Uncertainty Analysis Based on Probability Bounds (P-Box) Approach in Probabilistic Safety Assessment

A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results.     

Urban Aboriginal use of fringe financial institutions: Survey evidence from Prince George, British Columbia

This paper analyses the use of fringe financial institutions (FFIs), such as payday loan and check cashing providers, by urban Aboriginal people based on a survey undertaken in Prince George, British Columbia. We found that 60% of FFIs’ clients surveyed self-identified as Aboriginal. Their characteristics, compared to the non-Aboriginal FFI clients, included having lower average incomes, lower levels of education, more likely to be female, a higher incidence of being unemployed, higher levels of financial exclusion, and less satisfaction with the service provided by FFIs. We find that government policy towards regulating the FFI industry is inadequate for meeting the basic financial needs of urban Aboriginal people.     

Vinayak & me: Hindutva and the politics of naming

This article examines political and intellectual influences of Vinayak Damodar Savarkar's Hindutva: Who is a Hindu? (published in 1923) on the development of nationalism in India. The article argues that contemporary political concerns about national identity raised in India have historical links that go back to Savarkar's text, especially the discourses on the politics of naming. The article considers the implications of these writings on one of Savarkar's disciples: Dr Dattatrey Parchure, an individual who had acquired notoriety for his role in Mohandas K. Gandhi's assassination in 1948. By examining Dr Parchure's life-story, the article shows how transformations in the family, the medical profession, masculinity and religion were directly intertwined with an emergent Hindu nationalism. But more specifically, the article examines Dr Parchure's decision to name children Vinayak as a way of evoking a mental image of Savarkar as an icon in everyday life and to propagate the ideals of Hindutva . The article posits that the politics of figures like Dr Parchure and Savarkar reflect a conjuncture in the development of nationalism in India during the colonial period, and their legacies continue to influence the making of today's post-colonial nationalism.     

Household wealth and the measurement of economic well-being in the United States

We analyze the level and distribution of economic well-being in the United States during the 1980s and 1990s based on the standard measure of money income and a measure in which income from wealth is calculated as the sum of lifetime annuity from nonhome wealth and imputed rental-equivalent for owner-occupied homes. Over the 1982–2000 period, median well-being increases faster when these adjustments are made than when standard money income is used. This adjustment also widens the income gap between African-Americans and whites but increases the relative well-being of the elderly. Adding imputed rent and annuities from household wealth to household income considerably increases measured inequality and the share of income from wealth in inequality. However, both measures show about the same rise in inequality over the period. We also find an increasing share of wage and salary income in our expanded definition of income among the richest 1% over the period but do not find that the “working rich” have largely replaced rentiers at the top of the economic ladder.      

Some remarks on classical and bayesian reliability estimation of binomial and poisson distributions

The problems of estimating the reliability function and Pr{X₁+...+X_k ≤ Y} are considered. The random variables X’s and Y are assumed to follow binomial and Poisson distributions. Classical estimators available in the literature are discussed and Bayes estimators are derived. In order to obtain the estimators of these parametric functions, the basic role is played by the estimators of factorial moments of the two distributions.     

A generalized step-up-down multiple test procedure

A generalization of step-up and step-down multiple test procedures is proposed. This step-up-down procedure is useful when the objective is to reject a specified minimum number, q, out of a family of k hypotheses. If this basic objective is met at the first step, then it proceeds in a step-down manner to see if more than q hypotheses can be rejected. Otherwise it proceeds in a step-up manner to see if some number less than q hypotheses can be rejected. The usual step-down procedure is the special case where q = 1, and the usual step-up procedure is the special case where q = k. Analytical and numerical comparisons between the powers of the step-up-down procedures with different choices of q are made to see how these powers depend on the actual number of false hypotheses. Examples of application include comparing the efficacy of a treatment to a control for multiple endpoints and testing the sensitivity of a clinical trial for comparing the efficacy of a new treatment with a set of standard treatments.     

Bayesian Estimation of Regression Coefficients Under Extended Balanced Loss Function

Appreciating the desirability of simultaneously using both the criteria of goodness of fitted model and clustering of estimates around true parameter values, an extended version of the balanced loss function is presented and the Bayesian estimation of regression coefficients is discussed. The thus obtained optimal estimator is then compared with the least squares estimator and posterior mean vector with respect to the criteria like posterior expected loss, Bayes risk, bias vector, mean squared error matrix and risk function.