Summary
This paper reviews the development of the inverse Gaussian distribution and of statistical methods based upon it from the paper of Schrödinger (1915) to the present (1978). After summarizing the properties of the distribution, the paper presents tests of hypotheses, estimation, confidence intervals, regression and “analysis of variance” based upon the inverse Gaussian. its potential role in reliability work is discussed and work on Bayesian statistics is reviewed briefly. An extensive set of references to the distribution is given.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association.Minimum variance unbiased estimates of the inverse Gaussian distribution function for all possible cases are given. A direct relationship is established between its density function and the normal density function, which throws more light on its salient features and possibly on its application in statistical inference. It is shown that the estimates are very similar in nature to those of the normal distribution and can be evaluated from the normal and Student's t distribution tables.
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