Optimal Subsampling for Multinomial Logistic Models With Big Data

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.5705/ss.202022.0277

Zhiqiang Ye, Jun Yu, Mingyao Ai

引用次数: 0

Abstract

This section is dedicated to presenting the explicit forms of πij(β)’s and their derivatives, which are important parts in searching the maximum likelihood estimator and in the theoretical proofs. The categorical probability πij(β) for Models (2.1)-(2.4) can be calculated directly, and the first derivative of πij(β) with respect to β can be gotten through ∂πij(β) ∂β = πij(β) ∂ log πij(β) ∂β , (S1.1)

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大数据下多项式Logistic模型的最优子抽样

本节给出π (β)及其导数的显式形式，π (β)及其导数是搜索极大似然估计量和理论证明的重要组成部分。可以直接计算出模型(2.1)-(2.4)的分类概率πij(β)，通过∂πij(β)∂β = πij(β)∂log πij(β)∂β， (S1.1)得到πij(β)对β的一阶导数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.

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