TEXT SIZE

search for



CrossRef (0)
A class of accelerated sequential procedures with applications to estimation problems for some distributions useful in reliability theory
Communications for Statistical Applications and Methods 2021;28:563-582
Published online September 30, 2021
© 2021 Korean Statistical Society.

Neeraj Joshi1,a, Sudeep R. Bapatb, Ashish Kumar Shuklac

aDepartment of Statistics, University of Delhi, India;
bDepartment of Operations Management and Quantitative Techniques, Indian Institute of Management, India;
cDepartment of Statistics, Ramanujan College (University of Delhi), India
Correspondence to: 1 Department of Statistics, University of Delhi, Delhi-110007, India. E-mail:stats.joshi@gmail.com
Received May 7, 2021; Revised June 26, 2021; Accepted June 26, 2021.
 Abstract
This paper deals with developing a general class of accelerated sequential procedures and obtaining the associated second-order approximations for the expected sample size and ‘regret’ (difference between the risks of the proposed accelerated sequential procedure and the optimum fixed sample size procedure) function. We establish that the estimation problems based on various lifetime distributions can be tackled with the help of the proposed class of accelerated sequential procedures. Extensive simulation analysis is presented in support of the accuracy of our proposed methodology using the Pareto distribution and a real data set on carbon fibers is also analyzed to demonstrate the practical utility. We also provide the brief details of some other inferential problems which can be seen as the applications of the proposed class of accelerated sequential procedures.
Keywords : accelerated sequential, carbon fibers, inverse Gaussian, minimum risk, multivariate normal, negative exponential, normal, Pareto, regret, second-order approximations