Wavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of Lee–Carter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.

This study examines the adoption of best-management practices (BMPs) in terms of the total number of practices implemented up to a certain period, using count data analysis. Poisson and negative binomial regressions were used to examine the likely determinants of producers' decisions to adopt greater numbers of technologies, and the specific case of dairy producers' adoption of BMPs was explored. Our results emphasize the significant effect of producers' awareness of the efforts to control non-point source pollution, information about BMPs, farm size, producer's educational attainment, and risk aversion on the number of BMPs adopted.

The search for an association between disease incidence and possible risk factors using surveillance data needs to account for possible spatial and temporal correlations in underlying risk. This can be especially difficult if there are missing values for some important covariates. We present a case study to show how this problem can be overcome in a Bayesian analysis framework by adding to the usual spatio-temporal model a component for modelling the missing data.

The introduction and the widespread use of the varicella vaccine in Taiwan has led to a 75–80% decrease in the incidence of varicella in children. However the vaccine's long-term impact on the incidence of herpes zoster (HZ) has attracted attention. By controlling gender, underlying diseases, and age effects, a Poisson regression was applied on the 2000–2008 chart records of 240 000 randomly selected residents who enrolled in the Universal National Health Insurance. The results show that, as the vaccine coverage in children increases, the incidence of varicella decreases. However, the incidence of HZ increased even before the implementation of the free varicella vaccination programme in 2004, particularly in females. The increase in the incidence of HZ cannot be entirely and directly attributed to the widespread vaccination of children. Continuous monitoring is needed to understand the secular trends in HZ before and after varicella vaccination in Taiwan and in other countries.

We consider variation of observed claim frequencies in non-life insurance, modeled by Poisson regression with overdispersion. In order to quantify how much variation between insurance policies that is captured by the rating factors, one may use the coefficient of determination, R2, the estimated proportion of total variation explained by the model. We introduce a novel coefficient of individual determination (CID), which excludes noise variance and is defined as the estimated fraction of total individual variation explained by the model. We argue that CID is a more relevant measure of explained variation than R2 for data with Poisson variation. We also generalize previously used estimates and tests of overdispersion and introduce new coefficients of individual explained and unexplained variance.

Application to a Swedish three year motor TPL data set reveals that only 0.5% of the total variation and 11% of the total individual variation is explained by a model with seven rating factors, including interaction between sex and age. Even though the amount of overdispersion is small (4.4% of the noise variance) it is still highly significant. The coefficient of variation of explained and unexplained individual variation is 29% and 81% respectively.

The feasibility of using risk markers to screen broiler chicken flocks and anticipate their risk of condemnation at meat inspection was examined in 404 randomly selected flocks in 15 French slaughterhouses in 2005. Condemnation rate and information about rearing conditions, health history, catching and loading, transport and slaughtering were collected. The Poisson regression model of the condemnation rate consisted of six simple and biologically relevant predictors: production type, frequency of farmer's visits during the starting period, health disorders during rearing, on-farm mortality, mortality during transport, and slaughter-line speed. Although accurate prediction of the condemnation rate for a given flock was not feasible, flocks with low or high risk of condemnation could be distinguished. These findings could be useful at various stages of chicken production, to monitor and improve farm husbandry practices, minimize the impact of transport conditions, and optimize meat inspection procedures.