Optimal subsampling for softmax regression

Yaqiong Yao () and HaiYing Wang ()
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Yaqiong Yao: University of Connecticut
HaiYing Wang: University of Connecticut

Statistical Papers, 2019, vol. 60, issue 2, No 15, 585-599

Abstract: Abstract To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113(522):829–844, 2018b) developed an optimal subsampling method for logistic regression. The purpose of this paper is to extend their method to softmax regression, which is also called multinomial logistic regression and is commonly used to model data with multiple categorical responses. We first derive the asymptotic distribution of the general subsampling estimator, and then derive optimal subsampling probabilities under the A-optimality criterion and the L-optimality criterion with a specific L matrix. Since the optimal subsampling probabilities depend on the unknowns, we adopt a two-stage adaptive procedure to address this issue and use numerical simulations to demonstrate its performance.

Keywords: Massive data; Subsampling; Optimality criterion; Softmax regression (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s00362-018-01068-6

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