An overview of model-robust regression

References

• Allen , D.M. 1947 . The Relationship Between Variable Selection and Data Augmentation and a Method for Prediction . Technometrics , 16 : 125 – 127 .
   Google Scholar
• Altman , N.S. 1992 . An Introduction to Kernel and Nearest-neighbor Nonparametric Regression . American Statistician , 46 : 175 – 185 .
   Web of Science ®Google Scholar
• Assaid , C.A. 1997 . “ Dual Model Regression: Robust to Model Misspecification ” . In Ph.D. Dissertation , Virginia Polyt. Inst. and State Univ .
   Google Scholar
• Burman , P. and Chaudhuri , P. 1992 . “ A Hybrid Approach to Parametric and Nonparametric Regression ” . In Technical Report No. 243 , University of California Davis . Division of Statistics
   Google Scholar
• Chiu , S.-T. 1990 . Why Bandwidth Selectors Tend to Choose Smaller Bandwidths, and a Remedy . Biometrika , 77 : 222 – 226 .
   Web of Science ®Google Scholar
• Chu , C.-K. and Manon , J.S. 1991 . Choosing a Kernel Regression Estimator . Statistical Science , 6 : 404 – 436 . (with discussion)
   Google Scholar
• Cleveland , W.S. 1979 . Robust Locally Weighted Regression and Smoothing Scatter Plots . Journal of the American Statistical Association , 74 : 829 – 836 .
   Web of Science ®Google Scholar
• Einspom , R.L. and Birch , J.B. 1993 . “ Model Robust Regression: Using Nonparametric Regression to Improve Parametric Regression Analyses ” . In Technical Report Number 93-5 , Virginia Polvt. Inst. and State Univ . Dept of Statistics
   Google Scholar
• Fan , J. 1992 . Design-adaptive Nonparametric Regression . Journal of the American Statistical Association , 87 : 998 – 1004 .
   Web of Science ®Google Scholar
• Fan , J. and Gijbels , I. 1996 . Local Polynomial Modelling and Its Applications , London : Chapman and Hall .
   Google Scholar
• Faraway , J.J. 1990 . Bootstrap Selection of Bandwidth and Confidence Bands for Nonparametric Regression . J. Statist. Comput. Simul , 37 : 37 – 44 .
   Google Scholar
• Gasser , T. and Müller , H.G. 1979 . “ Kernel estimation of Regression Functions ” . In Smoothing Techniques for Curve Estimation , Edited by: Gasser , . and Rosenblat , . Heidelberg : Springer-Verlag .
   Google Scholar
• Glad , I.K. 1995 . “ Contributions to Nonparametric Function Estimation and Bayesian Image Analysis ” . In Ph.D. Thesis , Norwegian Institute of Technology . No.58
   Google Scholar
• Gozalo , P.L. and Linto , O. 1994 . “ Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically ” . In Cowles Foundation Discussion Paper No. 1075 , Yale Univ .
   Google Scholar
• Härdle , W. 1990 . Applied Nonparametric Regression , New York : Cambridge University Press .
   Google Scholar
• Härdle , W. and Bowman , A.W. 1988 . Bootstrapping in Nonparametric Regression: Local Adaptive Smoothing and Confidence Bands . Journal of the American Statistical Association , 83 : 102 – 110 .
   Web of Science ®Google Scholar
• Härdle , W. , Hall , P. and Marron , J.S. 1988 . How Far are Automatically Chosen Regression Smoothing Parameters from their Optimum? . Journal of the American Statistical Association , 83 : 86 – 99 . (with discussion)
   Web of Science ®Google Scholar
• Härdle , W. and Marron , J.S. 1985 . Optimal Bandwidth Selection in Nonparametric Regression Function Estimation . Annals of Statistics , 13 : 1465 – 1481 .
   Web of Science ®Google Scholar
• Hastie , T. and Loader , C. 1993 . Local Regression: Automatic Kernel Carpentry . Statistical Science , 8 : 120 – 143 . (with discussion)
   Web of Science ®Google Scholar
• Hjort , N.L. and Giad , I.K. 1995 . Nonparametric Density Estimation with a Parametric Start . The Annals of Statistics , 23 : 882 – 904 .
