Optimal subsampling for softmax regression
WebMar 17, 2024 · This article focuses on quantile regression with massive data where the sample size n (greater than 1 0 6 in general) is extraordinarily large but the dimension d (smaller than 20 in general) is small. We first formulate the general subsampling procedure and establish the asymptotic property of the resultant estimator. WebDec 18, 2024 · To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113(522):829–844, 2024b ) 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 …
Optimal subsampling for softmax regression
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WebConstruct optimal subsampling probabilities that are invariant to model constraints. ... The optimal subsampling probabilities for softmax regression have been studied under the baseline constraint which treats one dimension of the multivariate response differently from other dimensions. In this paper, we show that different model constraints ... WebSep 17, 2015 · On the basis of the criterion, we provide two novel subsampling methods, the optimal subsampling and the predictor- length subsampling methods. The predictor …
WebApr 21, 2009 · The model is an extension of the clustered ordinal regression approach of Hedeker and Gibbons that includes the continuous outcome. To handle subsampling, we then derive a partial likelihood (PL) that is based on the bivariate model, and we give an expression for the PL score in Section 3. We show that consistent estimates can be … WebApr 6, 2024 · Rogers, Steven C. Youth Suicide is 100% Preventable: 4 Things Parents Need to Know CCMC Blog 02/25/2024 Carter EJ, Kaman K, Baron K, Miller M, Krol DM, Harpalani D, Aseltine RH, Pagoto S. (2024) Parent-reported penicillin allergies in children: A qualitative study.Am J Infect Control. Jan;51(1):56-61. doi: 10.1016/j.ajic.2024.04.008. Epub 2024 …
Websubsampling least squares regression. On the other hand, several scholars have developed optimal subsampling methods for parametric regression problems. For example, Wang et al. (2024a) proposed an inverse weighted subsampling method for logistic regression based on the A- or L-optimality criterion. Subsequently, a more efficient estimation
WebThis paper fills the gap by studying the subsampling method for a widely used missing data estimator, the augmented inverse probability weighting (AIPW) estimator. The response mean estimation problem with missing responses is discussed for illustration. A two-stage subsampling method is proposed via Poisson sampling framework. nsw mid north coast health intranetWebFor softmax regression, the optimal subsampling algorithm has been investigated in Yao and Wang (2024) under the baseline constraint, where one dimension of the multivariate … nike dunk high orange and whiteWebOptimal Subsampling for Softmax Regression 3 2 Model setup and optimal subsampling. Yaqiong Yao, Haiying Wang. Published 2024. Mathematics. To meet the challenge of … nsw mid north coast healthWebThe problem of variable selection in neural network regression models with dependent data is considered. In this framework, a test procedure based on the introduction of a measure for the variable re nike dunk high outfits womenWebJul 21, 2024 · We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions … nike dunk high pink and blackWebNov 5, 2024 · Title: Optimal Poisson Subsampling for Softmax Regression Authors: Yaqiong Yao, Jiahui Zou Award ID(s): 2105571 Publication Date: 2024-11-05 NSF-PAR ID: … nsw metal workers picnic dayWebOptimal subsampling for softmax regression Article Full-text available Apr 2024 Yaqiong Yao Haiying Wang To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113 (522):829–844,... nike dunk high pink white