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Everything about Kernel regression
Technique in statistics
In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Kernel regressionA 5-minute orientation
- Skim a recent paper: Random kernel k-nearest neighbors regressionInfluential work since 2022
- Pick up a classic book: Local bandwidth selection in nonparametric kernel regressionThe most-reprinted book on the subject
- Read the landmark paper: An Introduction to Kernel and Nearest-Neighbor Nonparametric RegressionCited 9,528 times
- Go sideways: Errors-in-variables modelA closely related topic
- Go sideways: Nonparametric regressionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Local Regression: Automatic Kernel Carpentry
Kernel dimension reduction in regression
Boundary modification for kernel regression
Kernel Discriminant Learning for Ordinal Regression
Choosing a Kernel Regression Estimator
Kernel Regression in Empirical Microeconomics
Kernel regression with functional response
🆕 Influential since 2022
📚 Books worth reading
- 📕Local bandwidth selection in nonparametric kernel regressionMichael Brockmann · 1993
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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