Results 141 to 150 of about 269 (169)
Hellinger distance and Kullback--Leibler loss for the kernel density estimator
The optimal window width, which asymptotically minimizes mean Hellinger distance between the kernel estimator and density, is known to be equivalent to the one that maximizes expected Kullback--Leibler loss for compactly supported densities. Implications
Kanazawa, Yuichiro
core
A Simple Nonparametric Least-Squares-Based Causal Inference for Heterogeneous Treatment Effects. [PDF]
J Nonparametr StatZhang Y +5 more
europepmc +1 more source
On the convergence rates of kernel estimator and hazard estimator for widely dependent samples. [PDF]
J Inequal Appl, 2018 Li Y, Zhou Y, Liu C.
europepmc +1 more source
Estimators of scale parameters in linear regression
This note discusses the asymptotic distribution of two scale and location invariant estimators of two scale parameters in the multiple linear regression model. Both of these estimators need an initial estimator of the regression parameter vector.
Susarla, V., Koul, H. L.
core
A Class of Structured High-Dimensional Dynamic Covariance Matrices. [PDF]
Commun Math StatYang J, Lian H, Zhang W.
europepmc +1 more source
Normal Laws for Two Entropy Estimators on Infinite Alphabets. [PDF]
Entropy (Basel), 2018 Chen C +4 more
europepmc +1 more source
Structure Identification, Estimation and Variable Selection for Varying Coefficient EV Models With Longitudinal Data. [PDF]
Stat MedZhao M +5 more
europepmc +1 more source
Semiparametric Single-Index Model for Estimating Optimal Individualized Treatment Strategy. [PDF]
Electron J Stat, 2017 Song R +5 more
europepmc +1 more source
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