Asymptotic Finite-Time Ruin Probabilities for a Class of Path-Dependent Heavy-Tailed Claim Amounts Using Poisson Spacings

In the compound Poisson risk model, several strong hypotheses may be found too restrictive to describe accurately the evolution of the reserves of an insurance company. This is especially true for a company that faces natural disaster risks like earthquake or flooding. For such risks, claim amounts are often inter-dependent and they may also depend on the history of the natural phenomenon. The present paper is concerned with a situation of this kind where each claim amount depends on the previous interclaim arrival time, or on past interclaim arrival times in a more complex way. Our main purpose is to evaluate, for large initial reserves, the asymptotic finite-time ruin probabilities of the company when the claim sizes have a heavy-tailed distribution. The approach is based more particularly on the analysis of spacings in a conditioned Poisson process.Risk process; finite-time ruin probabilities; asymptotic approximation for large initial reserves; path-dependent claims, heavy-tailed claim amounts; Poisson spacing;

A Randomized Controlled Trial Translating the Diabetes Prevention Program to a University Worksite, Ohio, 2012-2014

INTRODUCTION: Working adults spend much time at the workplace, an ideal setting for wellness programs targeting weight loss and disease prevention. Few randomized trials have evaluated the efficacy of worksite diabetes prevention programs. This study evaluated the efficacy of a worksite lifestyle intervention on metabolic and behavioral risk factors compared with usual care. METHODS: A pretest-posttest control group design with 3-month follow-up was used. Participants with prediabetes were recruited from a university worksite and randomized to receive a 16-week lifestyle intervention (n = 35) or usual care (n = 34). Participants were evaluated at baseline, postintervention, and 3-month follow-up. Dietary intake was measured by a food frequency questionnaire and level of physical activity by accelerometers. Repeated measures analysis of variance compared the change in outcomes between and within groups. RESULTS: Mean (standard error [SE]) weight loss was greater in the intervention (-5.5% [0.6%]) than in the control (-0.4% [0.5%]) group (P < .001) postintervention and was sustained at 3-month follow-up (P < .001). Mean (SE) reductions in fasting glucose were greater in the intervention (-8.6 [1.6] mg/dL) than in the control (-3.7 [1.6] mg/dL) group (P = .02) postintervention; both groups had significant glucose reductions at 3-month follow-up (P < .001). In the intervention group, the intake of total energy and the percentage of energy from all fats, saturated fats, and trans fats decreased, and the intake of dietary fiber increased (all P < .01) postintervention. CONCLUSION: The worksite intervention improved metabolic and behavioral risk factors among employees with prediabetes. The long-term impact on diabetes prevention and program sustainability warrant further investigation

Biomarkers of lupus nephritis determined by serial urine proteomics

Lupus nephritis is a frequent and serious complication of systemic lupus erythematosus (SLE), the treatment of which often requires the use of immunosuppressives that can have severe side effects. Here we determined the low-molecular weight proteome of serial lupus urine samples to uncover novel and predictive biomarkers of SLE renal flare. Urine from 25 flare cycles of 19 patients with WHO Class III, IV, and V SLE nephritis were obtained at baseline, pre-flare, flare and post-flare. Each sample was first fractionated to remove proteins larger than 30kDa, then applied onto weak cation exchanger protein chips for analysis by SELDI-TOF mass spectrometry. We found 176 protein ions of which 27 were differentially expressed between specific flare intervals. On-chip peptide sequencing by integrated tandem mass spectrometry positively identified the 20 and 25 amino-acid isoforms of hepcidin, as well as fragments of α1-antitrypsin and albumin among the selected differentially expressed protein ions. Hepcidin 20 increased 4 months before renal flare and returned to baseline at renal flare, whereas hepcidin 25 decreased at renal flare and returned to baseline 4 months after the flare. These studies provide a beginning proteomic analysis aimed at predicting impending renal relapse, relapse severity, and the potential for recovery after SLE nephritis flare

Statistical methods for modeling house prices and indices

Repeat sales techniques are a common approach for modeling house prices. This methodology presumes the previous sale price acts as a proxy for hedonic variables, such as size and number of bedrooms. Capturing the spirit of the repeat sales setup, the proposed model includes the previous price as a predictor of current price. However, the model also includes an adjustment so that the more time which has elapsed between sales, the less useful the previous price becomes. To incorporate this property into the model framework, a two-part, nonlinear model is proposed which consists of a general price index and an autoregressive component (AR). The latter element can be thought of as the result of a latent AR(1) process for each house which is observed only in time periods when sales occur. In the fitting process, all sales contribute to estimating the time effect but only repeat sales factor in the autoregressive coefficient estimate. The resulting index, constructed from the time effects, is therefore more representative of the housing market compared to existing repeat sales models which ignore single sales. Moreover, the proposed model outperforms benchmark models including the S&P/Case-Shiller® model in terms of predictive power when applied to single-family home sales from July 1985 through September 2004 for twenty U.S. metropolitan areas. Finally, an extension to this model is proposed to incorporate local effects. Here, zip code is introduced to the model as a random effect. Predictive performance is further improved with this addition

The aggregation paradox for statistical rankings and nonparametric tests.

The relationship between social choice aggregation rules and non-parametric statistical tests has been established for several cases. An outstanding, general question at this intersection is whether there exists a non-parametric test that is consistent upon aggregation of data sets (not subject to Yule-Simpson Aggregation Paradox reversals for any ordinal data). Inconsistency has been shown for several non-parametric tests, where the property bears fundamentally upon robustness (ambiguity) of non-parametric test (social choice) results. Using the binomial(n, p = 0.5) random variable CDF, we prove that aggregation of r(≥2) constituent data sets-each rendering a qualitatively-equivalent sign test for matched pairs result-reinforces and strengthens constituent results (sign test consistency). Further, we prove that magnitude of sign test consistency strengthens in significance-level of constituent results (strong-form consistency). We then find preliminary evidence that sign test consistency is preserved for a generalized form of aggregation. Application data illustrate (in)consistency in non-parametric settings, and links with information aggregation mechanisms (as well as paradoxes thereof) are discussed