Analysis of gene expression data subject to measurement error in binary responses and predictors | Publication

題名	Analysis of gene expression data subject to measurement error in binary responses and predictors
作者	陳立榜 Chen, Li-Pang
貢獻者	統計系
日期	2024-12
上傳時間	2026-01-19
摘要	Gene expression variables are usually used to classify specific diseases, such as acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). However, gene expression data usually encounter ultrahigh dimensionality and measurement error. Those complex features also affect the classification performance. The aim is to introduce a method called BOOME, which refers to BOOsting algorithm for measurement error in binary responses and ultrahigh-dimensional predictors. This method primarily focuses on logistic regression and probit models with responses and predictors contaminated with measurement error. The BOOME method aims to address the effects of measurement error and then employs a boosting procedure to make variable selections and estimations. Numerical experiments reveal that the BOOME method is valid for addressing measurement error effects and deriving reliable estimation results.
關聯	CFE-CMStatistics 2024, Econometrics and Statistics (EcoSta)
資料類型	conference

dc.contributor	統計系
dc.creator (作者)	陳立榜
dc.creator (作者)	Chen, Li-Pang
dc.date (日期)	2024-12
dc.date.accessioned	2026-01-19	-
dc.date.available	2026-01-19	-
dc.date.issued (上傳時間)	2026-01-19	-
dc.identifier.uri (URI)	https://ah.lib.nccu.edu.tw/item?item_id=180640	-
dc.description.abstract (摘要)	Gene expression variables are usually used to classify specific diseases, such as acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). However, gene expression data usually encounter ultrahigh dimensionality and measurement error. Those complex features also affect the classification performance. The aim is to introduce a method called BOOME, which refers to BOOsting algorithm for measurement error in binary responses and ultrahigh-dimensional predictors. This method primarily focuses on logistic regression and probit models with responses and predictors contaminated with measurement error. The BOOME method aims to address the effects of measurement error and then employs a boosting procedure to make variable selections and estimations. Numerical experiments reveal that the BOOME method is valid for addressing measurement error effects and deriving reliable estimation results.
dc.format.extent	193 bytes	-
dc.format.mimetype	text/html	-
dc.relation (關聯)	CFE-CMStatistics 2024, Econometrics and Statistics (EcoSta)
dc.title (題名)	Analysis of gene expression data subject to measurement error in binary responses and predictors
dc.type (資料類型)	conference