The Experts below are selected from a list of 6357 Experts worldwide ranked by ideXlab platform
Jinghao Xue - One of the best experts on this subject based on the ideXlab platform.
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dimension reduction for data with heterogeneous Missingness
arXiv: Machine Learning, 2021Co-Authors: Yurong Ling, Zijing Liu, Jinghao XueAbstract:Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard dimension reduction techniques. As a large number of dimension reduction approaches are based on the Gram matrix, we first investigate the effects of Missingness on dimension reduction by studying the statistical properties of the Gram matrix with or without Missingness, and then we present a bias-corrected Gram matrix with nice statistical properties under heterogeneous Missingness. Extensive empirical results, on both simulated and publicly available real datasets, show that the proposed unbiased Gram matrix can significantly improve a broad spectrum of representative dimension reduction approaches.
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dimension reduction for data with heterogeneous Missingness
Uncertainty in Artificial Intelligence, 2021Co-Authors: Yurong Ling, Zijing Liu, Jinghao XueAbstract:Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard dimension reduction techniques. As a large number of dimension reduction approaches are based on the Gram matrix, we first investigate the effects of Missingness on dimension reduction by studying the statistical properties of the Gram matrix with or without missing data, and then we present a bias-corrected Gram matrix with nice statistical properties under heterogeneous Missingness. Extensive empirical results, on both simulated and publicly available real datasets, show that the proposed unbiased Gram matrix can significantly improve a broad spectrum of representative dimension reduction approaches.
Jiwei Zhao - One of the best experts on this subject based on the ideXlab platform.
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a versatile estimation procedure without estimating the nonignorable Missingness mechanism
Journal of the American Statistical Association, 2021Co-Authors: Jiwei ZhaoAbstract:We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable Missingness and identifiability is ensured by the shadow variable approach. We propo...
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a nuisance free inference procedure accounting for the unknown Missingness with application to electronic health records
Entropy, 2020Co-Authors: Jiwei Zhao, Chi ChenAbstract:We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the Missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually imposed and the regularization technique is popularly used. Motivated by the fact that the Missingness mechanism, albeit usually treated as a nuisance, is difficult to specify correctly, we adopt the conditional likelihood approach so that the nuisance can be completely ignored throughout our procedure. We establish the asymptotic theory of the proposed estimator and develop an easy-to-implement algorithm via some data manipulation strategy. In particular, under the high-dimensional setting where regularization is needed, we propose a data perturbation method for the post-selection inference. The proposed methodology is especially appealing when the true Missingness mechanism tends to be missing not at random, e.g., patient reported outcomes or real world data such as electronic health records. The performance of the proposed method is evaluated by comprehensive simulation experiments as well as a study of the albumin level in the MIMIC-III database.
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a versatile estimation procedure without estimating the nonignorable Missingness mechanism
arXiv: Methodology, 2019Co-Authors: Jiwei ZhaoAbstract:We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable Missingness and identifiability is ensured by the shadow variable approach. We propose a versatile estimation procedure where modeling of Missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different scenarios. Comprehensive simulation studies are conducted to demonstrate the finite sample performance of the method. We apply the estimator to a children's mental health study to illustrate its usefulness.
Kaj Bo Veiersted - One of the best experts on this subject based on the ideXlab platform.
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simple descriptive missing data indicators in longitudinal studies with attrition intermittent missing data and a high number of follow ups
BMC Research Notes, 2018Co-Authors: Morten Wærsted, Taran Svenssen Børnick, Jos W. R. Twisk, Kaj Bo VeierstedAbstract:Missing data in longitudinal studies may constitute a source of bias. We suggest three simple missing data indicators for the initial phase of getting an overview of the Missingness pattern in a dataset with a high number of follow-ups. Possible use of the indicators is exemplified in two datasets allowing wave nonresponse; a Norwegian dataset of 420 subjects examined at 21 occasions during 6.5 years and a Dutch dataset of 350 subjects with ten repeated measurements over a period of 35 years. The indicators Last response (the timing of last response), Retention (the number of responded follow-ups), and Dispersion (the evenness of the distribution of responses) are introduced. The proposed indicators reveal different aspects of the missing data pattern, and may give the researcher a better insight into the pattern of Missingness in a study with several follow-ups, as a starting point for analyzing possible bias. Although the indicators are positively correlated to each other, potential predictors of Missingness can have a different relationship with different indicators leading to a better understanding of the missing data mechanism in longitudinal studies. These indictors may be useful descriptive tools when starting to look into a longitudinal dataset with many follow-ups.
