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Kernel Density Estimation/Evidence
Method evidence record

Kernel Density Estimation

Kernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities.

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Source record

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Kernel Density Estimation and Distribution Testing (KDE)
Taxonomic method record · regression-model / statistics
  • Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. · DOI 10.1214/aoms/1177728190
  • Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall / CRC Press. · ISBN 978-0412246203
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Related methods

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Same method familyAnderson-Darling Testmachine-suggested · Relational suggestion, not evidence.Same method familyLilliefors Testmachine-suggested · Relational suggestion, not evidence.Same method familyMood's Median Testmachine-suggested · Relational suggestion, not evidence.Same method familyQuantile Regressionmachine-suggested · Relational suggestion, not evidence.

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Sources

2 recorded citations, copied from the method source record.

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