<p><p>This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R. It covers mathematical, statistical as well as programming problems in computati
Basic Elements of Computational Statistics
β Scribed by Wolfgang Karl HΓ€rdle, Ostap Okhrin, Yarema Okhrin
- Publisher
- Springer
- Year
- 2017
- Tongue
- English
- Leaves
- 318
- Series
- Statistics and Computing
- Edition
- 1st ed. 2017
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R. It covers mathematical, statistical as well as programming problems in computational statistics and contains a wide variety of practical examples. In addition to the numerous R sniplets presented in the text, all computer programs (quantlets) and data sets to the book are available on GitHub and referred to in the book. This enables the reader to fully reproduce as well as modify and adjust all examples to their needs.
The book is intended for advanced undergraduate and first-year graduate students as well as for data analysts new to the job who would like a tour of the various statistical tools in a data analysis workshop. The experienced reader with a good knowledge of statistics and programming might skip some sections on univariate models and enjoy the various ma
thematical roots of multivariate techniques.
The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web.Β QuantNet and the corresponding Data-Driven Documents-based visualization allows readers to reproduce the tables, pictures and calculations inside this Springer book.
β¦ Subjects
Statistics;Education & Reference;Business & Money;Mathematical & Statistical;Software;Computers & Technology;Biological Sciences;Anatomy;Animals;Bacteriology;Biochemistry;Bioelectricity;Bioinformatics;Biology;Biophysics;Biotechnology;Botany;Ecology;Genetics;Paleontology;Plants;Taxonomic Classification;Zoology;Science & Math;Probability & Statistics;Applied;Mathematics;Science & Math;Statistics;Applied;Mathematics;Science & Math;Business & Finance;Accounting;Banking;Business Communication;Busines
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This is basically a review I wrote of this book nearly six years ago. I read the book with great interest. As six years have past there has definitely been a continual growth in the speed, memory capabilities and size of modern computers. So books like this may be obsolete and should be revised.A
Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resampling, partitioning, and multiple transformations of a dataset. They may also make use of randomly generated artificial
Computationally intensive methods have become widely used both for statistical inference and for exploratory analyses of data. The methods of computational statistics involve resampling, partitioning, and multiple transformations of a dataset. They may also make use of randomly generated artificial
Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Com