Article
Biology
Yang Liu, Yukun Liu, Pengfei Li, Lin Zhu
Summary: The paper introduces a maximum empirical likelihood estimation method for estimating abundance in the presence of missing covariates, showing it has smaller mean square error in simulations and more accurate coverage probabilities for confidence intervals than existing methods.
Article
Economics
Xuerong Chen, Denis Heng-Yan Leung, Jing Qin
Summary: In this study, we address the challenge of non-random missing data by considering an unspecified single index model for the propensity score and constructing a pseudo-likelihood based on complete data. The pseudo-likelihood provides asymptotically normal estimates and simulations demonstrate its favorable performance compared to existing methods.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Automation & Control Systems
Xinpeng Liu, Xianqiang Yang, Miao Yu
Summary: This paper investigates the identification of switched FIR systems in the presence of random missing outputs, addressing the practical problems of unknown number of local models and unknown switching mechanism. A probabilistic model is constructed from a Bayesian perspective, and an algorithm to estimate all unknown parameters is derived using the VB approach. Results from simulated examples and the mass-spring-damper system demonstrate the efficacy of the developed algorithm.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Statistics & Probability
Zhan Liu, Chun Yip Yau
Summary: This paper proposed a method for handling nonignorable missing data in longitudinal surveys, focusing on time series models. Results from simulation studies and an empirical example were presented to demonstrate the usefulness of the proposed methodology.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2021)
Article
Biology
D. M. Farewell, R. M. Daniel, S. R. Seaman
Summary: The paper presents a natural and extensible measure-theoretic approach to handling missingness at random and characterizes observed data within the standard missing-data framework. It is demonstrated that common missingness-at-random conditions are equivalent to specific stochastic processes being adapted to a set-indexed filtration, ensuring the usual factorization of likelihood ratios. The theory is shown to easily incorporate explanatory variables, describe longitudinal data in continuous time, and allow for a more general coarsening of observations.
Article
Statistics & Probability
Yang Zhao
Summary: This study generalized the semiparametric likelihood model for dealing with nonmonotone missing data patterns with any number of variables. It also introduced a semiparametric estimator for assessing model fit. Simulation studies and an analysis of a specific case-control study demonstrated the practical implementation of the new method.
STATISTICAL METHODS AND APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Xiaofeng Zhang, Rong Yuan
Summary: This paper investigates a stochastic chemostat model with multiplicative noise, proving the existence and uniqueness of global positive solutions and discussing the impact of forward absorbing sets and forward attracting sets on the long-term behavior of microorganisms. Comparison between different ways of modeling randomness in the chemostat model is conducted, supported by numerical simulations.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Statistics & Probability
Hongyuan Cao, Jason P. Fine
Summary: This paper introduces a weighted partial likelihood estimation method for handling time-dependent covariates in the proportional hazards model, demonstrating consistency and asymptotic normality in theory and providing a closed form variance estimator for inference conveniently implemented using standard software. The convergence rate is shown to be slower than fully observed covariates but equivalent to that of all lagged covariate values, with simulation studies supporting the theoretical findings. Data from an Alzheimer's study demonstrates the practical utility of the methodology.
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
(2021)
Review
Computer Science, Artificial Intelligence
Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying
Summary: This paper proposes a robust noise model that incorporates a mixture of Gaussian noise modeling strategy into a baseline classification model. The number of mixture components is automatically selected using the penalized likelihood method. The proposed model defines hyperparameters from the error representation and achieves the best performance compared to conventional classification methods.
NEURAL COMPUTING & APPLICATIONS
(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
Statistics & Probability
Tadayoshi Fushiki
Summary: This study investigates the properties of the estimators obtained by applying the maximum likelihood method and the Bayesian method when data has missing values assumed to be missing at random (MAR), and the true distribution may or may not belong to the assumed statistical model.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2022)
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
Biology
Hairu Wang, Zhiping Lu, Yukun Liu
Summary: Missing data can be divided into three categories: missing completely at random(MCAR), missing at random (MAR), and missing not at random (MNAR). Valid statistical approaches depend on correctly identifying the underlying missingness mechanism. This paper proposes two score tests based on a logistic model and a semiparametric location model to distinguish between the MAR and MNAR mechanisms. The simulation and analysis of HIV data demonstrate the effectiveness of the score tests.
Article
Mathematics, Interdisciplinary Applications
Roderick J. Little
Summary: This paper reviews assumptions about missing data mechanisms and discusses statistical analysis methods related to missing data, including Rubin's MAR definition and its limitations, as well as some sufficient conditions. It also explores other definitions and methods related to missing data, and presents an argument for weakening the conditions for frequentist maximum likelihood inference.
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 8, 2021
(2021)
Article
Biology
Chixiang Chen, Biyi Shen, Aiyi Liu, Rongling Wu, Ming Wang
Summary: Longitudinal data are often missing in outcomes and time-dependent risk factors, posing a significant challenge in handling the various missing patterns and mechanisms. A novel semiparametric framework is proposed for analyzing such data with missing responses and covariates, with innovative calibrated propensity scores for robust estimation. This approach shows promising performance and advantages compared to existing methods.
