Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis

Authors:
Yongsong Qin;Shichao Zhang;Chengqi Zhang
Affiliations:
Guangxi Normal University, China;Zhejiang Normal University, China and University of Technology, Australia;University of Technology, Australia
Venue:
International Journal of Data Warehousing and Mining
Year:
2010

Citing 3
Cited 2

Data Squashing by Empirical Likelihood

Data Mining and Knowledge Discovery
Confidence intervals for marginal parameters under fractional linear regression imputation for missing data

Journal of Multivariate Analysis
Shell-neighbor method and its application in missing data imputation

Applied Intelligence

Estimating Semi-Parametric Missing Values with Iterative Imputation

International Journal of Data Warehousing and Mining
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

International Journal of Data Warehousing and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

The k-nearest neighbor kNN imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing or incomplete data that is a combination of the kNN imputation and bootstrap calibrated EL Empirical Likelihood. The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with kNN imputation method.