Vista Equipo: Information criteria on multimodel selection of parametric regression: Biological applications

Information criteria on multimodel selection of parametric regression: Biological applications

In carrying out modelling analysis of experimental data results important to obtain a measure of the relative fit of the models as a primary selection criterion. In this sense, there are few studies based on multi-model selection techniques for the theoretical representation of data sets, so it is c...

Descripción completa

Detalles Bibliográficos
Autores Principales:	Lopez, Daniela Moraga, Palacios, Cristian Román
Formato:	Artículo (Article)
Lenguaje:	Inglés (English)
Publicado:	Universidad Santo Tomás 2015
Materias:	AIC BIC mínimos cuadrados regresión.
Acceso en línea:	http://hdl.handle.net/11634/24879

id	ir-11634-24879
recordtype	dspace
spelling	ir-11634-248792020-06-16T21:39:24Z Information criteria on multimodel selection of parametric regression: Biological applications Criterios de información en la selección multimodelo de regresiones paramétricas: aplicaciones biológicas Lopez, Daniela Moraga Palacios, Cristian Román AIC BIC mínimos cuadrados regresión. In carrying out modelling analysis of experimental data results important to obtain a measure of the relative fit of the models as a primary selection criterion. In this sense, there are few studies based on multi-model selection techniques for the theoretical representation of data sets, so it is common to incur in a misinterpretation of the existing patterns, or even more, the incorrect extrapolation and prediction based on the wrong model. This paper is intended to evaluate in 40 sets of data from various publications researches the effectiveness of the regression model designated by the authors by contrasting six regression models with the Akaike and Bayesian information criteria and to discuss its implications on subsequent interpretations made. It was found that the linear regression model was successful only in 13.35% of the datasets (AIC= 15%; BIC = 11.7%), but in the other hand, the logarithmic model was the most successful model in 38.5% of the cases (AIC= 35%; BIC= 41.1%) which casts doubt on the efficiency of the linear regression over other types of regression under biological data. It is clear then that the features discussed from regression analysis regardless multi-model selection depends on the subjectivity of the researcher and often incurs in selecting a model that involves greater losses of the information contained in the data set. Cuando se lleva a cabo análisis de modelamiento usando datos experimentales es importante obtener una medida de la conﬁabilidad del ajuste relativo de cada modelo como un criterio principal de seleccion. En este sentido, existen pocos estudios basados en técnicas de selección multimodelo para realizar representaciones teóricas de conjuntos de datos, por lo que es común incurrir en una mala interpretación de los patrones existentes, o más aún, extrapolar incorrectamente y basar predicciones en modelos equívocos. Este documentos está enfocado en evaluar en 40 conjuntos de datos provenientes de varios estudios ecológicos publicados, la efectividad de la regresión lineal designada por los autores al contrastarla con seis modelos de regresión usando los criterios de información de Akaike y Bayesiano, y además discutir las implicaciones de las interpretaciones subsecuentes de acuerdo al modelo. Se encontró que el modelo de regresión lineal fue exitoso en solo el 13.35% de los conjuntos de datos (15% de los conjuntos de datos para AIC y 11.7% de los datos para BIC), pero por otro lado, el modelo logarítmico fue m´as exitoso en el 38.5% de los casos (35% de los conjuntos de datos para AIC y 41.1% de los datos para BIC), generando dudas sobre la eﬁciencia del modelo de regresi´on lineal sobre los otros tipos de regresión en datos biológicos. 2015-07-01 2020-06-16T21:39:24Z 2020-06-16T21:39:24Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1487 10.15332/s2027-3355.2015.0001.03 http://hdl.handle.net/11634/24879 eng https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1487/2197 https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1487/3610 https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1487/5262 https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1487/5263 https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1487/5264 Derechos de autor 2015 Comunicaciones en Estadística application/pdf text/plain image/jpeg application/zip application/zip Universidad Santo Tomás Comunicaciones en Estadística; Vol. 8 Núm. 1 (2015); 45-57 2339-3076 2027-3355 Comunicaciones en Estadística; Vol. 8 No. 1 (2015); 45-57
institution	Universidad Santo Tomas
collection	DSpace
language	Inglés (English)
topic	AIC BIC mínimos cuadrados regresión.
spellingShingle	AIC BIC mínimos cuadrados regresión. Lopez, Daniela Moraga Palacios, Cristian Román Information criteria on multimodel selection of parametric regression: Biological applications
description	In carrying out modelling analysis of experimental data results important to obtain a measure of the relative fit of the models as a primary selection criterion. In this sense, there are few studies based on multi-model selection techniques for the theoretical representation of data sets, so it is common to incur in a misinterpretation of the existing patterns, or even more, the incorrect extrapolation and prediction based on the wrong model. This paper is intended to evaluate in 40 sets of data from various publications researches the effectiveness of the regression model designated by the authors by contrasting six regression models with the Akaike and Bayesian information criteria and to discuss its implications on subsequent interpretations made. It was found that the linear regression model was successful only in 13.35% of the datasets (AIC= 15%; BIC = 11.7%), but in the other hand, the logarithmic model was the most successful model in 38.5% of the cases (AIC= 35%; BIC= 41.1%) which casts doubt on the efficiency of the linear regression over other types of regression under biological data. It is clear then that the features discussed from regression analysis regardless multi-model selection depends on the subjectivity of the researcher and often incurs in selecting a model that involves greater losses of the information contained in the data set.
format	Artículo (Article)
author	Lopez, Daniela Moraga Palacios, Cristian Román
author_facet	Lopez, Daniela Moraga Palacios, Cristian Román
author_sort	Lopez, Daniela Moraga
title	Information criteria on multimodel selection of parametric regression: Biological applications
title_short	Information criteria on multimodel selection of parametric regression: Biological applications
title_full	Information criteria on multimodel selection of parametric regression: Biological applications
title_fullStr	Information criteria on multimodel selection of parametric regression: Biological applications
title_full_unstemmed	Information criteria on multimodel selection of parametric regression: Biological applications
title_sort	information criteria on multimodel selection of parametric regression: biological applications
publisher	Universidad Santo Tomás
publishDate	2015
url	http://hdl.handle.net/11634/24879
_version_	1712105352573485056
score	12,111491

Information criteria on multimodel selection of parametric regression: Biological applications

Ejemplares similares