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Unsupervised learning of Dirichlet process mixture models with missing data

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Science China(Information Sciences) 2016年第1期59卷 161-174页

作者： Xunan ZHANG Shiji SONG Lei ZHU Keyou YOU Cheng WU Department of Automation Tsinghua University China Ocean Mineral Resources R&D Association

This study presents a novel approach to unsupervised learning for clustering with missing *** first extend a finite mixture model to the infinite case by considering Dirichlet process mixtures, which can automatically determine the number of mixture components or clusters. Furthermore, we view the missing features as latent variables and compute the posterior distributions using the variational Bayesian expectation maximization algorithm, which optimizes the evidence lower bound on the complete-data log marginal likelihood. We demonstrate the performance on several artificial data sets with missing values. The experimental results indicate that the proposed method outperforms some classic imputation methods. We finally present an application to seabed hydrothermal sulfide color images analysis problem.

关键词： Dirichlet processes missing data clustering variational Bayesian image analysis

New Chain Imputation Methods for Estimating Population Mean in the Presence of missing data Using Two Auxiliary Variables

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Communications in Mathematics and Statistics 2023年第2期11卷 325-340页

作者： Shashi Bhushan Abhay Pratap Pandey Department of Mathematics and Statistics Dr.Shakuntala Misra National Rehabilitation UniversityLucknowIndia Department of Statistics Ramanujan College University of DelhiNew DelhiIndia

This article deals with some new chain imputation methods by using two auxiliary variables under missing completely at random(MCAR)*** proposed generalized classes of chain imputation methods are tested from the viewpoint of optimality in terms of *** proposed imputation methods can be considered as an efficient extension to the work of Singh and Horn(Metrika 51:267-276,2000),Singh and Deo(Stat Pap 44:555-579,2003),Singh(Stat A J Theor Appl Stat 43(5):499-511,2009),Kadilar and Cingi(Commun Stat Theory Methods 37:2226-2236,2008)and Diana and Perri(Commun Stat Theory Methods 39:3245-3251,2010).The performance of the proposed chain imputation methods is investigated relative to the conventional chain-type imputation *** theoretical results are derived and comparative study is conducted and the results are found to be quite encouraging providing the improvement over the discussed work.

关键词： missing data Imputation MCAR

Empirical Likelihood Confidence Intervals for the Differences of Quantiles with missing data

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Acta Mathematicae Applicatae Sinica 2009年第1期25卷 105-116页

作者： Yong-song Qin Yong-jiang Qian School of Mathematical Sciences Guangxi Normal University Guilin Guangxi 541004 China

Suppose that there are two nonparametric populations x and y with missing data on both of them. We are interested in constructing confidence intervals on the quantile differences of x and y. Random imputation is used.... 详细信息

关键词： Empirical likelihood confidence Interval quantile missing data imputation

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Comparison of missing data Imputation Methods in Time Series Forecasting

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Computers, Materials & Continua 2022年第1期70卷 767-779页

作者： Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim Division of Software Convergence Hanshin UniversityGyeonggi18101Korea Contents Convergence Software Research Institute Kyonggi UniversityGyeonggi16227Korea Division of AI Computer Science and Engineering Kyonggi UniversityGyeonggi16227Korea

Time series forecasting has become an important aspect of data analysis and has many real-world ***,undesirable missing values are often encountered,which may adversely affect many forecasting *** this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time *** approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created *** an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation ***,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting *** results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.

关键词： missing data imputation method time series forecasting LSTM

Semi-empirical Likelihood Confidence Intervals for the Differences of Quantiles with missing data

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Acta Mathematica Sinica,English Series 2009年第5期25卷 845-854页

作者： Yong Song QIN Jun Chao ZHANG School of Mathematical Sciences Guangxi Normal University Guilin 541004 P. R. China Department of Mathematics Harbin University Harbin 150086 P. R. China

Detecting population （group） differences is useful in many applications, such as medical research. In this paper, we explore the probabilistic theory for identifying the quantile differences .between two populations. Suppose that there are two populations x and y with missing data on both of them, where x is nonparametric and y is parametric. We are interested in constructing confidence intervals on the quantile differences of x and y. Random hot deck imputation is used to fill in missing data. Semi-empirical likelihood confidence intervals on the differences are constructed.

