Objective: We aimed to investigate the influence of the genetic variability of candidate genes on survival at old age in good health. Methods: First, on the basis of a synthetic survival curve constructed using historic mortality data taken from the Italian population from 1890 onward, we defined three age classes ranging from 18 to 106 years. Second, we assembled a multinomial logistic regression model to evaluate the effect of dichotomous variables (genotypes) on the probability to be assigned to a specific category (age class). Third, we applied the regression model to a cross-sectional dataset (10 genes; 972 subjects selected for healthy status) categorized according to age and sex. Results: We found that genetic factors influence survival at advanced age in good health in a sex- and age-specific way. Furthermore, we found that genetic variability plays a stronger role in males than in females and that, in both genders, its impact is especially important at very old ages. Conclusions: The analyses presented here underline the age-specific effect of the gene network in modulating survival at advanced age in good health.

In financial analysis it is useful to study the dependence between two or more time series as well as the temporal dependence in a univariate time series. This article is concerned with the statistical modeling of the dependence structure in a univariate financial time series using the concept of copula. We treat the series of financial returns as a first order Markov process. The Archimedean two-parameter BB7 copula is adopted to describe the underlying dependence structure between two consecutive returns, while the log-Dagum distribution is employed to model the margins marked by skewness and kurtosis. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. Furthermore, we apply the model to the daily returns of four stocks and, finally, we illustrate how its fitting to data can be improved when the dependence between consecutive returns is described through a copula function.

In this work, we show that the Dagum distribution [ 3 Dagum, C. 1977. A new model of personal income distribution: Specification and estimation. Econ. Appl., XXX: 413–436. [Google Scholar] ] may be a competitive model for describing data which include censored observations in lifetime and reliability problems. Maximum likelihood estimates of the three parameters of the Dagum distribution are determined from samples with type I right and type II doubly censored data. We perform an empirical analysis using published censored data sets: in certain cases, the Dagum distribution fits the data better than other parametric distributions that are more commonly used in survival and reliability analysis. Graphical comparisons confirm that the Dagum model behaves better than a number of competitive distributions in describing the empirical hazard rate of the analyzed data. A probability plot to provide graphical check of the appropriateness of the Dagum model for right censored data is constructed, and the details are given in the appendix. Finally, a simulation study that shows the good performance of the maximum likelihood estimators of the Dagum shape parameters for finite type II doubly censored samples is carried out.