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Some inference problems for inverse Gaussian data


Some inference problems for inverse Gaussian data

Sen, Debaraj (2004) Some inference problems for inverse Gaussian data. PhD thesis, Concordia University.

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This thesis deals with some inference problems related with inverse Gaussian models. In Chapter 2, we investigate the properties of an estimator of mean of an inverse Gaussian population that is motivated from finite population sampling [see Chaubey and Dwivedi (1982)]. We demonstrate that when the coefficient of variation is large, the new estimator performs much better than the usual estimator of the mean, namely the sample average. In Chapter 3, we provide simple approximating formulae for the first four moments of the new estimator which may be used to approximate its finite sample distribution. Chapter 4 investigates some properties of the preliminary test estimator for mean of an IG population. Such an estimator was proposed and studied in detail in the statistical literature for Gaussian and other distributions [see Bancroft (1944), Ahmed (1992)]. Our conclusions for the inverse Gaussian model are similar to the case for Gaussian model. Next, in Chapter 5, overlap measures for two inverse Gaussian densities are studied on the lines of Mulekar and Mishra (1994, 2000)

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (PhD)
Authors:Sen, Debaraj
Pagination:xii, 111 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Ph. D.
Thesis Supervisor(s):Chaubey, Yogendra P
Identification Number:QA 175.32 I54S46 2004
ID Code:8296
Deposited By: Concordia University Library
Deposited On:18 Aug 2011 18:21
Last Modified:13 Jul 2020 20:03
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