Envelope method with ignorable missing data

Ma, Linquan; Liu, Lan; Yang, Wei

doi:10.1214/21-ejs1881

Cited by 4 publications

(3 citation statements)

References 45 publications

(59 reference statements)

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“…Moreover, the missing data techniques may be combined with the mixed effects model to further relax conditions. Ma et al (2019) discussed the envelope method under the ignorable missingness of predictors and covariates. In this paper, we assume that the measures collected at each visit are balanced and repeated measures may be collected at different time points across individuals.…”

Section: Discussionmentioning

confidence: 99%

Mixed Effects Envelope Models

Shi

Liu

2021

Preprint

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When multiple measures are collected repeatedly over time, redundancy typically exists among responses. The envelope method was recently proposed to reduce the dimension of responses without loss of information in regression with multivariate responses. It can gain substantial efficiency over the standard least squares estimator. In this paper, we generalize the envelope method to mixed effects models for longitudinal data with possibly unbalanced design and time-varying predictors. We show that our model provides more efficient estimators than the standard estimators in mixed effects models. Improved accuracy and efficiency of the proposed method over the standard mixed effects model estimator are observed in both the simulations and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.

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Section: Discussionmentioning

confidence: 99%

Mixed Effects Envelope Models

Shi

Liu

2021

Preprint

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“…There is no universal rule to know whether a missing dataset is MCAR, MAR or NMAR. Thus, it is always important to observe and understand the dataset in order to better decide how to impute missing data [15,16].…”

Section: Table I the Categories Of Missing Datamentioning

confidence: 99%

An Improved GAIN-Based Model for Image Imputation

Sadegh¹,

Mohameden²,

LemineSalihi³

et al. 2023

IJETAE

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Missing data represent a main issue having a considerable impact not only on the inference quality but also on the prediction relevance. A problem that researchers have constantly tried to solve, ranging from Statistics to Deep Learning through Machine Learning. Recently, a growing interest is being given to Deep Learning models and particularly to Generative Adversarial Networks (GAN) given their interesting performance and their adaptability to various scenarios. In this paper, we propose an improvement of Generative Adversarial Imputation Nets (GAIN) which is one of the main GAN-based models having proved its worth in image imputation. The results found confirm the proposed improvement efficiency.

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“…Since the introduction of the envelope estimator, there has been an explosion of work on using envelope-based dimension reduction methods for multivariate regression models [10,35,36,11,9,8,13,14,24,34,6,32,26]. Our paper focuses on one popular extension and improvement of the original work, the inner envelope estimator of Su and Cook [36].…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Efficient Dimension Reduction in multivariate regression with an Inner Envelope

Ma¹,

Kang²,

Liu³

2022

Preprint

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Recently, Su and Cook [36] proposed a dimension reduction technique called the inner envelope which can be substantially more efficient than the original envelope [10] or existing dimension reduction techniques for multivariate regression. However, their technique relied on a linear model with normally distributed error, which may be violated in practice. In this work, we propose a semiparametric variant of the inner envelope that does not rely on the linear model nor the normality assumption. We show that our proposal leads to globally and locally efficient estimators of the inner envelope spaces. We also present a computationally tractable algorithm to estimate the inner envelope. Our simulations and real data analysis show that our method is both robust and efficient compared to existing dimension reduction methods in a diverse array of settings.

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Envelope method with ignorable missing data

Cited by 4 publications

References 45 publications

Mixed Effects Envelope Models

Mixed Effects Envelope Models

An Improved GAIN-Based Model for Image Imputation

Semiparametric Efficient Dimension Reduction in multivariate regression with an Inner Envelope

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