Bayesian Non-Parametric Estimation of Smooth Hazard Rates for Seismic Hazard Assessment

Abstract.  Hazard rate estimation is an alternative to density estimation for positive variables that is of interest when variables are times to event. In particular, it is here shown that hazard rate estimation is useful for seismic hazard assessment. This paper suggests a simple, but flexible, Bayesian method for non-parametric hazard rate estimation, based on building the prior hazard rate as the convolution mixture of a Gaussian kernel with an exponential jump-size compound Poisson process. Conditions are given for a compound Poisson process prior to be well-defined and to select smooth hazard rates, an elicitation procedure is devised to assign a constant prior expected hazard rate while controlling prior variability, and a Markov chain Monte Carlo approximation of the posterior distribution is obtained. Finally, the suggested method is validated in a simulation study, and some Italian seismic event data are analysed.     

Bayesian optimal design for changepoint problems

We investigate Bayesian optimal designs for changepoint problems. We find robust optimal designs which allow for arbitrary distributions before and after the change, arbitrary prior densities on the parameters before and after the change, and any log‐concave prior density on the changepoint. We define a new design measure for Bayesian optimal design problems as a means of finding the optimal design. Our results apply to any design criterion function concave in the design measure. We illustrate our results by finding the optimal design in a problem motivated by a previous clinical trial. The Canadian Journal of Statistics 37: 495–513; 2009 © 2009 Statistical Society of Canada     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data

Summary.  Estimates of the number of prevalent human immunodeficiency virus infections are used in England and Wales to monitor development of the human immunodeficiency virus–acquired immune deficiency syndrome epidemic and for planning purposes. The population is split into risk groups, and estimates of risk group size and of risk group prevalence and diagnosis rates are combined to derive estimates of the number of undiagnosed infections and of the overall number of infected individuals. In traditional approaches, each risk group size, prevalence or diagnosis rate parameter must be informed by just one summary statistic. Yet a rich array of surveillance and other data is available, providing information on parameters and on functions of parameters, and raising the possibility of inconsistency between sources of evidence in some parts of the parameter space. We develop a Bayesian framework for synthesis of surveillance and other information, implemented through Markov chain Monte Carlo methods. The sources of data are found to be inconsistent under their accepted interpretation, but the inconsistencies can be resolved by introducing additional 'bias adjustment' parameters. The best-fitting model incorporates a hierarchical structure to spread information more evenly over the parameter space. We suggest that multiparameter evidence synthesis opens new avenues in epidemiology based on the coherent summary of available data, assessment of consistency and bias modelling.     

慈善信息披露的主体问题

要实现慈善信息的充分披露,将信息披露主体仅仅局限于公益慈善组织本身是远远不够的,必须构建由慈善组织、行政主管部门、独立评估机构和媒体共同组成的多主体、全方位的慈善信息披露机制,以慈善组织的自愿性披露为主,以行政主管部门的义务性披露为辅,以独立评估机构和媒体进行的信息披露作为补充,各信息披露主体各司其职,各负其责,分工合作。     

Applying to college: Do information deficits lower the likelihood of college-eligible students from less-privileged families to pursue their college intentions?: Evidence from a field experiment

Information deficits are considered an important source of why students from less-privileged families do not enroll in college, even when they are college-eligible and intend to go to college. In this paper, we examine whether correct and detailed information on the costs of and returns to higher education increases the likelihood of college applications of less-privileged high school graduates who expressed college intentions in their junior high school year. We employ an experimental design with a randomly assigned 25-minute information treatment about funding opportunities for, and returns to, higher education given at Berlin schools awarding university entrance qualifications. Our analyses show that our information treatment indeed substantially increases the likelihood of treated less-privileged students to apply to college. Our study indicates that our low-cost provision of financial information not only increased their college knowledge but also substantially changed their college application behavior, despite other existing barriers, like economic constraints.     

Information seeking in secondary schools: A multilevel network approach

In this study, we investigate information seeking interactions in secondary schools from a multilevel network approach. Based on network-related theories, we examine the facilitating role of formal subunits. We apply exponential random graph models for multilevel networks and summarize our findings by using a meta-analysis technique. Our results indicate that formal subunits (e.g. subject departments) can, to some extent, facilitate interactions, in loosely coupled organizations (e.g. secondary schools). Finally, this study shows that a multilevel network approach can provide a more informative representation of information seeking ties in knowledge-intensive organizations.     

Estimation of Pr(Y < X) for the two-parameter generalized exponential records

This article considers the maximum likelihood and Bayes estimation of the stress–strength reliability based on two-parameter generalized exponential records. Here, we extend the results of Baklizi [Computational Statistics and Data Analysis 52 (2008), 3468–3473] to explain a wide variety of real datasets. We also consider the estimation of R when the same shape parameter is known. The results for exponential distribution can be obtained as a special case with different scale parameters.     

Residual life estimation under time-varying conditions based on a Wiener process

Residual life (RL) estimation plays an important role in prognostics and health management. In operating conditions, components usually experience stresses continuously varying over time, which have an impact on the degradation processes. This paper investigates a Wiener process model to track and predict the RL under time-varying conditions. The item-to-item variation is captured by the drift parameter and the degradation characteristic of the whole population is described by the diffusion parameter. The bootstrap method and Bayesian theorem are employed to estimate and update the distribution parameters of ‘a’ and ‘b’, which are the coefficients of the linear drifting process in the degradation model. Once new degradation information becomes available, the RL distributions considering the future operating condition are derived. The proposed method is tested on Lithium-ion battery devices under three levels of charging/discharging rates. The results are further validated by a simulation method.     

BAYESIAN ESTIMATION OF RUIN PROBABILITIES WITH A HETEROGENEOUS AND HEAVY-TAILED INSURANCE CLAIM-SIZE DISTRIBUTION

This paper describes a Bayesian approach to make inference for risk reserve processes with an unknown claim‐size distribution. A flexible model based on mixtures of Erlang distributions is proposed to approximate the special features frequently observed in insurance claim sizes, such as long tails and heterogeneity. A Bayesian density estimation approach for the claim sizes is implemented using reversible jump Markov chain Monte Carlo methods. An advantage of the considered mixture model is that it belongs to the class of phase‐type distributions, and thus explicit evaluations of the ruin probabilities are possible. Furthermore, from a statistical point of view, the parametric structure of the mixtures of the Erlang distribution offers some advantages compared with the whole over‐parametrized family of phase‐type distributions. Given the observed claim arrivals and claim sizes, we show how to estimate the ruin probabilities, as a function of the initial capital, and predictive intervals that give a measure of the uncertainty in the estimations.