Statistical computing in C++ and R:
"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 acti...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2012
|
| Schriftenreihe: | The R Series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "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"-- |
| Beschreibung: | Includes bibliographical references (p. 531-535) and index |
| Beschreibung: | XV, 540 S. graph. Darst. 27 cm |
| ISBN: | 9781420066500 |
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Datensatz im Suchindex
| _version_ | 1804149244086452224 |
|---|---|
| adam_text | Titel: Statistical computing in C++ and R
Autor: Eubank, Randall L
Jahr: 2012
Contents
Preface vii
List of Algorithms xv
1 Introduction 1
1.1 Programming paradigms 2
1.2 Object-oriented programming 3
1.3 What lies ahead 4
2 Computer representation of numbers 9
2.1 Introduction 9
2.2 Storage in C++ 10
2.3 Integere 13
2.4 Floating-point representation 16
2.5 Errors 21
2.6 Computing a sample variance 26
2.7 Storage in R 30
2.8 Exercises 31
3 A sketch of C++ 35
3.1 Introduction 35
3.2 Variables and scope 37
3.3 Arithmetic and logical Operators 38
3.4 Control structures 39
3.5 Using arrays and pointers 42
3.6 Functions 49
3.7 Classes, objects and methods 54
3.8 Miscellaneous topics 60
3.8.1 Structs 60
3.8.2 The this pointer 60
3.8.3 const correctness 62
3.8.4 Forward references 68
3.8.5 Strings 70
3.8.6 Namespaces 73
3.8.7 Handling errors 73
3.8.8 Timing a program 79
3.9 Matrix and vector classes 79
3.10 Input, Output and templates 95
3.11 Function templates 102
3.12 Exercises 103
xii CONTENTS
4 Generation of pseudo-random numbers 111
4.1 Introduction 111
4.2 Congruential methods 112
4.3 Lehmer type generators in C++ 116
4.4 An FM2 class 119
4.5 Other generation methods 123
4.6 Nonuniform generation 130
4.7 Generating random normals 138
4.8 Generating random numbers in R 142
4.9 Using the R Standalone Math Library 143
4.10 Exercises 146
5 Programming in R 153
5.1 Introduction 153
5.2 File input and output 153
5.3 Classes, methods and namespaces 164
5.4 Writing R functions 167
5.5 Avoiding loops in R 174
5.6 An example 179
5.7 Using C/C++ code in R 186
5.8 Exercises 192
6 Creating classes and methods in R 199
6.1 Introduction 199
6.2 Creating a new class 200
6.3 Generic methods 205
6.4 An example 212
6.5 Exercises 223
7 Numerical linear algebra 227
7.1 Introduction 227
7.2 Solving linear equations 227
7.2.1 Solving triangulär Systems 227
7.2.2 Gaussian elimination 229
7.2.3 Cholesky decomposition 233
7.2.4 Banded matrices 236
7.2.5 An application: linear smoothing splines 240
7.2.6 Banded matrices via inheritance 247
7.3 Eigenvalues and eigenvectors 258
7.4 Singular value decomposition 265
7.5 Least Squares 269
7.6 The Template Numerical Toolkit 272
7.7 Exercises 278
8 Numerical optimization 283
8.1 Introduction 283
8.2 Function objects 283
8.3 Golden section 289
8.3.1 Dealing with multiple minima 295
8.3.2 An application: linear smoothing splines revisited 299
8.4 Newton s method 303
CONTENTS xiii
8.5 Maximum likelihood 312
8.6 Random search 318
8.7 Exercises 322
9 Abstract data structures 327
9.1 Introduction 327
9.2 ADT dictionary 329
9.2.1 Dynamic arrays and quicksort 329
9.2.2 Linked lists and mergesort 337
9.2.3 Stacks and queues 344
9.2.4 Hash tables 346
9.3 ADT priority queue 358
9.3.1 Heaps 359
9.3.2 A simple heap in C++ 363
9.4 ADT ordered set 368
9.4.1 A simple C++ binary search tree 370
9.4.2 Balancing binary trees 373
9.5 Pointer arithmetic, iterators and templates 376
