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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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On generalized latent factor modeling and inference for high-dimensional binomial data.

Ting Fung Ma1, Fangfang Wang2, Jun Zhu3

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.

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|October 6, 2022
PubMed
Summary
This summary is machine-generated.

We introduce a new statistical model for analyzing discrete data, like binomial responses. This method is efficient and scalable for large datasets, accurately estimating effects and latent structures in genetic studies.

Keywords:
Discrete bounded dataeigenanalysisgene-environment associationgeneralized linear mixed modelsub-Gaussian error

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Area of Science:

  • Statistics
  • Genetics
  • Bioinformatics

Background:

  • Analyzing discrete and bounded response variables, particularly binomial outcomes, presents statistical challenges.
  • High-dimensional data in terms of subjects and features requires computationally efficient and scalable methods.
  • Latent factor models are valuable for uncovering underlying structures in complex datasets.

Purpose of the Study:

  • To develop a hierarchical generalized latent factor model for discrete and bounded response variables.
  • To propose a novel, computationally efficient, and scalable two-step estimation procedure and statistical inference.
  • To establish the validity and asymptotic properties of the proposed estimation method.

Main Methods:

  • A hierarchical generalized latent factor model was developed.
  • A novel two-step estimation procedure and statistical inference were created.
  • Asymptotic properties of effect size, latent structure, and number of latent factors were established.

Main Results:

  • The proposed method is computationally efficient and scalable for high-dimensional data.
  • The estimation procedure demonstrates validity, including accurate estimation of latent structures and effect sizes.
  • The methodology successfully determined the estimated number of latent factors.

Conclusions:

  • The hierarchical generalized latent factor model provides a valid and efficient approach for analyzing discrete and bounded response variables.
  • The novel two-step estimation procedure is suitable for high-dimensional data common in fields like gene-environment association studies.
  • The method accurately estimates latent structures, effect sizes, and the number of latent factors, confirmed by simulation and application.