Principles of Statistical Inference

Cox, D. R. (2006). Principles of Statistical Inference. Cambridge University Press.

Type

Book

Intellectual base

Textbook

Year

2006

DOI

10.1017/cbo9780511813559

ISBN

9780521866736

Link

https://doi.org/10.1017/CBO9780511813559

Cited from

statistics_experimental_design

Cited by

2 citations across 2 artifacts.

Primes

• Distributional Assumption
  • The structural insight is robust: all modeling—statistical inference, machine learning, risk assessment, causal inference, time-series forecasting—rests on distributional assumptions that shape which questions can be answered efficiently and which remain computationally intractable, as Cox (2006) emphasizes in his survey of foundational principles of statistical inference.
• Statistical Inference
  • Statistical inference, as Cox (2006) frames it, is the reasoning by which observations on a finite sample are used to draw conclusions about an underlying population, process, hypothesis, or mechanism, with explicit accounting for the uncertainty introduced by sampling variability and model assumptions.

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