Density Estimation for Statistics and Data Analysis¶
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Discretization-Induced Artifact
- Statistics: histogram-binning artefacts — gaps, modes, and bimodality that shift with bin width and anchor and vanish under kernel-density estimation.
This sourceHistogram modes and gaps depend on bin width and anchor and dissolve under kernel-density estimation; the invariance test against binning choices.
Supported in partVerified against the publisher's abstract
Silverman's book supplies the method behind the example — kernel smoothing as the check on binning artefacts — but its abstract never states the bimodality or the team's two-segment conclusion.
“The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects.”
- Statistics: histogram-binning artefacts — gaps, modes, and bimodality that shift with bin width and anchor and vanish under kernel-density estimation.
- Modifiable Areal Unit Problem
- In histograms and exploratory analysis, the same continuous data displays as unimodal or bimodal depending on bin choice, with kernel-density bandwidth as the continuous analog.
This sourceHistogram appearance (unimodal vs bimodal) depends on bin choice; kernel-density bandwidth is the continuous analog of bin width.
- In histograms and exploratory analysis, the same continuous data displays as unimodal or bimodal depending on bin choice, with kernel-density bandwidth as the continuous analog.
Domain-specific¶
Verification¶
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Does it back the claim? Read against the text for 1 of 3 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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Registry ID ref:04405f23e9fa · see in the full table