Optimal subsampling for softmax regression
WebThis idea was generalized in [11] to softmax regression. An optimal subsampling method under the A-optimality criterion (OSMAC) for logistic re-gression inspired by the idea of … 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 …
Optimal subsampling for softmax regression
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WebThis method was named as optimal subsampling methods motivated 4 fromtheA-optimalitycriterion(OSMAC),andwasimprovedinWang(2024b)byadopt-5 ing unweighted target functions for subsamples and Poisson subsampling. In addition 6 to logistic regression, OSMAC was investigated to include softmax regression (Yao and WebJul 21, 2024 · We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions …
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. WebSubsampling techniques are efficient methods for handling big data. Quite a few optimal sampling methods have been developed for parametric models in which the loss …
WebMar 25, 2024 · We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling … Expand. 59. PDF. Save. Alert. Optimal Sampling for Generalized Linear Models Under Measurement Constraints. Tao Zhang, Y. Ning, D. Ruppert; 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 …
WebFeb 1, 2024 · Furthermore, the optimal subsampling probabilities are derived according to the A-optimality criterion. It is shown that the estimator based on the optimal … trump tweets coffee table bookWebThe increasing volumes of data bring new challenges for parameter estimation in softmax regression, and the optimal subsampling method is an effective way to solve them. … trump twitter and emhWebConstruct 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 ... trump tweets new cabinet postWebApr 1, 2024 · Abstract: The information-based optimal subdata selection (IBOSS) is a computationally efficient method to select informative data points from large data sets … philippines geopolitical analysisWebSoftmax regression, a generalization of Logistic re-gression (LR) in the setting of multi-class classi-cation, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax ... philippines geothermalWebDec 1, 2024 · Wang H Ma Y Optimal subsampling for quantile regression in big data Biometrika 2024 108 1 99 112 4226192 10.1093/biomet/asaa043 1462.62248 Google Scholar; Wang H Zhu R Ma P Optimal subsampling for large sample logistic regression J. Am. Stat. Assoc. 2024 113 522 829 844 3832230 10.1080/01621459.2024.1292914 … philippines geographic regionWeb844, 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 … trump twice acquitted