Data analysis using regression models with missing observations and long-memory: an application study

  • Authors:

  • Pilar Iglesias;Hector Jorquera;Wilfredo Palma

  • Affiliations:

  • Department of Statistics, P. Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile;Department of Chemical & Bioprocess Engineering, P. Universidad Catóólica de Chile, Santiago, Chile;Department of Statistics, P. Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2006

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Abstract

The objective of this work is to propose a statistical methodology to handle regression data exhibiting long memory errors and missing values. This type of data appears very often in many areas, including hydrology and environmental sciences, among others. A generalized linear model is proposed to deal with this problem and an estimation strategy is developed that combines both classical and Bayesian approaches. The estimation methodology proposed is illustrated with an application to air pollution data which shows the impact of the long memory in the statistical inference and of the missing values on the computations. From a Bayesian standpoint, genuine priors are considered for the parameters of the model which are justified within the context of the air pollution model derivation.