How to estimate robust sandwich standard errors when estimating parameters using optim () in R? Ask Question Asked 7 years, 9 months ago Modified 7 years, 9 months ago

Aug 10, 2016 · Maybe you should get yourself an introductory book about numerical optimization. You will need to take into account your function in order to decide for the algorithm. Among the algorithms …

Jul 24, 2018 · This essentially states that under the robust optimization framework, the most optimal way to handle noise in your data X X, is to use Ridge Regression. The robust optimization framework …

May 12, 2020 · A Comparison of Diagonal Weighted Least Squares Robust Estimation Techniques for Ordinal Data, Maximum Likelihood (ML) and Diagonally Weighted Least Squares (DWLS) Estimation …

Dec 19, 2012 · This suggests an algorithm adapted to your situation: start with some form of robust regression, but when taking small steps during the optimization, simply assume in the next step that …

Adam is generally regarded as being fairly robust to the choice of hyper parameters, though the learning rate sometimes needs to be changed from the suggested default. if this is true its a big deal because …

Mar 17, 2016 · Linear SVMs and logistic regression generally perform comparably in practice. Use SVM with a nonlinear kernel if you have reason to believe your data won't be linearly separable (or you …

Dec 29, 2016 · After reviewing a set of lectures on convex optimization, Newton's method seems to be a far superior algorithm than gradient descent to find globally optimal solutions, because Newton's …

Jul 3, 2021 · I think this is due to the similarity of optimization of Ordinary Least square with MSE, and Least Absolute regression with MAE. It means that I really need an alternative unbiased universal …

Apr 15, 2018 · I am looking for advantages and disadvantages of the Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS), the L-BFGS-B and PORT algorithm in optimization. Which one promises …