{"id":524,"date":"2017-09-24T06:43:23","date_gmt":"2017-09-24T06:43:23","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=524"},"modified":"2017-09-24T06:43:55","modified_gmt":"2017-09-24T06:43:55","slug":"estimation-mean-inverse-gaussian-population-based-coefficient-variation","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/estimation-mean-inverse-gaussian-population-based-coefficient-variation\/","title":{"rendered":"Estimation of mean in an inverse Gaussian population based on the coefficient of variation"},"content":{"rendered":"
Srivastava (1974, 1980) and Chaubey and Dwivedi (1982) investigated some es-
\ntimators of mean of a normal population utilizing an estimate of the coefficient
\nof variation. However, the normal model may not hold for positive or positively
\nskewed data, hence an alternative model may have to be employed. This paper
\nuses the inverse Gaussian model for such data and extends the results of Chaubey
\nand Dwivedi (1982) for the normal population to similar analysis for the inverse
\nGaussian population. It is found that the new estimator may result in large gains
\nin efficiency over the sample mean for large values of the coefficient of variation.<\/p>\n