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Likelihood Ratio Testing under Measurement Errors

Composite Tests under Corrupted Data

Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université, 75005 Paris, France
Institute of Information Theory and Automation, The Czech Academy of Sciences, 18208 Prague, Czech Republic
Faculty of Mathematics and Physics, Charles University, 18207 Prague, Czech Republic
Department of ECE, Indian Institute of Technology, Palakkad 560012, India
Safran Aircraft Engines, 77550 Moissy-Cramayel, France
Author to whom correspondence should be addressed.
Entropy 2019, 21(1), 63;
Received: 11 November 2018 / Revised: 8 January 2019 / Accepted: 10 January 2019 / Published: 14 January 2019
This paper focuses on test procedures under corrupted data. We assume that the observations Z i are mismeasured, due to the presence of measurement errors. Thus, instead of Z i for i = 1 , , n, we observe X i = Z i + δ V i, with an unknown parameter δ and an unobservable random variable V i. It is assumed that the random variables Z i are i.i.d., as are the X i and the V i. The test procedure aims at deciding between two simple hyptheses pertaining to the density of the variable Z i, namely f 0 and g 0. In this setting, the density of the V i is supposed to be known. The procedure which we propose aggregates likelihood ratios for a collection of values of δ. A new definition of least-favorable hypotheses for the aggregate family of tests is presented, and a relation with the Kullback-Leibler divergence between the sets f δ δ and g δ δ is presented. Finite-sample lower bounds for the power of these tests are presented, both through analytical inequalities and through simulation under the least-favorable hypotheses. Since no optimality holds for the aggregation of likelihood ratio tests, a similar procedure is proposed, replacing the individual likelihood ratio by some divergence based test statistics. It is shown and discussed that the resulting aggregated test may perform better than the aggregate likelihood ratio procedure. View Full-Text
Keywords: composite hypotheses; corrupted data; least-favorable hypotheses; Neyman Pearson test; divergence based testing; Chernoff Stein lemma composite hypotheses; corrupted data; least-favorable hypotheses; Neyman Pearson test; divergence based testing; Chernoff Stein lemma
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MDPI and ACS Style

Broniatowski, M.; Jurečková, J.; Moses, A.K.; Miranda, E. Composite Tests under Corrupted Data. Entropy 2019, 21, 63.

AMA Style

Broniatowski M, Jurečková J, Moses AK, Miranda E. Composite Tests under Corrupted Data. Entropy. 2019; 21(1):63.

Chicago/Turabian Style

Broniatowski, Michel, Jana Jurečková, Ashok K. Moses, and Emilie Miranda. 2019. "Composite Tests under Corrupted Data" Entropy 21, no. 1: 63.

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