An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
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Summary
Kernel and nearest-neighbor regression estimators are local versions of univariate location estimators, and so they can readily be introduced to beginning students and consulting clients who are familiar with such summaries as the sample mean and median.
- Type
- article
- Published
- 1992-08-01
- Cited by
- 5,559
- References
- 50
- Access
- Open access
- OpenAlex
- https://openalex.org/W1985258161
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17002880
Keywords
Nonparametric regression, Nonparametric statistics, Kernel regression, k-nearest neighbors algorithm, Statistics
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- Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification
- Robust Locally Weighted Regression and Smoothing Scatterplots
- Optimal Bandwidth Selection in Nonparametric Regression Function Estimation
- Testing the Goodness of Fit of a Linear Model via Nonparametric Regression Techniques
- A new family of mathematical models describing the human growth curve.
- On the use of nonparametric regression for model checking
- Residual variance and residual pattern in nonlinear regression
- Some Remarks about the Gasser-Sroka-Jennen-Steinmetz Variance Estimator
- The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction
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