Parameter Estimation in Engineering and ScienceIntroduction to and survey of parameter estimation; Probability; Introduction to statistics; Parameter estimation methods; Introduction to linear estimation; Matrix analysis for linear parameter estimation; Minimization of sum of squares functions for models nonlinear in parameters; Design of optimal experiments. |
Contents
Introduction to and Survey of Parameter Estimation | 1 |
REFERENCES | 24 |
PROBLEMS | 78 |
Introduction to Statistics | 84 |
REFERENCES | 114 |
REFERENCES | 129 |
REFERENCES | 204 |
Matrix Analysis for Linear Parameter Estimation | 213 |
APPENDIX 6B MATRIX INVERSION LEMMA | 326 |
Minimization of Sum of Squares Functions for Models | 334 |
| 414 | |
Design of Optimal Experiments | 419 |
REFERENCES | 474 |
Appendix A Identifiability Condition | 481 |
Appendix B Estimators and Covariances for Various Estimation | 489 |
| 495 | |
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Common terms and phrases
analysis approximate assumed autoregressive b₁ b₂ Box-Kanemasu calculated confidence interval confidence region considered constant variance constraints continuous random variable covariance matrix criterion degrees of freedom dependent variable derivative design of experiments diagonal differential equation distribution function equal to zero estimation problem example expected value finite Gauss method Hence inverse iteration known large number MAP estimation maximized maximum likelihood estimation measurement errors minimum ML estimation n₁ nonlinear normal distribution obtained ordinary least squares parameter estimation parameter vector prior information probability density function procedure random variable regression residuals sample Section sensitivity coefficients sensors shown in Fig Solution squares function ẞ₁ ẞ₂ standard assumptions standard deviation statistical sum of squares t₁ temperature theorem thermocouples tion unbiased estimator valid X₁ Y₁ zero mean
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Page 414 - Optimization: Theory and Practice", McGraw-Hill Book Company, New York, 1970, pp.