1. Bayes prediction results for the inverse gaussian distribution utilizing guess values of parameters
- Author
-
Satyanshu K. Upadhyay, R. Agrawal, and Umesh Singh
- Subjects
Class (set theory) ,Basis (linear algebra) ,Sample (statistics) ,Condensed Matter Physics ,Atomic and Molecular Physics, and Optics ,Surfaces, Coatings and Films ,Electronic, Optical and Magnetic Materials ,Set (abstract data type) ,Inverse Gaussian distribution ,symbols.namesake ,Bayes' theorem ,Distribution (mathematics) ,Statistics ,symbols ,Applied mathematics ,Electrical and Electronic Engineering ,Safety, Risk, Reliability and Quality ,Constant (mathematics) ,Mathematics - Abstract
The paper deals with the problem of predicting, on the basis of an observed sample from an inverse Gaussian distribution, a single future observation; as well as the mean of a set of future observations from the same distribution when guess values of the parameters are available. Using the general class of a prior which places a constant weight on the guess values, the paper derives the predictive p.d.f. and hence the prediction limits. The behaviour of the proposed limits as compared with the Padgett approximate classical limits [W. J. Padgett, J. Statist. Comput. Simul. 14 , 291–299 (1982)] is studied using an example. The proposed limits are found to be better than the classical limits.
- Published
- 1994