How to cite this paper
Navaee, M., Mobin, M., Vardani, M & Ahmadi, N. (2002). A Semi parametric approach to dual modeling.Management Science Letters , 2(2), 665-672.
Refrences
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Anderson-Cook, C.M., & Prewitt, K. (2005). Some guidelines for using nonparametric methods for modeling data from response surface designs. Journal of Modern Applied Statistical Methods, 4, 106-119.
Bartlett, M.S., & Kendall, D.G. (1946). The statistical analysis of variance heterogeneity and the logarithmic transformation. Journal of Royal Statistical Society Series B, 8, 128- 138.
Box, G., & Draper B. (1987). Empirical Model Building and Response Surface, Wiley, New York.
Box, G.E.P., & Meyer, R.D. (1986). Dispersion effects from fractional designs. Technimetrics, 28, 19-27.
Box, G.E.P., & Draper, N.R., (1987). Empirical Model Building and Response Surface. Wiley, New York.
Burman, P., & Chaudhuri, P. (1992). A Hybrid Approach to Parametric and Nonparametric Regression, Technical Report No. 243, Division of Statistics, University of California Davis, CA, USA.
Einsporn, R., & Birch, J.B. (1993). Model robust regression: using nonparametric regression to improve parametric regression analyses. Technical Report 93-5.Department of Statistics, Virginia Polytechnic Institute & State University, Blacksburg, VA.
Fan, J., & Gijbels, I. (1996). Local Polynomial Modeling and its Applications. Chapman & Hall, London.
Fan, J., Heckman, N.E., Wand, M.P., (1995). Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. Journal of American Statistical Association, 90, 141-150.
Lin, X., & Carroll, R.J. (2000). Nonparametric function estimation for clustered data when the predictor is measured without/with error. Journal of the American Statistical Association.95, 520-534.
Mays, J. E., Birch, J. B., & R. L. Einsporn. (2000). An overview of model robust regression. Journal of Statistical Computation and Simulation. 66, 79-100.
Mays, J., Brich, J., & Starnes, B. (2001). Model robust regression: combining parametric, nonparametric and semi parametric methods. Journal of Nonparametric Statistics, 13, 245-270.
Pickle, S. M., Robinson, T.J., Birch, J.B., Anderson-Cook, C.M. (2008). A semi-parametric approach to robust parameter design. Journal of Statistical Planning and inference, 138, 114-131.
Robinson, T. J., Birch, J. and Alden Starnes, B. (2010). A semi-parametric approach to dual modeling when no replication exists. Journal of Statistical Planning and Inference, 140, 2860-2869.
Rahman, M., Gokhale, D.V., & Ullah, A. (1997). A Note on Combining Parametric and Nonparametric Regression. Communications in Statistics-Simulation and Computation, 26, 519-529.
Robinson T.J., & Birch, J.B. (2000). Model misspecification in parametric dual modeling. Journal of Statistical Computation and Simulation, 66, 113-126.
Ruppert, D., Wand, M.R., & Carroll, R. J. (2003). Semiparametric Regression. Cambridge University Press.
Starnes, B.A. (1999). Asymptotic results for model robust regression. Unpublished dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA.
Anderson-Cook, C.M., & Prewitt, K. (2005). Some guidelines for using nonparametric methods for modeling data from response surface designs. Journal of Modern Applied Statistical Methods, 4, 106-119.
Bartlett, M.S., & Kendall, D.G. (1946). The statistical analysis of variance heterogeneity and the logarithmic transformation. Journal of Royal Statistical Society Series B, 8, 128- 138.
Box, G., & Draper B. (1987). Empirical Model Building and Response Surface, Wiley, New York.
Box, G.E.P., & Meyer, R.D. (1986). Dispersion effects from fractional designs. Technimetrics, 28, 19-27.
Box, G.E.P., & Draper, N.R., (1987). Empirical Model Building and Response Surface. Wiley, New York.
Burman, P., & Chaudhuri, P. (1992). A Hybrid Approach to Parametric and Nonparametric Regression, Technical Report No. 243, Division of Statistics, University of California Davis, CA, USA.
Einsporn, R., & Birch, J.B. (1993). Model robust regression: using nonparametric regression to improve parametric regression analyses. Technical Report 93-5.Department of Statistics, Virginia Polytechnic Institute & State University, Blacksburg, VA.
Fan, J., & Gijbels, I. (1996). Local Polynomial Modeling and its Applications. Chapman & Hall, London.
Fan, J., Heckman, N.E., Wand, M.P., (1995). Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. Journal of American Statistical Association, 90, 141-150.
Lin, X., & Carroll, R.J. (2000). Nonparametric function estimation for clustered data when the predictor is measured without/with error. Journal of the American Statistical Association.95, 520-534.
Mays, J. E., Birch, J. B., & R. L. Einsporn. (2000). An overview of model robust regression. Journal of Statistical Computation and Simulation. 66, 79-100.
Mays, J., Brich, J., & Starnes, B. (2001). Model robust regression: combining parametric, nonparametric and semi parametric methods. Journal of Nonparametric Statistics, 13, 245-270.
Pickle, S. M., Robinson, T.J., Birch, J.B., Anderson-Cook, C.M. (2008). A semi-parametric approach to robust parameter design. Journal of Statistical Planning and inference, 138, 114-131.
Robinson, T. J., Birch, J. and Alden Starnes, B. (2010). A semi-parametric approach to dual modeling when no replication exists. Journal of Statistical Planning and Inference, 140, 2860-2869.
Rahman, M., Gokhale, D.V., & Ullah, A. (1997). A Note on Combining Parametric and Nonparametric Regression. Communications in Statistics-Simulation and Computation, 26, 519-529.
Robinson T.J., & Birch, J.B. (2000). Model misspecification in parametric dual modeling. Journal of Statistical Computation and Simulation, 66, 113-126.
Ruppert, D., Wand, M.R., & Carroll, R. J. (2003). Semiparametric Regression. Cambridge University Press.
Starnes, B.A. (1999). Asymptotic results for model robust regression. Unpublished dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA.