Computational Statistics Handbook with Matlab

Computational Statistics Handbook with Matlab

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Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience with its implementation, and practice with computational software. Focusing on the computational aspects of statistics rather than the theoretical, "Computational Statistics Handbook with MATLAB" uses a down-to-earth approach that makes statistics accessible to a wide range of users. The authors integrate the use of MATLAB throughout the book, allowing readers to see the actual implementation of algorithms, but also include step-by-step procedures to allow implementation with any suitable software.The book concentrates on the simulation/Monte Carlo point of view, and contains algorithms for exploratory data analysis, modeling, Monte Carlo simulation, pattern recognition, bootstrap, classification, cross-validation methods, probability density estimation, random number generation, and other computational statistics methods. Emphasis on the practical aspects of statistics, details of the latest techniques, and real implementation experience make the "Computational Statistics Handbook with MATLAB" more than just the first book to use MATLAB to solve computational problems in statistics. It also forms an outstanding, introduction to statistics for anyone in the many disciplines that involve data more

Product details

  • Hardback | 616 pages
  • 157.5 x 233.7 x 40.6mm | 1,020.59g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • indices
  • 1584882298
  • 9781584882299

Review quote

[T]his book is perfectly appropriate as a textbook for an introductory course on computational statistics. It covers many useful topics, which in combination with the well-documented code, make the underlying concepts easy to grasp by the students. Overall, this is a very nice book to be used in an undergraduate or Masters level computational statistics course. It would also prove useful to researchers in other fields that want to learn and implement quickly some advanced statistical more

Table of contents

Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Statistical Pattern Recognition. Nonparametric Regression. Markov Chain Monte Carlo Methods. Spatial Statistics. more

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