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Statistics (ii): Classical Estimation and Hypothesis Testing

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Part of the Statistics for Biology and Health book series (SBH)

Abstract

An introduction to classical estimation and hypothesis testing procedures was given in Chapter 3. However, the theoretical aspects of these procedures were not then discussed. In particular, optimality aspects of the two procedures were not addressed. We now give a brief introduction to both classical estimation theory and classical hypothesis testing theory, focusing on optimality aspects of these theories. Extensive treatments are given respectively in Lehmann (1991) and Lehmann (1986).

Keywords

  • Null Hypothesis
  • Alternative Hypothesis
  • Maximum Likelihood Estimator
  • Unbiased Estimator
  • Classical Estimation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-1-4757-3247-4_8
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© 2001 Springer Science+Business Media New York

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Ewens, W.J., Grant, G.R. (2001). Statistics (ii): Classical Estimation and Hypothesis Testing. In: Statistical Methods in Bioinformatics. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3247-4_8

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  • DOI: https://doi.org/10.1007/978-1-4757-3247-4_8

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4757-3249-8

  • Online ISBN: 978-1-4757-3247-4

  • eBook Packages: Springer Book Archive