TY - JOUR
T1 - Inference for Reliability and Stress-Strength for a Scaled Burr Type X Distribution
AU - Surles, J. G.
AU - Padgett, W. J.
N1 - Funding Information:
We would like to thank the referees for their insightful and helpful comments and suggestions. This research was partially supported by the National Science Foundation under grant numbers DMS-9503104 and DMS-9877107 to the University of South Carolina.
PY - 2001
Y1 - 2001
N2 - Inference for R = P(Y < X) is considered when X and Y are independently distributed as scaled Burr type X random variables. Under this model, exact inference procedures for R cannot be found. Hence, based on the expected Fisher information matrix which is derived here, asymptotic inference procedures for R and other general functions of the parameters are developed. A bootstrap method to estimate variance for the maximum likelihood estimators is also discussed. To illustrate these techniques, an example using carbon fiber strength data is given. Simulations to assess the effectiveness of these techniques, as well as other concerns, are presented.
AB - Inference for R = P(Y < X) is considered when X and Y are independently distributed as scaled Burr type X random variables. Under this model, exact inference procedures for R cannot be found. Hence, based on the expected Fisher information matrix which is derived here, asymptotic inference procedures for R and other general functions of the parameters are developed. A bootstrap method to estimate variance for the maximum likelihood estimators is also discussed. To illustrate these techniques, an example using carbon fiber strength data is given. Simulations to assess the effectiveness of these techniques, as well as other concerns, are presented.
KW - Burr distributions
KW - Carbon composite materials
KW - Fisher information
KW - Maximum likelihood
KW - Stress-strength model
UR - http://www.scopus.com/inward/record.url?scp=0035377893&partnerID=8YFLogxK
U2 - 10.1023/A:1011352923990
DO - 10.1023/A:1011352923990
M3 - Article
C2 - 11458657
AN - SCOPUS:0035377893
SN - 1380-7870
VL - 7
SP - 187
EP - 200
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 2
ER -