Density estimation and regression analysis on S^d in the presence of measurement error

01/08/2023
by   Jeong Min Jeon, et al.
0

This paper studies density estimation and regression analysis with contaminated data observed on the unit hypersphere S^d. Our methodology and theory are based on harmonic analysis on general S^d. We establish novel nonparametric density and regression estimators, and study their asymptotic properties including the rates of convergence and asymptotic distributions. We also provide asymptotic confidence intervals based on the asymptotic distributions of the estimators and on the empirical likelihood technique. We present practical details on implementation as well as the results of numerical studies.

READ FULL TEXT

page 28

page 29

research
10/25/2017

Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel methods

In this article we perform an asymptotic analysis of Bayesian parallel d...
research
10/17/2020

Empirical likelihood and uniform convergence rates for dyadic kernel density estimation

This paper studies the asymptotic properties of and improved inference m...
research
11/24/2020

Nonparametric Asymptotic Distributions of Pianka's and MacArthur-Levins Measures

This article studies the asymptotic behaviors of nonparametric estimator...
research
07/20/2022

On minimax density estimation via measure transport

We study the convergence properties, in Hellinger and related distances,...
research
03/07/2018

Estimation of edge density in noisy networks

While it is common practice in applied network analysis to report variou...
research
01/08/2020

Conditional density estimation with covariate measurement error

We consider estimating the density of a response conditioning on an erro...
research
06/03/2019

Asymptotic Properties of Neural Network Sieve Estimators

Neural networks are one of the most popularly used methods in machine le...

Please sign up or login with your details

Forgot password? Click here to reset