On generalized multi‐state start‐up demonstration tests

Xian Zhao, Ge Sun, Weijuan Xie and Cong Lin

Applied Stochastic Models in Business and Industry, 2015, vol. 31, issue 3, 325-338

Abstract: In view of the fact that start‐up results of products are usually multi‐state, based on the traditional two‐state start‐up demonstration tests, such as consecutive successes total failures (CSTF), total successes consecutive failures (TSCF), consecutive successes consecutive failures (CSCF) and total successes total failures (TSTF), four multi‐state start‐up demonstration tests are proposed in this paper. By using finite Markov chain imbedding approach, the acceptance and rejection probabilities, the probability mass function, the distribution function and the expectation of the number of the start‐up tests to be terminated are given. We also provide a procedure to select the optimal parameter values. Besides, the estimations of possibly unknown probabilities are given by using maximum likelihood estimation. Finally, a numerical example that contains two tables is given to illustrate the advantages of multi‐state start‐up demonstration tests. The first table is presented to illustrate that the multi‐state start‐up demonstration tests are superior to two‐state start‐up demonstration tests. The second one is to illustrate that the four‐state models (proposed in this paper) are superior to CSTF and CS (1) CS (1,2) TF (proposed by Smith and Griffith) with the same values of α and β set by Smith and Griffith. Copyright © 2014 John Wiley & Sons, Ltd.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.1002/asmb.2041

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:31:y:2015:i:3:p:325-338

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().