''Nonparametric Estimation, Regression, and Prediction, Under Minimal Regularity Conditions" Upon graduation, joined Department of Statistics, University of Toronto.
Hence, inference is as good asymptotically as standard methods under standard regularity conditions, but also is identification robust.
It is shown that the MDPDE is strongly consistent and asymptotically normal under regularity conditions.
By construction, the bases satisfy regularity conditions at r="0 for any tensorial field.
As a corollary, we state mild regularity conditions under which our approximation consistently estimates true leave-k-out cross-validation for any fixed k.
We present Monte Carlo experiments corroborating our theoretical results and showing that standard methods can yield misleading inference when our new, stronger regularity conditions are violated.
Under certain regularity conditions, the SIVLS estimator of the parametric component is shown to be consistent with a limiting normal distribution that is amenable to inference.
We prove the proposed approach is (weakly) consistent under some technical regularity conditions and we analytically characterize its rate of convergence.
Regularity conditions in the CLT for linear eigenvalue statistics of Wigner matrices (with Percy Wong), Advances in Mathematics, Vol. 249, (2013), 37-87.
Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived.
We also know the asymptotic efficiency of sample standard deviation when the probability density function satisfies more general regularity conditions than the Gaussian density.
