Filippo Domma

Sex and Age Specificity of Susceptibility Genes Modulating Survival at Old Age

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.

Malnutrition, infection and arteriovenous fistula failure: Is there a link?

Introduction The histology of neointimal hyperplasia, the primary cause of arteriovenous fistula (AVF) stenosis, resembles the histology of atherosclerosis. We evaluated classic atherogenic risk factors such as hypertension, smoking, diabetes, cholesterol, and evaluated the role of expanded risk factors such as: cytomegalovirus (CMV), Helicobacter pylori (H. pylori), Chlamydia pneumoniae (C. pneumoniae), infection, and malnutrition, as possible causes of AVF failure in hemodialysis (HD) patients. Methods AVF of 91 HD patients were monitored by on-line blood flow measurement (Qac); levels of albumin, fibrinogen, C-reactive protein and plasma cholesterol were recorded. Nutrition was evaluated via the Malnutrition Inflammation Score and the normalized protein intake (nPCR). Seropositivity to CMV, C. pneumoniae and H. Pylori were assessed. Results Twenty-one patients had at least one episode of vascular access thrombosis; 17 patients had stenotic lesions. Analysis of survival tables revealed that patients who had high IgG CMV antibody levels had a higher probability of AVF failure than patients with lower CMV antibody levels. The difference in the empirical survival functions was statistically significant when we stratified by CMV antibody levels, unlike H. pylori or C. pneumoniae. In a logistic regression model, CMV, increased cholesterol, and decreases in nPCR and Qac significantly increased the risk of AVF failure. Conclusion Our study suggests that CMV infection, total plasma cholesterol, decreased Qac, and nPCR are important risk factors of AVF failure in HD patients.

Maximum likelihood estimation in Dagum distribution with censored samples

In this work, we show that the Dagum distribution [3] 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.

Some properties of the bivariate Burr type III distribution

In this paper, we extend some results related to the dependence structure of the bivariate Burr type III distribution, proposed by Rodriguez [Multivariate Burr III distributions, Part I. Theoretical Properties, Research Publication GMR-3232, General Motors Research Laboratories, Warren, Michigan, 1980; Frequency surfaces, system of, in Encyclopedia of Statistical Sciences, Vol. 3, 1983, Wiley, New York, pp. 232–247]. Using copula representations of bivariate distributions, in the first part of the work, we study some dependence properties and ordering, and we prove that this model can also describe situations of negative dependence. In the second part, we study some dependence measures such as the Kendalls tau, medial correlation and tail dependence. Finally, we show that the correlation coefficient exists and can also be negative.

Some developments on the log-Dagum distribution

Skewed and fat-tailed distributions frequently occur in many applications. Models proposed to deal with skewness and kurtosis may be difficult to treat because the density function cannot usually be written in a closed form and the moments might not exist. The log-Dagum distribution is a flexible and simple model obtained by a logarithmic transformation of the Dagum random variable. In this paper, some characteristics of the model are illustrated and the estimation of the parameters is considered. An application is given with the purpose of modeling kurtosis and skewness that mark the financial return distribution.

The Beta-Dagum Distribution: Definition and Properties

This article introduces a five-parameter Beta-Dagum distribution from which moments, hazard and entropy, and reliability measures are then derived. These properties show the high flexibility of the said distribution. The maximum likelihood estimators of the Beta-Dagum parameters are examined and the expected Fisher information matrix provided. Next, a simulation study is carried out which shows the good performance of maximum likelihood estimators for finite samples. Finally, the usefulness of the new distribution is illustrated through real data sets.

A stress–strength model with dependent variables to measure household financial fragility

The paper is inspired by the stress–strength models in the reliability literature, in which given the strength (Y) and the stress (X) of a component, its reliability is measured by P(X < Y). In this literature, X and Y are typically modeled as independent. Since in many applications such an assumption might not be realistic, we propose a copula approach in order to take into account the dependence between X and Y. We then apply a copula-based approach to the measurement of household financial fragility. Specifically, we define as financially fragile those households whose yearly consumption (X) is higher than income (Y), so that P(X > Y) is the measure of interest and X and Y are clearly not independent. Modeling income and consumption as non-identically Dagum distributed variables and their dependence by a Frank copula, we show that the proposed method improves the estimation of household financial fragility. Using data from the 2008 wave of the Bank of Italy’s Survey on Household Income and Wealth we point out that neglecting the existing dependence in fact overestimates the actual household fragility.

Statistical Modeling of Temporal Dependence in Financial Data via a Copula Function

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.

Asymptotic Distribution of the Maximum Likelihood Estimators of the Parameters of the Right-Truncated Dagum Distribution

In this work we have determined the asymptotic distribution of the maximum likelihood estimators of the parameters β, λ, and δ for the right-truncated Dagum model. Some numerical comparisons show that, for each combination of the parameters and for each sample size, the variance of maximum likelihood estimators increases as the truncation point decreases, i.e., with the increase in the cut of the right tail of distribution.

A new class of distribution functions for lifetime data

This paper deals with the issue of building a parametric model from the empirical and/or qualitative information about the hazard rate. We propose a new class of models for survival data analysis. This class is characterized by a distribution function which includes, in its expression, a function that defines the sign of the first derivative of a monotonic transformation of the hazard rate. We show that certain parametric models used in survival analysis belong to the proposed class. Finally, by using the proposed method, we build two new distributions which allow us to achieve a highly flexible hazard rate. The first one is based on an m-degree polynomial and allows us to get BT, IFR and UBT-BT hazard rates, while the second, based on trigonometric functions, enables us to obtain monotonically increasing or decreasing hazard rates or hazard rates with a non-monotonic behavior. The usefulness of the new method is illustrated through two applications to real data.