General Exponential Models for Discrete Observations

Abstract

A class of models generalizing exponential families is defined via the algebraic structure of the sufficient statistics. The maximum likelihood estimate for the unknown parameter is shown to exist and be unique. The sequence of sufficient statistics from successive repetitions of experiments corresponding to a general exponential model is shown to form an extreme family of Markov chains as defined by Lauritzen (1974).

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1974
Accession Number
AD0783080

Entities

People

  • Steffen L. Lauritzen

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Data Science
  • Information Science
  • Markov Chains
  • Mathematics
  • Observation
  • Sequences
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.