Some Concerns About Imputation Methods for Missing Data

2022 ◽  
Author(s):  
Rie Toyomoto ◽  
Satoshi Funada ◽  
Toshi A. Furukawa
Keyword(s):  
Missing Data ◽  
Imputation Methods

Comparison of five imputation methods in handling missing data in a continuous frequency table

2021 ◽  
Author(s):  
M. B. Mohammed ◽  
H. S. Zulkafli ◽  
M. B. Adam ◽  
N. Ali ◽  
I. A. Baba
Keyword(s):  
Missing Data ◽  
Imputation Methods ◽  
Frequency Table ◽  
Continuous Frequency

scholarly journals Comparison of Imputation Methods on Retrospective Breast Cancer Data in Tanzania: A Case Study of Muhimbili and Ocean Road Hospitals

2021 ◽  
Author(s):  
Rahibu A. Abassi ◽  
Amina S. Msengwa ◽  
Rocky R. J. Akarro
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Missing Data ◽  
Binary Logistic Regression ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Imputation Methods ◽  
Multiple Imputations ◽  
Predictive Mean Matching ◽  
Mean Square Errors

Abstract Background Clinical data are at risk of having missing or incomplete values for several reasons including patients’ failure to attend clinical measurements, wrong interpretations of measurements, and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission of missed observation during statistical analysis especially if a dataset is considerably small. The objective of this study is to compare several imputation methods in terms of efficiency in filling-in the missing data so as to increase the prediction and classification accuracy in breast cancer dataset. Methods Five imputation methods namely series mean, k-nearest neighbour, hot deck, predictive mean matching, and multiple imputations were applied to replace the missing values to the real breast cancer dataset. The efficiency of imputation methods was compared by using the Root Mean Square Errors and Mean Absolute Errors to obtain a suitable complete dataset. Binary logistic regression and linear discrimination classifiers were applied to the imputed dataset to compare their efficacy on classification and discrimination. Results The evaluation of imputation methods revealed that the predictive mean matching method was better off compared to other imputation methods. In addition, the binary logistic regression and linear discriminant analyses yield almost similar values on overall classification rates, sensitivity and specificity. Conclusion The predictive mean matching imputation showed higher accuracy in estimating and replacing missing/incomplete data values in a real breast cancer dataset under the study. It is a more effective and good method to handle missing data in this scenario. We recommend to replace missing data by using predictive mean matching since it is a plausible approach toward multiple imputations for numerical variables, as it improves estimation and prediction accuracy over the use complete-case analysis especially when percentage of missing data is not very small.

scholarly journals A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5947
Author(s):  
Liang Zhang
Keyword(s):  
Pattern Recognition ◽  
Missing Data ◽  
Energy Systems ◽  
Building Energy ◽  
Imputation Method ◽  
Validation Dataset ◽  
Data Imputation ◽  
Validation Data ◽  
Imputation Methods ◽  
Building Energy Systems

Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.

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