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Introduction to Probability Theory and Statistics

  • Gang Zheng
  • Yaning Yang
  • Xiaofeng Zhu
  • Robert C. Elston
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Basic probability theory and statistical models and procedures for the analysis of genetic studies are covered in Chap. 1. This chapter starts with an introduction to basic distribution theory and common distributions that are used in the book, including the uniform, multinomial, normal, t-, F-, Beta, Gamma, chi-squared and hypergeometric distributions. The basic distributions for order statistics are also given. Several types of stochastic convergence used in the book are summarized. Maximum likelihood estimation and its large sample properties are discussed. Various tests, including the efficient Score test, likelihood ratio test and Wald test, are studied with or without nuisance parameters. Multiple testing issues related to testing association with multiple genetic markers and related to hypothesis testing with an unknown genetic model are briefly reviewed. This chapter also covers the Delta method, the EM algorithm, basic concepts of sample size and power calculations, and asymptotic relative efficiency.

Keywords

Probability Density Function Maximum Likelihood Estimate Nuisance Parameter Multivariate Normal Distribution Joint Probability Density Function 
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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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Gang Zheng
    • 1
  • Yaning Yang
    • 2
  • Xiaofeng Zhu
    • 3
  • Robert C. Elston
    • 3
  1. 1.BethesdaUSA
  2. 2.School of Management, Dept. Statistics & FinanceUniversity of Science & Technology of ChinaHefeiPeople’s Republic of China
  3. 3.School of Medicine, Dept. Epidemiology & BiostatisticsCase Western Reserve UniversityClevelandUSA

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