deamer: Deconvolution density estimation with adaptive methods for a variable prone to measurement error
deamer provides deconvolution algorithms for the non-parametric estimation of the density f of an error-prone variable x with additive noise e. The model is y = x + e where the noisy variable y is observed, while x is unobserved. Estimation may be performed for i) a known density of the error ii) with an auxiliary sample of pure noise and iii) with an auxiliary sample of replicate (repeated) measurements. Estimation is performed using adaptive model selection and penalized contrasts.
- Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution from Claire Lacour.
- Date of publication
- 2012-08-05 06:07:55
- j.stirnemann <firstname.lastname@example.org>
- Objects of class 'deamer'
- Density estimation with known error density
- Non-parametric deconvolution density estimation of variables...
- Density estimation using an auxiliary sample of replicate...
- Density estimation using an auxiliary sample of pure errors
- Laplace distribution
- Mean integrated squared error
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