Distribution Of Error In Least Squares Solution Of An Overdetermined System Of Linear Simultaneous Equations
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Distribution Of Error In Least Squares Solution Of An Overdetermined System Of Linear Simultaneous Equations
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Author : C. David Miller
language : en
Publisher:
Release Date : 1972
Distribution Of Error In Least Squares Solution Of An Overdetermined System Of Linear Simultaneous Equations written by C. David Miller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972 with Equations, Simultaneous categories.
Probability density functions were derived for errors in the evaluation of unknowns by the least squares method in system of nonhomogeneous linear equations. Coefficients of the unknowns were assumed correct and computational precision were also assumed. A vector space was used, with number of dimensions equal to the number of equations. An error vector was defined and assumed to have uniform distribution of orientation throughout the vector space. The density functions are shown to be insensitive to the biasing effects of the source of the system of equations.
Nasa Technical Note
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Author :
language : en
Publisher:
Release Date : 1972
Nasa Technical Note written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972 with categories.
Abstracts Of Nasa Asrdi Publications Relevant To Aerospace Safety Research
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Author : G. Mandel
language : en
Publisher:
Release Date : 1973
Abstracts Of Nasa Asrdi Publications Relevant To Aerospace Safety Research written by G. Mandel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1973 with categories.
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1992-03
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-03 with Aeronautics categories.
Nbs Special Publication
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Author :
language : en
Publisher:
Release Date : 1970
Nbs Special Publication written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1970 with Weights and measures categories.
Journal Of Research Of The National Institute Of Standards And Technology
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Author :
language : en
Publisher:
Release Date : 1994
Journal Of Research Of The National Institute Of Standards And Technology written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Measurement categories.
Government Reports Announcements
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Author :
language : en
Publisher:
Release Date : 1972
Government Reports Announcements written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972 with Technology categories.
Applications Of Linear And Nonlinear Models
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Author : Erik W. Grafarend
language : en
Publisher: Springer Nature
Release Date : 2022-10-01
Applications Of Linear And Nonlinear Models written by Erik W. Grafarend and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-01 with Science categories.
This book provides numerous examples of linear and nonlinear model applications. Here, we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view and a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters, we concentrate on underdetermined and overdetermined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE, and total least squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so-called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann–Plucker coordinates, criterion matrices of type Taylor–Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overjet. This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (ii) The general integer least squares problem is formulated, and the optimality of the least squares solution is shown. (iii) The relation to the closest vector problem is considered, and the notion of reduced lattice basis is introduced. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. (2) Bayes methods that covers (i) general principle of Bayesian modeling. Explain the notion of prior distribution and posterior distribution. Choose the pragmatic approach for exploring the advantages of iterative Bayesian calculations and hierarchical modeling. (ii) Present the Bayes methods for linear models with normal distributed errors, including noninformative priors, conjugate priors, normal gamma distributions and (iii) short outview to modern application of Bayesian modeling. Useful in case of nonlinear models or linear models with no normal distribution: Monte Carlo (MC), Markov chain Monte Carlo (MCMC), approximative Bayesian computation (ABC) methods. (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Summarize the properties of TLS, (iii) explain the idea of simulation extrapolation (SIMEX) estimators, (iv) introduce the symmetrized SIMEX (SYMEX) estimator and its relation to TLS, and (v) short outview to nonlinear EIV models. The chapter on algebraic solution of nonlinear system of equations has also been updated in line with the new emerging field of hybrid numeric-symbolic solutions to systems of nonlinear equations, ermined system of nonlinear equations on curved manifolds. The von Mises–Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra, and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger algorithm, especially the C. F. Gauss combinatorial algorithm.
Government Reports Announcements Index
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Author :
language : en
Publisher:
Release Date : 1972
Government Reports Announcements Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1972 with Science categories.
Applications Of Linear And Nonlinear Models
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Author : Erik Grafarend
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-08-15
Applications Of Linear And Nonlinear Models written by Erik Grafarend and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-15 with Science categories.
Here we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view as well as a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss-Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters we concentrate on underdetermined and overdeterimined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE and Total Least Squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann-Pluecker coordinates, criterion matrices of type Taylor-Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overdetermined system of nonlinear equations on curved manifolds. The von Mises-Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter eight is devoted to probabilistic regression, the special Gauss-Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four Appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger Algorithm, especially the C. F. Gauss combinatorial algorithm.