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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/3303

Title: Stopping rules and the likelihood function
Authors: LINDSEY, James
Issue Date: 1997
Publisher: ELSEVIER SCIENCE BV
Citation: JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 59(1). p. 167-177
Abstract: Conditions are investigated whereby the likelihood function contains all of the relevant information from the data necessary for inference, with no knowledge of the sample design. Certain designs which result in the same reported likelihood for the final stopped experiment in fact have different underlying likelihood functions. For a likelihood function to be valid, it must, at least, contain the minimum information necessary for the experiment to be performable; this is shown to be the minimal filtration of the experiment.
Notes: LIMBURGS UNIV CTR,B-3590 DIEPENBEEK,BELGIUM.
URI: http://hdl.handle.net/1942/3303
DOI: 10.1016/S0378-3758(96)00096-1
ISI #: A1997WP62900012
ISSN: 0378-3758
Type: Journal Contribution
Appears in Collections: Research publications

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