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Título : Forest Analytics with R : An Introduction Tipo de documento: documento electrónico Autores: Robinson, Andrew P ; SpringerLink (Online service) ; Hamann, Jeff D Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Use R Número de páginas: XIV, 354 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-7762-5 Idioma : Inglés (eng) Palabras clave: Statistics Forestry management Environmental sciences for Life Sciences, Medicine, Health Sciences Management Math. Appl. in Science Clasificación: 51 Matemáticas Resumen: Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others Nota de contenido: Introduction -- Forest data management -- Data analysis for common inventory methods -- Imputation and Interpolation -- Fitting dimensional distributions -- Linear and non-linear models -- Fitting linear hierarchical models -- Simulations -- Forest estate planning and optimization En línea: http://dx.doi.org/10.1007/978-1-4419-7762-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33168 Forest Analytics with R : An Introduction [documento electrónico] / Robinson, Andrew P ; SpringerLink (Online service) ; Hamann, Jeff D . - New York, NY : Springer New York, 2011 . - XIV, 354 p : online resource. - (Use R) .
ISBN : 978-1-4419-7762-5
Idioma : Inglés (eng)
Palabras clave: Statistics Forestry management Environmental sciences for Life Sciences, Medicine, Health Sciences Management Math. Appl. in Science Clasificación: 51 Matemáticas Resumen: Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others Nota de contenido: Introduction -- Forest data management -- Data analysis for common inventory methods -- Imputation and Interpolation -- Fitting dimensional distributions -- Linear and non-linear models -- Fitting linear hierarchical models -- Simulations -- Forest estate planning and optimization En línea: http://dx.doi.org/10.1007/978-1-4419-7762-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33168 Ejemplares
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Título : Formulas Useful for Linear Regression Analysis and Related Matrix Theory : It's Only Formulas But We Like Them Tipo de documento: documento electrónico Autores: Puntanen, Simo ; SpringerLink (Online service) ; Styan, George P. H ; Isotalo, Jarkko Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: SpringerBriefs in Statistics, ISSN 2191-544X Número de páginas: XII, 125 p. 3 illus., 2 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-32931-9 Idioma : Inglés (eng) Palabras clave: Statistics Matrix theory Algebra Econometrics Statistical Theory and Methods Linear Multilinear Algebras, for Business/Economics/Mathematical Finance/Insurance Clasificación: 51 Matemáticas Resumen: This is an unusual book because it contains a great deal of formulas. Hence it is a blend of monograph, textbook, and handbook. It is intended for students and researchers who need quick access to useful formulas appearing in the linear regression model and related matrix theory. This is not a regular textbook - this is supporting material for courses given in linear statistical models. Such courses are extremely common at universities with quantitative statistical analysis programs Nota de contenido: The Model Matrix -- Fitted Values and Residuals -- Regression Coefficients -- Alternative Estimators -- Decompositions of Sums of Squares -- Partial Correlations -- Distributions -- Testing Hypotheses -- Diagnostics -- BLUE: Some Helpful Identities -- Estimability -- Best Linear Unbiased Estimator -- The Watson Efficiency -- Linear Sufficiency and Admissibility -- Best Linear Unbiased Predictor -- Mixed Model -- Multivariate Linear Model -- Inverse of a Partitioned Matrix -- Generalized Inverses -- Projectors -- Eigenvalues -- Discriminant Analysis -- Factor Analysis -- Canonical Correlations -- Matrix Decompositions -- Principal Component Analysis -- Löwner Ordering -- Rank Rules -- Inequalities -- Kronecker Product -- Matrix Derivatives En línea: http://dx.doi.org/10.1007/978-3-642-32931-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32528 Formulas Useful for Linear Regression Analysis and Related Matrix Theory : It's Only Formulas But We Like Them [documento electrónico] / Puntanen, Simo ; SpringerLink (Online service) ; Styan, George P. H ; Isotalo, Jarkko . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013 . - XII, 125 p. 3 illus., 2 illus. in color : online resource. - (SpringerBriefs in Statistics, ISSN 2191-544X) .
