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- W2952646068 abstract "For linear regression models who are not exactly sparse in the sense that the coefficients of the insignificant variables are not exactly zero, the working models obtained by a variable selection are often biased. Even in sparse cases, after a variable selection, when some significant variables are missing, the working models are biased as well. Thus, under such situations, root-n consistent estimation and accurate prediction could not be expected. In this paper, a novel remodelling method is proposed to produce an unbiased model when quasi-instrumental variables are introduced. The root-n estimation consistency and the asymptotic normality can be achieved, and the prediction accuracy can be promoted as well. The performance of the new method is examined through simulation studies." @default.
- W2952646068 created "2019-06-27" @default.
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- W2952646068 date "2014-07-15" @default.
- W2952646068 modified "2023-10-16" @default.
- W2952646068 title "Inference for biased models: a quasi-instrumental variable approach" @default.
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- W2952646068 doi "https://doi.org/10.48550/arxiv.1407.4184" @default.
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