Alexandre Berred

Prediction of record values

Given a number of record values from independent and identically distributed random variables with a continuous distribution function F, our aim is to predict future record values under suitable assumptions on the tail of F. In this paper, we are primarily concerned with finding reasonable tolerance regions for future record values. Two methods are proposed. The first one deals with the case where we observe only record values. The second one makes use of the information brought by the complete sample.

Properties and estimation of the mean past lifetime

Let T be a lifetime random variable. In order to study the properties of T in reliability theory and survival analysis, several measures are proposed in the literature. Among these measures, hazard rate, mean residual lifetime, reversed hazard rate and the mean past lifetime (MPL) play important roles. In the present paper, we focus mainly on the MPL. We investigate its properties in connection with other reliability measures. Some results on partial ordering and characterization are also given. Finally, we deal with its statistical estimation.

On new scheduling policy for the improvement of firm RTDBSs performances

Earliest deadline first (EDF) is one of the main scheduling policies used in real-time database systems (RTDBSs) for transactions processing. With EDF, prioritized transactions are not necessarily the most important in the system. Moreover, it is well-known that EDF is not efficient in overload conditions. In this paper, we introduce the notion of transaction importance and present a new priority assignment technique based on both transactions importance and deadlines. This assignment policy leads to a new scheduling policy, called generalized earliest deadline first (GEDF). In order to show the benefits of using GEDF for managing real-time transactions, we have designed an RTDBS simulator and carried out Monte Carlo simulations.

On the Influence of Record Terms in the Addition of Independent Random Variables

We discuss some problems connected with the role of record values and maximal values generated by sequences of random variables X1, X2,…, X n in the process of the growth of sums X1 +···+ Xn, n = 1, 2,….

An Estimator of the Exponent of Regular Variation Based on K-Record Values

Let {X n:n ≥ 1} be an i.i.d. sequence of random variables with a continuous distribution function F. Under the assumption that the upper tail of Fis regularly varying with exponent 1/α, α > 0, we study the asymptotic properties of an estimator of α based on k-record values.

Semiparametric Estimation in Copulas with the Same Marginals

We consider semiparametric multivariate data models based on copula representation of the common distribution function. A copula is characterized by a parameter of association and marginal distribution functions. This parameter and the marginal distributions are unknown. In this article, we study the estimator of the parameter of association in copulas with the marginal distribution functions assumed as nuisance parameters restricted by the assumption that the components are identically distributed. Results of this work could be used to construct special kinds of tests of homogeneity for random vectors having dependent components.

Tail dependence coefficient of generalized hyperbolic distribution

The generalized hyperbolic distribution was introduced in [3], and has been developed by many authors, see among others [17], [12], [16], [2], especially in relation with several applications in the Finance, see [15], [6]. This family contains and generalizes many familiar distributions such as the Student t, Gaussian, variance gamma, Cauchy and others. The aim of this paper is to investigate the upper tail dependence coefficient in the case of the generalized hyperbolic distribution. More precisely, a bivariate random variable X = (X1,X2) follows a generalized hyperbolic distribution GH(λ ,α,β ,δ ,γ) if

Signal Localization: A New Approach in Signal Discovery

A new approach for statistical association signal identification is developed in this paper. We consider a strategy for nonprecise signal identification by extending the well-known signal detection and signal identification methods applicable to the multiple testing problem. Collection of statistical instruments under the presented approach is much broader than under the traditional signal identification methods, allowing more efficient signal discovery. Further assessments of maximal value and average statistics in signal discovery are improved. While our method does not attempt to detect individual predictors, it instead detects sets of predictors that are jointly associated with the outcome. Therefore, an important application would be in genome wide association study (GWAS), where it can be used to detect genes which influence the phenotype but do not contain any individually significant single nucleotide polymorphism (SNP). We compare power of the signal identification method based on extremes of single p-values with the signal localization method based on average statistics for logarithms of p-values. A simulation analysis informs the application of signal localization using the average statistics for wide signals discovery in Gaussian white noise process. We apply average statistics and the localization method to GWAS to discover better gene influences of regulating loci in a Chinese cohort developed for risk of nasopharyngeal carcinoma (NPC).