Divergence estimators have emerged as quintessential tools in statistical inference, particularly in contexts where traditional likelihood‐based methods fail under model misspecification or data ...

Convergence rates of kernel density estimators for stationary time series are well studied. For invertible linear processes, we construct a new density estimator that ...

PROVIDENCE, R.I. [Brown University] — How do you sift through hundreds of billions of bits of information and make accurate inferences from such gargantuan sets of data" Brown University mathematician ...

In decision theoretic estimation of parameters in Euclidean space ℝp, the action space is chosen to be the convex closure of the estimand space. In this paper, the concept has been extended to the ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

Positioned at the intersection of quantitative finance, statistical learning, and modern AI, the research addresses a ...