A note on moments of order statistics from exchangeable variates

Joshi (1973) and Balakrishnan and Malik (1985) have derived some some identities for the moments of order statistics from independent and identically distributed random variables. In this paper, we make use of a basic result due to David and Joshi (1968) and show that these identities for the moments also hold when the order statistics arise from exchangeable variables.     

Two identities involving order statistics in the presence of an outlier

In this note, we derive two simple identities involving order statistics from a sample of size n in the presence of an outlier. These generalize the results of Joshi (1973). These identities will be quite useful in checking the computation of the single moments of order statistics from an outlier model.     

Computing the moments of order statistics from nonidentical random variables

In this paper, we establish new representations, identities and recurrence relations of order statistics (o.s.) arising from general independent nonidentically distributed random variables (r.v.s). These recurrence relations will enable one to compute all moments of all o.s. in a simple manner. Applications for some known distributions are given.     

On special linear identities for order statistics

Linear identities for the distribution functions of order statistics from an iid sample are defined. It is shown that such identities are true for all distributions or to some discrete distributions taking a finite number of values.     

Recurrence Relations And Identities For Moments Of Order Statistics,II; Specific Continuous Distributions

In an earlier paper, Malik et al. (1987) have reviewed several recurrence relations and identities available for the single and product moments of order statistics from an arbitrary continuous distribution. In this paper, we review several such relations and identities established for both single and product moments of order statistics from some specific continuous distributions. We also mention some important applications or. these results.     

Identities and recurrence relations for order statistics corresponding to ncnidentically distributed variable

In this paper, we derive two simple identities and some recurrencerelations involving order statistics from a sample of size n in case there is a possibility of one or more outliers being present.     

Recurrence relations mid identities for moments of order statistics,i: arbitrary continuous distribution

In this paper, we review several recurrence relations and identities established for the single and product moments of order statistics from an arbitrary continuous distribution. We point out the interrelationships between many of these recurrence relations. We discuss the results giving the bounds for the number of single and double integrals needed to be evaluated in order to compute the first, second and product moments of order statistics in a sample of size n from an arbitrary continuous distribution, given these moments in samples of sizes n-1 and less. Improvements of these bounds for the case of symmetric continuous distributions are also discussed     

Order statistics from discrete distributions

Several recurrence relations and identities available for single and product moments of order1 statistics in a sample size n from an arbitrary continuous distribution are extended for the discrete case,, Making use of these recurrence relations it is shown that it is sufficient to evaluate just two single moments and (n-l)/2 product moments when n is odd and two single moments and {n-2)/2 product moments when n is even, in order to evaluate the first, second and product moments of order statistics in a sample of size n drawn from an arbitrary discrete distribution, given these moments in samples of sizes n-1 and less.. A series representation for the product moments of order statistics is derived.. Besides enabling us to obtain an exact and explicit expression for the product moments of order statistics from the geometric distribution, it. makes the computation of the product moments of order statistics from other discrete distributions easy too.     

Generalized elliptical distributions

A family of distributions generated by an operator acting on generalized normal density is introduced. This family contains as particular cases many known distributions, including the generalized normal, generalized t, and generalized gamma distributions. Several mathematical properties of the family (including expansions, characteristic function, moments, cumulants, and order statistics properties) are derived. Estimation procedures are derived too by the method of moments, method of maximum likelihood, and the method of empirical characteristic function. A real data application is presented. Finally, extensions to the multivariate case are outlined.     

Theoretical L-moments and TL-moments using combinatorial identities and finite operators

Moments have been traditionally used to characterize a probability distribution. Recently, linear moments (L-moments) and trimmed L-moments (TL-moments) are appealing alternatives to the conventional moments. This paper focuses on the computation of theoretical L-moments and TL-moments and emphasizes the use of combinatorial identities. We are able to derive new closed-form formulas of L-moments and TL-moments for continuous probability distributions. Finally, closed-form formulas for the L-moments for the exponential distribution and the uniform distribution are also obtained.     

Relation for joint densities of progressively censored order statistics

A relation between four joint densities of progressively type-II censored order statistics is shown, which is well known in the particular case of ordinary order statistics. The result leads to identities for product moments and for moments of contrasts.     

Some identities among moments of order statistics

Some new identities among the m oments of order statistics are derived. These are more general in nature and are applicable when moments of Some extreme order statistics do not exist.     

The heat equation and Stein's identity: Connections,applications

This article presents two expectation identities and a series of applications. One of the identities uses the heat equation, and we show that in some families of distributions the identity characterizes the normal distribution. We also show that it is essentially equivalent to Stein's identity. The applications we have presented are of a broad range. They include exact formulas and bounds for moments, an improvement and a reversal of Jensen's inequality, linking unbiased estimation to elliptic partial differential equations, applications to decision theory and Bayesian statistics, and an application to counting matchings in graph theory. Some examples are also given.     

