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

iglesias, P; Jorquera, H; Palma, W

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. © 2005 Elsevier B.V. All rights reserved.

Más información

Título según WOS: Data analysis using regression models with missing observations and long-memory: an application study
Título según SCOPUS: Data analysis using regression models with missing observations and long-memory: An application study
Título de la Revista: COMPUTATIONAL STATISTICS DATA ANALYSIS
Volumen: 50
Número: 8
Editorial: Elsevier
Fecha de publicación: 2006
Página de inicio: 2028
Página final: 2043
Idioma: English
URL: http://linkinghub.elsevier.com/retrieve/pii/S0167947305000496
DOI:

10.1016/j.csda.2005.03.007

Notas: ISI, SCOPUS