Uncertainty Analysis for Computer Model Projections of Hurricane Losses

Projecting losses associated with hurricanes is a complex and difficult undertaking that is wrought with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real-time estimates grew from 2 to 3 billion dollars in losses late on August 12 to a peak of 50 billion dollars for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm hit the resort areas of Charlotte Harbor near Punta Gorda and then went on to Orlando in the central part of the state, with early poststorm estimates converging on a damage estimate in the 28 to 31 billion dollars range. Comparable damage to central Florida had not been seen since Hurricane Donna in 1960. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has recognized the role of computer models in projecting losses from hurricanes. The FCHLPM established a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a Holland B parameter hurricane wind field model. Uncertainty analysis quantifies the expected percentage reduction in the uncertainty of wind speed and loss that is attributable to each of the input variables.     

Tables of the exact quantiles of the top-down correlation coefficient for n - 3(1)14

Iman and Connver (1985, 1987) have suggested the top-down correlation coefficient as a measure of association when n objects are ranked by two or more independent sources and interest centers primarily on agreement in the top rankings, with disagreements on items at the bottom of the rankings being of little or no importance. The top-down correlation coefficient results from computing the ordinary Pearson correlation coefficient on Savage scores. Quantiles of the exact distribution of the top-down correlation coefficient based on the assumption of independent rankings are provided for n = 3(1)14.     

Approximations of the critical region of the fbietkan statistic

The Friedman (1937) test for the randomized complete block design is used to test the hypothesis of no treatment effect among k treatments with b blocks. Difficulty in determination of the size of the critical region for this hypothesis is com¬pounded by the facts that (1) the most recent extension of exact tables for the distribution of the test statistic by Odeh (1977) go up only to the case with k6 and b6, and (2) the usual chi-square approximation is grossly inaccurate for most commonly used combinations of (k,b). The purpose of this paper 2 is to compare two new approximations with the usual x² and F large sample approximations. This work represents an extension to the two-way layout of work done earlier by the authors for the one-way Kruskal-Wallis test statistic.     

Some exact tables for the squared ranks test

Since the squared ranks test was first proposed by Taha in 1964 it has been mentioned by several authors as a test that is easy to use, with good power in many situations. It is almost as easy to use as the Wilcoxon rank sum test, and has greater power when two populations differ in their scale parameters rather than in their location parameters. This paper discuss the versatility of the squared ranks test, introduces a test which uses squared ranks, and presents some exact tables     

The Repeatability of Uncertainty and Sensitivity Analyses for Complex Probabilistic Risk Assessments

The performance of a probabilistic risk assessment (PRA) for a nuclear power plant is a complex undertaking, involving the assembly of an accident frequency analysis, an accident progression analysis, a source term analysis, and a consequence analysis. Each of these analyses is, in itself, quite complex. Uncertainties enter into a PRA from each of these analyses. An important focus in recent PRAs has been to incorporate these uncertainties at each stage of the analysis, propagate the subsequent uncertainties through the entire analysis, and include uncertainty in the final results. Monte Carlo procedures based on Latin hypercube sampling provide one way to perform propagations of this type. In this paper, the results of two complete and independent Monte Carlo calculations for a recently completed PRA for a nuclear power plant are compared as a means of providing empirical evidence on the repeatability of uncertainty and sensitivity analyses for large-scale PRA calculations. These calculations use the same variables and analysis structure with two independently generated Latin hypercube samples. The results of the two calculations show a high degree of repeatability for the analysis of a very complex system.     

A Matrix-Based Approach to Uncertainty and Sensitivity Analysis for Fault Trees1

System unavailabilities for large complex systems such as nuclear power plants are often evaluated through use of fault tree analysis. The system unavailability is obtained from a Boolean representation of a system fault tree. Even after truncation of higher order terms these expressions can be quite large, involving thousands of terms. A general matrix notation is proposed for the representation of Boolean expressions which facilitates uncertainty and sensitivity analysis calculations.     

Pathological Gambling, Problem Gambling and Sleep Complaints: An Analysis of the National Comorbidity Survey: Replication (NCS-R)

The purpose of this study is to investigate the relationship between sleep disturbances and gambling behavior. Data from the National Comorbidity Survey—Replication (NCS-R) was used to examine the relationship between three specific sleep complaints (difficulty initiating sleep [DIS], difficulty maintaining sleep [DMS], and early morning awakening [EMA]) and gambling behavior. Bivariate logistic regression models were used to control for potentially confounding psychiatric disorders and age. Almost half of respondents with problem gambling behavior (45.9%) and two thirds (67.7%) of respondents with pathological gambling behavior reported at least one sleep compliant. Compared to respondents with no gambling pathology, respondents with pathological gambling were significantly more likely to report at least one sleep complaint (Adjusted Odds Ratio [AOR] = 3.444, 95% CI = 1.538–7.713), to report all sleep complaints (AOR = 3.449, 95% CI = 1.503–7.914), and to report any individual complaint (DIS: OR = 2.300, 95% CI = 1.069–4.946; DMS: AOR = 4.604, 95% CI = 2.093–10.129; EMA: AOR = 3.968, 95% CI = 1.856–8.481). The relationship between problem gambling and sleep complaints were more modest (any sleep complaint: AOR = 1.794, 95% CI = 1.142–2.818; all three sleep complaints: AOR = 2.144, 95% CI = 1.169–3.931; DIS: AOR = 1.961, 95% CI = 1.204–3.194; DMS: AOR = 1.551, 95% CI = 0.951–2.529; EMA: AOR = 1.796, 95% CI = 1.099–2.935). Given the individual and societal ramifications linked with the presence of sleep problems, this study presents another health-related repercussion associated with gambling pathology rarely discussed in the literature.