Variability, Information, and Prediction

  • Bertrand Clarke
  • Ernest Fokoué
  • Hao Helen Zhang
Part of the Springer Series in Statistics book series (SSS)

Introductory statistics courses often start with summary statistics, then develop a notion of probability, and finally turn to parametric models – mostly the normal – for inference. By the end of the course, the student has seen estimation and hypothesis testing for means, proportions, ANOVA, and maybe linear regression. This is a good approach for a first encounter with statistical thinking. The student who goes on takes a familiar series of courses: survey sampling, regression, Bayesian inference, multivariate analysis, nonparametrics and so forth, up to the crowning glories of decision theory, measure theory, and asymptotics. In aggregate, these courses develop a view of statistics that continues to provide insights and challenges.


Nonparametric Regression Edgeworth Expansion Pivotal Quantity List Search Hammersley Point 
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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  • Bertrand Clarke
    • 1
  • Ernest Fokoué
    • 2
  • Hao Helen Zhang
    • 3
  1. 1.University of MiamiMiamiCanada
  2. 2.Department of Science & MathematicsKettering UniversityFlintUSA
  3. 3.Department of StatisticsNorth Carolina State University Program in Statistical GeneticsRaleighUSA

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