ISBN : 978-3-642-32931-9
Idioma : Inglés (eng)
Palabras clave: Statistics Matrix theory Algebra Econometrics Statistical Theory and Methods Linear Multilinear Algebras, for Business/Economics/Mathematical Finance/Insurance Clasificación: 51 Matemáticas Resumen: This is an unusual book because it contains a great deal of formulas. Hence it is a blend of monograph, textbook, and handbook. It is intended for students and researchers who need quick access to useful formulas appearing in the linear regression model and related matrix theory. This is not a regular textbook - this is supporting material for courses given in linear statistical models. Such courses are extremely common at universities with quantitative statistical analysis programs Nota de contenido: The Model Matrix -- Fitted Values and Residuals -- Regression Coefficients -- Alternative Estimators -- Decompositions of Sums of Squares -- Partial Correlations -- Distributions -- Testing Hypotheses -- Diagnostics -- BLUE: Some Helpful Identities -- Estimability -- Best Linear Unbiased Estimator -- The Watson Efficiency -- Linear Sufficiency and Admissibility -- Best Linear Unbiased Predictor -- Mixed Model -- Multivariate Linear Model -- Inverse of a Partitioned Matrix -- Generalized Inverses -- Projectors -- Eigenvalues -- Discriminant Analysis -- Factor Analysis -- Canonical Correlations -- Matrix Decompositions -- Principal Component Analysis -- Löwner Ordering -- Rank Rules -- Inequalities -- Kronecker Product -- Matrix Derivatives En línea: http://dx.doi.org/10.1007/978-3-642-32931-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32528 Ejemplares
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Título : Marginal Models : For Dependent, Clustered, and Longitudinal Categorical Data Tipo de documento: documento electrónico Autores: Hagenaars, Jacques A ; SpringerLink (Online service) ; Croon, Marcel A ; Wicher Bergsma Editorial: New York, NY : Springer New York Fecha de publicación: 2009 Colección: Statistics for Social and Behavioral Sciences, ISSN 2199-7357 Número de páginas: XI, 268 p Il.: online resource ISBN/ISSN/DL: 978-0-387-09610-0 Idioma : Inglés (eng) Palabras clave: Mathematics System theory Statistics Systems Theory, Control Statistical Theory and Methods for Business/Economics/Mathematical Finance/Insurance Social Science, Behavorial Education, Public Policy, Law Clasificación: 51 Matemáticas Resumen: Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show. In the social, behavioral, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. The marginal models of this book provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level. The maximum likelihood method, with its attractive statistical properties, is used for fitting the models. This book has mainly been written with applied researchers in mind. It includes many real world examples, explains the types of research questions for which marginal modeling is useful, and provides a detailed description of how to apply marginal models for a great diversity of research questions. All these examples are presented on the book's website (www.cmm.st), along with user friendly programs. Wicher Bergsma is Senior Lecturer at the London School of Economics and Political Science. His current research interests are categorical data analysis, measurement of association, nonparametric regression, and maximum likelihood estimation. Marcel Croon is associate professor at Tilburg University. He is especially interested in measurement problems, structural equation modeling, latent variables, and random effect models. Jacques Hagenaars is full professor at Tilburg University and at present chair of the board of IOPS, the Dutch PhD School for Sociometrics and Psychometrics. His main research interests are research designs, longitudinal research, categorical data analysis and latent variable models Nota de contenido: Loglinear Marginal Models -- Nonloglinear Marginal Models -- Marginal Analysis of Longitudinal Data -- Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs -- Marginal modeling with latent variables -- Conclusions, Extensions, and Applications En línea: http://dx.doi.org/10.1007/b12532 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33824 Marginal Models : For Dependent, Clustered, and Longitudinal Categorical Data [documento electrónico] / Hagenaars, Jacques A ; SpringerLink (Online service) ; Croon, Marcel A ; Wicher Bergsma . - New York, NY : Springer New York, 2009 . - XI, 268 p : online resource. - (Statistics for Social and Behavioral Sciences, ISSN 2199-7357) .
