Local Transformation Kernel Density Estimation of Loss Distributions
Bibliographic Data
| ID | 19419460 |
|---|---|
| Authors | Jim Gustafsson (University of Copenhagen), Matthias Hagmann, Jens Perch Nielsen (0000-0002-2798-0817, City, University of London), Olivier Scaillet (0000-0002-3948-6891, University of Geneva) |
| Year | 2009 |
| Volume | 27 |
| Issue | 2 |
| Pages | 161-175 |
| Publication date | 2009-04-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1198/jbes.2009.0011 |
| OpenAlex | W3121668058 |
| Language | EN |
| Citations received | 1 |
| References cited | 24 |
We develop a tailor-made semiparametric asymmetric kernel density estimator for the estimation of actuarial loss distributions. The estimator is obtained by transforming the data with the generalized Champernowne distribution initially fitted to the data. Then the density of the transformed data is estimated by use of local asymmetric kernel methods to obtain superior estimation properties in the tails. We find in a vast simulation study that the proposed semiparametric estimation procedure performs well relative to alternative estimators. An application to operational loss data illustrates the proposed method
Density estimation · Econometrics · Economics · Estimation · Estimator · Kernel density estimation · Kernel method · Mathematical analysis · Mathematical optimization · Multivariate kernel density estimation · Semiparametric model · Statistics · Support vector machine · Variable kernel density estimation · Bayesian Methods and Mixture Models · Computer Science · Insurance, Mortality, Demography, Risk Management · Mathematics · Statistical Methods and Inference · Applied Mathematics · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |