Biomaterial Database

Statistical analysis of marginal count failure data. 2001

M R Karim, and W Yamamoto, and K Suzuki

Graduate School of Information Systems, University of Electro-Communications, Chofu, Tokyo 182-8585, Japan. karim@se.uec.ac.jp

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.

UI	MeSH Term	Description	Entries
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D014481	United States	A country in NORTH AMERICA between CANADA and MEXICO.
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016012	Poisson Distribution	A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.	Distribution, Poisson
D019544	Equipment Failure Analysis	The evaluation of incidents involving the loss of function of a device. These evaluations are used for a variety of purposes such as to determine the failure rates, the causes of failures, costs of failures, and the reliability and maintainability of devices.	Materials Failure Analysis,Prosthesis Failure Analysis,Analysis, Equipment Failure,Analysis, Materials Failure,Analysis, Prosthesis Failure,Analyses, Equipment Failure,Analyses, Materials Failure,Analyses, Prosthesis Failure,Equipment Failure Analyses,Failure Analyses, Equipment,Failure Analyses, Materials,Failure Analyses, Prosthesis,Failure Analysis, Equipment,Failure Analysis, Materials,Failure Analysis, Prosthesis,Materials Failure Analyses,Prosthesis Failure Analyses