evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation
evmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated extreme value models provide a suitable tail...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2018-04-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/3084 |
| _version_ | 1818060661897297920 |
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| author | Yang Hu Carl Scarrott |
| author_facet | Yang Hu Carl Scarrott |
| author_sort | Yang Hu |
| collection | DOAJ |
| description | evmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated extreme value models provide a suitable tail approximation. The package implements almost all existing extreme value mixture models, which permit objective threshold estimation and uncertainty quantification. Some traditional diagnostic plots for threshold choice are provided. Kernel density estimation with a range of kernels is provided, including cross-validation maximum likelihood inference for the bandwidth. A key contribution over existing kernel smoothing packages in R is that a wide range of boundary corrected kernel density estimators are implemented, which are designed for populations with bounded support. These non-parametric density estimators are also incorporated into the extreme value mixture model framework to describe the density below the threshold. The quartet of density, distribution, quantile and random number generation functions is provided along with parameter estimation by likelihood inference and standard model fit diagnostics, for both the mixture models and kernel density estimators. The key features of the mixture models and (boundary corrected) kernel density estimators are described and their implementation using the package demonstrated. |
| first_indexed | 2024-12-10T13:35:58Z |
| format | Article |
| id | doaj.art-7f5f8cebcda3411e933e095cfdbcf285 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-10T13:35:58Z |
| publishDate | 2018-04-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-7f5f8cebcda3411e933e095cfdbcf2852022-12-22T01:46:49ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602018-04-0184112710.18637/jss.v084.i051204evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density EstimationYang HuCarl Scarrottevmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated extreme value models provide a suitable tail approximation. The package implements almost all existing extreme value mixture models, which permit objective threshold estimation and uncertainty quantification. Some traditional diagnostic plots for threshold choice are provided. Kernel density estimation with a range of kernels is provided, including cross-validation maximum likelihood inference for the bandwidth. A key contribution over existing kernel smoothing packages in R is that a wide range of boundary corrected kernel density estimators are implemented, which are designed for populations with bounded support. These non-parametric density estimators are also incorporated into the extreme value mixture model framework to describe the density below the threshold. The quartet of density, distribution, quantile and random number generation functions is provided along with parameter estimation by likelihood inference and standard model fit diagnostics, for both the mixture models and kernel density estimators. The key features of the mixture models and (boundary corrected) kernel density estimators are described and their implementation using the package demonstrated.https://www.jstatsoft.org/index.php/jss/article/view/3084extreme value mixture modelthreshold estimationboundary corrected kernel density estimation |
| spellingShingle | Yang Hu Carl Scarrott evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation Journal of Statistical Software extreme value mixture model threshold estimation boundary corrected kernel density estimation |
| title | evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation |
| title_full | evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation |
| title_fullStr | evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation |
| title_full_unstemmed | evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation |
| title_short | evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation |
| title_sort | evmix an r package for extreme value mixture modeling threshold estimation and boundary corrected kernel density estimation |
| topic | extreme value mixture model threshold estimation boundary corrected kernel density estimation |
| url | https://www.jstatsoft.org/index.php/jss/article/view/3084 |
| work_keys_str_mv | AT yanghu evmixanrpackageforextremevaluemixturemodelingthresholdestimationandboundarycorrectedkerneldensityestimation AT carlscarrott evmixanrpackageforextremevaluemixturemodelingthresholdestimationandboundarycorrectedkerneldensityestimation |