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Study on the missing data Mechanisms and Imputation Methods

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Open Journal of Statistics 2021年第4期11卷 477-492页

作者： Abdullah Z. Alruhaymi Charles J. Kim Department of Electrical Engineering and Computer Science Howard University Washington DC USA

The absence of some data values in any observed dataset has been a real hindrance to achieving valid results in statistical research. This paper aimed at the missing data widespread problem faced by analysts and statisticians in academia and professional environments. Some data-driven methods were studied to obtain accurate data. Projects that highly rely on data face this missing data problem. And since machine learning models are only as good as the data used to train them, the missing data problem has a real impact on the solutions developed for real-world problems. Therefore, in this dissertation, there is an attempt to solve this problem using different mechanisms. This is done by testing the effectiveness of both traditional and modern data imputation techniques by determining the loss of statistical power when these different approaches are used to tackle the missing data problem. At the end of this research dissertation, it should be easy to establish which methods are the best when handling the research problem. It is recommended that using Multivariate Imputation by Chained Equations (MICE) for MAR missingness is the best approach to dealing with missing data.

关键词： missing data Mechanisms Imputation Techniques Models

Using Statistical Learning to Treat missing data: A Case of HIV/TB Co-Infection in Kenya

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Journal of data Analysis and Information Processing 2020年第3期8卷 110-133页

作者： Joshua O. Mwaro Linda Chaba Collins Odhiambo Strathmore Institute of Mathematical Sciences Strathmore University Nairobi Kenya

In this study, we investigate the effects of missing data when estimating HIV/TB co-infection. We revisit the concept of missing data and examine three available approaches for dealing with missingness. The main objective is to identify the best method for correcting missing data in TB/HIV Co-infection setting. We employ both empirical data analysis and extensive simulation study to examine the effects of missing data, the accuracy, sensitivity, specificity and train and test error for different approaches. The novelty of this work hinges on the use of modern statistical learning algorithm when treating missingness. In the empirical analysis, both HIV data and TB-HIV co-infection data imputations were performed, and the missing values were imputed using different approaches. In the simulation study, sets of 0% (Complete case), 10%, 30%, 50% and 80% of the data were drawn randomly and replaced with missing values. Results show complete cases only had a co-infection rate (95% Confidence Interval band) of 29% (25%, 33%), weighted method 27% (23%, 31%), likelihood-based approach 26% (24%, 28%) and multiple imputation approach 21% (20%, 22%). In conclusion, MI remains the best approach for dealing with missing data and failure to apply it, results to overestimation of HIV/TB co-infection rate by 8%.

关键词： missing data HIV/TB Co-Infection Imputation missing at Random Count data

Fault Diagnosis Based on Deep Learning Subject to missing data

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Fault Diagnosis Based on Deep Learning Subject to Missing Da...

An efficient and doubly robust empirical likelihood approach for estimating equations with missing data

第30届中国控制与决策会议

作者： Weibo Liu Dan Wei Funa Zhou School of Computer and Information Engineering Henan University

With the development of modern industry and computer technology, deep learning is widely used in the field of fault diagnosis, but it still faces many challenges, such as data missing due to different sampling rate of different sensor or other data packet of dropout of control system network. Observation data with missing values will seriously affect the result of fault diagnosis. Imputation methods such as regression imputation method intend to solve the problem of data missing to a certain extent. However, as the missing rate increases and the cross-correlation coefficient between observation variable decreases, the traditional imputation method will fail in extracting the potential feature involved in the missing data. In this paper, a neural network imputation method is proposed to estimate the missed observation. Once online observation data with missing value is available, neural network imputation method is first used to get a structural complete observation sample. Then DNN trained by structural complete data can be effectively classify different fault with high accuracy. Experiment analysis shows the efficiency of the proposed method.

关键词： fault diagnosis missing data deep learning imputation methods deep neural network

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An efficient and doubly robust empirical likelihood approach...

Statistical inference for nonignorable missing-data problems:a selective review

第十届海峡两岸统计与概率研讨会

作者： Tianqing Liu Xiaohui Yuan Zhaohai Li Aiyi Liu Jilin University Changchun University of Technology George Washington University National Institute of Child Health and Human Development

This paper considers an empirical likelihood inference for parameters defined by generalized estimating equations, when data are missing at random. The efficiency of existing estimators depends critically on correctly specifying the conditional expectation of the estimating function given the observed components of the random observations. When the conditional expectation is misspecified, the efficiency of estimation can be severely compromised even with correctly-specified propensity function(for missingness). We propose an efficient estimator which enjoys the double-robustness property and can achieve the semiparametric efficiency bound within the class of estimating functions that are generated by the estimating function of the generalized estimating equations, if both the propensity model and the regression model(for the conditional expectation) are specified correctly. Moreover, if the propensity function is specified correctly, the proposed estimator still achieves a semiparametric efficiency lower bound within a more general class of estimating functions, even if the regression model is misspecified. Simulation results suggest that the proposed estimators are robust against misspecification of the propensity model or regression model and outperform many existing competitors in terms of smaller mean-square errors. Our approach requires neither resampling nor kernel smoothing. A real data example from a dietary intervention study for children with type 1 diabetes is used to illustrate the proposed approach.

