Bayesian Kernel Regression for Noisy Inputs Based on Nadaraya–Watson Estimator Constructed from Noiseless Training Data

DOI10.1142/S2424922X20500047zbMath1462.62513OpenAlexW3042126369MaRDI QIDQ5858144

Takeshi Okadome, Ryo Hanafusa

Publication date: 9 April 2021

Published in: Advances in Data Science and Adaptive Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s2424922x20500047

zbMATH Keywords

parameter estimationhidden Markov modelNadaraya-Watson estimatornoisy inputminimum expected squared lossnoiseless training data

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Markov processes: estimation; hidden Markov models (62M05) White noise theory (60H40)

Uses Software

• UCI-ml
• Spearmint

Cites Work

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• Method of Moments Estimation in Linear Regression with Errors in both Variables
• Multivariate Density Estimation
• Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
• NONPARAMETRIC REGRESSION IN THE PRESENCE OF MEASUREMENT ERROR
• Measurement Error in Nonlinear Models

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