Envelope method with ignorable missing data.

Linquan Ma, Lan Liu, Wei Yang

Abstract read

In one paragraph

Article in Electronic journal of statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Linquan MaDepartment of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Lan LiuSchool of Statistics, University of Minnesota at Twin Cities, Minneapolis, Minnesota, USA.

Wei YangPerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Continuation of the Coordinating Center for the Chronic Renal Insufficiency Cohort (CRIC) StudyU24DK060990 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Amanda Hyre Anderson, Laura M Dember · 2018 to 2026

$21.5M

NIDDK NIH HHS U24 DK060990

6 · The paper itself

Abstract

Envelope method was recently proposed as a method to reduce the dimension of responses in multivariate regressions. However, when there exists missing data, the envelope method using the complete case observations may lead to biased and inefficient results. In this paper, we generalize the envelope estimation when the predictors and/or the responses are missing at random. Specifically, we incorporate the envelope structure in the expectation-maximization (EM) algorithm. As the parameters under the envelope method are not pointwise identifiable, the EM algorithm for the envelope method was not straightforward and requires a special decomposition. Our method is guaranteed to be more efficient, or at least as efficient as, the standard EM algorithm. Moreover, our method has the potential to outperform the full data MLE. We give asymptotic properties of our method under both normal and non-normal cases. The efficiency gain over the standard EM is confirmed in simulation studies and in an application to the Chronic Renal Insufficiency Cohort (CRIC) study.

Indexed as

Efficiency gainEM-algorithmMissing dataMultivariate regressionSufficient dimension reduction

Identifiers

PMID37842008

DOI10.1214/21-ejs1881

PMCPMC10571183

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.