ISBN : 978-0-387-09610-0
Idioma : Inglés (eng)
Palabras clave: Mathematics System theory Statistics Systems Theory, Control Statistical Theory and Methods for Business/Economics/Mathematical Finance/Insurance Social Science, Behavorial Education, Public Policy, Law Clasificación: 51 Matemáticas Resumen: Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show. In the social, behavioral, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. The marginal models of this book provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level. The maximum likelihood method, with its attractive statistical properties, is used for fitting the models. This book has mainly been written with applied researchers in mind. It includes many real world examples, explains the types of research questions for which marginal modeling is useful, and provides a detailed description of how to apply marginal models for a great diversity of research questions. All these examples are presented on the book's website (www.cmm.st), along with user friendly programs. Wicher Bergsma is Senior Lecturer at the London School of Economics and Political Science. His current research interests are categorical data analysis, measurement of association, nonparametric regression, and maximum likelihood estimation. Marcel Croon is associate professor at Tilburg University. He is especially interested in measurement problems, structural equation modeling, latent variables, and random effect models. Jacques Hagenaars is full professor at Tilburg University and at present chair of the board of IOPS, the Dutch PhD School for Sociometrics and Psychometrics. His main research interests are research designs, longitudinal research, categorical data analysis and latent variable models Nota de contenido: Loglinear Marginal Models -- Nonloglinear Marginal Models -- Marginal Analysis of Longitudinal Data -- Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs -- Marginal modeling with latent variables -- Conclusions, Extensions, and Applications En línea: http://dx.doi.org/10.1007/b12532 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33824 Ejemplares
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Título : Reliability, Life Testing and the Prediction of Service Lives : For Engineers and Scientists Tipo de documento: documento electrónico Autores: Saunders, Sam C ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XIV, 308 p Il.: online resource ISBN/ISSN/DL: 978-0-387-48538-6 Idioma : Inglés (eng) Palabras clave: Computer science software Reusability Probabilities Statistics Engineering Applied mathematics Quality control Reliability Industrial safety Science Performance and Engineering, general Appl.Mathematics/Computational Methods of Control, Reliability, Safety Risk for Physics, Science, Chemistry Earth Sciences Probability Theory Stochastic Processes Clasificación: 51 Matemáticas Resumen: This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. Yet tutorial problems of this nature are virtually never encountered in coursework. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used. Most reliability practitioners will be employed where personal interaction between disciplines is a necessity. A section is included on communication skills to facilitate model selection and formulation based on verifiable assumptions, rather than favorable conclusions. However, whether the answer is "right" can never be ascertained. Past and current applications of stochastic modeling to life-length can only be a guide for future adaptations under different conditions, with new materials in unknown usages. This book unifies the study of cumulative-damage distributions, namely, Wald and Tweedie (i.e., inverse-Gaussian and its reciprocal) with "fatigue-life." These distributions are most useful when the coefficient-of-variation is more appropriate than is the variance as a measure of dispersion. It is shown, uniquely, that the same hyperbolic-sine transformation of each life length variate has a Chi-square one-df distribution. This property is useful in the sample statistics. These IHRA distributions realistically model life-length, strength or duration of load under linear cumulative damage and can be combined as approximations in non-linear situations. Sam C. Saunders has served as a research engineer for 17 years at the Boeing Scientific Research Laboratories, 20 years as a consultant to the Advisory Committee for Nuclear Safeguards, 10 years as a consultant to NIST, was a principal in the consulting firms Mathematical Analysis Research Corporation and Scientific Consulting Service; and was for 26 years a professor of Applied Mathematics/Statistics at Washington State University. He is a Fellow of the American Statistical Association and a former editor of Technometrics Nota de contenido: Requisites -- Elements of Reliability -- Partitions and Selection -- Coherent Systems -- Applicable Life Distributions -- Philosophy, Science, and Sense -- Nonparametric Life Estimators -- Weibull Analysis -- Examine Data, Diagnose and Consult -- Cumulative Damage Distributions -- Analysis of Dispersion -- Damage Processes -- Service Life of Structures -- Strength and Durability -- Maintenance of Systems -- Mathematical Appendix En línea: http://dx.doi.org/10.1007/978-0-387-48538-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34476 Reliability, Life Testing and the Prediction of Service Lives : For Engineers and Scientists [documento electrónico] / Saunders, Sam C ; SpringerLink (Online service) . - New York, NY : Springer New York, 2007 . - XIV, 308 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-48538-6
