Extrapolation errors in linear model trees

On a symmetrized simulation extrapolation estimator in linear errors-in-variables models

Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...

Linear Model Selection when Covariates Contain Errors.

Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters. We bypass this inherent difficulty by using special properties on moment relation...

Bucket Errors in MBE Trees

Incremental Linear Model Trees on Big Data

The data revolution has a similar deep impact to the society as the industrial and the digital revolution. Due to the digital revolution computer were more and more integrated into daily life. Consequently, data volume was steadily increasing. It is assumed that the increase of data volume will even be, for the time being, exponential. This is due to new concepts as, e.g., social networks or th...