A Practitioner's Guide to Analyzing Reliability Experiments with Random Blocks and Subsampling
被引:24
作者:
Kensler, Jennifer L. K.
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机构:
Air Force Inst Technol, Sci Test & Anal Tech Test & Evaluat Ctr Excellenc, Wright Patterson AFB, OH 45433 USAAir Force Inst Technol, Sci Test & Anal Tech Test & Evaluat Ctr Excellenc, Wright Patterson AFB, OH 45433 USA
Kensler, Jennifer L. K.
[1
]
Freeman, Laura J.
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h-index: 0
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Inst Def Anal, Alexandria, VA USAAir Force Inst Technol, Sci Test & Anal Tech Test & Evaluat Ctr Excellenc, Wright Patterson AFB, OH 45433 USA
Freeman, Laura J.
[2
]
Vining, G. Geoffrey
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Virginia Tech, Dept Stat, Blacksburg, VA USAAir Force Inst Technol, Sci Test & Anal Tech Test & Evaluat Ctr Excellenc, Wright Patterson AFB, OH 45433 USA
Vining, G. Geoffrey
[3
]
机构:
[1] Air Force Inst Technol, Sci Test & Anal Tech Test & Evaluat Ctr Excellenc, Wright Patterson AFB, OH 45433 USA
maximum likelihood estimation;
nonnormal data;
regression estimation;
reliability estimation;
restrictions on randomization;
STANDARD-DEVIATION;
WEIBULL;
BIAS;
D O I:
10.1080/08982112.2014.887101
中图分类号:
T [工业技术];
学科分类号:
08 ;
摘要:
Reliability experiments provide important information regarding the life of a product, including how various factors affect product life. Current analyses of reliability data usually assume a completely randomized design. However, reliability experiments frequently contain subsampling, which represents a restriction on randomization. A typical experiment involves applying treatments to test stands, with several items placed on each test stand. In addition, raw materials used in experiments are often produced in batches, leading to a design involving blocks. This article proposes a method using Weibull regression for analyzing reliability experiments with random blocks and subsampling. An illustration of the method is provided.