You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Sufficiency, Prediction and Extreme Models
Steffen L. Lauritzen
Scandinavian Journal of Statistics
Vol. 1, No. 3 (1974), pp. 128-134
Published by: Wiley on behalf of Board of the Foundation of the Scandinavian Journal of Statistics
Stable URL: http://www.jstor.org/stable/4615564
Page Count: 7
You can always find the topics here!Topics: Maximum likelihood estimation, Statistical models, Statism, Statistics, Probabilities, Modeling, Statistical theories, Topological theorems, Stochastic processes, Markov chains
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
A modified concept of sufficiency, relevant in connection with statistical analysis of stochastic processes, is defined and its basic properties investigated. A method of prediction that applies when the probability structure is partly unknown is introduced and the method is shown to possess certain important invariance properties. The concept of an extreme model is defined and its probabilistic and statistical properties discussed. Existence of maximum likelihood estimators and predictors is established under weak regularity assumptions. For technical convenience, only discrete-valued stochastic processes are considered throughout the paper.
Scandinavian Journal of Statistics © 1974 Board of the Foundation of the Scandinavian Journal of Statistics