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Author: Stephen Boyd
Publisher: Cambridge University Press
Published: 2018-06-07
Total Pages: 477
ISBN-13: 1316518965
DOWNLOAD EBOOKA groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Author: Thomas Kailath
Publisher:
Published: 1976
Total Pages: 186
ISBN-13:
DOWNLOAD EBOOKAuthor: Jürgen Groß
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 400
ISBN-13: 364255864X
DOWNLOAD EBOOKThe book covers the basic theory of linear regression models and presents a comprehensive survey of different estimation techniques as alternatives and complements to least squares estimation. Proofs are given for the most relevant results, and the presented methods are illustrated with the help of numerical examples and graphics. Special emphasis is placed on practicability and possible applications. The book is rounded off by an introduction to the basics of decision theory and an appendix on matrix algebra.
Author: James W. Demmel
Publisher: SIAM
Published: 1997-08-01
Total Pages: 426
ISBN-13: 0898713897
DOWNLOAD EBOOKThis comprehensive textbook is designed for first-year graduate students from a variety of engineering and scientific disciplines.
Author: Steven L. Brunton
Publisher: Cambridge University Press
Published: 2022-05-05
Total Pages: 615
ISBN-13: 1009098489
DOWNLOAD EBOOKA textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author: Wen Shen
Publisher: World Scientific
Published: 2019-08-28
Total Pages: 339
ISBN-13: 9811204438
DOWNLOAD EBOOKThis book serves as a set of lecture notes for a senior undergraduate level course on the introduction to numerical computation, which was developed through 4 semesters of teaching the course over 10 years. The book requires minimum background knowledge from the students, including only a three-semester of calculus, and a bit on matrices.The book covers many of the introductory topics for a first course in numerical computation, which fits in the short time frame of a semester course. Topics range from polynomial approximations and interpolation, to numerical methods for ODEs and PDEs. Emphasis was made more on algorithm development, basic mathematical ideas behind the algorithms, and the implementation in Matlab.The book is supplemented by two sets of videos, available through the author's YouTube channel. Homework problem sets are provided for each chapter, and complete answer sets are available for instructors upon request.The second edition contains a set of selected advanced topics, written in a self-contained manner, suitable for self-learning or as additional material for an honored version of the course. Videos are also available for these added topics.
Author: Christiaan Heij
Publisher: OUP Oxford
Published: 2004-03-25
Total Pages: 1132
ISBN-13: 0191608408
DOWNLOAD EBOOKNowadays applied work in business and economics requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods with an application-oriented approach, this rigorous textbook provides students with a working understanding and hands-on experience of current econometrics. Taking a 'learning by doing' approach, it covers basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, and generalized method of moments), and addresses the creative process of model building with due attention to diagnostic testing and model improvement. Its last part is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations). · Real-world text examples and practical exercise questions stimulate active learning and show how econometrics can solve practical questions in modern business and economic management. · Focuses on the core of econometrics, regression, and covers two major advanced topics, choice data with applications in marketing and micro-economics, and time series data with applications in finance and macro-economics. · Learning-support features include concise, manageable sections of text, frequent cross-references to related and background material, summaries, computational schemes, keyword lists, suggested further reading, exercise sets, and online data sets and solutions. · Derivations and theory exercises are clearly marked for students in advanced courses. This textbook is perfect for advanced undergraduate students, new graduate students, and applied researchers in econometrics, business, and economics, and for researchers in other fields that draw on modern applied econometrics.
Author: Thomas Kailath
Publisher: Pearson
Published: 2000
Total Pages: 888
ISBN-13:
DOWNLOAD EBOOKThis original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Wiener and Kalman, but it does so in an original and novel manner that paves the way for further developments. This book contains a large collection of problems that complement it and are an important part of piece, in addition to numerous sections that offer interesting historical accounts and insights. The book also includes several results that appear in print for the first time. FEATURES/BENEFITS Takes a geometric point of view. Emphasis on the numerically favored array forms of many algorithms. Emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control. These features are generally absent in most prior treatments, ostensibly on the grounds that they are too abstract and complicated. It is the authors' hope that these misconceptions will be dispelled by the presentation herein, and that the fundamental simplicity and power of these ideas will be more widely recognized and exploited. Among other things, these features already yielded new insights and new results for linear and nonlinear problems in areas such as adaptive filtering, quadratic control, and estimation, including the recent Hà theories.
Author: T. Kailath
Publisher: Springer
Published: 2014-05-04
Total Pages: 189
ISBN-13: 3709128048
DOWNLOAD EBOOKAuthor: C. Radhakrishna Rao
Publisher: Springer Science & Business Media
Published: 2007-10-15
Total Pages: 583
ISBN-13: 3540742271
DOWNLOAD EBOOKRevised 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.
