958 resultados para LOG-POISSON STATISTICS
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Spatio-temporal clusters in 1997?2003 fire sequences of Tuscany region (central Italy) have been identified and analysed by using the scan statistic, a method which was devised to evidence clusters in epidemiology. Results showed that the method is reliable to find clusters of events and to evaluate their significance via Monte Carlo replication. The evaluation of the presence of spatial and temporal patterns in fire occurrence and their significance could have a great impact in forthcoming studies on fire occurrences prediction.
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Traditionally, it is assumed that the population size of cities in a country follows a Pareto distribution. This assumption is typically supported by nding evidence of Zipf's Law. Recent studies question this nding, highlighting that, while the Pareto distribution may t reasonably well when the data is truncated at the upper tail, i.e. for the largest cities of a country, the log-normal distribution may apply when all cities are considered. Moreover, conclusions may be sensitive to the choice of a particular truncation threshold, a yet overlooked issue in the literature. In this paper, then, we reassess the city size distribution in relation to its sensitivity to the choice of truncation point. In particular, we look at US Census data and apply a recursive-truncation approach to estimate Zipf's Law and a non-parametric alternative test where we consider each possible truncation point of the distribution of all cities. Results con rm the sensitivity of results to the truncation point. Moreover, repeating the analysis over simulated data con rms the di culty of distinguishing a Pareto tail from the tail of a log-normal and, in turn, identifying the city size distribution as a false or a weak Pareto law.
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This paper presents an initial challenge to tackle the every so "tricky" points encountered when dealing with energy accounting, and thereafter illustrates how such a system of accounting can be used when assessing for the metabolic changes in societies. The paper is divided in four main sections. The first three, present a general discussion on the main issues encountered when conducting energy analyses. The last section, subsequently, combines this heuristic approach to the actual formalization of it, in quantitative terms, for the analysis of possible energy scenarios. Section one covers the broader issue of how to account for the relevant categories used when accounting for Joules of energy; emphasizing on the clear distinction between Primary Energy Sources (PES) (which are the physical exploited entities that are used to derive useable energy forms (energy carriers)) and Energy Carriers (EC) (the actual useful energy that is transmitted for the appropriate end uses within a society). Section two sheds light on the concept of Energy Return on Investment (EROI). Here, it is emphasized that, there must already be a certain amount of energy carriers available to be able to extract/exploit Primary Energy Sources to thereafter generate a net supply of energy carriers. It is pointed out that this current trend of intense energy supply has only been possible to the great use and dependence on fossil energy. Section three follows up on the discussion of EROI, indicating that a single numeric indicator such as an output/input ratio is not sufficient in assessing for the performance of energetic systems. Rather an integrated approach that incorporates (i) how big the net supply of Joules of EC can be, given an amount of extracted PES (the external constraints); (ii) how much EC needs to be invested to extract an amount of PES; and (iii) the power level that it takes for both processes to succeed, is underlined. Section four, ultimately, puts the theoretical concepts at play, assessing for how the metabolic performances of societies can be accounted for within this analytical framework.
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We construct a new family of semi-discrete numerical schemes for the approximation of the one-dimensional periodic Vlasov-Poisson system. The methods are based on the coupling of discontinuous Galerkin approximation to the Vlasov equation and several finite element (conforming, non-conforming and mixed) approximations for the Poisson problem. We show optimal error estimates for the all proposed methods in the case of smooth compactly supported initial data. The issue of energy conservation is also analyzed for some of the methods.
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When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.
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We introduce and analyze two new semi-discrete numerical methods for the multi-dimensional Vlasov-Poisson system. The schemes are constructed by combing a discontinuous Galerkin approximation to the Vlasov equation together with a mixed finite element method for the Poisson problem. We show optimal error estimates in the case of smooth compactly supported initial data. We propose a scheme that preserves the total energy of the system.
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It has been argued that by truncating the sample space of the negative binomial and of the inverse Gaussian-Poisson mixture models at zero, one is allowed to extend the parameter space of the model. Here that is proved to be the case for the more general three parameter Tweedie-Poisson mixture model. It is also proved that the distributions in the extended part of the parameter space are not the zero truncation of mixed poisson distributions and that, other than for the negative binomial, they are not mixtures of zero truncated Poisson distributions either. By extending the parameter space one can improve the fit when the frequency of one is larger and the right tail is heavier than is allowed by the unextended model. Considering the extended model also allows one to use the basic maximum likelihood based inference tools when parameter estimates fall in the extended part of the parameter space, and hence when the m.l.e. does not exist under the unextended model. This extended truncated Tweedie-Poisson model is proved to be useful in the analysis of words and species frequency count data.
