elements computational statistics de gentle james (28 résultats)

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Paperback. Etat : new. Paperback. In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline called com- tational statistics. (Here, I am following Wegman, 1988, and distinguishing computationalstatisticsfromstatisticalcomputing, whichwetaketomean computational methods, including numerical analysis, for statisticians.) The computationally-intensive methods of modern statistics rely heavily on the developments in statistical computing and numerical analysis generally. Computational statistics shares two hallmarks with other computational sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery. Tools of the scienti?c method have generally been logical deduction (theory) and observation (experimentation). The computer, used to explore large numbers of scenarios, constitutes a new type of tool. Use of the computer to simulate alternatives and to present the research worker with information about these alternatives is a characteristic of thecomputationalsciences. Insomewaysthisusageisakintoexperimentation. The observations, however, are generated from an assumed model, and those simulated data are used toevaluate and study the model. Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Etat : New. pp. 444.

Etat : New. pp. 440.

Etat : New. pp. 444 Illus.

Etat : New. pp. 440 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Etat : New. pp. 444.

Etat : New. In.

Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

Hardcover. Etat : new. Hardcover. In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline called com- tational statistics. (Here, I am following Wegman, 1988, and distinguishing computationalstatisticsfromstatisticalcomputing, whichwetaketomean computational methods, including numerical analysis, for statisticians.) The computationally-intensive methods of modern statistics rely heavily on the developments in statistical computing and numerical analysis generally. Computational statistics shares two hallmarks with other computational sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery. Tools of the scienti?c method have generally been logical deduction (theory) and observation (experimentation). The computer, used to explore large numbers of scenarios, constitutes a new type of tool. Use of the computer to simulate alternatives and to present the research worker with information about these alternatives is a characteristic of thecomputationalsciences. Insomewaysthisusageisakintoexperimentation. The observations, however, are generated from an assumed model, and those simulated data are used toevaluate and study the model. This book describes techniques used in computational statistics and considers some of the areas of application, such as density estimation and model building, in which computationally-intensive methods are useful. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Etat : Neu. Neuware -In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline called ¿com- tational statistics¿. (Here, I am following Wegman, 1988, and distinguishing ¿computationalstatistics¿from¿statisticalcomputing¿, whichwetaketomean ¿computational methods, including numerical analysis, for statisticians¿.) The computationally-intensive methods of modern statistics rely heavily on the developments in statistical computing and numerical analysis generally. Computational statistics shares two hallmarks with other ¿computational¿ sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery. Tools of the scienti c method have generally been logical deduction (theory) and observation (experimentation). The computer, used to explore large numbers of scenarios, constitutes a new type of tool. Use of the computer to simulate alternatives and to present the research worker with information about these alternatives is a characteristic of thecomputationalsciences. Insomewaysthisusageisakintoexperimentation. The observations, however, are generated from an assumed model, and those simulated data are used toevaluate and study the model.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 444 pp. Englisch.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline called 'com- tational statistics'. (Here, I am following Wegman, 1988, and distinguishing 'computationalstatistics'from'statisticalcomputing', whichwetaketomean 'computational methods, including numerical analysis, for statisticians'.) The computationally-intensive methods of modern statistics rely heavily on the developments in statistical computing and numerical analysis generally. Computational statistics shares two hallmarks with other 'computational' sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery. Tools of the scienti c method have generally been logical deduction (theory) and observation (experimentation). The computer, used to explore large numbers of scenarios, constitutes a new type of tool. Use of the computer to simulate alternatives and to present the research worker with information about these alternatives is a characteristic of thecomputationalsciences. Insomewaysthisusageisakintoexperimentation. The observations, however, are generated from an assumed model, and those simulated data are used toevaluate and study the model.

Paperback. Etat : Like New. Like New. book.

Paperback. Etat : new. Paperback. In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline called com- tational statistics. (Here, I am following Wegman, 1988, and distinguishing computationalstatisticsfromstatisticalcomputing, whichwetaketomean computational methods, including numerical analysis, for statisticians.) The computationally-intensive methods of modern statistics rely heavily on the developments in statistical computing and numerical analysis generally. Computational statistics shares two hallmarks with other computational sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery. Tools of the scienti?c method have generally been logical deduction (theory) and observation (experimentation). The computer, used to explore large numbers of scenarios, constitutes a new type of tool. Use of the computer to simulate alternatives and to present the research worker with information about these alternatives is a characteristic of thecomputationalsciences. Insomewaysthisusageisakintoexperimentation. The observations, however, are generated from an assumed model, and those simulated data are used toevaluate and study the model. Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Etat : new. Hardcover. In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline called com- tational statistics. (Here, I am following Wegman, 1988, and distinguishing computationalstatisticsfromstatisticalcomputing, whichwetaketomean computational methods, including numerical analysis, for statisticians.) The computationally-intensive methods of modern statistics rely heavily on the developments in statistical computing and numerical analysis generally. Computational statistics shares two hallmarks with other computational sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery. Tools of the scienti?c method have generally been logical deduction (theory) and observation (experimentation). The computer, used to explore large numbers of scenarios, constitutes a new type of tool. Use of the computer to simulate alternatives and to present the research worker with information about these alternatives is a characteristic of thecomputationalsciences. Insomewaysthisusageisakintoexperimentation. The observations, however, are generated from an assumed model, and those simulated data are used toevaluate and study the model. This book describes techniques used in computational statistics and considers some of the areas of application, such as density estimation and model building, in which computationally-intensive methods are useful. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Etat : Used: Good. Occasion - Bon Etat - Éléments of computational statistics (2005) - Grand Format.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resampling, partitioning, and multiple transformations of a dataset. They may also make use of randomly generated artificial data. Implementation of these methods often requires advanced techniques in numerical analysis, so there is a close connection between computational statistics and statistical computing. This book describes techniques used in computational statistics, and addresses some areas of application of computationally intensive methods, such as density estimation, identification of structure in data, and model building. Although methods of statistical computing are not emphasized in this book, numerical techniques for transformations, for function approximation, and for optimization are explained in the context of the statistical methods. The book includes exercises, some with solutions. The book can be used as a text or supplementary text for various courses in modern statistics at the advanced undergraduate or graduate level, and it can also be used as a reference for statisticians who use computationally-intensive methods of analysis. Although some familiarity with probability and statistics is assumed, the book reviews basic methods of inference, and so is largely self-contained. James Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He has held several national offices in the American Statistical Association and has served as associate editor for journals of the ASA as well as for other journals in statistics and computing. He is the author of Random Number Generation and Monte Carlo Methods and Numerical Linear Algebra for Statistical Applications. This book describes techniques used in computational statistics and considers some of the areas of applications, such as density estimation and model building, in which computationally intensive methods are useful. In computational statistics, computation is viewed as an instrument of discovery; the role of the computer is not just to store data, perform computations, and produce graphs and tables, but additionally to suggest to the scientist alternative models and theories. Another characteristic of computational statistics is the computational intensity of the methods; even for datasets of medium size, high performance computers are required to perform the computations. Graphical displays and visualization methods are usually integral features of computational statistics. 444 pp. Englisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Will provide a more elementary introduction to these topics than other books available Gentle is the author of two other Springer books|In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Inde.