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Víctor Ignacio López-Ríos ;Revista Colombiana de Estadística 2014, 37 1

Author: Jaime Andrés Gaviria

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Revista Colombiana de Estadística ISSN: 0120-1751 revcoles_fcbog@unal.edu.co Universidad Nacional de Colombia Colombia Gaviria, Jaime Andrés; López-Ríos, Víctor Ignacio Locally D-Optimal Designs with Heteroscedasticity: A Comparison between Two Methodologies Revista Colombiana de Estadística, vol.
37, núm.
1, junio, 2014, pp.
95-110 Universidad Nacional de Colombia Bogotá, Colombia Available in: http:--www.redalyc.org-articulo.oa?id=89931327007 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Revista Colombiana de Estadística Junio 2014, volumen 37, no.
1, pp.
95 a 110 Locally D-Optimal Designs with Heteroscedasticity: A Comparison between Two Methodologies Diseños D-óptimos locales con heterocedasticidad: una comparación entre dos metodologías Jaime Andrés Gaviria a , Víctor Ignacio López-Ríos b Escuela de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Medellín, Colombia Abstract The classic theory of optimal experimental designs assumes that the errors of the model are independent and have a normal distribution with constant variance.
However, the assumption of homogeneity of variance is not always satisfied.
For example when the variability of the response is a function of the mean, it is probably that a heterogeneity model be more adequate than a homogeneous one.
To solve this problem there are two methods: The first one consists of incorporating a function which models the error variance in the model, the second one is to apply some of the Box-Cox transformations to both sides on the nonlinear regression model to achieve a homoscedastic model (Carroll & Ruppert 1988, Chapter 4).
In both cases it is possible to find the optimal design but the problem becomes more complex because it i...





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