Idioma : Inglés (eng)
Palabras clave: Computer science software Reusability Probabilities Statistics Engineering Applied mathematics Quality control Reliability Industrial safety Science Performance and Engineering, general Appl.Mathematics/Computational Methods of Control, Reliability, Safety Risk for Physics, Science, Chemistry Earth Sciences Probability Theory Stochastic Processes Clasificación: 51 Matemáticas Resumen: This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. Yet tutorial problems of this nature are virtually never encountered in coursework. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used. Most reliability practitioners will be employed where personal interaction between disciplines is a necessity. A section is included on communication skills to facilitate model selection and formulation based on verifiable assumptions, rather than favorable conclusions. However, whether the answer is "right" can never be ascertained. Past and current applications of stochastic modeling to life-length can only be a guide for future adaptations under different conditions, with new materials in unknown usages. This book unifies the study of cumulative-damage distributions, namely, Wald and Tweedie (i.e., inverse-Gaussian and its reciprocal) with "fatigue-life." These distributions are most useful when the coefficient-of-variation is more appropriate than is the variance as a measure of dispersion. It is shown, uniquely, that the same hyperbolic-sine transformation of each life length variate has a Chi-square one-df distribution. This property is useful in the sample statistics. These IHRA distributions realistically model life-length, strength or duration of load under linear cumulative damage and can be combined as approximations in non-linear situations. Sam C. Saunders has served as a research engineer for 17 years at the Boeing Scientific Research Laboratories, 20 years as a consultant to the Advisory Committee for Nuclear Safeguards, 10 years as a consultant to NIST, was a principal in the consulting firms Mathematical Analysis Research Corporation and Scientific Consulting Service; and was for 26 years a professor of Applied Mathematics/Statistics at Washington State University. He is a Fellow of the American Statistical Association and a former editor of Technometrics Nota de contenido: Requisites -- Elements of Reliability -- Partitions and Selection -- Coherent Systems -- Applicable Life Distributions -- Philosophy, Science, and Sense -- Nonparametric Life Estimators -- Weibull Analysis -- Examine Data, Diagnose and Consult -- Cumulative Damage Distributions -- Analysis of Dispersion -- Damage Processes -- Service Life of Structures -- Strength and Durability -- Maintenance of Systems -- Mathematical Appendix En línea: http://dx.doi.org/10.1007/978-0-387-48538-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34476 Ejemplares
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Título : A First Course in Statistics for Signal Analysis Tipo de documento: documento electrónico Autores: Woyczynski, Wojbor A ; SpringerLink (Online service) Editorial: Boston : Birkhäuser Boston Fecha de publicación: 2011 Número de páginas: XVI, 261 p. 76 illus Il.: online resource ISBN/ISSN/DL: 978-0-8176-8101-2 Idioma : Inglés (eng) Palabras clave: Statistics Fourier analysis Applied mathematics Engineering for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Analysis Signal, Image Speech Processing Statistical Theory Methods Applications of Mathematics Clasificación: 51 Matemáticas Resumen: 'A First Course in Statistics for Signal Analysis' is a small, dense, and inexpensive book that covers exactly what the title says: statistics for signal analysis. The book has much to recommend it. The author clearly understands the topics presented. The topics are covered in a rigorous manner, but not so rigorous as to be ostentatious. The sequence of topics is clearly targeted at the spectral properties of Gaussian stationary signals. Any student studying traditional communications and signal processing would benefit from an understanding of these topics...In summary, [the work] has much in its favor...This book is most appropriate for a graduate class in signal analysis. It also could be used as a secondary text in a statistics, signal processing, or communications class. —JASA> (Review of the First Edition) This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation. Topics and Features: Fourier series and transforms—fundamentally important in random signal analysis and processing—are developed from scratch, emphasizing the time-domain vs. frequency-domain duality; Basic concepts of probability theory, laws of large numbers, the central limit theorem, and statistical parametric inference procedures are presented so that no prior knowledge of probability and statistics is required; the