Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Machine Learning
Lazy lasso for local regression
Computational Statistics
Full bandwidth matrix selectors for gradient kernel density estimate
Computational Statistics & Data Analysis
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The intention is to provide a Bayesian formulation of regularized local linear regression, combined with techniques for optimal bandwidth selection. This approach arises from the idea that only those covariates that are found to be relevant for the regression function should be considered by the kernel function used to define the neighborhood of the point of interest. However, the regression function itself depends on the kernel function. A maximum posterior joint estimation of the regression parameters is given. Also, an alternative algorithm based on sampling techniques is developed for finding both the regression parameter distribution and the predictive distribution.