TY - JOUR
T1 - A new goodness-of-fit test for censored data with an application in monitoring processes
AU - Castro-Kuriss, Claudia
AU - Kelmansky, Diana M.
AU - Leiva, Victor
AU - Martinez, Elena J.
N1 - Funding Information:
The authors wish to thank the Editor and referees for their helpful comments that aided in improving this article. This study was partially supported by PICT 21407 from ANPCYT, X094 from the Universidad de Buenos Aires, and PIP5505 from CONICET grants, Argentina and by FONDECYT 1080326 and DIPUV 29-2006 grants, Chile.
PY - 2009/6
Y1 - 2009/6
N2 - In this article, we propose a new goodness-of-fit test for Type I or Type II censored samples from a completely specified distribution. This test is a generalization of Michael's test for censored data, which is based on the empirical distribution and a variance stabilizing transformation. Using Monte Carlo methods, the distributions of the test statistics are analyzed under the null hypothesis. Tables of quantiles of these statistics are also provided. The power of the proposed test is studied and compared to that of other well-known tests also using simulation. The proposed test is more powerful in most of the considered cases. Acceptance regions for the PP, QQ, and Michael's stabilized probability plots are derived, which enable one to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an application in quality control is presented as illustration.
AB - In this article, we propose a new goodness-of-fit test for Type I or Type II censored samples from a completely specified distribution. This test is a generalization of Michael's test for censored data, which is based on the empirical distribution and a variance stabilizing transformation. Using Monte Carlo methods, the distributions of the test statistics are analyzed under the null hypothesis. Tables of quantiles of these statistics are also provided. The power of the proposed test is studied and compared to that of other well-known tests also using simulation. The proposed test is more powerful in most of the considered cases. Acceptance regions for the PP, QQ, and Michael's stabilized probability plots are derived, which enable one to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an application in quality control is presented as illustration.
KW - And stabilized probability plots
KW - Kolmogorov-Smirnov test
KW - Monte Carlo simulation
KW - PP
KW - QQ
KW - Quality control
UR - http://www.scopus.com/inward/record.url?scp=70449636550&partnerID=8YFLogxK
U2 - 10.1080/03610910902833488
DO - 10.1080/03610910902833488
M3 - Article
AN - SCOPUS:70449636550
SN - 0361-0918
VL - 38
SP - 1161
EP - 1177
JO - Communications in Statistics: Simulation and Computation
JF - Communications in Statistics: Simulation and Computation
IS - 6
ER -