关键词： empirical likelihood confidence interval quantile missing data hot deck imputation

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Stochastic Gradient Descent for Linear Systems with missing data

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Numerical Mathematics(Theory,Methods and Applications) 2019年第1期12卷 1-20页

作者： Anna Ma Deanna Needell Claremont Graduate University ClaremontCA 91711USA University of California Los AngelesLos Angeles CA 90095USA

Traditional methods for solving linear systems have quickly become imprac-tical due to an increase in the size of available *** massive amounts of data is further complicated when the data is incomplete or has missing *** this work,we address the obstacles presented when working with large data and incom-plete data *** particular,we propose to adapt the Stochastic Gradient Descent method to address missing data in linear *** proposed algorithm,the Stochastic Gradient Descent for missing data method(mSGD),is introduced and theoretical convergence guarantees are *** addition,we include numerical experiments on simulated and real world data that demonstrate the usefulness of our method.

关键词： Linear systems missing data iterative methods least squares problems

data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第4期23卷 303-313页

作者： Feng SUN He XU Yu-han ZHAO Yu-dong ZHANG College of Mechanical and Electrical Engineering Harbin Engineering UniversityHarbin150001China

A control valve is one of the most widely used machines in hydraulic ***,it often works in harsh environments and failure occurs from time to *** intelligent and robust control valve fault diagnosis is therefore important for operation of the *** this study,a fault diagnosis based on the mathematical model(MM)imputation and the modified deep residual shrinkage network(MDRSN)is proposed to solve the problem that data-driven models for control valves are susceptible to changing operating conditions and missing *** multiple fault time-series samples of the control valve at different openings are collected for fault diagnosis to verify the effectiveness of the proposed *** effects of the proposed method in missing data imputation and fault diagnosis are *** with random and k-nearest neighbor(KNN)imputation,the accuracies of MM-based imputation are improved by 17.87%and 21.18%,in the circumstances of a20.00%data missing rate at valve opening from 10%to 28%.Furthermore,the results show that the proposed MDRSN can maintain high fault diagnosis accuracy with missing data.

关键词： Control valve missing data Fault diagnosis Mathematical model(MM) Deep residual shrinkage network(DRSN)

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missing data Imputation: A Comprehensive Review

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Journal of Computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： missing data Machine Learning Prediction Deep Learning Imputation

Fraction of missing Information (γ) at Different missing data Fractions in the 2012 NAMCS Physician Workflow Mail Survey

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Applied Mathematics 2016年第10期7卷 1057-1067页

作者： Qiyuan Pan Rong Wei National Center for Health Statistics Centers for Disease Control and Prevention Hyattsville MD USA

In his 1987 classic book on multiple imputation (MI), Rubin used the fraction of missing information, γ, to define the relative efficiency (RE) of MI as RE = (1 + γ/m)?1/2, where m is the number of imputations, leading to the conclusion that a small m (≤5) would be sufficient for MI. However, evidence has been accumulating that many more imputations are needed. Why would the apparently sufficient m deduced from the RE be actually too small? The answer may lie with γ. In this research, γ was determined at the fractions of missing data (δ) of 4%, 10%, 20%, and 29% using the 2012 Physician Workflow Mail Survey of the National Ambulatory Medical Care Survey (NAMCS). The γ values were strikingly small, ranging in the order of 10?6 to 0.01. As δ increased, γ usually increased but sometimes decreased. How the data were analysed had the dominating effects on γ, overshadowing the effect of δ. The results suggest that it is impossible to predict γ using δ and that it may not be appropriate to use the γ-based RE to determine sufficient m.

关键词： Multiple Imputation Fraction of missing Information (γ) Sufficient Number of Imputations missing data NAMCS