Lecture notes on ridge regression

The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this non-identifiability by augmentation of the loss function by a penalty (i.e. a function of regression coefficients). The ridge penalty is the sum of squared regression coefficients, giving rise to ridge regression. Here many aspect of ridge regression are reviewed e.g. moments, mean squared error, its equivalence to constrained estimation, and its relation to Bayesian regression. Finally, its behaviour and use are illustrated in simulation and on omics data. Subsequently, ridge regression is generalized to allow for a more general penalty. The ridge penalization framework is then translated to logistic regression and its properties are shown to carry over. To contrast ridge penalized estimation, the final chapter introduces its lasso counterpart

Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data

We study ridge estimation of the precision matrix in the high-dimensional setting where the number of variables is large relative to the sample size. We first review two archetypal ridge estimators and note that their utilized penalties do not coincide with common ridge penalties. Subsequently, starting from a common ridge penalty, analytic expressions are derived for two alternative ridge estimators of the precision matrix. The alternative estimators are compared to the archetypes with regard to eigenvalue shrinkage and risk. The alternatives are also compared to the graphical lasso within the context of graphical modeling. The comparisons may give reason to prefer the proposed alternative estimators

“Care is not just about care anymore”:Micro-level responses to institutional complexity and change in the Dutch home-care sector

Groenewegen, P. [Promotor]Broese Van Groenou, M.I. [Promotor

Bensarazid with L dopa in the treatment of Parkinson's disease

CITATION: Van Wieringen, A. 1974. Bensarazid with L-Dopa in the treatment of Parkinson's disease. S Afr Med J, 48(2):206-209.The original publication is available at http://www.samj.org.zaENGLISH ABSTRACT: A short review is given of the pharmacokinetics and pharmacodynamics of the decarboxylase inhibitor Ro 4-4602. The results obtained in 20 patients using this drug in combination with L dopa, are described. Reduction in the total dosage of L dopa by 1/6 to 1/10 the single preparation gave marked relief of nausea. The induction period of the dosage was smoother, and an optimum dose could be reached sooner with earlier signs of improvement in comparison with the single drug.Publisher’s versio

Bensarazid with L-dopa in the treatment of Parkinson's disease

A short review is given of the pharmokinetics and pharmacodynamics of the decarboxylase inhibitor Ro 44602. The results obtained in 20 patients using this drug in combination with L-dopa, (Madopar), are described. Reduction in the total dosage of L-dopa by one-sixth to one-tenth the single preparation gave marked relief of nausea. The induction period of the dosage was smoother, and an optimum dose could be reached sooner with earlier signs of improvement in comparison with the single drug.S. Afr. Med. J., 48, 206 (1974)