Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data
نویسندگان
چکیده
In this article, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work primarily motivated from many biomedical studies with correlated responses such as the cancer cell-line encyclopedia project. We assume that underlying coefficient matrix simultaneously low-rank and row-wise sparse. propose an intuitively appealing selection estimation framework based on marginal model likelihood, develop efficient computational algorithm for inference. establish novel theory nonlinear regression. general, allowing potential correlations between responses. new type nuclear norm penalty using smooth clipped absolute deviation, filling gap related non-convex penalization literature. theoretically demonstrate proposed approach improves accuracy by considering jointly through estimator when particular, non-asymptotic error bounds, both rank row support consistency method. Moreover, consistent rule select dimension matrix. Furthermore, extend methods joint Ising model, accounts dependence relationships. our analysis simulated data cell line data, outperform existing better predicting Supplementary materials article are available online.
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2022
ISSN: ['0162-1459', '1537-274X', '2326-6228', '1522-5445']
DOI: https://doi.org/10.1080/01621459.2022.2105704