All of Statistics

Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.

Type

Book

Intellectual base

Textbook

Year

2004

DOI

10.1007/978-0-387-21736-9

ISBN

9781441923226

Link

https://doi.org/10.1007/978-0-387-21736-9

Cited from

statistics_experimental_design

Cited by

3 citations across 3 artifacts.

Primes

• Distributional Assumption
  • Probability theory is the formal framework for reasoning about uncertainty in general; distributional assumption is a specific structural choice within that framework—the decision to restrict the infinite-dimensional space of all possible distributions to a finite-parameter family (e.g., "normal with unknown mean and variance" rather than "any distribution whatsoever"), a layered architecture Wasserman (2004) lays out clearly in his comprehensive overview of probability and statistical inference.

Domain-specific

• Statistic
  • The abstraction covers non-scalar statistics: the empirical distribution function \(F_n(t)\), an entire vector of order statistics, a contingency table, or a covariance matrix.
• Variance
  • Population variance is E[(X-E[X]) squared], with sample variants estimating it from observed deviations.

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