title: Statistical analysis of marginal count failure data.
abstract: Manufacturers want to assess the quality and reliability of their products. Specifically, they want to know the exact number of failures from the sales transacted during a particular month. Information available today is sometimes incomplete as many companies analyze their failure data simply comparing sales for a total month from a particular department with the total number of claims registered for that given month. This information--called marginal count data--is, thus, incomplete as it does not give the exact number of failures of the specific products that were sold in a particular month. In this paper we discuss nonparametric estimation of the mean numbers of failures for repairable products and the failure probabilities for nonrepairable products. We present a nonhomogeneous Poisson process model for repairable products and a multinomial model and its Poisson approximation for nonrepairable products. A numerical example is given and a simulation is carried out to evaluate the proposed methods of estimating failure probabilities under a number of possible situations.
authors.affiliation: Graduate School of Information Systems, University of Electro-Communications, Chofu, Tokyo 182-8585, Japan. karim@se.uec.ac.jp
authors.forename: M R
authors.initials: MR
authors.lastname: Karim
authors.forename: W
authors.initials: W
authors.lastname: Yamamoto
authors.forename: K
authors.initials: K
authors.lastname: Suzuki
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D019544:Equipment Failure Analysis; D015233:Models, Statistical; D016012:Poisson Distribution; D014481:United States