Decision Theory Applied to Selecting the Winners, Ranking, and Classification | Zendy

Nicholas T. Longford | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Decision Theory Applied to Selecting the Winners, Ranking, and Classification

Author(s) -

Nicholas T. Longford

Publication year - 2016

Publication title -

journal of educational and behavioral statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.066

H-Index - 59

eISSN - 1935-1054

pISSN - 1076-9986

DOI - 10.3102/1076998616644991

Subject(s) - ranking (information retrieval) , computer science , variable (mathematics) , set (abstract data type) , decision theory , value (mathematics) , machine learning , data mining , statistics , mathematics , artificial intelligence , mathematical analysis , programming language

We address the problem of selecting the best of a set of units based on a criterion variable, when its value is recorded for every unit subject to estimation, measurement, or another source of error. The solution is constructed in a decisiontheoretical framework, incorporating the consequences (ramifications) of the various kinds of error that can be committed. The related problems of classifying the units to a small number of groups and ranking them are solved by a similar approach. An application is presented involving retention rates in the undergraduate courses of a university

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research