Max-linear regression models with regularization

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Time-varying Linear Regression with Total Variation Regularization

We consider modeling time series data with time-varying linear regression, a model that allows the weight matrix to vary at every time point but penalizes this variation with the (multivariate) total variation norm. This corresponds to simultaneously learning the parameters of multiple linear systems as well as the change points that describe when the underlying process switches between linear ...

Generalized Lasso Regularization for Regression Models

Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...

Linear Regression Models with Logarithmic Transformations

Remember that we are using natural logarithms, where the base is e ≈ 2.71828. Logarithms may have other bases, for instance the decimal logarithm of base 10. (The base 10 logarithm is used in the definition of the Richter scale, for instance, measuring the intensity of earthquakes as Richter = log(intensity). This is why an earthquake of magnitude 9 is 100 times more powerful than an earthquake...