Logistic regression is preferrable over a simpler statistical test such as chi-squared test or Fisher’s exact test as it can incorporate more than one explanatory variable and deals with possible ...

To recap, linear ridge regression is essentially standard linear regression with L2 regularization added to prevent huge model coefficient values that can cause model overfitting. The weakness of ...

You can use linear regression to compare two or more variables, such as a specific stock with a benchmark, to determine their dependence, which can help make certain investment decisions.

This is where regression comes in. By using the regression function `svyglm()` in R, we can conduct a regression analysis that includes party differences in the same model as race. Using `svyglm()` ...

The KernelRegressor constructor accepts a dummy string value, "rbf" to indicate which kernel function to use. The parameter is a dummy because the RBF kernel function is hard-coded and so the ...

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population ...

Jean-Pierre Florens, Joël L. Horowitz, Ingrid Van Keilegom, Bias-Corrected Confidence Intervals in a Class of Linear Inverse Problems, Annals of Economics and Statistics, No. 128 (December 2017), pp.