Optimal model averaging estimator for expectile regressions

2022 ◽  
Vol 217 ◽  
pp. 204-223
Author(s):  
Yang Bai ◽  
Rongjie Jiang ◽  
Mengli Zhang
Keyword(s):  
Model Averaging ◽  
Optimal Model

scholarly journals When and when not to use optimal model averaging

2018 ◽  
Vol 61 (5) ◽  
pp. 2221-2240
Author(s):  
Michael Schomaker ◽  
Christian Heumann
Keyword(s):  
Model Averaging ◽  
Optimal Model

scholarly journals OPTIMAL MODEL AVERAGING OF VARYING COEFFICIENT MODELS

2019 ◽  
Author(s):  
Cong Li ◽  
Qi Li ◽  
JEFFREY RACINE ◽  
DAIQIANG ZHANG
Keyword(s):  
Model Averaging ◽  
Varying Coefficient Models ◽  
Optimal Model ◽  
Varying Coefficient

scholarly journals The diagnostic power of CD117, CD13, CD56, CD64, and MPO in rapid screening acute promyelocytic leukemia

2020 ◽  
Author(s):  
Vinh Thanh Tran ◽  
Thang Thanh Phan ◽  
Hong-Phuoc Mac ◽  
Tung Thanh Tran ◽  
Toan Trong Ho ◽  
...  
Keyword(s):  
Acute Promyelocytic Leukemia ◽  
Myeloid Leukemia ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Promyelocytic Leukemia ◽  
Rapid Screening ◽  
Optimal Model ◽  
Individual Values ◽  
Hla Dr ◽  
Auc Value

Abstract Objective The analogous immunophenotype between HLA-DR-negative acute myeloid leukemia (AML) and acute promyelocytic leukemia (APL) causes APL rapid screening to become difficult. This study aimed to identify the associated antigens for APL and the best model in clinical uses. Results A total of 36 APL (PML-RARA+) and 29 HLA-DR-negative non-APL patients were selected for this study. When a cut-off point of 20% events was applied to define positive or negative status, APL and non-APL patients share a similar immunophenotype of CD117, CD34, CD11b, CD13, CD33, and MPO (P > 0.05). However, expression intensity of CD117 (P = 0.002), CD13 (P < 0.001), CD35 (P < 0.001), CD64 (P < 0.001), and MPO (P < 0.001) in APL are significantly higher while CD56 (P = 0.049) is lower than in non-APL subjects. The Bayesian Model Averaging (BMA) analysis identified CD117 (≥ 49% events), CD13 (≥ 88% events), CD56 (≤ 25% events), CD64 (≥ 42% events), and MPO (≥ 97% events) antigens as an optimal model for APL diagnosis. A combination of these factors resulted in an area under curve (AUC) value of 0.98 together with 91.7% sensitivity and 93.1% specificity, which is higher than individual values (AUC were 0.76, 0.84, 0.65, 0.82, and 0.85, respectively) (P = 0.001).

scholarly journals The diagnostic power of CD117, CD13, CD56, CD64, and MPO in rapid screening acute promyelocytic leukemia

2020 ◽  
Author(s):  
Vinh Thanh Tran ◽  
Thang Thanh Phan ◽  
Hong-Phuoc Mac ◽  
Tung Thanh Tran ◽  
Toan Trong Ho ◽  
...  
Keyword(s):  
Acute Promyelocytic Leukemia ◽  
Myeloid Leukemia ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Promyelocytic Leukemia ◽  
Rapid Screening ◽  
Optimal Model ◽  
Hla Dr ◽  
Auc Value ◽  
Better Than

Abstract Objective: The same immuno-phenotype between HLA-DR-negative acute myeloid leukemia (AML) and acute promyelocytic leukemia (APL) causes APL rapid screening to become difficult. This study aimed to identify the associated antigens for APL and the best model in clinical uses.Results: A total of 36 APL (PML-RARA+) and 29 HLA-DR-negative non-APL patients enrolled in this study. When a cut-off point of 20% events was applied to define positive or negative status, APL and non-APL patients share a similar immuno-phenotype of CD117, CD34, CD11b, CD13, CD33, and MPO (P>0.05). However, expression intensity of CD117 (P=0.002), CD13 (P<0.001), CD35 (P<0.001), CD64 (P<0.001), and MPO (P<0.001) in APL are significantly higher while CD56 (P=0.049) is lower than in non-APL subjects. The Bayesian Model Averaging (BMA) analysis identified CD117 (≥49% events), CD13 (≥88% events), CD56 (≤25% events), CD64 (≥42% events), and MPO (≥97% events) antigens as an optimal model for APL diagnosis. A combination of these factors resulted in an area under curve (AUC) value of 0.98 together with 91.7% sensitivity and 93.1% specificity, which is better than individual markers (AUC were 0.76, 0.84, 0.65, 0.82, and 0.85, respectively) (P=0.001).

Optimal model averaging for multivariate regression models

2021 ◽  
pp. 104858
Author(s):  
Jun Liao ◽  
Alan T.K. Wan ◽  
Shuyuan He ◽  
Guohua Zou
Keyword(s):  
Multivariate Regression ◽  
Regression Models ◽  
Model Averaging ◽  
Optimal Model

scholarly journals Toward optimal model averaging in regression models with time series errors

2015 ◽  
Vol 189 (2) ◽  
pp. 321-334 ◽  
Author(s):  
Tzu-Chang F. Cheng ◽  
Ching-Kang Ing ◽  
Shu-Hui Yu
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Model Averaging ◽  
Optimal Model

scholarly journals The diagnostic power of CD117, CD13, CD56, CD64, and MPO in rapid screening acute promyelocytic leukemia

2020 ◽  
Author(s):  
Vinh Thanh Tran ◽  
Thang Thanh Phan ◽  
Hong-Phuoc Mac ◽  
Tung Thanh Tran ◽  
Toan Trong Ho ◽  
...  
Keyword(s):  
Acute Promyelocytic Leukemia ◽  
Myeloid Leukemia ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Promyelocytic Leukemia ◽  
Rapid Screening ◽  
Optimal Model ◽  
Hla Dr ◽  
Auc Value ◽  
Better Than

Abstract Objective: The same immuno-phenotype between HLA-DR-negative acute myeloid leukemia (AML) and acute promyelocytic leukemia (APL) causes APL rapid screening to become difficult. This study aimed to identify the associated antigens for APL and the best model in clinical uses.Results: A total of 36 APL (PML-RARA+) and 29 HLA-DR-negative non-APL patients enrolled in this study. When a cut-off point of 20% events was applied to define positive or negative status, APL and non-APL patients share a similar immuno-phenotype of CD117, CD34, CD11b, CD13, CD33, and MPO (P>0.05). However, expression intensity of CD117 (P=0.002), CD13 (P<0.001), CD35 (P<0.001), CD64 (P<0.001), and MPO (P<0.001) in APL are significantly higher while CD56 (P=0.049) is lower than in non-APL subjects. The Bayesian Model Averaging (BMA) analysis identified CD117 (≥49% events), CD13 (≥88% events), CD56 (≤25% events), CD64 (≥42% events), and MPO (≥97% events) antigens as an optimal model for APL diagnosis. A combination of these factors resulted in an area under curve (AUC) value of 0.98 together with 91.7% sensitivity and 93.1% specificity, which is better than individual markers (AUC were 0.76, 0.84, 0.65, 0.82, and 0.85, respectively) (P=0.001).

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