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OBJECTIVES: Age- and height-adjusted spirometric lung function of South Asian children is lower than those of white children. It is unclear whether this is purely genetic, or partly explained by the environment. In this study, we assessed whether cultural factors, socioeconomic status, intrauterine growth, environmental exposures, or a family and personal history of wheeze contribute to explaining the ethnic differences in spirometric lung function. METHODS: We studied children aged 9 to 14 years from a population-based cohort, including 1088 white children and 275 UK-born South Asians. Log-transformed spirometric data were analyzed using multiple linear regressions, adjusting for anthropometric factors. Five different additional models adjusted for (1) cultural factors, (2) indicators of socioeconomic status, (3) perinatal data reflecting intrauterine growth, (4) environmental exposures, and (5) personal and family history of wheeze. RESULTS: Height- and gender-adjusted forced vital capacity (FVC) and forced expired volume in 1 second (FEV1) were lower in South Asian than white children (relative difference -11% and -9% respectively, P < .001), but PEF and FEF50 were similar (P ≥ .5). FEV1/FVC was higher in South Asians (1.8%, P < .001). These differences remained largely unchanged in all 5 alternative models. CONCLUSIONS: Our study confirmed important differences in lung volumes between South Asian and white children. These were not attenuated after adjustment for cultural and socioeconomic factors and intrauterine growth, neither were they explained by differences in environmental exposures nor a personal or family history of wheeze. This suggests that differences in lung function may be mainly genetic in origin. The implication is that ethnicity-specific predicted values remain important specifically for South Asian children.
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One feature of the modern nutrition transition is the growing consumption of animal proteins. The most common approach in the quantitative analysis of this change used to be the study of averages of food consumption. But this kind of analysis seems to be incomplete without the knowledge of the number of consumers. Data about consumers are not usually published in historical statistics. This article introduces a methodological approach for reconstructing consumer populations. This methodology is based on some assumptions about the diffusion process of foodstuffs and the modeling of consumption patterns with a log-normal distribution. This estimating process is illustrated with the specific case of milk consumption in Spain between 1925 and 1981. These results fit quite well with other data and indirect sources available showing that this dietary change was a slow and late process. The reconstruction of consumer population could shed a new light in the study of nutritional transitions.
Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.
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Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.
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In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this model in order to consider alternatives. We propose a 2-finite mixture of bivariate Poisson regression models to demonstrate that the overdispersion in the data requires more structure if it is to be taken into account, and that a simple zero-inflated bivariate Poisson model does not suffice. At the same time, we show that a finite mixture of bivariate Poisson regression models embraces zero-inflated bivariate Poisson regression models as a special case. Additionally, we describe a model in which the mixing proportions are dependent on covariates when modelling the way in which each individual belongs to a separate cluster. Finally, an EM algorithm is provided in order to ensure the models’ ease-of-fit. These models are applied to the same automobile insurance claims data set as used in Bermúdez [2009] and it is shown that the modelling of the data set can be improved considerably.
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A statistical evaluation of the population dynamics of Panstrongylus geniculatus is based on a cohort experiment conducted under controlled laboratory conditions. Animals were fed on hen every 15 days. Egg incubation took 21 days; mean duration of 1st, 2nd, 3rd, 4th, and 5th instar nymphs was 25, 30, 58, 62, and 67 days, respectively; mean nymphal development time was 39 weeks and adult longevity was 72 weeks. Females reproduced during 30 weeks, producing an average of 61.6 eggs for female on its lifetime; the average number of eggs/female/week was 2.1. Total number of eggs produced by the cohort was 1379. Average hatch for the cohort was 88.9%; it was not affected by age of the mother. Age specific survival and reproduction tables were constructed. The following population parameters were evaluated, generation time was 36.1 weeks; net reproduction rate was 89.4; intrinsic rate of natural increase was 0.125; instantaneous birth and death rates were 0.163 and 0.039 respectively; finite rate of increase was 1.13; total reproductive value was 1196 and stable age distribution was 31.2% eggs, 64.7% nymphs and 4.1% adults. Finally the population characteristics of P. geniculatus lead to the conclusion that this species is a K strategist.