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Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology

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dc.contributor.author Minkoff, Alan S. en_US
dc.date.accessioned 2004-05-28T19:26:23Z
dc.date.available 2004-05-28T19:26:23Z
dc.date.issued 1981-02 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/5172
dc.description.abstract Evaluation of public programming currently tends toward plans that are set in advance of any sampling and adhered to throughout. Because increments in the knowledge profile during the course of an evaluation might beckon adjustment of the working procedure, fixed evaluation methodology may be cost-inefficient. It is desired to develop a methodology that is adaptive to changes in the knowledge profile. This might be most easily accomplished by borrowing ideas from some of the disciplines in which relevant problems occur. The most promising fields for this task include classical and Bayesian statistics, reliability theory, and dynamic programming. This paper reviews the techniques in classical statistics that seem most apt for handling the problem of adaptive changes in an evaluation to updated knowledge profiles, and considers the paths along which future research ought to be conducted. en_US
dc.format.extent 1744 bytes
dc.format.extent 2156379 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher Massachusetts Institute of Technology, Operations Research Center en_US
dc.relation.ispartofseries Operations Research Center Working Paper;OR 110-81 en_US
dc.title Preliminary Survey of Classical Statistical Techniques for Incorporation into Adaptive Evaluation Methodology en_US
dc.type Working Paper en_US


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