- Statist. Sci.
- Volume 13, Number 3 (1998), 248-276.
Logicist statistics. I. Models and modeling
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.
Statist. Sci., Volume 13, Number 3 (1998), 248-276.
First available in Project Euclid: 9 August 2002
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62A99: None of the above, but in this section
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
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