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Everything about Kernel smoother
Statistical technique
A kernel smoother is a statistical technique to estimate a real valued function as the weighted average of neighboring observed data. The weight is defined by the kernel, such that closer points are given higher weights. The estimated function is smooth, and the level of smoothness is set by a single parameter. Kernel smoothing is a type of weighted moving average. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Kernel smootherA 5-minute orientation
- Skim a recent paper: Multi-Kernel Correntropy Smoother for 6D Foot Motion Tracking With Inertial SensorsInfluential work since 2022
- Pick up a classic book: The Elements of Statistical LearningThe most-reprinted book on the subject
- Read the landmark paper: A Smoothed Backbone-Dependent Rotamer Library for Proteins Derived from Adaptive Kernel Density Estimates and RegressionsCited 1,015 times
- Go sideways: Local regressionA closely related topic
- Go sideways: Partially linear modelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A Smoothed Backbone-Dependent Rotamer Library for Proteins Derived from Adaptive Kernel Density Estimates and Regressions
Kernel Smoothing in Partial Linear Models
Improved anti-occlusion object tracking algorithm using Unscented Rauch-Tung-Striebel smoother and kernel correlation filter
Vehicle path reconstruction using Recursively Ensembled Low-pass filter (RELP) and adaptive tri-cubic kernel smoother
The Henderson Smoother in Reproducing Kernel Hilbert Space
Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)
Improved Spike-Based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning
Nonparametric kernel smoother on topology learning neural networks for incremental and ensemble regression
🆕 Influential since 2022
📚 Books worth reading
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
High Performance Computing for Computational Science - VECPAR 2012Michel Daydé · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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