This article proposes a novel solution for stretchy polynomial regression learning. The solution comes in primal and dual closed-forms similar to that of ridge regression. Essentially, the proposed solution stretches the covariance computation via a power term thereby compresses or amplifies the estimation. Our experiments on both synthetic data and real-world data show effectiveness of the pro...

This article establishes a comprehensive theory of the optimality, robustness, and cross-validation selection consistency for ridge regression under factor-augmented models with possibly dense idiosyncratic information. Using spectral analysis random matrices, we show that is asymptotically efficient in capturing both factor information by minimizing limiting predictive loss among entire class ...

For modeling count data, the Poisson regression model is widely used in which response variable takes non-negative integer values. However, presence of strong correlation between explanatory variables causes problem multicollinearity. Due to multicollinearity, variance maximum likelihood estimator (MLE) will be inflated causing parameters estimation become unstable. Multicollinearity can tackle...

Identifying influential observations is an important part of the model building process in linear regression. There are numerous diagnostic measures based on different approaches regression analysis. However, problem multicollinearity and may occur simultaneously. Therefore, we propose new two parameter ridge estimator defined by Lipovetsky Conklin (2005) alternative to usual ordinary We define...