Statistical Science

Logicist statistics. I. Models and modeling

A. P. Dempster

Full-text: Open access

Abstract

Arguments are presented to support increased emphasis on logical aspects of formal methods of analysis, depending on probability in the sense of R. A. Fisher. Formulating probabilistic models that convey uncertain knowledge of objective phenomena and using such models for inductive reasoning are central activities of individuals that introduce limited but necessary subjectivity into science. Statistical models are classified into overlapping types called here empirical, stochastic and predictive, all drawing on a common mathematical theory of probability, and all facilitating statements with logical and epistemic content. Contexts in which these ideas are intended to apply are discussed via three major examples.

Article information

Source
Statist. Sci., Volume 13, Number 3 (1998), 248-276.

Dates
First available in Project Euclid: 9 August 2002

Permanent link to this document
https://projecteuclid.org/euclid.ss/1028905887

Digital Object Identifier
doi:10.1214/ss/1028905887

Mathematical Reviews number (MathSciNet)
MR1665717

Zentralblatt MATH identifier
1099.62501

Subjects
Primary: 62A99: None of the above, but in this section

Keywords
Logicism and proceduralism specificity of analysis formal subjective probability complementarity subjective and objective formal and informal empirical, stochastic and predictive models U.S. national census screening for chronic disease global climate change

Citation

Dempster, A. P. Logicist statistics. I. Models and modeling. Statist. Sci. 13 (1998), no. 3, 248--276. doi:10.1214/ss/1028905887. https://projecteuclid.org/euclid.ss/1028905887


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