The Scaled Uniform Model Revisited

Research output: Contribution to journalArticlepeer-review

Abstract

Sufficiency, conditionality, and invariance are basic principles of statistical inference. Current mathematical statistics courses do not devote much teaching time to these classical principles, and even ignore the latter two, in order to teach modern methods. However, being the philosophical cornerstones of statistical inference, a minimal understanding of these principles should be part of any curriculum in statistics. The scaled uniform model is used here to demonstrate the importance and usefulness of the conditionality principle, which is probably the most basic and less familiar among the three.

Original languageAmerican English
Pages (from-to)98-100
Number of pages3
JournalAmerican Statistician
Volume74
Issue number1
DOIs
StatePublished - 2 Jan 2020

Keywords

  • Conditionality principle
  • Minimal sufficient
  • Scale family
  • Uniformly minimum variance unbiased estimator

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • General Mathematics

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