Principles of Statistical Inference¶
Cox, D. R. (2006). Principles of Statistical Inference. Cambridge University Press.
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.
Supported in partVerified against the publisher's abstract
The publisher abstract confirms Cox (2006) is a survey of foundational statistical inference, but says nothing about distributional assumptions determining which questions are computationally tractable.
“In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years.”
- 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.
Supported in partVerified against the publisher's abstract
The abstract confirms only that Cox's book is a survey of statistical inference and of uncertainty in conclusions from analyses; it does not state the sample-to-population definition or the four-way list the claim attributes to Cox.
“In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of 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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