Open Access
Issue |
ITM Web Conf.
Volume 23, 2018
XLVIII Seminar of Applied Mathematics
|
|
---|---|---|
Article Number | 00013 | |
Number of page(s) | 5 | |
DOI | https://doi.org/10.1051/itmconf/20182300013 | |
Published online | 07 November 2018 |
- K.D. Kim, J.H. Heo, J. Hydrology 260, 176-193 (2002) [CrossRef] [Google Scholar]
- R.A. Ferreyra, G. P. Podestá, C. D. Messina, D. Letson, J. Dardanelli, E. Guevara, S. Meira, Agric. For. Meteorol. 107, 177-192 (2001) [CrossRef] [Google Scholar]
- A. Michalski, Meteorol. Hydrol. Water Manage. 4, 41-46 (2016) [Google Scholar]
- S.J. Sheather, Stat. Sci. 19, 588-597 (2004) [CrossRef] [Google Scholar]
- G. Geenens, C. Wang, J. Comput. Graph. Stat. (to be published) [Google Scholar]
- M.P. Wand, M.C. Jones, Kernel Smoothing (Chapman and Hall, Boca Raton, 1995) [CrossRef] [Google Scholar]
- P. Hall, C. Minnotte, C. Zhang, Ann. Statist. 32, 2124-2141 (2004) [CrossRef] [Google Scholar]
- E. Schuster, Comm. Statist. Theory Methods 14, 1123-1136 (1985) [CrossRef] [Google Scholar]
- S.X. Chen, Ann. Inst. Statist. Math. 52, 471-480 (2000) [CrossRef] [Google Scholar]
- Jin, X. and Kawczak, J. Ann. Econ. Finance 4, 103-124 (2003) [Google Scholar]
- O. Scaillet, J. Nonparametr. Stat. 16, 217-226 (2004) [CrossRef] [Google Scholar]
- M. Hirukawa, M. Sakudo, J. Nonparametr. Stat. 27, 41-63 (2015) [CrossRef] [Google Scholar]
- G. Igarashi, Commun. Stat. Theory Methods 45, 6670 – 6687 (2016) [CrossRef] [Google Scholar]
- A. Charpentier, E. Flachaire, L’Actualité Économique 91, 141-159 (2015) [Google Scholar]
- G. Geenens, C. Wang, Local-likelihood transformation kernel density estimation for positive random variables (arXiv preprint, arXiv:1602.04862v1 [stat.ME], 2016) [Google Scholar]
- B. Rajagoplan, U. Lall, D. Tarboton, Stoch. Hydrol. Hydraul. 11, 523– 547 (1997) [CrossRef] [Google Scholar]
- S.J. Sheather, M.C. Jones, J.R. Stat. Soc. Ser. B Stat. Methodol. 53, 683-690 (1991) [Google Scholar]
- T. Mosthaf, A. Bardossy, Hydrol. Earth Syst. Sci. 21, 2643-2481 (2017) [CrossRef] [Google Scholar]
- W. Huang, D. Nychka, H. Zhang, Modeling Precipitation Extremes using Log-Histospline (arXiv preprint, arXiv:1802.09387v1, 2018) [Google Scholar]
- S. Beguería, S. Vicente-Serrano, SPEI: Calculation of the Standardised PrecipitationEvapotranspiration Index (R package, version 1.7, http://CRAN.R-project.org/package=SPEI, 2017) [Google Scholar]
- S. Węglarczyk, Statistics in environmental engineering (in Polish) (Technical University in Kraków Press, Kraków, 2010) [Google Scholar]
- M.L. Delignette-Muller, C. Dutang, J. Stat. Softw. 64, 1-34 (2015) [Google Scholar]
- J. Moss, M. Tveten, kdensity: Kernel Density Estimation with Parametric Starts and Asymmetric Kernels (R package, version 1.0, https://CRAN.R-project.org/package=kdensity, 2018) [Google Scholar]
- R Core Team, R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2015) [Google Scholar]
- E. Gąsiorek, E. Musiał, J. Ecol. Eng. 16, 44-53 (2015) [CrossRef] [Google Scholar]
- A. Stephenson, R News 2, 31-32 (2002) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.