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題名 De-noising analysis of noisy data under mixed graphical models
作者 陳立榜
Chen, Li-Pang
Yi, Grace Y.
貢獻者 統計系
關鍵詞 asymptotic bias; de-noise; error-prone variable; Graphical model; simulation-extrapolation algorithm
日期 2022-07
上傳時間 20-Oct-2022 16:06:26 (UTC+8)
摘要 Graphical models are useful to characterize the dependence structure of variables and have been commonly used for analysis of complex structured data. While various estimation methods have been developed under different graphical models, those methods are, however, inadequate to handle noisy data with measurement error. The development of most existing approaches relies on the implicit yet stringent assumption that the associated variables must be measured precisely. This assumption is unrealistic for many applications because mismeasurement in variables is usually presented in the data collection process. In this paper, we consider analysis of error-prone data under graphical models. To understand the impact of measurement error, we first study the asymptotic bias of the naive analysis which disregards the feature of measurement error in the variables. Furthermore, we develop a de-noising estimation procedure to account for measurement error effects. Theoretical results are established for the proposed method and numerical studies are reported to assess the finite sample performance of our proposed method.
關聯 Electronic Journal of Statistics, 16(2), pp.3861-3909
資料類型 article
DOI https://doi.org/10.1214/22-EJS2028
dc.contributor 統計系
dc.creator (作者) 陳立榜
dc.creator (作者) Chen, Li-Pang
dc.creator (作者) Yi, Grace Y.
dc.date (日期) 2022-07
dc.date.accessioned 20-Oct-2022 16:06:26 (UTC+8)-
dc.date.available 20-Oct-2022 16:06:26 (UTC+8)-
dc.date.issued (上傳時間) 20-Oct-2022 16:06:26 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142452-
dc.description.abstract (摘要) Graphical models are useful to characterize the dependence structure of variables and have been commonly used for analysis of complex structured data. While various estimation methods have been developed under different graphical models, those methods are, however, inadequate to handle noisy data with measurement error. The development of most existing approaches relies on the implicit yet stringent assumption that the associated variables must be measured precisely. This assumption is unrealistic for many applications because mismeasurement in variables is usually presented in the data collection process. In this paper, we consider analysis of error-prone data under graphical models. To understand the impact of measurement error, we first study the asymptotic bias of the naive analysis which disregards the feature of measurement error in the variables. Furthermore, we develop a de-noising estimation procedure to account for measurement error effects. Theoretical results are established for the proposed method and numerical studies are reported to assess the finite sample performance of our proposed method.
dc.format.extent 98 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Electronic Journal of Statistics, 16(2), pp.3861-3909
dc.subject (關鍵詞) asymptotic bias; de-noise; error-prone variable; Graphical model; simulation-extrapolation algorithm
dc.title (題名) De-noising analysis of noisy data under mixed graphical models
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1214/22-EJS2028
dc.doi.uri (DOI) https://doi.org/10.1214/22-EJS2028