Neural Comput - Density-difference estimation.

Tópicos

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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small estimation error incurred in the first stage can cause a big error in the second stage. In this letter, we propose a single-shot procedure for directly estimating the density difference without separately estimating two densities. We derive a nonparametric finite-sample error bound for the proposed single-shot density-difference estimator and show that it achieves the optimal convergence rate. We then show how the proposed density-difference estimator can be used in L?-distance approximation. Finally, we experimentally demonstrate the usefulness of the proposed method in robust distribution comparison such as class-prior estimation and change-point detection.

Resumo Limpo

address problem estim differ two probabl densiti naiv approach twostep procedur first estim two densiti separ comput differ howev procedur necessarili work well first step perform without regard second step thus small estim error incur first stage can caus big error second stage letter propos singleshot procedur direct estim densiti differ without separ estim two densiti deriv nonparametr finitesampl error bound propos singleshot densitydiffer estim show achiev optim converg rate show propos densitydiffer estim can use ldistanc approxim final experiment demonstr use propos method robust distribut comparison classprior estim changepoint detect

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