Third, a bivariate linear regression model was fit to the data set using the connectedness and liking scores as dependent variables; visit length and percentage of eye contact were separate ...

Lutzer, C.V. and Farnsworth, D.L. (2021) Pivot Points in Bivariate Linear Regression. Open Journal of Statistics, 11, 393-399.

The demo creates a linear support vector regression model, evaluates the model accuracy on the training and test data, and then uses the model to predict the target y value for x = [-0.1660, 0.4406, ...

Complementing this, studies on QR-based estimators in partially linear models with correlated errors have extended the utility of cross validation techniques in selecting shrinkage parameters ...

In bivariate survival analysis, these models introduce random effects – termed ‘frailties’ – that act multiplicatively on the baseline hazard functions, thereby providing a nuanced ...

Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI-like chatbots so good at understanding and using words. The work is ...

What Are Autoregressive Models? Autoregressive models are statistical models used for time series analysis, where current values are predicted based on a linear combination of past values. These ...

For our PAI course project, we are building several disease prediction systems, including heart disease, diabetes, Parkinson's, and breast cancer classification. Using machine learning algorithms, we ...

Dependent degradation processes of performance characteristics are ubiquitous in engineered systems. The initial state of each degradation process is usually random and relates to the degradation rate ...

5 Alternative methods Besides trimming and winsorizing, there are other methods to deal with outliers in regression, such as transforming the data, using robust estimators, or adding dummy variables.