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Variable Selection in Switching Dynamic Regression Models

  • Autores: Dayna P. Saldana-zepeda, Ciro Velasco Cruz, Victor H. Torres-preciado
  • Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 45, Nº. 1, 2022, págs. 231-263
  • Idioma: inglés
  • DOI: 10.15446/rce.v45n1.85385
  • Títulos paralelos:
    • Selección de variables en modelos de regresión dinámicos de cambios de régimen
  • Enlaces
  • Resumen
    • español

      Resumen Fenómenos dinámicos complejos en los que la dinámica está relacionada con eventos (modos) que provocan cambios estructurales a lo largo del tiempo, se aproximan mediante un sistema dinámico lineal de cambio de régimen (SDLR). Extendemos el SDLR al permitir que el error de medición sea específico del modo, una forma flexible de modelar datos no estacionarios. Además, para los modelos que son funciones de variables explicativas, adaptamos un método de selección de variables para identificar cuáles de ellas son significativas en cada modo. El modelo propuesto es un modelo bayesiano no paramétrico flexible que permite conocer el número de modos y su ubicación, y dentro de cada modo, identifica las variables significativas y estima los coeficientes de regresión. El desempeño del modelo se evalúa mediante simulación y se presentan dos ejemplos de aplicación de un conjunto de datos de series de tiempo meteorológicas de Barranquilla, Colombia.

    • English

      Abstract Complex dynamic phenomena in which dynamics is related to events (modes) that cause structural changes over time, are well described by the switching linear dynamical system (SLDS). We extend the SLDS by allowing the measurement noise to be mode-specific, a flexible way to model non stationary data. Additionally, for models that are functions of explanatory variables, we adapt a variable selection method to identify which of them are significant in each mode. Our proposed model is a flexible Bayesian nonparametric model that allows to learn about the number of modes and their location, and within each mode, it identifies the significant variables and estimates the regression coefficients. The model performance is evaluated by simulation and two application examples from a dataset of meteorological time series of Barranquilla, Colombia are presented.

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