朱至剛 教授
研究室：理A435
電話：03-8903536 傳真： 03-8900161
E-mail：chu@mail.ndhu.edu.tw
個人網頁：
學歷：
美國北卡羅來納大學統計博士
經歷：
研究領域：
無母數迴歸、時間序列分析
論文：
1. Hwang, R.C., Chung, H., and Chu, C.K. (2015). A two-stage probit model for predicting recovery rates. Accepted by Journal of Financial Services Research.
2. Siao, J.S., Hwang, R.C., and Chu, C.K. (2015). Predicting recovery rates using the logistic quantile regression with bounded outcomes. Accepted by Quantitative Finance.
3. Siao, J.S., Lin, Z.H., Hwang, R.C., and Chu, C.K. (2015). Is the S&P rating a good credit risk proxy? A stochastic frontier approach. Journal of the Chinese Statistical Association, 53, 107-127.
4. Hwang, R.C. and Chu, C.K. (2014). Forecasting forward defaults: A simple hazard model with competing risks. Quantitative Finance, 14, 1467-1477.
5. Hwang, R.C. and Chu, C.K. (2014). Forecasting forward defaults with the discrete-time hazard model. Journal of Forecasting, 33, 108-123.
6. Chu, C.K., Siao, J.S., Wang, L.C., and Deng, W.S., (2012). Estimation of 2D jump location curve and 3D jump location surface in nonparametric regression. Statistics and Computing, 22, 17-31.
7. Hwang, R.C., Siao, J.S., Chung, H., Chu, C.K. (2011). Assessing bankruptcy prediction models via information content of technical inefficiency. Journal of Productivity Analysis, 36, 263-273.
8. Cheng, K.F., Chu, C.K., and Hwang, R.C. (2010). Predicting bankruptcy using the discrete-time semiparametric hazard model. Quantitative Finance, 10, 1055-1066.
9. Hwang, R.C., Chung, H., and Chu, C.K. (2010). Predicting issuer credit ratings using a semiparametric method. Journal of Empirical Finance, 17, 120-137.
10.Hwang, R.C., Lin, Z.H., and Chu, C.K. (2009). Double smoothing robuster estimators in nonparametric regression. Sankhya A, 71, 298-330.
11.Sivertsena, A.H., Chu, C.K., Wang, L.C., Godtliebsenc, F., Heia, K., and Nilsen, H. (2009). Ridge detection with application to automatic fish fillet inspection. Journal of Food Engineering, 90, Pages 317-324.
12.Cheng, K.F. and Chu, C.K. (2008). Local likelihood method for estimating relative risk functions in case-control studies. Journal of Statistical Planning and Inference, 138, 3733-3748.
13.Hwang, R.C., Deng, W.S., and Chu, C.K. (2006). The local linear M-estimator with a robust initial estimate. Journal of Chinese Statistical Association, 44, 382-401.
14.Cheng, K.F., Chu, C.K., and Lin, D. (2006). Quick multivariate kernel density estimation for massive datasets. Applied Stochastic Models in Business and Industry, 22, 533-546.
15.Lin, J.H., Chang, T.K., and Chu, C.K. (2006). On study of a modified local constant M-smoother. Taiwanese Journal of Mathematics, 10, 1465-1483.
16.Chang, T.K., Deng, W.S., Lin, J.H., and Chu, C.K. (2005). Integrated cross-validation for the random design nonparametric regression. Taiwanese Journal of Mathematics, 9, 123-141.
17.Cheng, K.F. and Chu, C.K. (2004). Semiparametric density estimation under a two-sample density ratio model. Bernoulli, 10, 583-604.
18.Chu, C.K. and Deng, W.S. (2003). An interpolation method for adapting to sparse design in multivariate nonparametric regression. Journal of Statistical Planning and Inference, 116, 91-111.
19.Rue, H., Chu, C.K., Godtliebsen, F., and Marron, J.S. (2001). M-smoother with local linear fit. Journal of Nonparametric Statistics, 14, 155-168.
20.Lai, C.J. and Chu, C.K. (2001). Triple smoothing estimation of the regression function and its derivatives. Journal of Statistical Planning and Inference, 98, 157-175.
21.Godtliebsen, F., Chu, C.K., and Sorbye, S.H. (2001). An estimator for functional data with application to MRI. IEEE Transactions on Medical Imaging, 20, 36-44.
22.Deng, W.S., Chu, C.K., and Cheng, M.Y. (2001). A study of local linear ridge estimator estimators. Journal of Statistical Planning and Inference, 93, 225-238.
