A Text Book on the Method of Least Squares

A Text Book on the Method of Least Squares PDF Author: Mansfield Merriman
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 214

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Book Description

A Text Book on the Method of Least Squares

A Text Book on the Method of Least Squares PDF Author: Mansfield Merriman
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 214

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Book Description


A Text Book on the Method of Least Squares

A Text Book on the Method of Least Squares PDF Author: Mansfield Merriman
Publisher:
ISBN:
Category : Correlation (Statistics)
Languages : en
Pages : 218

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Data Analysis Using the Method of Least Squares

Data Analysis Using the Method of Least Squares PDF Author: John Wolberg
Publisher: Springer Science & Business Media
ISBN: 3540317201
Category : Mathematics
Languages : en
Pages : 257

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Book Description
Develops the full power of the least-squares method Enables engineers and scientists to apply the method to their specific problem Deals with linear as well as with non-linear least-squares, parametric as well as non-parametric methods

A Text-book on the Method of Least Squares ...

A Text-book on the Method of Least Squares ... PDF Author: Mansfield Merriman
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Solving Least Squares Problems

Solving Least Squares Problems PDF Author: Charles L. Lawson
Publisher: SIAM
ISBN: 0898713560
Category : Mathematics
Languages : en
Pages : 348

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Book Description
This Classic edition includes a new appendix which summarizes the major developments since the book was originally published in 1974. The additions are organized in short sections associated with each chapter. An additional 230 references have been added, bringing the bibliography to over 400 entries. Appendix C has been edited to reflect changes in the associated software package and software distribution method.

Least Squares Data Fitting with Applications

Least Squares Data Fitting with Applications PDF Author: Per Christian Hansen
Publisher: JHU Press
ISBN: 1421408589
Category : Mathematics
Languages : en
Pages : 325

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Book Description
A lucid explanation of the intricacies of both simple and complex least squares methods. As one of the classical statistical regression techniques, and often the first to be taught to new students, least squares fitting can be a very effective tool in data analysis. Given measured data, we establish a relationship between independent and dependent variables so that we can use the data predictively. The main concern of Least Squares Data Fitting with Applications is how to do this on a computer with efficient and robust computational methods for linear and nonlinear relationships. The presentation also establishes a link between the statistical setting and the computational issues. In a number of applications, the accuracy and efficiency of the least squares fit is central, and Per Christian Hansen, Víctor Pereyra, and Godela Scherer survey modern computational methods and illustrate them in fields ranging from engineering and environmental sciences to geophysics. Anyone working with problems of linear and nonlinear least squares fitting will find this book invaluable as a hands-on guide, with accessible text and carefully explained problems. Included are • an overview of computational methods together with their properties and advantages • topics from statistical regression analysis that help readers to understand and evaluate the computed solutions • many examples that illustrate the techniques and algorithms Least Squares Data Fitting with Applications can be used as a textbook for advanced undergraduate or graduate courses and professionals in the sciences and in engineering.

Linear Models and Generalizations

Linear Models and Generalizations PDF Author: C. Radhakrishna Rao
Publisher: Springer Science & Business Media
ISBN: 3540742271
Category : Mathematics
Languages : en
Pages : 583

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Book Description
Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory.

Introduction to Applied Linear Algebra

Introduction to Applied Linear Algebra PDF Author: Stephen Boyd
Publisher: Cambridge University Press
ISBN: 1316518965
Category : Business & Economics
Languages : en
Pages : 477

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Book Description
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Regression Analysis

Regression Analysis PDF Author: Ashish Sen
Publisher: Springer Science & Business Media
ISBN: 1461244706
Category : Psychology
Languages : en
Pages : 361

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Book Description
An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications. It is further enhanced through real-life examples drawn from many disciplines, showing the difficulties typically encountered in the practice of regression analysis. Consequently, this book provides a sound foundation in the theory of this important subject.

The Total Least Squares Problem

The Total Least Squares Problem PDF Author: Sabine Van Huffel
Publisher: SIAM
ISBN: 0898712750
Category : Mathematics
Languages : en
Pages : 302

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Book Description
This is the first book devoted entirely to total least squares. The authors give a unified presentation of the TLS problem. A description of its basic principles are given, the various algebraic, statistical and sensitivity properties of the problem are discussed, and generalizations are presented. Applications are surveyed to facilitate uses in an even wider range of applications. Whenever possible, comparison is made with the well-known least squares methods. A basic knowledge of numerical linear algebra, matrix computations, and some notion of elementary statistics is required of the reader; however, some background material is included to make the book reasonably self-contained.