Ma˚rtensson, J., Hjalmarsson, H.: How to make bias and variance errors insensitive to system and model complexity in identification. IEEE Transactions on Automatic Control 56(1), 100- 112 (2011)Martensson J., Hjalmarsson H. (2011). How to make bias and variance errors insensitive to ...
Bias: whether the estimator underestimates or overestimates a parameter. For example state inspectors in Houston, Texas, found that one in five gas pumps weren’t calibrated correctly. An incorrectly calibrated pump could cost a consumer up to 18 cubic inches per five gallons pumped. The bias me...
asset you're targeting because the fees and the structure will provide variance, particularly in the long term. Furthermore, many inverse ETFs aren't even designed to be 1-to-1, but instead offer "leveraged" inverse exposure intended to deliver two or three times the movement of cert...
leads. But to be confident that this difference is not just due to chance, you conduct a statistical test and find that the results are statistically significant. This tells you that the difference in performance between the two versions is likely real, and not just a result of random ...
We can understand regularization as an approach of adding an additional bias to a model to reduce the degree of overfitting in models that suffer from high variance. By adding regularization terms to the cost function, we penalize large model coefficients (weights); effectively, we are reducing ...
There is a great degree of variance in the average day trader's salary, with some day traders making six figures and others losing money.5 How To Get Started in Day Trading Getting started in day tradingisn't like dabbling in investing. Any would-be investor with a few hundred dollars ...
In brief: our algorithm suffers from high variance (overfitting) or high bias (underfitting). It may help to get a better grasp of our problem by plotting “learning curves” For example, here I plotted the average accuracies of a model (using 10-fold cross validation). The blue line (...
to the in-group country bias literature, we theorize an “origin-backfire” effect: consumers forgive domestic brand transgressionsless. Analyzing experimental, social media, and secondary-longitudinal data, we find that consumers treat domestic brand transgressors as home-country traitors deserving ...
To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intell
The industrial sector is a key contributor to environmental and social problems. Based on stakeholder theory and agency theory, this research proposes that green innovation strategies at the firm level can overcome the industry’s negative environmental impact. As a result, the focus of this ...