On the matching noise of some nonparametric imputation procedures

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in each model, utilize a substantially useful amount of information. Additionally, this information includes occurrences involving missing values as well as capturing the real dataset distribution easier than the parametric or nonparametric imputation techniques. Experimental results show that the author’s imputation methods outperform the existing methods in terms of imputation accuracy, in particular in the situation with high missing ratio.

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Nonparametric imputation method for nonresponse in surveys

Statistical Methods & Applications ◽

10.1007/s10260-019-00458-w ◽

2019 ◽

Vol 29 (1) ◽

pp. 25-48 ◽

Cited By ~ 1

Author(s):

Caren Hasler ◽

Radu V. Craiu

Keyword(s):

Imputation Method ◽

Nonparametric Imputation

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Regeneration imputation models for complex stands of southeastern British Columbia

The Forestry Chronicle ◽

10.5558/tfc80271-2 ◽

2004 ◽

Vol 80 (2) ◽

pp. 271-278 ◽

Cited By ~ 7

Author(s):

Badre T Hassani ◽

Valerie LeMay ◽

Peter L Marshall ◽

H. Temesgen ◽

Abdel-Azim Zumrawi

Keyword(s):

British Columbia ◽

Key Words ◽

Natural Regeneration ◽

Probability Distributions ◽

Nonparametric Imputation ◽

Multivariate Prediction ◽

Ease Of Access ◽

Using Data ◽

Overstory Trees ◽

Imputation Approach

Two imputation techniques for predicting natural regeneration in complex stands prevalent in southeastern British Columbia (BC) were compared using data from the Interior Cedar-Hemlock moist warm subzone variant 2 (ICHmw2) in the vicinity of Nelson, BC. Imputation approaches offer advantages over other modeling approaches in that they provide estimates of many variables at one time (multivariate) and there are no assumptions regarding the probability distributions of the variables to be predicted. For the tabular imputation, the average regeneration per ha was calculated for each combination of five site groups, two residual density classes, five time-since-disturbance intervals, species, and height classes. For Most Similar Neighbour (MSN) imputation, data with both regeneration information, and overstory trees and site information (called reference plots) were used to impute regeneration of plots with only overstory trees and site information (called target plots), by selecting the most similar plot. Of the two approaches studied, the MSN approach gave better results than tabular imputation. The tabular imputation approach is simpler to implement, since tables of results can be published and made available for use. However, the MSN software has been made freely available, resulting in greater ease of access. Key words: multi-species, multi-cohort, nonparametric imputation, multivariate prediction, regeneration estimation

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Nonparametric Imputation by Data Depth

Journal of the American Statistical Association ◽

10.1080/01621459.2018.1543123 ◽

2019 ◽

Vol 115 (529) ◽

pp. 241-253

Author(s):

Pavlo Mozharovskyi ◽

Julie Josse ◽

François Husson

Keyword(s):

Data Depth ◽

Nonparametric Imputation

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Estimating Semi-Parametric Missing Values with Iterative Imputation

Exploring Advances in Interdisciplinary Data Mining and Analytics ◽

10.4018/978-1-61350-474-1.ch009 ◽

2011 ◽

pp. 147-156

Author(s):

Shichao Zhang

Keyword(s):

Prior Knowledge ◽

Efficient Method ◽

Missing Values ◽

Imputation Accuracy ◽

Imputation Method ◽

Experimental Results ◽

Imputation Methods ◽

Regression Imputation ◽

Target Values ◽

Nonparametric Imputation

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in each model, utilize a substantially useful amount of information. Additionally, this information includes occurrences involving missing values as well as capturing the real dataset distribution easier than the parametric or nonparametric imputation techniques. Experimental results show that the author’s imputation methods outperform the existing methods in terms of imputation accuracy, in particular in the situation with high missing ratio.

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