Principal Components Analysis in the presence of missing data: the principle of available data

Principal Components Analysis in the presence of missing data: the principle of available data


Authors

  • Víctor Manuel Gonzalez Rojas Escuela de Estadística. Universidad del Valle, Cali. Colombia https://orcid.org/0000-0002-6526-7879
  • Gabriel Conde Arango Escuela de Estadística. Universidad del Valle, Cali. Colombia
  • Andrés Felipe Ochoa Muñoz Escuela de Estadística. Universidad del Valle, Cali. Colombia https://orcid.org/0000-0002-0003-1347

DOI:

https://doi.org/10.22517/23447214.20591

Keywords:

ACP, datos faltantes, datos disponibles, NIPALS, relaciones de transición

Abstract

En este trabajo proponemos utilizar el principio de datos disponibles derivado del algoritmo NIPALS (Nonlinear estimation by Iterative Partial Least Square) para trabajar el Análisis de Componentes Principales (ACP) en presencia de datos faltantes. Esta propuesta es importante puesto que no realiza imputación de datos, ni se descartan individuos de la base datos, el método propuesto trabaja con los elementos pares disponibles para conformar las matrices de cuasicorrelación en  y en la descomposición espectral de estas matrices permite a través de las relaciones de transición realizar un ACP convencional. Del estudio de simulación realizado se encontró que a medida que aumenta el porcentaje de datos faltantes disminuye la inercia explicada en el primer plano factorial. Se desarrolló el algoritmo de solución bajo el entorno de programación R y se anexa el código para uso libre.

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Published

2021-06-30

How to Cite

Gonzalez Rojas, V. M., Conde Arango, G., & Ochoa Muñoz, A. F. (2021). Principal Components Analysis in the presence of missing data: the principle of available data: Principal Components Analysis in the presence of missing data: the principle of available data. Scientia Et Technica, 26(2), 219–228. https://doi.org/10.22517/23447214.20591

Issue

Vol. 26 No. 2 (2021): Abril-Junio 2021

Section

Ciencias Básicas

License

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