only prerequisite is a basic two–three semester calculus sequence; Computer simulation algorithms of stationary random signals with a given power spectrum density; Complementary bibliography for readers who wish to pursue the study of random signals in greater depth; Many diverse examples and end-of-chapter problems and exercises. New to the Second Edition: Revised notation and terminology to better reflect the concepts under discussion; Many redrawn figures to better illustrate the scale of the quantities represented; Considerably expanded sections with new examples, illustrations, and commentary; Addition of more applied exercises; A large appendix containing solutions of selected problems from each of the nine chapters. Developed by the author over the course of many years of classroom use, A First Course in Statistics for Signal Analysis, Second Edition may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories Nota de contenido: Foreword to the Second Edition -- Introduction -- Notation -- Description of Signals -- Spectral Representation of Deterministic Signals: Fourier Series and Transforms -- Random Quantities and Random Vectors -- Stationary Signals -- Power Spectra of Random Signals -- Transmission of Stationary Signals through Linear Systems -- Optimization of Signal-to-Noise Ratio in Linear Systems -- Gaussian Signals, Covariance Matrices, and Sample Path Properties -- Spectral Representation of Discrete-Time Stationary Signals and Their Computer Simulations -- Solutions to Selected Exercises -- Bibliographical Comments -- Index En línea: http://dx.doi.org/10.1007/978-0-8176-8101-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33102 A First Course in Statistics for Signal Analysis [documento electrónico] / Woyczynski, Wojbor A ; SpringerLink (Online service) . - Boston : Birkhäuser Boston, 2011 . - XVI, 261 p. 76 illus : online resource.
ISBN : 978-0-8176-8101-2
Idioma : Inglés (eng)
Palabras clave: Statistics Fourier analysis Applied mathematics Engineering for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Analysis Signal, Image Speech Processing Statistical Theory Methods Applications of Mathematics Clasificación: 51 Matemáticas Resumen: 'A First Course in Statistics for Signal Analysis' is a small, dense, and inexpensive book that covers exactly what the title says: statistics for signal analysis. The book has much to recommend it. The author clearly understands the topics presented. The topics are covered in a rigorous manner, but not so rigorous as to be ostentatious. The sequence of topics is clearly targeted at the spectral properties of Gaussian stationary signals. Any student studying traditional communications and signal processing would benefit from an understanding of these topics...In summary, [the work] has much in its favor...This book is most appropriate for a graduate class in signal analysis. It also could be used as a secondary text in a statistics, signal processing, or communications class. —JASA> (Review of the First Edition) This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation. Topics and Features: Fourier series and transforms—fundamentally important in random signal analysis and processing—are developed from scratch, emphasizing the time-domain vs. frequency-domain duality; Basic concepts of probability theory, laws of large numbers, the central limit theorem, and statistical parametric inference procedures are presented so that no prior knowledge of probability and statistics is required; the only prerequisite is a basic two–three semester calculus sequence; Computer simulation algorithms of stationary random signals with a given power spectrum density; Complementary bibliography for readers who wish to pursue the study of random signals in greater depth; Many diverse examples and end-of-chapter problems and exercises. New to the Second Edition: Revised notation and terminology to better reflect the concepts under discussion; Many redrawn figures to better illustrate the scale of the quantities represented; Considerably expanded sections with new examples, illustrations, and commentary; Addition of more applied exercises; A large appendix containing solutions of selected problems from each of the nine chapters. Developed by the author over the course of many years of classroom use, A First Course in Statistics for Signal Analysis, Second Edition may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories Nota de contenido: Foreword to the Second Edition -- Introduction -- Notation -- Description of Signals -- Spectral Representation of Deterministic Signals: Fourier Series and Transforms -- Random Quantities and Random Vectors -- Stationary Signals -- Power Spectra of Random Signals -- Transmission of Stationary Signals through Linear Systems -- Optimization of Signal-to-Noise Ratio in Linear Systems -- Gaussian Signals, Covariance Matrices, and Sample Path Properties -- Spectral Representation of Discrete-Time Stationary Signals and Their Computer Simulations -- Solutions to Selected Exercises -- Bibliographical Comments -- Index En línea: http://dx.doi.org/10.1007/978-0-8176-8101-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33102 Ejemplares
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