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Order statistics from non-identical exponential random variables and some applications

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TLDR
In this paper, the authors established some recurrence relations satisfied by the single and product moments of order statistics arising from n independent and non-identically distributed exponential random variables, and used these results to examine the sensitivity of a robust linear estimator to the presence of multiple outliers in the sample.
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This article is published in Computational Statistics & Data Analysis.The article was published on 1994-09-01. It has received 46 citations till now. The article focuses on the topics: Order statistic & Robust statistics.

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Permanents, Order Statistics, Outliers, and Robustness

TL;DR: In this article, the robustness properties of several linear estimators when multiple outliers are possibly present in the sample are investigated. But the robust properties of linear estimator are not discussed.
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Multiuser Scheduling Schemes for Simultaneous Wireless Information and Power Transfer Over Fading Channels

TL;DR: An order-based equal throughput (ET) fair scheduler, which schedules the user having the minimum moving average throughput out of the users whose N-SNR orders fall into a given set of allowed orders, is proposed and it is shown that this scheme provides the users with proportionally fair average harvested energy values.
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Order statistics from non-identical right-truncated Lomax random variables with applications

TL;DR: In this article, the authors derived recurrence relations for the single and product moments of order statistics from n independent and non-identically distributed Lomax and right-truncated LOMax random variables, and applied these results by examining the robustness of censored BLUE's to the presence of multiple outliers.
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Evaluating project completion times when activity times are Weibull distributed

TL;DR: A development of the moments method based on Weibull distribution of activity time is presented, which provides an accurate estimate of the project completion time compared with other d.f.'s estimates.
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Progressive censoring from heterogeneous distributions with applications to robustness

TL;DR: In this paper, the generalized order statistics from heterogeneous distributions are introduced and their properties are investigated, and the case of proportional hazards leads to an interesting connection to the model of generalized ordering statistics.
References
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Journal ArticleDOI

On the Detection of Many Outliers

Bernard Rosner
- 01 May 1975 - 
TL;DR: In this article, the authors proposed several many outlier procedtues to detect more than one outlier in a sample and compared several different procedtuses in various sample sizes.
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Some Grubbs-Type Statistics for the Detection of Several Outliers

TL;DR: In this article, the problem of repeated application and masking is discussed and two new statistics are proposed to over-come these problems: Lk which is based on the k largest (observed) values and Ek which is in absolute value (in absolute value) residuals.

Order statistics for nonidentically distributed variables and permanents

TL;DR: Theory of permanents provides an effective tool in dealing with order statistics corresponding to random variables which are independent but possibly non-identically distributed as mentioned in this paper, which is illustrated by giving a characterization of symmetric random variables in terms of order statistics and by generalizing some known recurrence relations.
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Estimation of Expected Life in the Presence of an Outlier Observation

TL;DR: In this paper, the authors considered a life testing experiment consisting of n items, (n − 1) of which have the same expected life while one of them could have a much longer expected life.
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A Recursive Algorithm for Null Distributions for Outliers: I. Gamma Samples

TL;DR: In this paper, a recursive method is described to obtain certain distributional results relating to order statistics, which may be used to obtain the testing of single and multiple outliers, and some useful inequalities for tail probabilities.