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- W4283456708 abstract "The assumption that response and predictor belong to the same statistical unit may be violated in practice. Unbiased estimation and recovery of true label ordering based on unlabeled data are challenging tasks and have attracted increasing attentions in the recent literature. In this paper, we present a relatively complete analysis of label permutation problem for the generalized linear model with multivariate responses. The theory is established under different scenarios, with knowledge of true parameters, with partial knowledge of underlying label permutation matrix and without any knowledge. Our results remove the stringent conditions required by the current literature and are further extended to the missing observation setting which has never been considered in the field of label permutation problem. On computational side, we propose two methods, maximum likelihood estimation algorithm and two-step estimation algorithm, to accommodate for different settings. When the proportion of permuted labels is moderate, both methods work effectively. Multiple numerical experiments are provided and corroborate our theoretical findings." @default.
- W4283456708 created "2022-06-26" @default.
- W4283456708 creator A5034487716 @default.
- W4283456708 creator A5073070877 @default.
- W4283456708 date "2022-06-23" @default.
- W4283456708 modified "2023-10-03" @default.
- W4283456708 title "Regression with Label Permutation in Generalized Linear Model" @default.
- W4283456708 doi "https://doi.org/10.48550/arxiv.2206.11775" @default.
- W4283456708 hasPublicationYear "2022" @default.
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