The residual error is one of the most used criteria for the choice of regression model. The large majority of the criteria for model selection are also functions of the usual variance estimate for a regression model, as Akaike criterion information [Akaike, 1973] and Mallows’ criterion [Mallows, 1964]. The effectiveness of these criteria to approach the theoretical model is related to the good estimate of the theoretical residual error of the model. This efficiency is often limited by the sample size used for the estimates. The choice of a invalid model can have bad consequences on the objective of the research in particular on the forecasts, interpretations and the conclusions. We studied by Monte Carlo simulation the effects of the dat...
© 2019 Royal Statistical Society We develop model averaging estimation in the linear regression mode...
In linear regression models with autocorrelated errors, we apply the residual likelihood approach to...
Multivariate measurement error regression models with normal errors are investigated and residuals, ...
We obtain the residual information criterion RIC, a selection criterion based on the residual log-li...
This paper demonstrates the impact of particular factors – such as a non-normal error distribution, ...
RePEc Working Paper Series: No. 03/2011This review surveys a number of common Model Selection Algori...
Monte Carlo simulation methods was used to study the effects of the data structure on the quality of...
When using multiple regression models for predictive purposes, it may be desirable to exclude some p...
This note contrasts the importance of the analysis of model residual values in assessing the invalid...
Abstract: This paper makes the case for developing a statistical model to describe the behavior of t...
This manuscript addresses the problem of model selection, studied in the linear regression framework...
In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assum...
This paper develops new model selection criteria for regression with heteroskedastic and autocorrela...
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAM...
In this paper we will investigate the consequences of applying model selec-tion methods under regula...
© 2019 Royal Statistical Society We develop model averaging estimation in the linear regression mode...
In linear regression models with autocorrelated errors, we apply the residual likelihood approach to...
Multivariate measurement error regression models with normal errors are investigated and residuals, ...
We obtain the residual information criterion RIC, a selection criterion based on the residual log-li...
This paper demonstrates the impact of particular factors – such as a non-normal error distribution, ...
RePEc Working Paper Series: No. 03/2011This review surveys a number of common Model Selection Algori...
Monte Carlo simulation methods was used to study the effects of the data structure on the quality of...
When using multiple regression models for predictive purposes, it may be desirable to exclude some p...
This note contrasts the importance of the analysis of model residual values in assessing the invalid...
Abstract: This paper makes the case for developing a statistical model to describe the behavior of t...
This manuscript addresses the problem of model selection, studied in the linear regression framework...
In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assum...
This paper develops new model selection criteria for regression with heteroskedastic and autocorrela...
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAM...
In this paper we will investigate the consequences of applying model selec-tion methods under regula...
© 2019 Royal Statistical Society We develop model averaging estimation in the linear regression mode...
In linear regression models with autocorrelated errors, we apply the residual likelihood approach to...
Multivariate measurement error regression models with normal errors are investigated and residuals, ...