1 |
Barndorff-Nielsen, O. (1997). Normal inverse Gaussian distributions and stochastic volatility, Scandinavian Journal of Statistics, 24, 1-13.
DOI
ScienceOn
|
2 |
Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, London.
|
3 |
Coles, S. and Dixon, M. (1999). Likelihood-based inference for extreme value models, Extremes, 2, 5-23.
|
4 |
Embrechts, P., Kluppelberg, C. and Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance, Springer.
|
5 |
Hosking, J. and Wallis, J. (1987). Parameters and quantile estimation for the Generalized Pareto Distribution, Technometrics, 29, 339-349.
DOI
ScienceOn
|
6 |
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed., McGraw Hill.
|
7 |
Juarez, S. and Schucany, W. (2004). Robust and efficient estimation for the Generalized Pareto Distribution, Extremes, 7, 237-251.
DOI
|
8 |
Singh, V. P. and Guo, H. (1995). Parameter estimation for 3-parameter generalized Pareto distribution by the principle of maximum entropy (POME), Hydrological Sciences, 40, 165-181. 입니다.
DOI
ScienceOn
|
9 |
Song, J. and Song, S. (2012). A quantile estimation for massive data with Generalized Pareto Distribution, Computational Statistics and Data Analysis, 56, 143-150.
DOI
ScienceOn
|
10 |
Zhang, J. (2007). Likelihood moment estimation for the Generalized Pareto Distribution, Australian and New Zealand Journal of Statistics, 49, 69-77.
DOI
ScienceOn
|
11 |
Zhang, J. (2010). Improving on estimation for the Generalized Pareto Distribution, Technometrics, 52, 335-339.
DOI
ScienceOn
|
12 |
Zhang, J. and Stephens, M.(2009). A new and efficient estimation method for the generalized Pareto distribution, Technometrics, 51, 316-325.
DOI
ScienceOn
|