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SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA.

Bailey K Fosdick1, Peter D Hoff1

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Summary
This summary is machine-generated.

This study introduces a novel factor analytic model for human mortality data, improving regression analysis and predictions. The method accurately estimates country, time, and sex similarities for better mortality imputations.

Keywords:
Array normalBayesian estimationKronecker productimputationmultiway data

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Area of Science:

  • Statistics
  • Demography
  • Biostatistics

Background:

  • Human mortality data is often structured as multiway arrays (age, sex, country, year).
  • Traditional regression models inadequately handle dependencies across multiple dimensions.
  • Existing separable covariance models are computationally intensive and may lack parameter estimates.

Purpose of the Study:

  • To develop a flexible and robust statistical model for analyzing multiway human mortality data.
  • To address limitations of existing methods in handling multi-dimensional dependencies.
  • To improve the accuracy of mortality rate estimation and imputation.

Main Methods:

  • Proposed a submodel of separable covariance models with factor analytic structure for each dimension.
  • Utilized maximum likelihood and Bayesian estimation techniques.
  • Developed a likelihood ratio testing procedure for factor model rank selection.

Main Results:

  • The proposed factor analytic model outperforms simpler methods in cross-validation experiments.
  • Demonstrated the model's ability to estimate similarities between mortality rates across countries, time, and sexes.
  • Successfully imputed missing mortality data for several countries and years.

Conclusions:

  • The factor analytic approach provides an effective extension of factor analysis for array-valued mortality data.
  • This methodology enhances the accuracy of regression parameter estimates, standard errors, and predictions.
  • The model offers a powerful tool for mortality data imputation and understanding cross-dimensional similarities.