7 resultados para COM-Poisson distribution
em BORIS: Bern Open Repository and Information System - Berna - Suiça
Resumo:
BACKGROUND: First investigations of the interactions between weather and the incidence of acute myocardial infarctions date back to 1938. The early observation of a higher incidence of myocardial infarctions in the cold season could be confirmed in very different geographical regions and cohorts. While the influence of seasonal variations on the incidence of myocardial infarctions has been extensively documented, the impact of individual meteorological parameters on the disease has so far not been investigated systematically. Hence the present study intended to assess the impact of the essential variables of weather and climate on the incidence of myocardial infarctions. METHODS: The daily incidence of myocardial infarctions was calculated from a national hospitalization survey. The hourly weather and climate data were provided by the database of the national weather forecast. The epidemiological and meteorological data were correlated by multivariate analysis based on a generalized linear model assuming a log-link-function and a Poisson distribution. RESULTS: High ambient pressure, high pressure gradients, and heavy wind activity were associated with an increase in the incidence of the totally 6560 hospitalizations for myocardial infarction irrespective of the geographical region. Snow- and rainfall had inconsistent effects. Temperature, Foehn, and lightning showed no statistically significant impact. CONCLUSIONS: Ambient pressure, pressure gradient, and wind activity had a statistical impact on the incidence of myocardial infarctions in Switzerland from 1990 to 1994. To establish a cause-and-effect relationship more data are needed on the interaction between the pathophysiological mechanisms of the acute coronary syndrome and weather and climate variables.
Resumo:
OBJECTIVE To assess the impact of potential risk factors on the development of respiratory symptoms and their specific modification by breastfeeding in infants in the first year of life. STUDY DESIGN We prospectively studied 436 healthy term infants from the Bern-Basel Infant Lung Development cohort. The breastfeeding status, and incidence and severity of respiratory symptoms (score) were assessed weekly by telephone interview during the first year of life. Risk factors (eg, pre- and postnatal smoking exposure, mode of delivery, gestational age, maternal atopy, and number of older siblings) were obtained using standardized questionnaires. Weekly measurements of particulate matter <10 μg were provided by local monitoring stations. The associations were investigated using generalized additive mixed model with quasi Poisson distribution. RESULTS Breastfeeding reduced the incidence and severity of the respiratory symptom score mainly in the first 27 weeks of life (risk ratio 0.70; 95% CI 0.55-0.88). We found a protective effect of breastfeeding in girls but not in boys. During the first 27 weeks of life, breastfeeding attenuated the effects of maternal smoking during pregnancy, gestational age, and cesarean delivery on respiratory symptoms. There was no evidence for an interaction between breastfeeding and maternal atopy, number of older siblings, child care attendance, or particulate matter <10 μg. CONCLUSIONS This study shows the risk-specific effect of breastfeeding on respiratory symptoms in early life using the comprehensive time-series approach.
Resumo:
In this article we propose a bootstrap test for the probability of ruin in the compound Poisson risk process. We adopt the P-value approach, which leads to a more complete assessment of the underlying risk than the probability of ruin alone. We provide second-order accurate P-values for this testing problem and consider both parametric and nonparametric estimators of the individual claim amount distribution. Simulation studies show that the suggested bootstrap P-values are very accurate and outperform their analogues based on the asymptotic normal approximation.
Resumo:
Serial correlation of extreme midlatitude cyclones observed at the storm track exits is explained by deviations from a Poisson process. To model these deviations, we apply fractional Poisson processes (FPPs) to extreme midlatitude cyclones, which are defined by the 850 hPa relative vorticity of the ERA interim reanalysis during boreal winter (DJF) and summer (JJA) seasons. Extremes are defined by a 99% quantile threshold in the grid-point time series. In general, FPPs are based on long-term memory and lead to non-exponential return time distributions. The return times are described by a Weibull distribution to approximate the Mittag–Leffler function in the FPPs. The Weibull shape parameter yields a dispersion parameter that agrees with results found for midlatitude cyclones. The memory of the FPP, which is determined by detrended fluctuation analysis, provides an independent estimate for the shape parameter. Thus, the analysis exhibits a concise framework of the deviation from Poisson statistics (by a dispersion parameter), non-exponential return times and memory (correlation) on the basis of a single parameter. The results have potential implications for the predictability of extreme cyclones.
Resumo:
This paper deals with sequences of random variables belonging to a fixed chaos of order q generated by a Poisson random measure on a Polish space. The problem is investigated whether convergence of the third and fourth moment of such a suitably normalized sequence to the third and fourth moment of a centred Gamma law implies convergence in distribution of the involved random variables. A positive answer is obtained for q = 2 and q = 4. The proof of this four moments theorem is based on a number of new estimates for contraction norms. Applications concern homogeneous sums and U-statistics on the Poisson space.