참고문헌
- Carroll, R. J. and Hall, P. (1988). Optimal rates of convergence for deconvoluting a density. Journal of the American Statistical Association, 83, 1184-1886. https://doi.org/10.1080/01621459.1988.10478718
- Carroll, R. J., Ruppert, D. and Stefanski, L. (1995). Measurement error in nonlinear models, Chapman and Hall, London.
- Dilaigle, A. and Gijbels, I. (2007). Frequent problems in calculating integrals and optimizing objective functions: A case study in density estimation. Statistics and Computing, 17, 349-355. https://doi.org/10.1007/s11222-007-9024-0
- Eggermont, P. and LaRiccia, V. (1997). Nonlinearly smoothed EM density estimation with augmented smoothing parameter selection for nonparametric deconvolution problems. Journal of the American Statistical Association, 92, 1451-1458. https://doi.org/10.1080/01621459.1997.10473666
- Fan, J. (1991). On the optimal rates of convergence for nonparametric deconvolution problem. Annals of Statistics, 19, 1257-1272. https://doi.org/10.1214/aos/1176348248
- Gunn, S. R. (1998). Support vector machines for classification and regression, Technical report, University of Southampton, Southampton, UK.
- Hall, P. and Qiu, P. (2005). Discrete-transform approach to deconvolution problems. Biometrika, 92, 135-148. https://doi.org/10.1093/biomet/92.1.135
- Hazelton, M. L. and Turlach, B. A. (2009). Nonparametric density deconvolution by weighted kernel estimators. Statistics and Computing, 19, 217-228. https://doi.org/10.1007/s11222-008-9086-7
- Koo, J.-Y. and Park, B. U. (1996). B-spline deconvolution based on the EM algorithm. Journal of Statistical Computation and Simulation, 54, 275-288. https://doi.org/10.1080/00949659608811735
- Lee, S. and Taylor, R. L.(2008). A note on support vector density estimation for the deconvolution problem. Communications in Statistics: Theory and Methods, 37, 328-336. https://doi.org/10.1080/03610920701653086
- Lee, S. (2010). A Support vector method for the deconvolution problem. Communications of the Korean Statistical Society, 17, 451-457. https://doi.org/10.5351/CKSS.2010.17.3.451
- Lee, S. (2012). A Note on deconvolution estimators when measurements errors are normal. Communications of the Korean Statistical Society, 19, 517-526. https://doi.org/10.5351/CKSS.2012.19.4.517
- Liu, M. C. and Taylor, R. L. (1989). A Consistent nonparametric density estimator for the deconvolution problem. The Canadian Journal of Statistics, 17, 427-438. https://doi.org/10.2307/3315482
- Louis, T. A. (1991). Using empirical Bayes methods in biopharmaceutical research. Statistics in Medicine, 10, 811-827. https://doi.org/10.1002/sim.4780100604
- Mendelsohn, J. and Rice, R. (1982). Deconvolution of micro uorometric histograms with B splines. Journal of the American Statistical Association, 77, 748-753.
- Mukherjee, S. and Vapnik, V. (1999). Support vector method for multivariate density estimation. Proceedings in Neural Information Processing Systems, 659-665.
- Pensky, M. and Vidakovic, B. (1999). Adaptive wavelet estimator for nonparametric density deconvolutoin. Annals of Statistics, 27, 2033-2053. https://doi.org/10.1214/aos/1017939249
- Stefanski, L. and Carroll, R. J. (1990). Deconvoluting kernel density estimators. Statistics, 21, 169-184. https://doi.org/10.1080/02331889008802238
- Wang, X. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39, Issue 10.