Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data-Reference-Cited by-同舟云学术

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Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data

Published:2022-09-20 Issue: Volume: Page:1-15
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Park Seyoung¹^ORCID,Lee Eun Ryung¹^ORCID,Zhao Hongyu²

Affiliation:

1. Department of Statistics, Sungkyunkwan University, Seoul, Korea

2. Department of Biostatistics, Yale University, New Haven, CT

Funder

nsf

Intramural Research Program, National Institutes of Health Clinical Center

National Research Foundation of Korea

NIH

NSF

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2022.2105704

Reference86 articles.

1. Determining the Number of Factors in Approximate Factor Models

2. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

3. Rationalizing Drug Response in Cancer Cell Lines

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A reduced-rank approach to predicting multiple binary responses through machine learning;Statistics and Computing;2023-10-13

2. Quantile forward regression for high-dimensional survival data;Lifetime Data Analysis;2023-07-02

3. Estimation and visualization of heterogeneous treatment effects for multiple outcomes;Statistics in Medicine;2022-12-27

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