Elements Computational Statistics by Gentle James (34 results)

Hardcover. Condition: Good. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

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Condition: 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.

Condition: New. pp. 444 Illus.

Condition: New. pp. 440.

Condition: New. pp. 444.

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Condition: New. pp. 444.

Taschenbuch. Condition: 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.

Condition: New. Series: Statistics and Computing. Num Pages: 420 pages, biography. BIC Classification: UFM. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 22. Weight in Grams: 613. . 2010. Softcover reprint of the original 1st ed. 2002. Paperback. . . . .

Taschenbuch. Condition: 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.

Paperback. Condition: 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.

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Condition: New. Series: Statistics and Computing. Num Pages: 420 pages, biography. BIC Classification: UFM. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 22. Weight in Grams: 613. . 2010. Softcover reprint of the original 1st ed. 2002. Paperback. . . . . Books ship from the US and Ireland.

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Hardcover. Condition: 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.

Condition: New. Describes techniques used in computational statistics and considers some of the areas of application, such as density estimation and model building. This book explains numerical techniques for transformations, for function approximation, and for optimization. It is suitable for various courses in modern statistics. Series: Statistics and Computing. Num Pages: 420 pages, biography. BIC Classification: PBT; UYA. Category: (G) General (US: Trade); (UU) Undergraduate. Dimension: 234 x 156 x 25. Weight in Grams: 796. . 2005. 1st. ed. 2002. Corr. 2nd printing 2005. Hardback. . . . .

Paperback. Condition: 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.