Under second-order smoothness and a new boundary condition (A), k-NN matching estimators have squared bias of order (k/n)^{min(4/d,3)}, giving parametric rates for d<=4 and ATE efficiency for d=1,2,3.
Strong universal consistent estimate of the minimum mean squared error
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.ST 1years
2025 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Convergence rate for Nearest Neighbour matching: geometry of the domain and higher-order regularity
Under second-order smoothness and a new boundary condition (A), k-NN matching estimators have squared bias of order (k/n)^{min(4/d,3)}, giving parametric rates for d<=4 and ATE efficiency for d=1,2,3.