicity: Airborne Occupational Exposure to Ethanol and Iodine

Dean Mattison explains hazard identification, hazard characterization and exposure characterization as furnishing a foundation for Risk assessment generally. He then illustrates their application in assessing the fetal Risk posed by two common substances. Ultimately, he argues that only after Risks have been so examined can women of child bearing age (or anyone) decide what if any measures are appropriate to avoid them

The CRaTER Special Issue of Space Weather: Building the observational foundation to deduce biological effects of space radiation

[1] The United States is preparing for exploration beyond low-Earth Orbit (LEO). However, the space radiation environment poses significant risks. The radiation hazard is potentially severe but not sufficiently well characterized to determine if long missions outside LEO can be accomplished with acceptable risk [Cucinotta et al., 2001; Schwadron et al., 2010; Cucinotta et al., 2010]. Radiation hazards may be over- or under-stated through incomplete characterization in terms of net quantities such as accumulated dose. Time-dependent characterization often changes acute risk estimates [NCRP, 1989; Cucinotta, 1999; Cucinotta et al., 2000; George et al., 2002]. For example, events with high accumulated doses but sufficiently low dose rates (/h) pose significantly reduced risks. Protons, heavy ions, and neutrons all contribute significantly to the radiation hazard. However, each form of radiation presents different biological effectiveness. As a result, quality factors and radiation-specific weighting factors are needed to assess biological effectiveness of different forms of radiation [e.g., NCRP 116, 1993] (Figure 1). More complete characterization must account for time-dependent radiation effects according to organ type, primary and secondary radiation composition, and acute effects (vomiting, sickness, and, at high exposures, death) versus chronic effects (such as cancer)

About adaptive coding on countable alphabets

This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring AC code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy % of small source classes by Opper and Haussler (1997) and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate

The Burr XII-Burr XII Distribution: Mathematical Properties and Characterizations

We introduce a new continuous distribution called the Burr XII-Burr XII distribution. Some of its properties are derived. The method of maximum likelihood is used to estimate the unknown parameters. An application is provided with details to illustrate the importance of the new. The new model provides adequate fits as compared to other related models with smallest values for A-IC, B-IC, CA-IC and HQ-IC. Characterization results are presented based on two truncated moments, hazard function as well as based on the conditional expectation

An adverse selection model of optimal unemployment insurance

We derive the shape of optimal unemployment insurance (UI) contracts when agents can exert search effort but face different search costs and have private information about their type. We derive a recursive solution of our dynamic adverse selection problem with repeated moral hazard. Conditions under which the UI agency should always offer separating contracts are identified. We show that the good searcher receives an information rent and that the bad searcher receives the minimal entitlement. From a methodological point of view, we achieve a precise characterization of the sets of jointly feasible entitlements. This allows us to map our analytical results one-toone to a numerical algorithm. According to our results the contract for the good searcher has a decreasing benefit profile, as the one he would be offered in a pure moral hazard environment. In contrast, the contract of the bad searcher is distorted by an adverse selection effect, so that it tends to have an upward-sloping benefit profile. We provide a comparative static analysis of changes in various parameters of our model. --Unemployment Insurance,Adverse Selection,Moral Hazard

Characterizations and Infinite Divisibility of Certain Recently Introduced Distributions IV

Certain characterizations of recently proposed univariate continuous distributions are presented in different directions. This work contains a good number of reintroduced distributions and may serve as a source of preventing the reinvention and/or duplication of the existing distributions in the future

Weighted Distributions: A Brief Review, Perspective and Characterizations

The weighted distributions are widely used in many fields such as medicine, ecology and reliability, to name a few, for the development of proper statistical models. Weighted distributions are milestone for efficient modeling of statistical data and prediction when the standard distributions are not appropriate. A good deal of studies related to the weight distributions have been published in the literature. In this article, a brief review of these distributions is carried out. Implications of the differing weight models for future research as well as some possible strategies are discussed. Finally, characterizations of these distributions based on a simple relationship between two truncated moments are presented

Needs and challenges for assessing the environmental impacts of engineered nanomaterials (ENMs).

The potential environmental impact of nanomaterials is a critical concern and the ability to assess these potential impacts is top priority for the progress of sustainable nanotechnology. Risk assessment tools are needed to enable decision makers to rapidly assess the potential risks that may be imposed by engineered nanomaterials (ENMs), particularly when confronted by the reality of limited hazard or exposure data. In this review, we examine a range of available risk assessment frameworks considering the contexts in which different stakeholders may need to assess the potential environmental impacts of ENMs. Assessment frameworks and tools that are suitable for the different decision analysis scenarios are then identified. In addition, we identify the gaps that currently exist between the needs of decision makers, for a range of decision scenarios, and the abilities of present frameworks and tools to meet those needs