Asymptotic sampling distribution of inverse coefficient-of-variation and its applications

1994 ◽  
Vol 43 (4) ◽  
pp. 630-633 ◽  
Author(s):  
K.K. Sharma ◽  
H. Krishna
Keyword(s):  
Coefficient Of Variation ◽  
Sampling Distribution ◽  
Asymptotic Sampling ◽  
Inverse Coefficient

scholarly journals Asymptotic Inference for Optimal Rerandomization Designs

2021 ◽  
Vol 1 (1) ◽  
pp. 49-58
Author(s):  
Mårten Schultzberg ◽  
Per Johansson
Keyword(s):  
Experimental Design ◽  
Normal Distribution ◽  
Mahalanobis Distance ◽  
Design Strategy ◽  
Sampling Distribution ◽  
Optimal Designs ◽  
Asymptotic Inference ◽  
Optimal Allocations ◽  
Asymptotic Sampling

AbstractRecently a computational-based experimental design strategy called rerandomization has been proposed as an alternative or complement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to a balance criterion, and then randomize within the set of acceptable allocations. Based on the Mahalanobis distance criterion for balancing the covariates, we show that asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations. Finally, we show that for the optimal and near optimal designs, the quite complex asymptotic sampling distribution derived by Li et al. (2018), is well approximated by a normal distribution.

scholarly journals Closed-Form Asymptotic Sampling Distributions under the Coalescent with Recombination for an Arbitrary Number of Loci

2012 ◽  
Vol 44 (2) ◽  
pp. 391-407 ◽  
Author(s):  
Anand Bhaskar ◽  
Yun S. Song
Keyword(s):  
Asymptotic Expansion ◽  
Closed Form ◽  
Recombination Rate ◽  
Functional Form ◽  
Asymptotic Series ◽  
Sampling Distribution ◽  
Challenging Problem ◽  
Sampling Distributions ◽  
Asymptotic Sampling ◽  
New Framework

Obtaining a closed-form sampling distribution for the coalescent with recombination is a challenging problem. In the case of two loci, a new framework based on an asymptotic series has recently been developed to derive closed-form results when the recombination rate is moderate to large. In this paper, an arbitrary number of loci is considered and combinatorial approaches are employed to find closed-form expressions for the first couple of terms in an asymptotic expansion of the multi-locus sampling distribution. These expressions are universal in the sense that their functional form in terms of the marginal one-locus distributions applies to all finite- and infinite-alleles models of mutation.

scholarly journals Closed-Form Asymptotic Sampling Distributions under the Coalescent with Recombination for an Arbitrary Number of Loci

2012 ◽  
Vol 44 (02) ◽  
pp. 391-407 ◽  
Author(s):  
Anand Bhaskar ◽  
Yun S. Song
Keyword(s):  
Asymptotic Expansion ◽  
Closed Form ◽  
Recombination Rate ◽  
Functional Form ◽  
Asymptotic Series ◽  
Sampling Distribution ◽  
Challenging Problem ◽  
Sampling Distributions ◽  
Asymptotic Sampling ◽  
New Framework

Obtaining a closed-form sampling distribution for the coalescent with recombination is a challenging problem. In the case of two loci, a new framework based on an asymptotic series has recently been developed to derive closed-form results when the recombination rate is moderate to large. In this paper, an arbitrary number of loci is considered and combinatorial approaches are employed to find closed-form expressions for the first couple of terms in an asymptotic expansion of the multi-locus sampling distribution. These expressions are universal in the sense that their functional form in terms of the marginal one-locus distributions applies to all finite- and infinite-alleles models of mutation.

scholarly journals Three-Stage Sequential Estimation of the Inverse Coefficient of Variation of the Normal Distribution

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 69 ◽  
Author(s):  
Ali Yousef ◽  
Hosny Hamdy
Keyword(s):  
Confidence Interval ◽  
Normal Distribution ◽  
Coefficient Of Variation ◽  
Sequential Estimation ◽  
Point Estimation ◽  
Squared Error Loss Function ◽  
Fixed Sample ◽  
Sampling Cost ◽  
Better Than ◽  
Inverse Coefficient

This paper sequentially estimates the inverse coefficient of variation of the normal distribution using Hall’s three-stage procedure. We find theorems that facilitate finding a confidence interval for the inverse coefficient of variation that has pre-determined width and coverage probability. We also discuss the sensitivity of the constructed confidence interval to detect a possible shift in the inverse coefficient of variation. Finally, we find the asymptotic regret encountered in point estimation of the inverse coefficient of variation under the squared-error loss function with linear sampling cost. The asymptotic regret provides negative values, which indicate that the three-stage sampling does better than the optimal fixed sample size had the population inverse coefficient of variation been known.

Restudy on the Sampling Distribution of Coefficient of Variation for a Normal Population

2012 ◽  
Vol 151 ◽  
pp. 678-684
Author(s):  
Jian Mei Xu ◽  
Lun Bai
Keyword(s):  
Coefficient Of Variation ◽  
Normal Population ◽  
Sampling Distribution ◽  
Affecting Factors ◽  
Distribution Shape ◽  
Mean And Variance ◽  
The Mean

The sampling distribution of the coefficient of variation for a normal population is theoretically deduced, as well as its mean and variance. The conditions under which the mean and variance of the sampling distribution exist are studied, and the affecting factors on the sampling distribution shape are discussed.

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