A note on sufficiency and information loss
β Scribed by Paul Kabaila
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 230 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
β¦ Synopsis
We adopt the frequentist approach to statistical inference together with a recognition of the validity of conditional inference as described by Cox (1958). We describe a type of model which shows clearly the nature of a form of information which may be lost due to basing inference solely on a sufficient statistic.
π SIMILAR VOLUMES
Under the general Gauss-Markov model y = X/~ + e, linear transformations Fy of the observable random vector y which preserve enough information to release the best linear unbiased predictor/estimator of E as a linear function of Fy are considered. Transformations with this property are called 'linea
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