9.5.1 Iterators 376
9.5.2 A linked list template class 379
9.6 Exercises 386
10 Data structures in C++ 391
10.1 Introduction 391
10.2 Container basics 391
10.3 Vector and deque 393
10.3.1 Streaming data 399
10.3.2 Flexible data input 404
10.3.3 Guess5 revisited 407
10.4 The C++ list Container 415
10.4.1 An example 417
10.4.2 A chaining hash table 423
10.5 Queues 429
10.6 The map and set Containers 437
10.7 Algorithm basics 439
10.8 Exercises 445
11 Parallel Computing in C++ and R 449
11.1 Introduction 449
11.2 OpenMP 450
11.3 Basic MPI commands for C++ 455
11.4 Parallel processing in R 460
11.5 Parallel random number generation 467
11.6 Exercises 476
A An introduction to Unix 479
A.l Getting around and finding things 480
A.2 Seeing what s there 480
A.3 Creating and destroying things 481
A.4 Things that are running and how to stop them 483
xiv CONTENTS
B An introduction to R 485
B.l R as a calculator 488
B.2 R as a graphics engine 492
B.3 R for Statistical analysis 496
C C++ library extensions (TRI) 501
C.l Pseudo-random numbers 501
C.2 Hash tables 503
C.3 Tuples 509
D The Matrix and Vector classes 511
E The ranGen class 527
References 531
Index 536
|
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| isbn | 9781420066500 |
| language | English |
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| spelling | Eubank, Randall L. 1952- Verfasser (DE-588)132752220 aut Statistical computing in C++ and R Randall L. Eubank ; Ana Kupresanin Boca Raton [u.a.] CRC Press 2012 XV, 540 S. graph. Darst. 27 cm txt rdacontent n rdamedia nc rdacarrier The R Series Includes bibliographical references (p. 531-535) and index "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"-- Datenverarbeitung Statistik R Programm (DE-588)4705956-4 gnd rswk-swf C++ (DE-588)4193909-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Computerunterstütztes Verfahren (DE-588)4139030-1 gnd rswk-swf Statistics Data processing C++ (Computer program language) R (Computer program language) Statistik (DE-588)4056995-0 s C++ (DE-588)4193909-8 s R Programm (DE-588)4705956-4 s Computerunterstütztes Verfahren (DE-588)4139030-1 s DE-604 Kupresanin, Ana Verfasser (DE-588)1021093246 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025104765&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Eubank, Randall L. 1952- Kupresanin, Ana Statistical computing in C++ and R Datenverarbeitung Statistik R Programm (DE-588)4705956-4 gnd C++ (DE-588)4193909-8 gnd Statistik (DE-588)4056995-0 gnd Computerunterstütztes Verfahren (DE-588)4139030-1 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4193909-8 (DE-588)4056995-0 (DE-588)4139030-1 |
| title | Statistical computing in C++ and R |
| title_auth | Statistical computing in C++ and R |
| title_exact_search | Statistical computing in C++ and R |
| title_full | Statistical computing in C++ and R Randall L. Eubank ; Ana Kupresanin |
| title_fullStr | Statistical computing in C++ and R Randall L. Eubank ; Ana Kupresanin |
| title_full_unstemmed | Statistical computing in C++ and R Randall L. Eubank ; Ana Kupresanin |
| title_short | Statistical computing in C++ and R |
| title_sort | statistical computing in c and r |
| topic | Datenverarbeitung Statistik R Programm (DE-588)4705956-4 gnd C++ (DE-588)4193909-8 gnd Statistik (DE-588)4056995-0 gnd Computerunterstütztes Verfahren (DE-588)4139030-1 gnd |
| topic_facet | Datenverarbeitung Statistik R Programm C++ Computerunterstütztes Verfahren |
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