Deconvolution density estimation with penalised MLE
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866908859290877952 |
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| author | Cai, Yun Gu, Hong Kenney, Toby |
| author_facet | Cai, Yun Gu, Hong Kenney, Toby |
| contents | Deconvolution is the important problem of estimating the distribution of a quantity of interest from a sample with additive measurement error. Nearly all methods in the literature are based on Fourier transformation because it is mathematically a very neat solution. However, in practice these methods are unstable, and produce bad estimates when signal-noise ratio or sample size are low. In this paper, we develop a new deconvolution method based on maximum likelihood with a smoothness penalty. We show that our new method has much better performance than existing methods, particularly for small sample size or signal-noise ratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_11205 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Deconvolution density estimation with penalised MLE Cai, Yun Gu, Hong Kenney, Toby Methodology 62G07 Deconvolution is the important problem of estimating the distribution of a quantity of interest from a sample with additive measurement error. Nearly all methods in the literature are based on Fourier transformation because it is mathematically a very neat solution. However, in practice these methods are unstable, and produce bad estimates when signal-noise ratio or sample size are low. In this paper, we develop a new deconvolution method based on maximum likelihood with a smoothness penalty. We show that our new method has much better performance than existing methods, particularly for small sample size or signal-noise ratio. |
| title | Deconvolution density estimation with penalised MLE |
| topic | Methodology 62G07 |
| url | https://arxiv.org/abs/2105.11205 |