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Simple descriptive missing data indicators in longitudinal studies with attrition, intermittent missing data and a high number of follow-ups
BMC, 2018Co-Authors: Morten Wærsted, Taran Svenssen Børnick, Jos W. R. Twisk, Kaj Bo VeierstedAbstract:Abstract Objective Missing data in longitudinal studies may constitute a source of bias. We suggest three simple missing data indicators for the initial phase of getting an overview of the Missingness pattern in a dataset with a high number of follow-ups. Possible use of the indicators is exemplified in two datasets allowing wave nonresponse; a Norwegian dataset of 420 subjects examined at 21 occasions during 6.5 years and a Dutch dataset of 350 subjects with ten repeated measurements over a period of 35 years. Results The indicators Last response (the timing of last response), Retention (the number of responded follow-ups), and Dispersion (the evenness of the distribution of responses) are introduced. The proposed indicators reveal different aspects of the missing data pattern, and may give the researcher a better insight into the pattern of Missingness in a study with several follow-ups, as a starting point for analyzing possible bias. Although the indicators are positively correlated to each other, potential predictors of Missingness can have a different relationship with different indicators leading to a better understanding of the missing data mechanism in longitudinal studies. These indictors may be useful descriptive tools when starting to look into a longitudinal dataset with many follow-ups
Yurong Ling - One of the best experts on this subject based on the ideXlab platform.
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dimension reduction for data with heterogeneous Missingness
arXiv: Machine Learning, 2021Co-Authors: Yurong Ling, Zijing Liu, Jinghao XueAbstract:Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard dimension reduction techniques. As a large number of dimension reduction approaches are based on the Gram matrix, we first investigate the effects of Missingness on dimension reduction by studying the statistical properties of the Gram matrix with or without Missingness, and then we present a bias-corrected Gram matrix with nice statistical properties under heterogeneous Missingness. Extensive empirical results, on both simulated and publicly available real datasets, show that the proposed unbiased Gram matrix can significantly improve a broad spectrum of representative dimension reduction approaches.
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dimension reduction for data with heterogeneous Missingness
Uncertainty in Artificial Intelligence, 2021Co-Authors: Yurong Ling, Zijing Liu, Jinghao XueAbstract:Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard dimension reduction techniques. As a large number of dimension reduction approaches are based on the Gram matrix, we first investigate the effects of Missingness on dimension reduction by studying the statistical properties of the Gram matrix with or without missing data, and then we present a bias-corrected Gram matrix with nice statistical properties under heterogeneous Missingness. Extensive empirical results, on both simulated and publicly available real datasets, show that the proposed unbiased Gram matrix can significantly improve a broad spectrum of representative dimension reduction approaches.
Bradley P Carlin - One of the best experts on this subject based on the ideXlab platform.
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bayesian hierarchical models for network meta analysis incorporating nonignorable Missingness
Statistical Methods in Medical Research, 2017Co-Authors: Jing Zhang, Haitao Chu, Hwanhee Hong, Beth A Virnig, Bradley P CarlinAbstract:Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random. However, in randomized controlled trials, nonignorable Missingness or Missingness not at random may occur due to deliberate choices of treatments at the design stage. In addition, those undertaking a network meta-analysis may selectively choose treatments to include in the analysis, which may also lead to Missingness not at random. In this paper, we extend our previous work to incorporate Missingness not at ran...
