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A note on multiple imputation for method of moments estimation

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Shu Yang, Jae Kwang Kim

Methodology · Submitted 27 Aug 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim A new variance estimator based on over-imputation is proposed to provide asymptotically valid inference for method of moments estimation.

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arXiv ID1508.06977 (version 1)
Pith ID77X5JHJK, Pith's permanent record of this paper and its review
CitationJae Kwang Kim, Shu Yang. "A note on multiple imputation for method of moments estimation." arXiv:1508.06977 (2015). https://pith.science/paper/77X5JHJK
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,224 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.17 · human participants 0.05 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.09 · compares with prior results 0.73 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Multiple imputation is a popular imputation method for general purpose estimation. Rubin(1987) provided an easily applicable formula for the variance estimation of multiple imputation. However, the validity of the multiple imputation inference requires the congeniality condition of Meng(1994), which is not necessarily satisfied for method of moments estimation. This paper presents the asymptotic bias of Rubin's variance estimator when the method of moments estimator is used as a complete-sample estimator in the multiple imputation procedure. A new variance estimator based on over-imputation is proposed to provide asymptotically valid inference for method of moments estimation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Multiple imputation is a popular imputation method for general purpose estimation. background 1.00 0.03 0.02 asserted 1.00
s1 Rubin(1987) provided an easily applicable formula for the variance estimation of multiple imputation. background 1.00 0.03 0.02 asserted 1.00
s2 However, the validity of the multiple imputation inference requires the congeniality condition of Meng(1994), which is not necessarily satisfied for method of moments estimation. gap 0.61 0.05 0.09 asserted 0.87
s3 This paper presents the asymptotic bias of Rubin's variance estimator when the method of moments estimator is used as a complete-sample estimator in the multiple imputation procedure. contribution 0.54 0.89 0.23 asserted 0.99
s4 A new variance estimator based on over-imputation is proposed to provide asymptotically valid inference for method of moments estimation. contribution 0.96 0.93 0.69 asserted 0.97

What kind of paper the classifier reports this is: method or tool 0.76 · evidence from proof or derivation 0.98.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Multiple imputation is a popular imputation method for general purpose estimation. Rubin(1987) provided an easily applicable formula for the variance estimation of multiple imputation. However, the validity of the multiple imputation inference requires the congeniality condition of Meng(1994), which is not necessarily satisfied for method of moments estimation. This paper presents the asymptotic bias of Rubin's variance estimator when the method of moments estimator is used as a complete-sample estimator in the multiple imputation procedure. A new variance estimator based on over-imputation is proposed to provide asymptotically valid inference for method of moments estimation.

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