Article
Mathematics, Interdisciplinary Applications
Lei Wang, Siying Sun, Zheng Xia
Summary: This paper introduces an empirical likelihood-based inference for parameters defined by the general estimating equations, showing consistency and asymptotic normality of the resulting estimator. The authors propose a two-stage estimation procedure using dimension-reduced kernel estimators and AIPW-MI methods, demonstrating the finite-sample performance through simulation and application to HIV-CD4 data.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2021)
Article
Health Care Sciences & Services
Carl van Walraven, Christopher McCudden, Peter C. Austin
Summary: In this study, the association between laboratory test results and test order status was examined. The results showed that the missing data in laboratory tests are not missing at random. The likelihood of testing also affects the test results. Therefore, imputing missing laboratory data may lead to biased results.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2023)
Article
Agriculture, Multidisciplinary
Axiu Mao, Meilu Zhu, Endai Huang, Xi Yao, Kai Liu
Summary: Automated animal activity recognition (AAR) has made significant progress in livestock management efficiency, animal health, and welfare monitoring. However, the energy consumption and battery life of sensing devices limit the reduction of the sampling rate in practical applications. This study proposes a novel method that uses knowledge from high-sampling-rate data to improve the performance of AAR at low sampling rates.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Electrical & Electronic
Qin Shu, Jinyan He, Chang Wang
Summary: This article proposes a method for estimating utility harmonic impedance based on semiparametric estimation, which does not rely on conventional assumptions. The method utilizes the stochastic characteristic of utility harmonic current, obtains impedance through kernel density estimation, and validates its performance superiority over existing methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Statistics & Probability
Jin Jin, Liuquan Sun
Summary: In this paper, a general semi-parametric hazards regression model is proposed to deal with missing covariate observations in survival analysis. Weighted estimators and fully augmented weighted estimators are introduced and shown to be consistent and asymptotically normal. Simulation studies and application to leukemia data demonstrate the effectiveness of the proposed methods.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Mathematics, Applied
Jin Jin, Peng Ye, Liuquan Sun
Summary: In this article, a class of weighted estimating equations is proposed for handling missing covariate data in biomedical studies. The approach effectively addresses the estimation of selection probabilities in both parametric and non-parametric modeling schemes.
SCIENCE CHINA-MATHEMATICS
(2022)
Article
Biochemical Research Methods
Weijia Kong, Bertrand Jern Han Wong, Harvard Wai Hann Hui, Kai Peng Lim, Yulan Wang, Limsoon Wong, Wilson Wen Bin Goh
Summary: Missing values can have negative effects on data analysis and machine learning model development. A new mixed-model method called ProJect (Protein inJection) is proposed for missing value imputation, which is an improvement over existing methods. ProJect consistently outperforms other methods in tests on various high-throughput data types. It handles different types of missing values and achieves more accurate and reliable imputation outcomes.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Mathematics
Alicia Cordero, Beny Neta, Juan R. Torregrosa
Summary: In this paper, a novel iterative scheme with memory for finding roots with unknown multiplicities is proposed, enhancing efficiency and serving as a seed for generating higher order methods with similar characteristics. After studying its convergence order and stability, the scheme is numerically compared with similar memory-less schemes, demonstrating its good properties.
Article
Automation & Control Systems
Abhishek Grover, Brejesh Lall
Summary: This study proposes an online method for estimating missing data in a network of sensors. By utilizing the Karhunen-Loeve Expansion and a rolling window approach, the algorithm predicts missing observations and updates model parameters. The utility of the algorithm is demonstrated through empirical analysis.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Mathematical & Computational Biology
Meng Liu, Yang Zhao
Summary: In this study, new WGEEs for missing at random data were proposed, with a unified approach to improve estimation efficiency. The proposed method showed consistent and more efficient results in simulation studies with both continuous response and binary response data.
STATISTICS IN MEDICINE
(2022)
Article
Physics, Multidisciplinary
Maximilian Kertel, Markus Pauly
Summary: In this work, we rigorously apply the Expectation Maximization algorithm to determine the marginal distributions and dependence structure in a Gaussian copula model with missing data. We show how to avoid prior assumptions on the marginals through semiparametric modeling and explain how expert knowledge can be incorporated. Simulation results demonstrate that the distribution learned using this algorithm is closer to the true distribution than existing methods, and the inclusion of domain knowledge provides benefits.
Article
Engineering, Marine
Youngrong Kim, Sverre Steen, Helene Muri
Summary: This article presents a method to estimate ship principal data by using regression analysis to handle missing values, and it demonstrates the effectiveness and applicability of the method through a case study.
Article
Engineering, Civil
Xin Jing, Jungang Luo, Jingmin Wang, Ganggang Zuo, Na Wei
Summary: This paper proposes a multiple-imputation method, MICE-RF, which integrates chain equations and random forest to deal with hydro-meteorological missing values. MICE-RF provides the best imputation accuracy, supports water resources management in time, and describes imputation uncertainty.
WATER RESOURCES MANAGEMENT
(2022)
Article
Statistics & Probability
Linghui Jin, Yanyan Liu, Lisha Guo
Summary: In this study, a semiparametric likelihood estimator was proposed to improve study efficiency for survival data with non-random missing covariate entries. The estimation utilizes supplementary information on the covariate and fills the gap in deriving the asymptotic theory of the resulting estimator. The theoretical development leverages the theory of modern empirical process.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2021)
Article
Computer Science, Artificial Intelligence
Giovanni Amormino da Silva Junior, Alisson Marques da Silva
Summary: This paper introduces a new evolving fuzzy approach, eNFN-MDP, for handling single and multiple missing values in data samples, imputing estimated values and computing outputs. The approach demonstrates good performance as a simple and efficient alternative for data imputation, as shown through forecasting examples and experimental comparisons.