Domains of attraction of asymptotic distributions of extreme generalized order statistics

Abstract

In extreme value theory for ordinary order statistics, there are many results that characterize the domains of attraction of the three extreme value distributions. In this article, we consider a subclass of generalized order statistics for which also three types of limit distributions occur. We characterize the domains of attraction of these limit distributions by means of necessary and/or sufficient conditions for an underlying distribution function to belong to the respective domain of attraction. Moreover, we compare the domains of attraction of the limit distributions for extreme generalized order statistics with the domains of attraction of the extreme value distributions.     

A General Recurrence Relation for the Moments of Bivariate and Trivariate Order Statistics

In this article, by using the dropping argument, a general recurrence relation satisfied by the joint cumulative distribution functions of order statistics from any arbitrary bivariate distribution function is established. This recurrence relation is the first bivariate version of the basic triangle rule for order statistics arisen from univariate distribution function. Finally, this relation is extended to the trivariate case. These lead to similar identities for product moments (of any order) of order statistics.     

A survey and comparison of methods for estimating extreme right tail-area quantiles

The purpose of this paper is to survey many of the methods for estimating extreme right tail-area quantiles in order to determine which method or methods gives the best approximations. The problem is to find a good estimate of x_p defined by 1 - F(x _p) = p where p is a very small number for a random sample from an unknown distribution. An extension of this problem is to determine the number of largest order statistics that should be used to make an estimate. From extensive computer simulations trying to minimize relative error, conclusions can be drawn based on the value of p. For p = .02, the exponential tail method by Breiman, et al using a method by Pickands for determining the number of order statistics to use works best for light to heavy tailed distributions. For extremely heavy tailed distributions, a method proposed by Hosking and Wallis seems to be the most accurate at p = .02 and p = .002. The quadratic tail method by Breiman, et al appears best for light to moderately heavy tailed distributions at p = .002 and for all distributions at p = .0002.     

Tests for generalized exponential laws based on the empirical Mellin transform

Recently, many standard families of distributions have been generalized by exponentiating their cumulative distribution function (CDF). In this paper, test statistics are constructed based on CDF–transformed observations and the corresponding moments of arbitrary positive order. Simulation results for generalized exponential distributions show that the proposed test compares well with standard methods based on the empirical distribution function.     

Testing order restricted hypotheses with proportional hazards

Inferences for survival curves based on right censored continuous or grouped data are studied. Testing homogeneity with an ordered restricted alternative and testing the order restriction as the null hypothesis are considered. Under a proportional hazards model, the ordering on the survival curves corresponds to an ordering on the regression coefficients. Approximate likelihood methods are obtained by applying order restricted procedures to the estimates of the regression coefficients. Ordered analogues to the log rank test which are based on the score statistics are considered also. Chi-bar-squared distributions, which have been studied extensively, are shown to provide reasonable approximations to the null distributions of these tests statistics. Using Monte Carlo techniques, the powers of these two types of tests are compared with those that are available in the literature.     

An Estimator of Shannon Entropy of Beta-Generated Distributions and a Goodness-of-Fit Test

The aim of this article is twofold: on the one hand to introduce and study some of the statistical properties of an estimator for the Shannon entropy and on the other hand to develop a goodness-of-fit test for beta-generated distributions and the distribution of order statistics. Beta-generated distributions are a broad class of univariate distributions which has received great attention during the last 15 years, as it obeys nice properties and it extends the distribution of order statistics. The proposed estimator of Shannon entropy of beta-generated distributions is motivated by the respective Vasicek’s estimator, as the latter one is tailored to the class of the beta-generated distributions and the distribution of order statistics. The estimator of Shannon entropy is defined and its consistency is studied. It is, moreover, exploited to build a goodness-of-fit test for the beta-generated distribution and the distribution of order statistics. Simulations are performed to examine the small- and moderate-sample properties of the proposed estimator and to compare the power of the proposed test with the power of competitors under a variety of alternatives.     

Characterizations via regression of generalized order statistics

In this paper, we present some characterizations of distributions based on the regression of generalized order statistics. In the case of adjacent generalized order statistics, the conditional expectation of one generalized order statistic given the other one completely characterizes distributions depending on the type of regression function. In the case of non-adjacent generalized order statistics, the characterization of distributions using conditional expectations becomes more complicated. The results presented in the paper unify and extend some of the existing results involving order statistics and record values.