关键词： Doubly robust inference Empirical likelihood Estimating equations Generalized moment method missing data Ratio of correlated observations

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Statistical Theory and Related Fields 2018年第2期2卷 105-133页

作者： Niansheng Tang Yuanyuan Ju Key Lab of Statistical Modelling and data Analysis of Yunnan Province Yunnan UniversityKunming650091People’s Republic of China

Nonignorable missing data are frequently encountered in various settings, such as economics,sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection. For estimation of meanfunctionals, we review semiparametric method and empirical likelihood (EL) approach. For estimation of parameters in exponential family nonlinear structural equation models, we introduceexpectation-maximisation algorithm, Bayesian approach, and Bayesian EL method. For influenceanalysis, we investigate the case-deletion method and local influence analysis method fromthe frequentist and Bayesian viewpoints. For model selection, we present the modified Akaikeinformation criterion and penalised method.

关键词： Bayesian method empirical likelihood method expectation-maximisation algorithm local influence analysis missing data model selection

A Test for a Normal Covariance Matrix with missing data

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校园英语 2015年第12期 255-255页

作者：禹建奇桂林理工大学理学院

The problem of testing the normal covariance matrix equal to a specified matrix with missing data is *** exact Chi-Square test statistic based on the incomplete data is derived.

关键词： missing data exact test Chi-Square test

A Practical Approach for missing Wireless Sensor Networks data Recovery

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China Communications 2024年第5期21卷 202-217页

作者： Song Xiaoxiang Guo Yan Li Ning Ren Bing The College of Communications Engineering Army Engineering University of PLANanjing 210007China The 63891 Unit of PLA Luoyang 471000China

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data ***,missing data is almost inevitable in the process of data acquisition and *** methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs ***,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data ***,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior *** designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is ***,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established ***,an improved fast matching pursuit algorithm is proposed to solve the *** results show that the proposed method can effectively recover the missing WSNs data.

关键词： average cross correlation matching pursuit missing data wireless sensor networks

Comparison of two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials: a simulation study

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Medical data Mining 2024年第1期7卷 10-15页

作者： Jing-Bo Zhai Tian-Ci Guo Wei-Jie Yu School of Public Health Tianjin University of Traditional Chinese MedicineTianjin 301617China.

Background:missing data are frequently occurred in clinical *** to the development of precision medicine,there is an increased interest in N-of-1 *** models are one of main statistical methods for analyzing the data of N-of-1 *** simulation study aimed to compare two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 ***:The simulated data of N-of-1 trials with different coefficients of autocorrelation,effect sizes and missing ratios are obtained by SAS 9.1 *** missing values are filled with mean filling and regression filling respectively in the condition of different coefficients of autocorrelation,effect sizes and missing ratios by SPSS 25.0 *** models are built to estimate the posterior means by Winbugs 14 ***:When the missing ratio is relatively small,e.g.5%,missing values have relatively little effect on the *** effects may be underestimated when the coefficient of autocorrelation increases and no filling is ***,it may be overestimated when mean or regression filling is used,and the results after mean filling are closer to the actual effect than regression *** the case of moderate missing ratio,the estimated effect after mean filling is closer to the actual effect compared to regression *** a large missing ratio(20%)occurs,data missing can lead to significantly underestimate the *** this case,the estimated effect after regression filling is closer to the actual effect compared to mean ***:data missing can affect the estimated therapeutic effects using Bayesian models in N-of-1 *** present study suggests that mean filling can be used under situation of missing ratio≤10%.Otherwise,regression filling may be preferable.

关键词： N-of-1 trial Bayesian missing data simulation study

Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 427-431页

作者： Chao Ren Han Yu Yan Xu Zhao Yang Dong School of Computer Science and Engineering Nanyang Technological UniversitySingapore School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore

This letter proposes a reliable transfer learning(RTL)method for pre-fault dynamic security assessment(DSA)in power systems to improve DSA performance in the presence of potentially related unknown *** takes individual discrepancies into consideration and can handle unknown faults with incomplete *** experiment results demonstrate high DSA accuracy and computational efficiency of the proposed RTL *** analysis shows RTL can guarantee system performance.

关键词： Adversarial training dynamic security assessment maximum classifier discrepancy missing data transfer learning