Approximations for a fixed sample size selection procedure for negative binomial populations

1991 ◽  
Vol 20 (5-6) ◽  
pp. 1767-1776 ◽  
Author(s):  
Madhuri S. Mulekar ◽  
Linda J. Young
Keyword(s):  
Sample Size ◽  
Negative Binomial ◽  
Selection Procedure ◽  
Size Selection ◽  
Fixed Sample Size ◽  
Fixed Sample ◽  
Binomial Populations

Stopping rules for proofreading

1989 ◽  
Vol 26 (02) ◽  
pp. 304-313 ◽  
Author(s):  
T. S. Ferguson ◽  
J. P. Hardwick
Keyword(s):  
Sample Size ◽  
Optimal Stopping ◽  
Random Number ◽  
Poisson Model ◽  
Stopping Rule ◽  
Stopping Rules ◽  
Binomial Model ◽  
Fixed Sample Size ◽  
Fixed Sample ◽  
Optimal Stopping Rule

A manuscript with an unknown random numberMof misprints is subjected to a series of proofreadings in an effort to detect and correct the misprints. On thenthproofreading, each remaining misprint is detected independently with probabilitypn– 1. Each proofreading costs an amountCP> 0, and if one stops afternproofreadings, each misprint overlooked costs an amountcn> 0. Two models are treated based on the distribution ofM.In the Poisson model, the optimal stopping rule is seen to be a fixed sample size rule. In the binomial model, the myopic rule is optimal in many important cases. A generalization is made to problems in which individual misprints may have distinct probabilities of detection and distinct overlook costs.

Fixed-Sample Size Sampling Plan

2008 ◽  
pp. 1464-1464
Author(s):  
E. S. Krafsur ◽  
R. D. Moon ◽  
R. Albajes ◽  
O. Alomar ◽  
Elisabetta Chiappini ◽  
...  
Keyword(s):  
Sample Size ◽  
Sampling Plan ◽  
Fixed Sample Size ◽  
Fixed Sample

New exact Bayesian prediction of the range for the exponential lifetime based on fixed and random sample sizes

2017 ◽  
Vol 6 (1-2) ◽  
pp. 169
Author(s):  
A. H. Abd Ellah
Keyword(s):  
Sample Size ◽  
Random Sample ◽  
Real Data ◽  
Predictive Distribution ◽  
Data Sets ◽  
Life Testing ◽  
Random Sample Size ◽  
Fixed Sample Size ◽  
Fixed Sample ◽  
Special Case

We consider the problem of predictive interval for the range of the future observations from an exponential distribution. Two cases are considered, (1) Fixed sample size (FSS). (2) Random sample size (RSS). Further, I derive the predictive function for both FSS and RSS in closely forms. Random sample size is appeared in many application of life testing. Fixed sample size is a special case from the case of random sample size. Illustrative examples are given. Factors of the predictive distribution are given. A comparison in savings is made with the above method. To show the applications of our results, we present some simulation experiments. Finally, we apply our results to some real data sets in life testing.

scholarly journals Modified chain regression type estimator for population mean in the presence of non- response

2015 ◽  
Vol 3 (2) ◽  
pp. 165
Author(s):  
Brij Khare ◽  
Habib Rehman
Keyword(s):  
Sample Size ◽  
Empirical Studies ◽  
Fixed Cost ◽  
Second Phase ◽  
Population Mean ◽  
Fixed Sample Size ◽  
Fixed Sample ◽  
Phase Sample ◽  
Available Information

<p>A modified chain regression type estimator for  population mean in the presence of non-response have been proposed replacing Hansen &amp; Hurwitz (1946) estimator for population mean by Searls (1964) type improved estimator and using Hansen &amp; Hurwitz (1946) estimator for  based on available information comparing to the study character  in the second phase sample. The expressions for MSE for fixed sample size   and also fixed cost   have been obtained. The empirical studies show that the proposed estimator is more efficient than the relevant estimators in the case of fixed sample size as well as for fixed cost.</p>

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