Estimation of coefficient of variation in a weighted inverse Gaussian model
โ Scribed by Gupta, Ramesh C. ;Akman, Olcay
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 363 KB
- Volume
- 12
- Category
- Article
- ISSN
- 8755-0024
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โฆ Synopsis
The coefficient of variation is an important parameter in many physical, biological and medical sciences.
In this paper we study the estimation of the square of the coefficient of variation in a weighted inverse Gaussian model which is a mixture of the inverse Gaussian and the length biased inverse Gaussian distribution. This represents a rich family of distributions for different values of the mixing parameter and can be used for modelling various life testing situations. The maximum likelihood as well as the Bayes estimates of the parameters are obtained. These estimates are used to derive the estimates of the square of the coefficient of variation of the model under study. Several important data sets are analysed to illustrate the results.
๐ SIMILAR VOLUMES
## Summa y Following SEN & GEBI~ (1975) an estimator for the population mean on the current occasion is proposed. It has been shown that the estimator is more efficient than 'mean per unit estimate' for all kind of populations. The eetimatiors suggeated by SEN & GERIG (1976) and SEABM (1964) are ,
## Abstract The problem of estimating the common mean of two normal populations __N(ฯ, a1ฯ2__) and __N(ฯ, a2ฯ2__) where the coefficients of variation of two populations respectively, are known constants, on the basis of two independent random samples, one from each population, is considered. The mi