The NLIN Procedure

Bard, Y. (1970), “Comparison of Gradient Methods for the Solution of the Nonlinear Parameter Estimation Problem,” SIAM Journal on Numerical Analysis, 7, 157–186.

Bard, Y. (1974), Nonlinear Parameter Estimation, New York: Academic Press.

Bates, D. M. and Watts, D. G. (1980), “Relative Curvature Measures of Nonlinearity (with Discussion),” Journal of the Royal Statistical Society, Series B, 42, 1–25.

Bates, D. M. and Watts, D. G. (1981), “A Relative Offset Orthogonality Convergence Criterion for Nonlinear Least Squares,” Technometrics, 23, 179–183.

Bates, D. M. and Watts, D. G. (1988), Nonlinear Regression Analysis and Its Applications, New York: John Wiley & Sons.

Beaton, A. E. and Tukey, J. W. (1974), “The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data,” Technometrics, 16, 147–185.

Box, M. J. (1971), “Bias in Nonlinear Estimation (with Discussion),” Journal of the Royal Statistical Society, Series B, 33, 171–201.

Charnes, A., Frome, E. L., and Yu, P. L. (1976), “The Equivalence of Generalized Least Squares and Maximum Likelihood Estimation in the Exponential Family,” Journal of the American Statistical Association, 71, 169–172.

Clarke, G. P. Y. (1987), “Approximate Confidence Limits for a Parameter Function in Nonlinear Regression,” Journal of the American Statistical Association, 82, 221–230.

Cook, R. D. and Tsai, C.-L. (1985), “Residuals in Nonlinear Regression,” Biometrika, 72, 23–29.

Cook, R. D. and Weisberg, S. (1990), “Confidence Curves in Nonlinear Regression,” Journal of the American Statistical Association, 85, 544–551.

Cox, D. R. (1970), Analysis of Binary Data, London: Metheun.

Donaldson, J. R. and Schnabel, R. B. (1987), “Computational Experience with Confidence Regions and Confidence Intervals for Nonlinear Least Squares,” Technometrics, 29, 67–82.

Finney, D. J. (1971), Probit Analysis, 3rd Edition, Cambridge: Cambridge University Press.

Gallant, A. R. (1975), “Nonlinear Regression,” American Statistician, 29, 73–81.

Gill, P. E., Murray, W., and Wright, M. H. (1981), Practical Optimization, New York: Academic Press.

Goodnight, J. H. (1979), “A Tutorial on the Sweep Operator,” American Statistician, 33, 149–158.

Hartley, H. O. (1961), “The Modified Gauss-Newton Method for the Fitting of Non-linear Regression Functions by Least Squares,” Technometrics, 3, 269–280.

Holland, P. W. and Welsch, R. E. (1977), “Robust Regression Using Iteratively Reweighted Least-Squares,” Communications in Statistics—Theory and Methods, 6, 813–827.

Hougaard, P. (1982), “Parameterizations of Nonlinear Models,” Journal of the Royal Statistical Society, Series B, 44, 244–252.

Hougaard, P. (1985), “The Appropriateness of the Asymptotic Distribution in a Nonlinear Regression Model in Relation to Curvature,” Journal of the Royal Statistical Society, Series B, 47, 103–114.

Huber, P. J. (1964), “Robust Estimation of a Location Parameter,” Annals of Mathematical Statistics, 35, 73–101.

Huber, P. J. (1973), “Robust Regression: Asymptotics, Conjectures, and Monte Carlo,” Annals of Statistics, 1, 799–821.

Jennrich, R. I. (1969), “Asymptotic Properties of Nonlinear Least Squares Estimators,” Annals of Mathematical Statistics, 40, 633–643.

Jennrich, R. I. and Moore, R. H. (1975), “Maximum Likelihood Estimation by Means of Nonlinear Least Squares,” American Statistical Association, 1975 Proceedings of the Statistical Computing Section, 57–65.

Jennrich, R. I. and Sampson, P. F. (1968), “Application of Stepwise Regression to Nonlinear Estimation,” Technometrics, 10, 63–72.

Judge, G. G., Griffiths, W. E., Hill, R. C., and Lee, T.-C. (1980), The Theory and Practice of Econometrics, New York: John Wiley & Sons.

Kennedy, W. J., Jr. and Gentle, J. E. (1980), Statistical Computing, New York: Marcel Dekker.

Lee, E. T. (1974), “A Computer Program for Linear Logistic Regression Analysis,” Computer Programs in Biomedicine, 4, 80–92.

Marquardt, D. W. (1963), “An Algorithm for Least-Squares Estimation of Nonlinear Parameters,” Journal of the Society for Industrial and Applied Mathematics, 11, 431–441.

McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, 2nd Edition, London: Chapman & Hall.

Nelder, J. A. and Wedderburn, R. W. M. (1972), “Generalized Linear Models,” Journal of the Royal Statistical Society, Series A, 135, 370–384.

Pinheiro, J. C. and Bates, D. M. (1995), “Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model,” Journal of Computational and Graphical Statistics, 4, 12–35.

Pringle, R. M. and Rayner, A. A. (1971), Generalized Inverse Matrices with Applications to Statistics, New York: Hafner Publishing.

Ratkowsky, D. (1983), Nonlinear Regression Modeling, New York: Marcel Dekker.

Ratkowsky, D. (1990), Handbook of Nonlinear Regression Models, New York: Marcel Dekker.

Schabenberger, O. and Pierce, F. J. (2002), Contemporary Statistical Models for the Plant and Soil Sciences, Boca Raton, FL: CRC Press.

Schabenberger, O., Tharp, B. E., Kells, J. J., and Penner, D. (1999), “Statistical Tests for Hormesis and Effective Dosages in Herbicide Dose Response,” Agronomy Journal, 91, 713–721.

Seber, G. A. F. and Wild, C. J. (1989), Nonlinear Regression, New York: John Wiley & Sons.

St. Laurent, R. T. and Cook, R. D. (1992), “Leverages and Superleverages in Nonlinear Regression,” Journal of the American Statistical Association, 87, 985–990.

St. Laurent, R. T. and Cook, R. D. (1993), “Leverages, Local Influence, and Curvature in Nonlinear Regression,” Biometrika, 80, 99–106.