Statistical computing in C++ and R /
Material type: TextSeries: Chapman & Hall/CRC the R seriesPublication details: Boca Raton : CRC Press, c2012Description: xv, 540 p. : illISBN: 9781420066500 (hardback)Subject(s): Statistics | C++ (Computer program language) | R (Computer program language) | MATHEMATICS / Probability & Statistics / GeneralDDC classification: 519.50285/5133 Summary: "Preface When one looks at a book with 'statistical computing' in the title, the expectation is most likely for a treatment of the topic that has close ties to numerical analysis. There are many texts written from this perspective that provide valuable resources for those who are actively involved in the solution of computing problems that arise in statistics. The presentation in the present text represents a departure from this classical emphasis in that it concentrates on the writing of code rather than the development and study of algorithms, per se. The goal is to provide a treatment of statistical computing that lays a foundation for original code development in a research environment. The advancement of statistical methodology is now inextricably linked to the use of computers. New methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. As a result, many statisticians expend significant amounts of their creative energy while sitting in front of a computer monitor. The end products from the vast majority of these efforts are unlikely to be reflected in changes to core aspects of numerical methods or computer hardware. Nonetheless, they are modern statisticians that are (often very) involved in computing. This book is written with that particular audience in mind. What does a modern statistician need to know about computing? Our belief is that they need to understand at least the basic principles of algorithmic thinking. The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Kannur University Central Library | Stack | 519.50285/5133 EUB/S (Browse shelf (Opens below)) | Available | 38287 |
Browsing Kannur University Central Library shelves, Collection: Stack Close shelf browser (Hides shelf browser)
519.50285 KEE/G Graphics for statistics and and data analysis With R. | 519.50285 PAD/P Practical statistical methods :A SAS programming approach | 519.50285 RFO R for statistics | 519.50285/5133 EUB/S Statistical computing in C++ and R / | 519.502855133 GRO/A Advanced R solutions | 519.502855133 RIZ/S Statistical computing with R. | 519.503 UPT/D Dictionary of statistics |
"Preface When one looks at a book with 'statistical computing' in the title, the expectation is most likely for a treatment of the topic that has close ties to numerical analysis. There are many texts written from this perspective that provide valuable resources for those who are actively involved in the solution of computing problems that arise in statistics. The presentation in the present text represents a departure from this classical emphasis in that it concentrates on the writing of code rather than the development and study of algorithms, per se. The goal is to provide a treatment of statistical computing that lays a foundation for original code development in a research environment. The advancement of statistical methodology is now inextricably linked to the use of computers. New methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. As a result, many statisticians expend significant amounts of their creative energy while sitting in front of a computer monitor. The end products from the vast majority of these efforts are unlikely to be reflected in changes to core aspects of numerical methods or computer hardware. Nonetheless, they are modern statisticians that are (often very) involved in computing. This book is written with that particular audience in mind. What does a modern statistician need to know about computing? Our belief is that they need to understand at least the basic principles of algorithmic thinking. The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems"--
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