Approximations et majorations optimales des statistiques d'ordre d'un échantillon
Asymptotic behavior of the empirical process for gaussian data presenting seasonal long-memory
We study the asymptotic behavior of the empirical process when the underlying data are gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and -Statistics.
Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory
We study the asymptotic behavior of the empirical process when the underlying data are Gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and U-Statistics.
Asymptotic covariances of empirical processes
Asymptotic distribution of Gumbel statistic in a semi-parametric approach.
Asymptotic normality and convergence rates of linear rank statistics under alternatives
Asymptotic Normality of Linear Functions of Concomitants of Order Statistics
Asymptotic Properties of Minimum Contrast Estimators for Parameters of Boolean Models.
Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics
2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.This paper is concerned with the problem of deriving Bayesian prediction bounds for the future observations (two-sample prediction) from the inverse Weibull distribution based on generalized order statistics (GOS). Study the two side interval Bayesian prediction, point prediction under symmetric and asymmetric loss functions and the maximum likelihood (ML) prediction using "plug-in" procedure for future observations from the inverse...
Bayesian inference in life tests based on exponential model with outliers when sample size is a random variable.
This paper deals with the problem of prediction of the order statistics in a future sample. Underlying model is exponential. Outlier is present in the sample drawn and the sample size is considered a random variable. Firstly, an outlier of type θδ in the exponential model, is treated. Actual predictive distribution of the order statistics is obtained. As an extension, the two-sample problem is also taken up. Finally, an outlier of type θ + δ is dealt with and now the predictive distribution is expressed...
Bias-robust estimates based on order statistics and spacings in the exponential model
Bounds on expectations of record range and record increment from distributions with bounded support.
Bounds on the expectations of th record increments.
Characteristic properties of generalized order statistics from exponential distributions
Exponential distributions are characterized by distributional properties of generalized order statistics. These characterizations include known results for ordinary order statistics and record values as particular cases.
Characterizations of distributions by moments of order statistics when the sample size is random
We give characterizations of the uniform distribution in terms of moments of order statistics when the sample size is random. Special cases of a random sample size (logarithmic series, geometrical, binomial, negative binomial, and Poisson distribution) are also considered.
Characterizations of Distributions Via Two Expected Values of Order Statistics.
Characterizations of exponential distributions by spacings of generalized order statistics
-3Properties of spacings of generalized order statistics based on IFR and DFR distributions are shown to characterize exponential distributions.
Characterizations of power distributions via moments of order statistics and record values
Power distributions can be characterized by equalities involving three moments of order statistics. Similar equalities involving three moments of k-record values can also be used for such a characterization. The case of samples with random sizes is also considered.
Chung-type functional laws of the iterated logarithm for tail empirical processes
Comparison of order statistics in a random sequence to the same statistics with I.I.D. variables
The paper is motivated by the stochastic comparison of the reliability of non-repairable -out-of- systems. The lifetime of such a system with nonidentical components is compared with the lifetime of a system with identical components. Formally the problem is as follows. Let be positive independent random variables with common distribution . For and , let consider and . Remark that this is no more than a change of scale for each term. For let us define to be the th order statistics...