Carrasco, JMF, Figueroa-Zuñiga, JI, Leiva, V et al. (2 more authors) (2020) An errors-in-variables model based on the Birnbaum-Saunders and its diagnostics with an application to earthquake data. Stochastic Environmental Research and Risk Assessment, 34 (2). pp. 369-380. ISSN 1436-3240
Abstract
Regression modelling where explanatory variables are measured with error is a common problem in applied sciences. However, if inappropriate analysis methods are applied, then unreliable conclusions can be made. This work deals with estimation and diagnostic analytics in regression modelling based on the Birnbaum–Saunders distribution using additive measurement errors. The maximum pseudo-likelihood and regression calibration methods are used for parameter estimation. We also carry out a residual analysis and apply global and local diagnostic techniques in order to detect anomalous and potentially influential observations. Simulations are conducted to validate the proposed approach and to evaluate performance. A real-world data set, related to earthquakes, is used to illustrate the new approach.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © Springer-Verlag GmbH Germany, part of Springer Nature 2020. This is an author produced version of an article published in Stochastic Environmental Research and Risk Assessment. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Diagnostic techniques; Likelihood methods; Measurement errors; Monte Carlo simulation; Ox and R software; Regression analysis |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jan 2020 12:09 |
Last Modified: | 01 Feb 2021 01:38 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00477-020-01767-3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155563 |