Data analysis using the method of least squares: Extracting the most information from experiments

Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments

Volumn , Issue , 2006, Pages 1-250

  Wolberg, J ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

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EID: 84892233338     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/3-540-31720-1     Document Type: Book

References (52)

1. M. Abramowitz, I. Stegen, Handbook of Mathematical Functions, NBS, 1964.
2. Aronson, D. Evidence Based Technical Analysis, J. Wiley, 2006.
3. M. Azoff, Neural Network Time Series Forecasting of Financial Markets, Wiley, 1994.
4. Y. Bard, Nonlinear Parameter Estimation, Academic Press, 1974.
5. R.E. Bellman, Adaptive Control Processes, Princeton University Press, 1961.
6. R. J. Bauer, Genetic Algorithms and Investment Strategies, Wiley, 1994.
7. P. Bevington, D. Keith, Data Reduction and Error Analysis for the Physical Sciences, McGraw Hill, 2003.
8. A. Constantinides, Numerical Methods for Chemical Engineers with MATLAB Applications, Prentice Hall, 1999.
9. W.W. Daniel, Applied Nonparametric Statistics, 2nd Edition d, PWS-Kent, 1990.
10. M. Davidian, D. Giltinan, Nonlinear Models for Repeated Measurement Data, Chapman and Hall, 1995.
11. W.E. Deming, Statistical Adjustment of Data, Wiley, 1943.
12. N.R. Draper, H. Smith, Applied Regression Analysis, Wiley, 1966.
13. J. Freund, Mathematical Statistics (5th Edition), Prentice Hall, 1992.
14. G.E. Forsythe, Generation and Use of Orthogonal Polynomials for Data-Fitting with a Digital Computer, J. Soc. Of Applied Math., Vol 5, No. 2, June 1957.
15. C.F. Gauss, C.H. Davis, Theory of the Motion of the Heavenly Bodies Moving about the Sun in Conic Sections: A Translation of Gauss's "Theoria Motus", Little Brown, 1857.
16. T. Gasser, H. G. Muller, W. Kohler, L. Molianari, and A. Prader, Nonparametric regression analysis of growth curves, Annals of Statistics, 12, 210-229, 1984.
17. P. Gans, Data Fitting in the Chemical Sciences, Wiley, 1992.
18. E. Gately, Neural Networks for Financial Forecasting, Wiley, 1995.
19. W. Hardle, Applied Nonparametric Regression, Cambridge University Press, 1990.
20. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer Series in Statistics, 2001.
21. S. Huet, A. Bouvier, M. Poursat, E. Jolivet, Statistical Tools for Nonlinear Regression. A Practical Guide with S-PLUS and R Ex-amples, Springer Series in Statistics, 2003.
22. K. Levenberg, Quart. Applied Mathematics, Vol 2, 164, 1944.
23. D.W. Marquardt, J. Soc. Ind. Applied Mathematics, Vol 11, 431, 1963.
24. B. D. McCullough, Econometric Software Reliability: EViews, LIMDEP, SHAZAM and TSP, Journal of Applied Econometrics, Volume 14, Issue 2, 1999.
25. M. Merriman, The Elements of the Method of Least Squares, J. Wiley & Sons, 1877.
26. W. Mendenhall, T. Sincich, Statistics for Engineers and Scientists, 3rd Edition d,Macmillan, 1992.
27. R. H. Moore, R. K. Zeigler, The Solution of the General Least Squares Problem with Special Reference to High Speed Computers, LA-2367 Report, March 1960.
28. D. Mount, S. Arya, ANN: Library for Approximate Nearest Neighbor Searching, University of Maryland, 1998.
29. H. Motulsky, A. Christopoulos, GraphPad Prism-Fitting Models To Biological Data using Linear and Nonlinear Regression, GraphPad Software, 2003.
30. D. Pyle, Data Preparation for Data Mining, Morgan Kaufmann, 1999.
31. C. R. Rao, Linear Statistical Inference and its Applications, 2nd Edition, Wiley, 1973.
32. A. Ralston, P. Rabinowitz, A First Course in Numerical Analysis, McGraw Hill, 1978.
33. A.P. Refenes, Neural Networks in the Capital Markets, Wiley, 1995.
34. H. Samet, The Design and Analysis of Spatial Data Structures, Addison Wesley Longman, 1990.
35. Oliver Schabenberger, Nonlinear Regression with the SAS System, http://www.ats.ucla.edu/stat/sas/library/SASNLin-os.htm, 1998.
36. C. E. Shannon, A Mathematical Theory of Communication. The Bell System Technical Journal, Vol 27, 379-423, 623-656, July, Oct 1948. also http://cm.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf.
37. S. Siegel, N. J. Castellan, Nonparametric Statistics for the Behavioral Sciences, McGraw Hill, 1988.
38. Skiena, S. S. Nearest Neighbor Search. 8.6.5 in The Algorithm Design Manual. Springer-Verlag, pp. 361-363, 1997.
39. Smid, M. "Closest-Point Problems in Computational Geometry." Ch. 20 in Handbook of Computational Geometry. North-Holland, pp. 877-935, 2000.
40. S. M. Stigler, The History of Statistics: the Measurement of Uncertainty before 1900, Harvard University Press, 1986.
41. Statistica Electronic Textbook, www.statsoft.com/textbook/ stathome.html, Distribution Tables, StatSoft Inc, 2003.
42. E. Tadmor, A. Agrawal, J. Wolberg, An Efficient Data Structure for Graded Atomistic Simulations, Proc of the 29th Israel Conf. of Mechanical Eng, May 2003
43. J. Tvrdik, I. Krivy, Comparison of Algorithms for Nonlinear Regression Estimates, Compstat'2004 Symposium, Physcia-Verlag/Springer 2004.
44. A. Ullah and H. D. Vinod, General Nonparametric Regression Estimation and Testing in Econometrics, Chapter 4, Handbook of Statistics 11, North Holland, 1993.
45. W. Venables, B. Ripley, Modern Applied Statistics with S, 4th Edition, Springer-Verlag, 2002.
46. R. E. Walpole, R. H. Meyers, Probability and Statistics for Engineers and Scientists, (5th edition), McMillan, 1993.
47. Wilks, S. S., Mathematical Statistics, Wiley, 1962.
48. J. R. Wolberg, T.J. Thompson, I. Kaplan, A Study of the Fast Fission Effect in Lattices of Uranium Rods in Heavy Water, M.I.T. Nuclear Engineering Report 9661, February, 1962.
49. J. R. Wolberg, Prediction Analysis, Van Nostrand, 1967.
50. J. R. Wolberg, Application of Computers to Engineering Analysis, McGraw Hill, 1971.
51. J. R. Wolberg, Expert Trading Systems: Modeling Financial Markets with Kernel Regression, Wiley, 2000.
52. J. Zelis, SPSS Advanced Statistics 6.1, UCL Institut de Statistique, 1998.