Open AccessProceedings Article
Kernel-Based Multi-Imputation for Missing Data
Shichao Zhang,Yongsong Qin,Xiaofeng Zhu,Jilian Zhang,Chengqi Zhang +4 more
- 19 May 2006
- pp 106-111
3
TL;DR: A Kernel-Based Nonparametric Multiple imputation method is proposed under MAR and MCAR missing mechanisms in nonparametric regression settings, and it is demonstrated that the imputation performs better than the well-known NORM algorithm.
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Abstract: A Kernel-Based Nonparametric Multiple imputation method is proposed under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. We experimentally evaluate our approach, and demonstrate that our imputation performs better than the well-known NORM algorithm.
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Citations
Rebooting data-driven soft-sensors in process industries: A review of kernel methods
TL;DR: A systematic review of various state-of-the-art kernel-based methods with applications for data pre-processing, sample selection, variable selection, model construction and reliability analysis of soft-sensors.
119
Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm
Mlungisi Duma,Tshilidzi Marwala,Bhekisipho Twala,Fulufhelo V. Nelwamondo +3 more
- 01 Dec 2013
TL;DR: The results demonstrate that when missing data imputation is performed using the proposed hybrid method, the classification improves and the robustness to the amount of missing data is increased relative to the mean/mode method for data missing completely at random, missing at random (MAR), and not missing at Random (NMAR).
30
A Novel Two-Phase Method for the Classification of Incomplete Data
Xiuyun Qu,Bo Yuan,Wenhuang Liu +2 more
- 26 Dec 2009
TL;DR: A novel two-phase method is developed to deal with the challenge of incomplete data on classification problems by dividing the dataset into disjoint subsets based on the attributes with missing values.
4
References
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Statistical Analysis with Missing Data
Roderick J. A. Little,Donald B. Rubin +1 more
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TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
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Statistical Analysis With Missing Data
TL;DR: Generalized Estimating Equations is a good introductory book for analyzing continuous and discrete correlated data using GEE methods and provides good guidance for analyzing correlated data in biomedical studies and survey studies.
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Analysis of Incomplete Multivariate Data
J.L. Schafer
- 01 Aug 1997
TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.
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Statistical Analysis with Missing Data.
TL;DR: This comprehensive book explores statistical analysis with missing data, covering complete-case and available-case analysis, single imputation methods, likelihood-based approaches, and applications to various models, including multivariate normal and mixed normal data.
4.3K