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題名 Missing data imputation using classification and regression trees
作者 張育瑋
Chang, Yu-Wei;Chen, Cheng-Yang
貢獻者 統計系
關鍵詞 Classification and regression trees; Missing data; Missing data imputation; Resampling
日期 2024-06
上傳時間 2024-07-17
摘要 Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART). Methods We consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit approval data for illustration. Results The method that performs the best strongly depends on the correlation between variables. For imputing missing ordinal categorical variables, the rpart package with surrogate variables is recommended under correlations larger than 0 with missing completely at random (MCAR) and missing at random (MAR) conditions. Under missing not at random (MNAR), chi-squared test methods and the rpart package with surrogate variables are suggested. For imputing missing quantitative variables, the iterative imputation method is most recommended under moderate correlation conditions.
關聯 PeerJ Computer Science, 10, e2119
資料類型 article
DOI https://doi.org/10.7717/peerj-cs.2119
dc.contributor 統計系
dc.creator (作者) 張育瑋
dc.creator (作者) Chang, Yu-Wei;Chen, Cheng-Yang
dc.date (日期) 2024-06
dc.date.accessioned 2024-07-17-
dc.date.available 2024-07-17-
dc.date.issued (上傳時間) 2024-07-17-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152336-
dc.description.abstract (摘要) Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART). Methods We consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit approval data for illustration. Results The method that performs the best strongly depends on the correlation between variables. For imputing missing ordinal categorical variables, the rpart package with surrogate variables is recommended under correlations larger than 0 with missing completely at random (MCAR) and missing at random (MAR) conditions. Under missing not at random (MNAR), chi-squared test methods and the rpart package with surrogate variables are suggested. For imputing missing quantitative variables, the iterative imputation method is most recommended under moderate correlation conditions.
dc.format.extent 101 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) PeerJ Computer Science, 10, e2119
dc.subject (關鍵詞) Classification and regression trees; Missing data; Missing data imputation; Resampling
dc.title (題名) Missing data imputation using classification and regression trees
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.7717/peerj-cs.2119
dc.doi.uri (DOI) https://doi.org/10.7717/peerj-cs.2119