<p>(A) Simulation curve for fixed 0.1 <i>C<sub>R</sub></i>+ additional <i>C<sub>I</sub></i> with all other parameters set to the default values and best fit regression curves for different degree of polynomials, (B) Root mean square error values for the leave-one-out cross validation (5 time points for each run, 10 independent runs for 25 µs), (C) Root mean square error values for the leave-one-out cross validation for simultaneous variation in <i>α</i> and C (five <i>α</i> values:1.6, 1.8, 2.0, 2.2, 2.4) and (eight <i>C</i> values: 0.1, 0.15, …, 0.45).</p
In this paper, a formal test on prediction errors is developed for the cross-validation of regressio...
Abstract Background A random multiple-regression model that simultaneously fit all allele substituti...
<p>A) Error rate produced by different classification algorithms as a function of the number of pred...
Bold font indicates improvement of the “trend+var” method, compared to the “trend” method. k is the ...
BackgroundA random multiple-regression model that simultaneously fit all allele substitution effects...
We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessme...
We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessme...
Model fit statistics (R-squared, AIC and BIC) and mean squared prediction error from 10-fold cross v...
BackgroundA random multiple-regression model that simultaneously fit all allele substitution effects...
<p>The tradeoff between overfit and underfit for one of the five cross-validation data splits. Model...
textWhen testing structural equation models, researchers attempt to establish a model that will gen...
<p>The coefficients for each factor that was included in the model generated from the training datas...
textWhen testing structural equation models, researchers attempt to establish a model that will gen...
<p>The cross-validation approaches for different kernels were run on our training set including 198 ...
We give an explict example of parameter estimation for a univariate radial basis function using cros...
In this paper, a formal test on prediction errors is developed for the cross-validation of regressio...
Abstract Background A random multiple-regression model that simultaneously fit all allele substituti...
<p>A) Error rate produced by different classification algorithms as a function of the number of pred...
Bold font indicates improvement of the “trend+var” method, compared to the “trend” method. k is the ...
BackgroundA random multiple-regression model that simultaneously fit all allele substitution effects...
We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessme...
We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessme...
Model fit statistics (R-squared, AIC and BIC) and mean squared prediction error from 10-fold cross v...
BackgroundA random multiple-regression model that simultaneously fit all allele substitution effects...
<p>The tradeoff between overfit and underfit for one of the five cross-validation data splits. Model...
textWhen testing structural equation models, researchers attempt to establish a model that will gen...
<p>The coefficients for each factor that was included in the model generated from the training datas...
textWhen testing structural equation models, researchers attempt to establish a model that will gen...
<p>The cross-validation approaches for different kernels were run on our training set including 198 ...
We give an explict example of parameter estimation for a univariate radial basis function using cros...
In this paper, a formal test on prediction errors is developed for the cross-validation of regressio...
Abstract Background A random multiple-regression model that simultaneously fit all allele substituti...
<p>A) Error rate produced by different classification algorithms as a function of the number of pred...