Computer Intensive Methods in Statistics (Record no. 5456)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 06036cam a2200517Mu 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9780429202322 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FlBoTFG |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240213122829.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu---unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 191130s2019 flu o 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | OCoLC-P |
| Language of cataloging | eng |
| Transcribing agency | OCoLC-P |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780429510946 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0429510942 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780429202322 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0429202326 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780429514371 |
| Qualifying information | (electronic bk. : EPUB) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0429514379 |
| Qualifying information | (electronic bk. : EPUB) |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1129160328 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC-P)1129160328 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA276.4 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | BUS |
| Subject category code subdivision | 061000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 021030 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | MAT |
| Subject category code subdivision | 029000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UFM |
| Source | bicssc |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.50285 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Zwanzig, Silvelyn. |
| 245 10 - TITLE STATEMENT | |
| Title | Computer Intensive Methods in Statistics |
| Medium | [electronic resource]. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Boca Raton : |
| Name of publisher, distributor, etc. | CRC Press LLC, |
| Date of publication, distribution, etc. | 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (227 p.) |
| 500 ## - GENERAL NOTE | |
| General note | Description based upon print version of record. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover; Half Title; Title Page; Copyright Page; Contents; Preface; Introduction; 1. Random Variable Generation; 1.1 Basic Methods; 1.1.1 Congruential Generators; 1.1.2 The KISS Generator; 1.1.3 Beyond Uniform Distributions; 1.2 Transformation Methods; 1.3 Accept-Reject Methods; 1.3.1 Envelope Accept-Reject Methods; 1.4 Problems; 2. Monte Carlo Methods; 2.1 Independent Monte Carlo Methods; 2.1.1 Importance Sampling; 2.1.2 The Rule of Thumb for Importance Sampling; 2.2 Markov Chain Monte Carlo; 2.2.1 Metropolis-Hastings Algorithm; 2.2.2 Special MCMC Algorithms; 2.2.3 Adaptive MCMC |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 2.2.4 Perfect Simulation2.2.5 The Gibbs Sampler; 2.3 Approximate Bayesian Computation Methods; 2.4 Problems; 3. Bootstrap; 3.1 General Principle; 3.1.1 Unified Bootstrap Framework; 3.1.2 Bootstrap and Monte Carlo; 3.1.3 Conditional and Unconditional Distribution; 3.2 Basic Bootstrap; 3.2.1 Plug-in Principle; 3.2.2 Why is Bootstrap Good?; 3.2.3 Example where Bootstrap Fails; 3.3 Bootstrap Confidence Sets; 3.3.1 The Pivotal Method; 3.3.2 Bootstrap Pivotal Methods; 3.3.2.1 Percentile Bootstrap Confidence Interval; 3.3.2.2 Basic Bootstrap Confidence Interval |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 3.3.2.3 Studentized Bootstrap Confidence Interval3.3.3 Transformed Bootstrap Confidence Intervals; 3.3.4 Prepivoting Confidence Set; 3.3.5 BCa-Confidence Interval; 3.4 Bootstrap Hypothesis Tests; 3.4.1 Parametric Bootstrap Hypothesis Test; 3.4.2 Nonparametric Bootstrap Hypothesis Test; 3.4.3 Advanced Bootstrap Hypothesis Tests; 3.5 Bootstrap in Regression; 3.5.1 Model-Based Bootstrap; 3.5.2 Parametric Bootstrap Regression; 3.5.3 Casewise Bootstrap in Correlation Model; 3.6 Bootstrap for Time Series; 3.7 Problems; 4. Simulation-Based Methods; 4.1 EM Algorithm; 4.2 SIMEX; 4.3 Variable Selection |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 4.3.1 F-Backward and F-Forward Procedures4.3.2 FSR-Forward Procedure; 4.3.3 SimSel; 4.4 Problems; 5. Density Estimation; 5.1 Background; 5.2 Histogram; 5.3 Kernel Density Estimator; 5.3.1 Statistical Properties; 5.3.2 Bandwidth Selection in Practice; 5.4 Nearest Neighbor Estimator; 5.5 Orthogonal Series Estimator; 5.6 Minimax Convergence Rate; 5.7 Problems; 6. Nonparametric Regression; 6.1 Background; 6.2 Kernel Regression Smoothing; 6.3 Local Regression; 6.4 Classes of Restricted Estimators; 6.4.1 Ridge Regression; 6.4.2 Lasso; 6.5 Spline Estimators; 6.5.1 Base Splines |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 6.5.2 Smoothing Splines6.6 Wavelet Estimators; 6.6.1 Wavelet Base; 6.6.2 Wavelet Smoothing; 6.7 Choosing the Smoothing Parameter; 6.8 Bootstrap in Regression; 6.9 Problems; References; Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners. Features Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | OCLC-licensed vendor bibliographic record. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | BUSINESS & ECONOMICS / Statistics |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS / Database Management / Data Mining |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MATHEMATICS / Probability & Statistics / General |
| Source of heading or term | bisacsh |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Statistics |
| General subdivision | Data processing. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mahjani, Behrang. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Taylor & Francis |
| Uniform Resource Identifier | <a href="https://www.taylorfrancis.com/books/9780429202322">https://www.taylorfrancis.com/books/9780429202322</a> |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | OCLC metadata license agreement |
| Uniform Resource Identifier | <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a> |
No items available.
