Support vector regressionThis is a discussion of the article Frankel, Jennings and Lee, based on my discussion at the 2015 JAE conference in Rochester. Following standard protocol (examples of other discussion articles), I did not include a formal abstract.doi:10.1016/j.jacceco.2016.07.003Richard FrankelJared JenningsJoshua...
Subsequently, we ran a second mixed-effect logistic regression to estimate the relationship between each of the nine types of situational change with event segmentation probability for both samples. In this analysis, the multicollinearity was not a concern since all variance inflation factors (VIF) we...
(2019) examine the impact of the GSVI on the stock returns of the S&P BSE 500 companies using a quantile regression model over the period 2012 to 2019. They find that a higher GSVI predicts positive and significant returns in the first and second weeks. Finally, the study supports the ...
Define the reduction function. The reduction function must reduce the output of the reduction layer to a scalar value. The Grad-CAM map displays the importance of different parts of the image to that scalar. In this regression problem, the network predicts the angle of rotation of the image....
Our kernel regression theory can be applied separately to each element of the target function vector (Methods), and a generalization error can be calculated by adding the error due to each vector component. We can visualize the complexity of the two tasks by plotting the projection of the data...
8. Linear regression and variance partitioning To quantify the effect of biogeographic isolation on bird, mammal, and bat diversity, we used regression and variance partitioning. For each of our four response variables (species richness, phylogenetic alpha diversity, functional richness, and mean ...
mixed-effects logistic regression (R version 4.2.1, ‘glmer’ function) was used to estimate the segmentation probability (criterion) as the function of the number of changes and group as the two predictors. We constructed two mixed-effects regressions models for distinct objectives: (i) predictin...
Ridge regression, also known as L2 Regularization, is a regression technique that introduces a small amount of bias to reduce overfitting. It does this by minimizing the sum of squared residuals plus a penalty, where the penalty is equal to lambda times the slope squared. Lambda refers to the...
Two responses (here and here) criticize the article, but I thought I should compare to the obvious Bayesian approach: GP regression. I used Carl’s code to perform the regression in Matlab, with squared exponential covariance function, and I optimized the hyperparameters using the minimise ...
I concur with yours and others' assessments in this thread (e.g., with Quote #14) there has been a regression with Intel Fortran compiler 18.0 version compared to, say, version corresponding to Intel Parallel Studio 2016. Hopefully you will feedback comments from this thread at your supp...