The exponential distribution analog of the Grubbs–Weaver method

Abstract

Grubbs and Weaver (1947 Grubbs, F. E., and C. L. Weaver. 1947. The best unbiased estimate of population standard deviation based on group ranges. Journal of the American Statistical Association 42 (238):224–41. doi: 10.2307/2280652.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggest a minimum-variance unbiased estimator for the population standard deviation of a normal random variable, where a random sample is drawn and a weighted sum of the ranges of subsamples is calculated. The optimal choice involves using as many subsamples of size eight as possible. They verified their results numerically for samples of size up to 100, and conjectured that their “rule of eights” is valid for all sample sizes. Here we examine the analogous problem where the underlying distribution is exponential and find that a “rule of fours” yields optimality and prove the result rigorously.     

Being disabled,being a manager: ‘glass partitions’ and conditional identities in the contemporary workplace

This article critically explores the working lives and views of disabled senior staff working in UK organisations. The qualitative research at the core of the article establishes that some disabled people are confounding established notions of disabled people only working in peripheral employment roles by exploring the working lives and perceptions of disabled managers. The findings do, however, point to continued barriers to what disabled staff in senior positions can be seen to do and be organisationally. Here both practical and ontological risk inheres in organisationally induced change, openness about impairment and risky identities. Such ideas, it is argued, present limits to further promotion and workplace inclusion for some disabled managers.     

基于高维分步投影的多重分区聚类算法

数据分布密度划分的聚类算法是数据挖掘聚类算法的主要方法之一。针对传统密度划分聚类算法存在运算复杂、运行效率不高等缺陷，设计高维分步投影的多重分区聚类算法；以高维分布投影密度为依据，对数据集进行多重分区，产生数据集的子簇空间，并进行子簇合并，形成理想的聚类结果；依据该算法进行实验，结果证明该算法具有运算简单和运行效率高等优良性。     

On Block Skip Free Transition Matrices and First Passage Times

Starting from an abstract setting which extends the property “skip free to the left” for transition matrices to a partition of the state space, we develop bounds for the mean hitting time of a Markov chain to an arbitrary subset from an arbitrary initial law. We apply our theory to the embedded Markov chains associated with the M/G/1 and the GI/M/1 queueing systems. We also illustrate its applicability with an asymptotic analysis of a non-reversible Markovian star queueing network with losses.     

A test statistic for weighted runs

A new test statistic based on runs of weighted deviations is introduced. Its use for observations sampled from independent normal distributions is worked out in detail. It supplements the classic χ² test which ignores the ordering of observations and provides additional sensitivity to local deviations from expectations. The exact distribution of the statistic in the non-parametric case is derived and an algorithm to compute p-values is presented. The computational complexity of the algorithm is derived employing a novel identity for integer partitions.     

A bound connected with 26 factorial search designs of resolution 3.1

In this paper, we give a lower bound for the number of treatments required

for a plan to be a main effect plus one plan for 2^m (m = 6) factorial experiments, The lower bound problem is important in the event of generating new designs with similar properties or when one wants to study the criteria of optimality for such designs.     

Extended stirling family of discrete probability distributions

In this paper. introduced is a family of discrete probability distributions. whose probability function includes explieitly the Striling-Carlitz polynomial of the first or the second kind. The new family extends the stirling family of distributions. Sibuya (1988) includes the conditional distributions of the orginal ones and enlarges the application area.     

Stability and Efficiency of Partitions in Matching Problems

We define two versions of stability and efficiency of partitions and analyze their relationships for some matching rules. The stability and efficiency of a partition depends on the matching rule φ. The results are stated under various membership property rights axioms. It is shown that in a world where agents can freely exit from and enter coalitions, whenever the matching rule is individually rational and Pareto optimal, the set of φ-stable and φ-efficient partitions coincide and it is unique: the grand coalition. Then we define a weaker version of stability and efficiency, namely specific to a given preference profile and find some negative results for stable matching rules.     

A convolution algorithm for calculating coefficients of jensen's bivariate f distribution

An algorithm for computing probabilities from Jensen's Bivariate F Distribution was given by McAllister, Lee and Holland (1981). One portion of their algorithm involves the calculation of coefficients that require summing over all nonnegative integer partitions of 0,1,2,…,N of size r. Presented here is an alternative method for generating the coefficients by successive convolutions which significantly reduces computation time.     

Common Knowledge with Monotone Statistics

When individual statistics are aggregated through a strictly monotone function to an aggregate statistic, common knowledge of the value of the aggregate statistic does not imply, in general, that the individual statistics are either equal or constant. This paper discusses circumstances where constancy and equality both hold. The first case arises when partitions are independently drawn, and each individual's information is determined by their own partition and some public signal. In this case common knowledge of the value of the aggregator function implies (with probability one) that the individual statistics are constant, so that in the case where the individual statistics have the same expected value, they must all be equal. The second circumstance is where private statistics are related: affiliation of individual statistics and a lattice condition imply that the individual statistics are equal when the value of the aggregate statistic is common knowledge.