AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
Computers and Biomedical Research
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Variance and Bias for General Loss Functions
Machine Learning
Efficient modeling and inference for event-related fMRI data
Computational Statistics & Data Analysis
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Stochastic modeling for large-scale datasets usually involves a varying-dimensional model space. This paper investigates the asymptotic properties, when the number of parameters grows with the available sample size, of the minimum-BD estimators and classifiers under a broad and important class of Bregman divergence (BD), which encompasses nearly all of the commonly used loss functions in the regression analysis, classification procedures and machine learning literature. Unlike the maximum likelihood estimators which require the joint likelihood of observations, the minimum-BD estimators are useful for a range of models where the joint likelihood is unavailable or incomplete. Statistical inference tools developed for the class of large dimensional minimum-BD estimators and related classifiers are evaluated via simulation studies, and are illustrated by analysis of a real dataset.