http://orcid.org/0000-0003-1109-6768
References
- Lee T, Lee S. Normal mixture quasi maximum likelihood estimator for GARCH models. Scand J Statist. 2009;36:157–170.
- Kim M, Lee S. Comparison of semiparametric methods to estimate VaR and ES. Korean J Appl Statist. 2016;29:171–180. (in Korean). doi: 10.5351/KJAS.2016.29.1.171
- Engle RF, Manganelli S. CAViaR: conditional autoregressive value at risk by regression quantiles. J Bus Econ Statist. 2004;22:367–381. doi: 10.1198/073500104000000370
- Noh J, Lee S. Quantile regression for location-scale time series models with conditional hetroscedasticity. Scand J Statist. 2016;43:700–720. doi: 10.1111/sjos.12199
- Kim M, Lee S. Nonlinear expectile regression with application to value-at-risk and expected shortfall estimation. Comput Statist Data Anal. 2016;94:1–19. doi: 10.1016/j.csda.2015.07.011
- Hansen BE. Autoregressive conditional density estimation. Int Econ Rev. 1994;35:705–730. (Philadelphia). doi: 10.2307/2527081
- Kim M, Lee S. Inferences for conditionally heteroscedastic location-scale time series models with ASTD and AEPD innovations. Unpublished manuscript. 2016.
- D'Agostino RB, Stephens MA. Goodness-of-fit techniques. New York: Marcel Dekker; 1986.
- Lee S, Vonta I, Karagrigoriou A. A maximum entropy type test of fit. Comput Stat Data Anal. 2011;55:2635–2643. doi: 10.1016/j.csda.2011.03.012
- Lee S. A maximum entropy type test of fit: composite hypothesis case. Comp Statist Data Anal. 2013;57:59–67. doi: 10.1016/j.csda.2012.06.006
- Lee J, Lee S, Park S. Maximum entropy test for GARCH models. Statist Method. 2015;22:8–16. doi: 10.1016/j.stamet.2014.05.002
- Durbin J. Weak convergence of the sample distribution function when parameters are estimated. Ann Statist. 1973;1:279–290. doi: 10.1214/aos/1176342365
- Lee S, Wei CZ. On residual empirical processes of stochastic regression models with applications to time series. Ann Statist. 1999;27:237–261. doi: 10.1214/aos/1018031109
- Lee S, Taniguchi M. Asymptotic theory for ARCH-SM models: LAN and residual empirical process. Statist Sinica. 2005;15:215–234.
- Lee S, Kim M. On entropy-based goodness-of-fit test for asymmetric Student-t and exponential power distributions. J Statist Comp Simul. 2017;87:187–197. doi: 10.1080/00949655.2016.1196690
- Lee S, Kim M. On entropy test for conditionally heteroscedastic location-scale time series models. Entropy. 2017;19(8):388. doi: 10.3390/e19080388
- Efron B. Bootstrap methods: another look at the jackknife. Ann Statist. 1979;7:1–26. doi: 10.1214/aos/1176344552
- Efron B, Tibshirani R. An introduction to the bootstrap. New York: Chapman & Hall; 1994.
- Stute W, Manteiga WG, Quindimil MP. Bootstrap based goodness-of-fit tests. Metrika. 1993;40:243–256. doi: 10.1007/BF02613687
- Babu GJ, Rao CR. Goodness-of-fit tests when parameters are estimated. Sankhya: Indian J Statist. 2004;66:63–74.
- Shimizu K. Bootstrapping stationary ARMA-GARCH models. Germany: Vieweg+Teubner Verlag; 2010.
- Lee S, Lee T. Inference for Box-Cox transformed threshold GARCH models with nuisance parameters. Scand J Statist. 2012;39:568–589. doi: 10.1111/j.1467-9469.2012.00805.x
- Davidson J. Stochastic limit theory: an introduction for econometricians. New York: Oxford university press; 1994.
- Billingsley P. Convergence of probability measures. New York: Wiley; 1968.
- Hall P, Heyde CC. Martingale limit theory and its applications. New Yorky: Academic Press; 1980.