Add to ORKGIn model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so that the test error only at the test input points at hand is minimized. In this paper, we follow this idea and derive an estimator of the test error at the given test input points for linear regression. Our estimator is proved to be an unbiased estimator of the test error at the given test input points under certain conditions. Through the simulations with artificial and standard benchmark data sets, we show that the proposed method is successfull...
This article focuses on variable selection for partially linear models when the covariates are measu...
We propose variable selection procedures based on penalized score functions derived for linear measu...
Model selection strategies for machine learning algorithms typically involve the numerical optimisat...
In model selection procedures in supervised learning, a model is usually chosen so that the expected...
© 2017 American Statistical Association. Prediction precision is arguably the most relevant criterio...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
<p>Prediction precision is arguably the most relevant criterion of a model in practice and is often ...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
Since the training error tends to underestimate the true test error, an appropriate test error estim...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
A common assumption in supervised learning is that the training and test input points follow the sam...
An important statistical application is the problem of determining an appropriate set of input varia...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
This article focuses on variable selection for partially linear models when the covariates are measu...
We propose variable selection procedures based on penalized score functions derived for linear measu...
Model selection strategies for machine learning algorithms typically involve the numerical optimisat...
In model selection procedures in supervised learning, a model is usually chosen so that the expected...
© 2017 American Statistical Association. Prediction precision is arguably the most relevant criterio...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
<p>Prediction precision is arguably the most relevant criterion of a model in practice and is often ...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
Since the training error tends to underestimate the true test error, an appropriate test error estim...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
A common assumption in supervised learning is that the training and test input points follow the sam...
An important statistical application is the problem of determining an appropriate set of input varia...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
This article focuses on variable selection for partially linear models when the covariates are measu...
We propose variable selection procedures based on penalized score functions derived for linear measu...
Model selection strategies for machine learning algorithms typically involve the numerical optimisat...