Semiparametric density estimation under a two-sample density ratio model

By Kuang-Fu Cheng, Chih-Kang Chu. 2004

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Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections (2019, methodology): “error), various direct ratio estimators have been proposed [37], including the moment matching approach [13], probabilistic classification approach [3, 5, 33], and ratio matching approach [17, 27, 38] The proposed DualDICE algorithm, as a direct approach for density ratio estimation, bears some simi”
Merging information for semiparametric density estimation (2004, methodology): “The problem of density estimation under the two-sample density ratio model is also the subject of a recent contribution by Cheng and Chu (2004).”
Least-squares two-sample test (2011, methodology): “Thanks to the analytic-form expression, uLSIF is computationally more efficient than alternative density ratio estimators which involve non-linear optimization (Qin, 1998; Cheng & Chu, 2004; Huang et al., 2007; Sugiyama et al., 2008; Nguyen et al., 2010).”
Likelihood-Free Inference by Ratio Estimation (2016, methodology): “, 2014), of which estimation through logistic regression is widely used and has some favourable asymptotic properties (Geyer, 1994; Qin, 1998; Cheng and Chu, 2004; Bickel et al., 2007).”
A Least-squares Approach to Direct Importance Estimation (2009, methodology): “…we can formulate our optimization problem as follows. min α∈Rb [ 1 2 α⊤Ĥα− ĥ⊤α+λ1⊤b α ] subject toα≥ 0b, (6) where 0b and 1b are theb-dimensional vectors with all zeros and ones, respectively; the vector inequalityα≥ 0b is applied in the element-wise manner, that is,αℓ≥ 0 for ℓ = 1,2, . . . ,b.”
Conditional Density Estimation via Least-Squares Density Ratio Estimation (2010, methodology): “The kernel mean matching method (Huang et al., 2007) and the logistic regression based method (Qin, 1998; Cheng & Chu, 2004; Bickel et al., 2007) also allow one to directly estimate a density ratio q(x)/q′(x).”
Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation (2012, background): “2009), the probabilistic classification approach (Qin 1998; Cheng and Chu 2004), the density matching approach (Sugiyama et al.”