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Fast precision estimation in high-dimensional multivariate joint models.

Vahid Nassiri1, Anna Ivanova1, Geert Molenberghs1,2

  • 1I-BioStat, KU Leuven, Kapucijnenvoer 35 blok d - box 7001, BE3000 Leuven, Belgium.

Biometrical Journal. Biometrische Zeitschrift
|June 17, 2017
PubMed
Summary
This summary is machine-generated.

A novel method significantly speeds up precision calculations for high-dimensional multivariate joint models. This approach offers substantial computational time savings for statistical analysis, improving efficiency in complex data modeling.

Keywords:
Joint modelMultiple outputationRandom effects

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • High-dimensional multivariate joint models are crucial for analyzing complex datasets.
  • Calculating the precision of parameter estimates in these models can be computationally intensive.

Purpose of the Study:

  • To propose a computationally efficient method for estimating the precision of parameters in high-dimensional multivariate joint models.
  • To leverage the multiple imputation idea and a pairwise approach for enhanced speed.

Main Methods:

  • The study introduces a novel approach based on the multiple imputation concept.
  • A pairwise strategy is employed for calculating parameter estimate precision.
  • The method is validated through simulations and real-world data analysis.

Main Results:

  • Simulations demonstrated computational speedups exceeding 2500 times compared to existing methods.
  • Real-world data analysis showed a time gain of over 330 times.
  • The proposed method significantly reduces computational burden for high-dimensional joint models.

Conclusions:

  • The proposed fast computation method is effective for high-dimensional multivariate joint models.
  • This approach offers a practical solution for accelerating statistical analyses in complex data settings.
  • The method provides substantial computational advantages in both simulated and real data scenarios.