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Showing results 1 to 9 of 9

1
AN IMPUTATION APPROACH FOR HANDLING MIXED-MODE SURVEYS

Park, Seunghwan; Kim, Jae Kwang; Park, Sangun, ANNALS OF APPLIED STATISTICS, v.10, no.2, pp.1063 - 1085, 2016-06

2
Finite sample properties of multiple imputation estimators

Kim, Jae Kwang, ANNALS OF STATISTICS, v.32, no.2, pp.766 - 783, 2004-04

3
Fractional hot deck imputation

Kim, Jae Kwang; Fuller, W, BIOMETRIKA, v.91, no.3, pp.559 - 578, 2004-09

4
Learning With End-Users in Distribution Grids: Topology and Parameter Estimation

Park, Sejun; Deka, Deepjyoti; Backhaus, Scott; Chertkov, Michael, IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, v.7, no.3, pp.1428 - 1440, 2020-09

5
Learning-Based Adaptive Imputation Method with kNN Algorithm for Missing Power Data

Kim, Minkyung; Park, Sangdon; Lee, Joohyung; Joo, Yongjae; Choi, Jun Kyun, ENERGIES, v.10, no.10, 2017-10

6
Missing data imputation for transfer passenger flow identified from in-station WiFi systems

Jiang, Wenhua; Zheng, Nan; Kim, Inhi, TRANSPORTMETRICA B-TRANSPORT DYNAMICS, v.11, no.1, pp.325 - 342, 2023-02

7
On the bias of the multiple-imputation variance estimator in survey sampling

Kim, Jae Kwang; Brick, JM; Fuller, WA; Kalton, G, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, v.68, pp.509 - 521, 2006

8
Regression fractional hot deck imputation

Kim, Jae Kwang, JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.36, no.3, pp.423 - 434, 2007-09

9
Training algorithm with incomplete data for feed-forward neural networks

Yoon, SY; Lee, Soo-Young, NEURAL PROCESSING LETTERS, v.10, no.3, pp.171 - 179, 1999-12

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