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Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data

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Rou Zhong, Chunming Zhang, Jingxiao Zhang

Methodology · Submitted 9 Jun 2022

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

The authors' main claim The theoretical properties are established in terms of both consistency and sparsistency, and the simulation studies further verify the satisfying performance of our method when compared with existing approaches.

how it is stated · asserted 1.00 read the record →

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arXiv ID2206.04315 (version 1)
Pith IDGLGMGQF4, Pith's permanent record of this paper and its review
CitationChunming Zhang, Jingxiao Zhang, Rou Zhong. "Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data." arXiv:2206.04315 (2022). https://pith.science/paper/GLGMGQF4
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,449 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.86 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.93 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

In longitudinal study, it is common that response and covariate are not measured at the same time, which complicates the analysis to a large extent. In this paper, we take into account the estimation of generalized varying coefficient model with such asynchronous observations. A penalized kernel-weighted estimating equation is constructed through kernel technique in the framework of functional data analysis. Moreover, local sparsity is also considered in the estimating equation to improve the interpretability of the estimate. We extend the iteratively reweighted least squares (IRLS) algorithm in our computation. The theoretical properties are established in terms of both consistency and sparsistency, and the simulation studies further verify the satisfying performance of our method when compared with existing approaches. The method is applied to an AIDS study to reveal its practical merits.

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 In longitudinal study, it is common that response and covariate are not measured at the same time, which complicates the analysis to a large extent. background 1.00 0.05 0.02 asserted 0.99
s1 In this paper, we take into account the estimation of generalized varying coefficient model with such asynchronous observations. contribution 0.99 0.87 0.05 asserted 0.92
s2 A penalized kernel-weighted estimating equation is constructed through kernel technique in the framework of functional data analysis. method 0.99 0.92 0.04 asserted 1.00
s3 Moreover, local sparsity is also considered in the estimating equation to improve the interpretability of the estimate. method 0.66 0.87 0.06 asserted 0.97
s4 We extend the iteratively reweighted least squares (IRLS) algorithm in our computation. method 0.98 0.92 0.02 asserted 1.00
s5 The theoretical properties are established in terms of both consistency and sparsistency, and the simulation studies further verify the satisfying performance of our method when compared with existing approaches. result 0.92 0.95 0.64 asserted 1.00
s6 The method is applied to an AIDS study to reveal its practical merits. contribution 0.41 0.93 0.16 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.85.

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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

In longitudinal study, it is common that response and covariate are not measured at the same time, which complicates the analysis to a large extent. In this paper, we take into account the estimation of generalized varying coefficient model with such asynchronous observations. A penalized kernel-weighted estimating equation is constructed through kernel technique in the framework of functional data analysis. Moreover, local sparsity is also considered in the estimating equation to improve the interpretability of the estimate. We extend the iteratively reweighted least squares (IRLS) algorithm in our computation. The theoretical properties are established in terms of both consistency and sparsistency, and the simulation studies further verify the satisfying performance of our method when compared with existing approaches. The method is applied to an AIDS study to reveal its practical merits.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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