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Optimal sampling for repeated binary measurements
Author(s) -
Quintana Fernando A.,
Müller Peter
Publication year - 2004
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316000
Subject(s) - sequence (biology) , sampling (signal processing) , markov chain , binary number , pseudorandom binary sequence , mathematics , term (time) , dirichlet distribution , nonparametric statistics , computer science , mathematical optimization , statistics , arithmetic , mathematical analysis , genetics , physics , filter (signal processing) , quantum mechanics , computer vision , biology , boundary value problem
The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k . The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.