Logicist statistics. I. Models and modeling
A. P. Dempster
Source: Statist. Sci. Volume 13, Number 3 (1998), 248-276.
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.
Primary Subjects:
62A99
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
Full-text: Open access
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Permanent link to this document: http://projecteuclid.org/euclid.ss/1028905887
Mathematical Reviews number (MathSciNet):
MR1665717
Digital Object Identifier: doi:10.1214/ss/1028905887
Zentralblatt MATH identifier:
01963442
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