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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/148693


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    題名: Bias approximations for likelihood‐based estimators
    作者: 翁久幸
    Weng, Ruby Chiu-Hsing;Coad, D. Stephen
    貢獻者: 統計系
    關鍵詞: bias calculation;l2penalized likelihood;maximum likelihood estimation;stopping time;very weak approximation;Woodroofe–Stein identity
    日期: 2021-12
    上傳時間: 2023-12-13 13:54:59 (UTC+8)
    摘要: Bias approximation has played an important rôle in statistical inference, and numerous bias calculation techniques have been proposed under different contexts. We provide a unified approach to approximating the bias of the maximum likelihood estimator and the l2 penalized likelihood estimator for both linear and nonlinear models, where the design variables are allowed to be random and the sample size can be a stopping time. The proposed method is based on the Woodroofe–Stein identity and is justified by very weak approximations. The accuracy of the derived bias formulas is assessed by simulation for several examples. The bias of the ridge estimator in high-dimensional settings is also discussed.
    關聯: Scandinavian Journal of Statistics, Vol.48, No.4, pp.1474-1497
    資料類型: article
    DOI 連結: https://doi.org/10.1111/sjos.12499
    DOI: 10.1111/sjos.12499
    顯示於類別:[統計學系] 期刊論文

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