23.Deng, W.S. and Chu, C.K. (1999). On study of kernel regression function polygons. Journal of Nonparametric Statistics, 12, 597-609.
24.Chu, C.K., Glad, I., Godtliebsen, F., and Marron, J.S. (1998). Edge preserving smoothers for image processing (with discussion). Journal of the American Statistical Association, 93, 526-556.
25.Godtliebsen, F. and Chu, C.K. (1995). Estimation of the number of true grey levels, their values and relative frequencies in a noisy image. Journal of the American Statistical Association, 90, 890-899.
26.Chu, C.K. and Cheng, P.E. (1995). Estimation of jump points and jump values of a density function. Statistica Sinica, 6, 79-95.
27.Chu, C.K. and Cheng, P.E. (1995). Nonparametric regression estimation with missing data. Journal of Statistical Planning and Inference, 48, 85-99.
28.Chu, C.K. and Cheng, K.F. (1995). Nonparametric regression estimates using misclassified binary responses. Biometrika, 82, 315-325.
29.Chu, C.K. (1995). The effect of the smoothness of the regression function on a bandwidth selector in nonparametric regression. Communications of Statistics --- Simulation and Computation, 24, 61-77.
30.Chu, C.K. (1995). Bandwidth selection in nonparametric regression with general errors. Journal of Statistical Planning and Inference, 44, 265-275.
31.Cheng, P.E. and Chu, C.K. (1995). Estimation of distribution function and quantile with missing data. Statistica Sinica, 6, 63-78.
32.Wu, J.S. and Chu, C.K. (1994). Nonparametric estimation of a regression function with dependent observations. Stochastic Processes and Their Applications, 50, 149-160.
33.Wei, C.Z. and Chu, C.K. (1994). A regression point of view toward density estimation. Journal of Nonparametric Statistics, 4, 191-201.
34.Chu, C.K. (1994). Binned modified cross-validation with dependent errors. Communications of Statistics --- Theory and Methods, 23, 3513-3537.
35.Chu, C.K. (1994). Estimation of change-points in nonparametric regression function through kernel density estimation. Communications of Statistics --- Theory and Methods, 23, 3037-3062.
36.Wu, J.S. and Chu, C.K. (1993). Nonparametric function estimation and bandwidth selection for discontinuous regression functions. Statistica Sinica, 3, 557-576.
37.Wu, J.S. and Chu, C.K. (1993). Modification for boundary effects and jump points in nonparametric regression. Journal of Nonparametric Statistics, 2, 341-354.
38.Wu, J.S. and Chu, C.K. (1993). Kernel type estimators of jump points and values of a regression function. Annals of Statistics, 21, 1545-1566.
39.Chu, C.K. (1993). Nonparametric bivariate density estimation. Chinese Journal of Mathematics, 21, 115-124.
40.Chu, C.K. (1993). A new version of the Gasser-Muller estimator. Journal of Nonparametric Statistics, 3, 187-193.
41.Wu, J.S. and Chu, C.K. (1992). Double smoothing for kernel estimators in nonparametric regression. Journal of Nonparametric Statistics, 1, 375-386.
42.Wu, J.H., Chen, W.S., Teng, C.I., and Chu, C.K. (1992). Asymptotic behavior of local linear smoothers when the regression function and its derivatives have points of discontinuity. Journal of Chinese Statistical Association, 31, 73-87.
43.Teng, C.I., Chen, W.S., Wu, J.H., and Chu, C.K. (1992). Local linear smoothers of the regression function and its derivatives. Journal of Chinese Statistical Association, 30, 63-76.
44.Chu, C.K. (1992). Double smoothing bandwidth selection with dependent errors. Chinese Journal of Mathematics, 20, 223-240.
45.Chu, C.K. (1992). A simplified local linear smoother. Journal of Chinese Statistical Association, 30, 77-86.
46.Chen, W.S., Teng, C.I., Wu, J.H., and Chu, C.K. (1992). Asymptotic behavior of nonparametric regression function estimators when there exists random variation among fixed design points. Journal of Chinese Statistical Association, 30, 133-145.
47.Chu, C.K. and Marron, J.S. (1991). Choosing a kernel regression estimator (with discussion). Statistical Science, 6, 404-436.
48.Chu, C.K. and Marron, J.S. (1991). Comparison of two bandwidth selectors with dependent errors. Annals of Statistics, 19, 1906-1918.
研討會論文:
1.K.F. Cheng, D.Lin and C.K. Chu(2001).Quick kernel density estimation for massive data sets.九十年度採礦學會採礦研討會，中央大學。