The predictor that minimizes mean-squared prediction error is used to derive a goodness-of-fit measure that offers an asymptotically valid model selection criterion for a wide variety of regression models. In particular, a new goodness-of-fit criterion (cr2) is proposed for censored or otherwise limited dependent variables. The new goodness-of-fit measure is then applied to the analysis of duration.goodness-of-fit, optimal predictor, nonlinear, multivariate, instrumental variables, deration, JET Classification Numbers: C50, C52, C41,
<p>Note: Variables correspond to those described in the section ‘Bivariate analysis’. AIC is Akaike ...
<p>(A) Population prediction versus observed concentration. (B) Individual prediction versus observe...
R-squared (R2) is a popular method for variable selection in linear regression models. R2 based on L...
This work is devoted to the description of linear, logistic, ordinal and multinominal regression mod...
Let Z1;:::; Zn be i.i.d. vectors, each consisting of a response and explanatory variables. Suppose w...
A procedure called GOLPE is suggested in order to detect those variables which increase the predicti...
For regression models other than the linear model, R-squared type goodness-of-fit summary statistics...
This brief note compares model selection procedures in regression. On the one hand there is an obser...
Maximum Likelihood (ML) in the linear model overfits when the number of predictors (M) exceeds the n...
A method is introduced for variable selection and prediction in linear regression problems where the...
Let Z1,..., Zn be i.i.d. vectors, each consisting of a response and a few explanatory variables. Sup...
Consider a regression model where Y|x ∼ D(h(x),θ) for some real valued function such as h(x) = xTβ w...
Ten techniques used for selection of useful predictors in multivariate calibration and in other case...
Regression models with good fitting but no predictive ability are sometimes chance correlations and ...
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accurac...
<p>Note: Variables correspond to those described in the section ‘Bivariate analysis’. AIC is Akaike ...
<p>(A) Population prediction versus observed concentration. (B) Individual prediction versus observe...
R-squared (R2) is a popular method for variable selection in linear regression models. R2 based on L...
This work is devoted to the description of linear, logistic, ordinal and multinominal regression mod...
Let Z1;:::; Zn be i.i.d. vectors, each consisting of a response and explanatory variables. Suppose w...
A procedure called GOLPE is suggested in order to detect those variables which increase the predicti...
For regression models other than the linear model, R-squared type goodness-of-fit summary statistics...
This brief note compares model selection procedures in regression. On the one hand there is an obser...
Maximum Likelihood (ML) in the linear model overfits when the number of predictors (M) exceeds the n...
A method is introduced for variable selection and prediction in linear regression problems where the...
Let Z1,..., Zn be i.i.d. vectors, each consisting of a response and a few explanatory variables. Sup...
Consider a regression model where Y|x ∼ D(h(x),θ) for some real valued function such as h(x) = xTβ w...
Ten techniques used for selection of useful predictors in multivariate calibration and in other case...
Regression models with good fitting but no predictive ability are sometimes chance correlations and ...
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accurac...
<p>Note: Variables correspond to those described in the section ‘Bivariate analysis’. AIC is Akaike ...
<p>(A) Population prediction versus observed concentration. (B) Individual prediction versus observe...
R-squared (R2) is a popular method for variable selection in linear regression models. R2 based on L...