Syntax Supported Only by the IML Procedure

References

  • Al-Baali, M., and Fletcher, R. (1985). “Variational Methods for Nonlinear Least Squares.” Journal of the Operations Research Society 36:405–421.

  • Al-Baali, M., and Fletcher, R. (1986). “An Efficient Line Search for Nonlinear Least Squares.” Journal of Optimization Theory and Applications 48:359–377.

  • Barreto, H., and Maharry, D. (2006). “Least Median of Squares and Regression through the Origin.” Computational Statistics and Data Analysis 50:1391–1397.

  • Barrodale, I., and Roberts, F. D. K. (1974). “Algorithm 478: Solution of an Overdetermined System of Equations in the upper L 1-Norm.” Communications of the ACM 17:319–320.

  • Beale, E. M. L. (1972). “A Derivation of Conjugate Gradients.” In Numerical Methods for Nonlinear Optimization, edited by F. A. Lootsma, 39–43. London: Academic Press.

  • Brownlee, K. A. (1965). Statistical Theory and Methodology in Science and Engineering. New York: John Wiley & Sons.

  • Cox, D. R., and Hinkley, D. V. (1974). Theoretical Statistics. London: Chapman & Hall.

  • Dennis, J. E., Gay, D. M., and Welsch, R. E. (1981). “An Adaptive Nonlinear Least-Squares Algorithm.” ACM Transactions on Mathematical Software 7:348–368.

  • Dennis, J. E., and Mei, H. H. W. (1979). “Two New Unconstrained Optimization Algorithms Which Use Function and Gradient Values.” Journal of Optimization Theory and Applications 28:453–482.

  • Eskow, E., and Schnabel, R. B. (1991). “Algorithm 695: Software for a New Modified Cholesky Factorization.” ACM Transactions on Mathematical Software 17:306–312.

  • Fletcher, R. (1987). Practical Methods of Optimization. 2nd ed. Chichester, UK: John Wiley & Sons.

  • Fletcher, R., and Xu, C. (1987). “Hybrid Methods for Nonlinear Least Squares.” Journal of Numerical Analysis 7:371–389.

  • Gay, D. M. (1983). “Subroutines for Unconstrained Minimization.” ACM Transactions on Mathematical Software 9:503–524.

  • George, J. A., and Liu, J. W. (1981). Computer Solutions of Large Sparse Positive Definite Systems. Englewood Cliffs, NJ: Prentice-Hall.

  • Gill, P. E., Murray, W., Saunders, M. A., and Wright, M. H. (1984). “Procedures for Optimization Problems with a Mixture of Bounds and General Linear Constraints.” ACM Transactions on Mathematical Software 10:282–298.

  • Gonin, R., and Money, A. H. (1989). Nonlinear upper L Subscript p-Norm Estimation. New York: Marcel Dekker.

  • Harris, F. J. (1978). “On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform.” Proceedings of the IEEE 66:51–83.

  • Heinzel, G., Rüdiger, A., and Schilling, R. (2002). Spectrum and Spectral Density Estimation by the Discrete Fourier Transform (DFT), Including a Comprehensive List of Window Functions and Some New Flat-Top Windows. Tech. rep., Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut), Teilinstitut Hannover.

  • Lee, W., and Gentle, J. E. (1986). “The LAV Procedure.” In SUGI Supplemental Library User’s Guide, 257–260. Cary, NC: SAS Institute Inc.

  • Lindström, P., and Wedin, P.-A. (1984). “A New Line-Search Algorithm for Nonlinear Least-Squares Problems.” Mathematical Programming 29:268–296.

  • Madsen, K., and Nielsen, H. B. (1993). “A Finite Smoothing Algorithm for Linear upper L 1 Estimation.” SIAM Journal on Optimization 3:223–235.

  • McKean, J. W., and Schrader, R. M. (1987). “Least Absolute Errors Analysis of Variance.” In Statistical Data Analysis Based on upper L 1 Norm and Related Methods, edited by Y. Dodge, 297–305. Amsterdam: North-Holland.

  • Moré, J. J. (1978). “The Levenberg-Marquardt Algorithm: Implementation and Theory.” In Lecture Notes in Mathematics, vol. 30, edited by G. A. Watson, 105–116. Berlin: Springer-Verlag.

  • Moré, J. J., and Sorensen, D. C. (1983). “Computing a Trust-Region Step.” SIAM Journal on Scientific and Statistical Computing 4:553–572.

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

  • Osborne, M. R. (1985). Finite Algorithms in Optimization and Data Analysis. New York: John Wiley & Sons.

  • Powell, M. J. D. (1977). “Restart Procedures for the Conjugate Gradient Method.” Mathematical Programming 12:241–254.

  • Powell, M. J. D. (1978a). “Algorithms for Nonlinear Constraints That Use Lagrangian Functions.” Mathematical Programming 14:224–248.

  • Powell, M. J. D. (1978b). “A Fast Algorithm for Nonlinearly Constrained Optimization Calculations.” In Lecture Notes in Mathematics, vol. 630, edited by G. A. Watson, 144–175. Berlin: Springer-Verlag.

  • Powell, M. J. D. (1982a). “Extensions to Subroutine VF02AD.” In Systems Modeling and Optimization, Lecture Notes in Control and Information Sciences, vol. 38, edited by R. F. Drenick and F. Kozin, 529–538. Berlin: Springer-Verlag.

  • Powell, M. J. D. (1982b). VMCWD: A Fortran Subroutine for Constrained Optimization. Technical Report DAMTP 1982/NA4, Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

  • Powell, M. J. D. (1992). A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation. Technical Report DAMTP 1992/NA5, Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

  • Rabiner, L. R., and Schafer, R. W. (2007). Introduction to Digital Speech Processing. Boston: Now Publishers.

  • Rousseeuw, P. J. (1984). “Least Median of Squares Regression.” Journal of the American Statistical Association 79:871–880.

  • Rousseeuw, P. J. (1985). “Multivariate Estimation with High Breakdown Point.” In Mathematical Statistics and Applications, edited by W. Grossmann, G. Pflug, I. Vincze, and W. Wertz, 283–297. Dordrecht, Netherlands: D. Reidel Publishing.

  • Rousseeuw, P. J., and Hubert, M. (1996). “Recent Development in PROGRESS.” Computational Statistics and Data Analysis 21:67–85.

  • Rousseeuw, P. J., and Hubert, M. (1997). Recent Developments in PROGRESS. Hayward, CA: Institute of Mathematical Statistics.

  • Rousseeuw, P. J., and Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: John Wiley & Sons.

  • Rousseeuw, P. J., and Van Driessen, K. (1998). Computing LTS Regression for Large Data Sets. Technical report, University of Antwerp.

  • Rousseeuw, P. J., and Van Driessen, K. (1999). “A Fast Algorithm for the Minimum Covariance Determinant Estimator.” Technometrics 41:212–223.

  • Rousseeuw, P. J., and Van Zomeren, B. C. (1990). “Unmasking Multivariate Outliers and Leverage Points.” Journal of the American Statistical Association 85:633–639.

  • Trethewey, M. W. (2000). “Window and Overlap Processing Effects on Power Estimates from Spectra.” Mechanical Systems and Signal Processing 14:267–278.

Last updated: March 08, 2024