Geary on inference in multiple regression and on closeness and the Taxi problem
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This paper deals with some minor aspects of Roy Geary's work. Two areas are selected for discussion ? (a) his work with Leser on "paradoxical" situations in multiple regression and (b) his work on estimation of the unknown upper bound, N, of a uniform distribution, based on a sample of n values from that distribution. This work is explained, expanded and evaluated. The concept of "paradoxes" in multiple regression is slightly extended and applied to the case of estimating means in a multinomial situation with a known covariance matrix. Geary's estimator of N is compared with several other estimators, on the basis, inter alia, of mean squared errors, in both the cases of a continuous distribution and a discrete distribution sampling without replacement. In the latter case, a "large N minimum mean squared error" estimator is derived and assessed.
Publisher:Economic & Social Research Institute
Type of material:Journal Article
Availability:Full text available