- 鄭紀倫
Publications
 
Papers
  • Cheng, C-L and Kukush, A, 2006, Non-existence of the first moment of the adjusted least squares estimator in multivariate errors-in-variables model, Metrika, 64(1), 41-46, [SCI] (IF 0.551).
  • H. Schneeweiss and C-L. Cheng, 2006, Bias of the structural quasi score estimator of a measurement error model under misspecification of the regressor distribution., Journal of Multivariate Analysis, 97(2), 455-473, [SCI] (IF 0.763).
  • C-L. Cheng and J. Riu, 2006, On estimating linear relationships when both variables are subject to heteroscedastic measurement errors, Technometrics, 48(4), 511-519, [SCI] (IF 1.012).
  • C-L. Cheng and C.-L. Tsai, 2004, The invariance of some score tests in the linear model with classical measurement error., Journal of the American Statistical Association, 99(467), 805-809, [SCI] (IF 1.978).
  • C-L. Cheng, and A. Kukush, 2004, A goodness-of-fit test in a polynomial errors-in-variables model, Ukrainian Mathematical Journal, 56(4), 641-661.
  • C-L. Cheng, H. Schneeweiss and M. Thamerus, 2000, A small sample estimator for a polynomial regression with errors in the variables, Journal of the Royal Statistical Society Series B, 62(4), 699-709, [SCI].
  • C-L. Cheng and H. Schneeweiss, 1998, Polynomial regression with errors in the variables, Journal of the Royal Statistical Society Series B, 60(1), 189-199, [SCI].
  • C-L. Cheng and J. W. Van Ness, 1997, Robust calibration, Technometrics, 39(4), 401-411, [SCI].
  • C-L. Cheng and J. W. Van Ness, 1994, On estimating the linear relationships when both variables are subject to errors, Journal of the Royal Statistical Society Series B, 56(1), 167-183, [SCI].
  • C-L. Cheng and J. W. Van Ness, 1992, Generalized M-estimators for errors-in-variables regression, The Annals of Statistics, 20(1), 385-397, [SCI].
  • C-L. Cheng, 1992, Robust linear regression via bounded influence M-estimators, Journal of Multivariate Analysis, 40(1), 158-171, [SCI].
  • C-L. Cheng and J. W. Van Ness, 1991, On the unreplicated ultrastructural model, Biometrika, 78(2), 442-445, [SCI].
Technical Reports
  • C-L. Cheng, 1997, On the polynomial Berkson model, On the polynomial Berkson model.
  • C-L. Cheng and C-L. Tsai, 1994, The comparisons of three different linear calibration estimators in measurement error models, The comparisons of three different linear calibration estimators in measurement error models.
  • C-L. Cheng, 1994, On generalized least squares and least squares, On generalized least squares and least squares.
  • C-L. Cheng and C-L. Tsai, 1993, Diagnostics in measurement error model, Diagnostics in measurement error model.
Book Chapters
  • C-L. Cheng and H. Schneeweiss, 2002, On the polynomial measurement error model, Total Least Squares and Errors-in-Variables Modeling, 131-143.
  • H. Schneeweiss, C-L. Cheng and R. Wolf, 2002, On the bias of structural estimation methods in a polynomial regression with measurement error when the distribution of the latent covariate is a mixture of normals, Contributions to Modern Econometrics-From Data Analysis to Economic Policy, 209-222.
  • C-L. Cheng and H. Schneeweiss, 1998, Note on two estimators for the polynomial regression with errors in the variables, Statistical Modeling: Proceedings of the 13th International Workshop on Statistical Modeling, 141-147.
  • C-L. Cheng and J. W. Van Ness, 1998, Errors in variables in econometrics, Econometrics in Theory and Practice: Festschrift for H. Schneeweiss, 3-13.
  • C-L. Cheng and J. W. Van Ness, 1997, Structural and functional models revisited, Recent Advances in Total Least Squares Techniques and Errors-in-Variables Modeling, 37-50.
  • C-L. Cheng and C-L. Tsai, 1995, Estimating linear measurement error models via M-estimators, Symposia Gaussiana: Proceedings of Second Gauss Symposium, Conference B: Statistical Sciences, 247-259.
  • C-L. Cheng and J. W. Van Ness, 1990, Bounded influence errors-in-variables regression, Statistical Analysis of Measurement Error Models and Applications, 227-241.
Books
  • C-L. Cheng and J. W. Van Ness, 1999, Statistical Regression with Measurement Error, 262.