Title page for ETD etd-05072010-102728


Type of Document Dissertation
Author Freeman, Laura June
Author's Email Address lamorgan@vt.edu
URN etd-05072010-102728
Title Statistical Methods for Reliability Data from Designed Experiments
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Vining, G. Geoffrey Committee Chair
Birch, Jeffrey B. Committee Member
Du, Pang Committee Member
Hong, Yili Committee Member
Kowalski, Scott M. Committee Member
Keywords
  • Random Effect
  • Nonlinear Mixed Models
  • Lifetime Data
  • Design of Experiments
  • Weibull Distribution
  • Weighted Least Squares
Date of Defense 2010-05-06
Availability restricted
Abstract
Product reliability is an important characteristic for all manufacturers, engineers and consumers. Industrial statisticians have been planning experiments for years to improve product quality and reliability. However, rarely do experts in the field of reliability have expertise in design of experiments (DOE) and the implications that experimental protocol have on data analysis. Additionally, statisticians who focus on DOE rarely work with reliability data. As a result, analysis methods for lifetime data for experimental designs that are more complex than a completely randomized design are extremely limited. This dissertation provides two new analysis methods for reliability data from life tests. We focus on data from a sub-sampling experimental design. The new analysis methods are illustrated on a popular reliability data set, which contains sub-sampling. Monte Carlo simulation studies evaluate the capabilities of the new modeling methods. Additionally, Monte Carlo simulation studies highlight the principles of experimental design in a reliability context. The dissertation provides multiple methods for statistical inference for the new analysis methods. Finally, implications for the reliability field are discussed, especially in future applications of the new analysis methods.

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