Nonparametric Bayes Estimation of Distribution Functions and the Study of Probability Density Estimates.
Abstract
In work under this grant, major results were obtained in the four broad areas of survival analysis and life testing, probability density estimates and laws of large numbers, estimation after testing, and robustness and distribution-free procedures. In particular, nonparametric estimators of the failure rate function and survival probability were developed under the assumption of increasing failure rate using both maximum likelihood and Bayesian approaches. These particular results have attracted wide attention due to their generality and applicability in survival analysis and reliability estimation from arbitrarily right-censored data. Also, consistency results for both univariate and multivariate kernel estimates for probability density functions and regression functions were obtained using techniques and results of function-space probability theory. Sequential procedures were developed and analyzed which provided interval estimators of the parameter of interest after testing certain hypotheses. Various robustness and nonparametric methods for incomplete samples and broken samples were also studied. Thus, maintenance policies and development of new, more reliable, equipment may be formulated using statistical procedures and theory from these results. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 30, 1980
- Accession Number
- ADA088250
Entities
People
- L. J. Wei
- R. L. Taylor
- William J. Padgett
Organizations
- University of South Carolina