Fundamental Concepts in Parametric Inference

  • Mayer Alvo
  • Philip L. H. Yu
Part of the Springer Series in the Data Sciences book series (SSDS)


In this chapter we review some terminology and basic concepts in probability and classical statistical inference which provide the notation and fundamental background to be used throughout this book. In the section on probability we describe some basic notions and list some common distributions along with their mean, variance, skewness, and kurtosis. We also describe various modes of convergence and end with central limit theorems. In the section on statistical inference, we begin with the subjects of estimation and hypothesis testing and proceed with the notions of contiguity and composite likelihood.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mayer Alvo
    • 1
  • Philip L. H. Yu
    • 2
  1. 1.Department of Mathematics and StatisticsUniversity of OttawaOttawaCanada
  2. 2.Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongChina

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