   Web of Science ®Google Scholar
• Mays , J.E. 1995 . “ Model Robust Regression: Combining Parametric, Nonparametric, and Semiparametric Methods ” . In Ph.D. Dissertation , Virginia Polyt. Inst. and State U .
   Google Scholar
• Mays , J.E. and Birch , J.B. 1996 . “ Model Robust Regression: Combining Parametric, Nonparametric, and Semiparametric Methods ” . In Technical Report Number 96-11 , Virginia Polyt. Inst. and State Univ . Dept. of Statistics
   Google Scholar
• Mays , J.E. and Birch , J.B. 1998 . “ Smoothing Considerations in Nonparametric and Semiparametric Regression ” . In Technical Report Number 98-2 , Virginia Polyt. Inst. and State Univ . Dept. of Statistics
   Google Scholar
• Mays J. E. Birch J. B. Starnes B. A. Model Robust Regression: Combining Parametric, Nonparametric, and Semiparametric Methods unpublished manuscrip
   Google Scholar
• Montgomery , D.C. and Peck , E.A. 1992 . Introduction to Linear Regression Analysis , New York : Wiley .
   Google Scholar
• Müller , H.G. and Stadtmüller , U. 1987 . Variable Bandwidth Kernel Estimators of Regression Curves . Annals of Statistics , 15 : 182 – 201 .
   Web of Science ®Google Scholar
• Myers , R.H. 1990 . Classical and Modern Regression with Applications , Boston, MA : PWS-KENT .
   Google Scholar
• Nottingham , Q.J. and Birch , J.B. 2000 . “ Model Robust Quanta1 Regression: An Alternative Analysis for Dose-Response Data ” . In Statistics in Medicine , in Press .
   Google Scholar
• Priestley , M.B. and Chao , M.T. 1972 . Nonparametric Function Fitting . J. Royal Stat. Soc , B,34 : 384 – 392 .
   Google Scholar
• Rahman , M. , Gokhale , D.V. and Ullah , A. 1997 . A Note on Combining Parametric and Nonparametric Regression . Communications in Statistics — Simulation and Computation , 26 : 519 – 529 .
   Web of Science ®Google Scholar
• Rice , J.A. 1984 . Bandwidth Choice for Nonparametric Regression . Annals of Statistics , 12 : 1215 – 1230 .
   Web of Science ®Google Scholar
• Robinson , P.M. 1988 . Root-N-Consistent Semiparametric Regression . Econometrica , 56 : 931 – 954 .
   Web of Science ®Google Scholar
• Robinson , T.J. 1997 . “ Dual Model Robust Regression ” . In Ph.D. Dissertation , Virginia Polyt. Inst. and State Univ .
   Google Scholar
• Ruppert , D. , Sheather , S.J. and Wand , M.P. 1995 . An Effective Bandwidth Selector for Local Linear Regression . Journal of the American Statistical Association , 90 : 1257 – 1270 .
   Web of Science ®Google Scholar
• SAS Institute Inc . 1996 . The SAS System for Microsoft Windows Cary, N.C Release 6.12
   Google Scholar
• Speckman , P. 1988 . Kernel Smoothing in Partial Linear Models . J. Royal Stat. Soc , B,50 : 413 – 436 .
   Google Scholar
• Stone , C.J. 1980 . Optimal Rates of Convergence for Nonparametric Estimators . Annals of Statistics , 8 : 1348 – 1360 .
   Web of Science ®Google Scholar
• Stone , C.J. 1982 . Optimal Global Rates of Convergence for Nonparametric Regression . Annals of Statistics , 10 : 1040 – 1053 .
   Web of Science ®Google Scholar
• Ullah , A. and Vinod , H.D. 1993 . “ General Nonparametric Regression Estimation and Testing in Econometrics ” . In Handbook of Statistics , Edited by: Maddala , G.S. , Rao , C.R. and Vinod , H.D. Vol. 11 , New York : Elsevier Science Publishers . In
   Google Scholar
• Wand , M.P. and Jones , M.C. 1995 . Kernel Smoothing , London : Chapman and Hall .
   Google Scholar
• Wooldridge , J.M. 1992 . A Test for Functional Form Against Nonparametric Alternatives . Econometric Theory , 8 : 452 – 475 .
   Web of Science ®Google Scholar