ABSTRACT
In this chapter, we discuss some selected topics in statistics. These topics include basic statistical concepts, § 2.1 and § 2.2, estimation, § 2.3, hypothesis testing, § 2.4, linear and generalized linear models, § 2.5, mixture models and EM algorithm, § 2.6, Bayesian analysis, § 2.7, samplers for Markov chain Monte Carlo simulations, § 2.8, feature selection in small-n-large-p models, § 2.9, and variable selection criteria for small-n-large-p models, § 2.10. In the discussion of these topics, we choose to elucidate the statistical principles rather than to dwell on the details so that the reader can have a deeper understanding of the essence of statistical analysis.
