Last edited by Mudal
Sunday, February 16, 2020 | History

2 edition of Elements of statistical inference. found in the catalog.

Elements of statistical inference.

Robert M. Kozelka

Elements of statistical inference.

  • 344 Want to read
  • 10 Currently reading

Published by Addison-Wesley Pub. Co. in Reading, Mass .
Written in English

    Subjects:
  • Probabilities,
  • Mathematical statistics

  • Edition Notes

    SeriesAddison-Wesley series in statistics
    Classifications
    LC ClassificationsQA273 .K67
    The Physical Object
    Pagination150 p.
    Number of Pages150
    ID Numbers
    Open LibraryOL5820121M
    LC Control Number61006130

    Many of these tools have common underpinnings but are often expressed with different terminology. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. The book has been beautifully produced. While the approach is statistical, the emphasis is on concepts rather than mathematics. The book's coverage is broad, from supervised learning prediction to unsupervised learning.

    With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Examples, illustration, and computer codes are all very helpful for the readers to understand the content. While the approach is statistical, the emphasis is on concepts rather than mathematics. The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book.

    Almost all of the chapters are revised This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.


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Elements of statistical inference. by Robert M. Kozelka Download PDF Ebook

One of the most beautifully produced books I've seen. Written by well-known specialists in applied statistics, the book provides a good practical orientation, with related theoretical issues coming out quite Elements of statistical inference. book. Many examples are given, with a liberal use of color graphics"--Jacket.

It should become a classic…It is especially good for statisticians interested in high-dimensional and high-volume data such as can be found in telephone records, satellite images, and genetic microarrays.

The many superb graphs add to this pleasure. This book describes the Elements of statistical inference. book ideas in these areas in a common conceptual framework. Many of these tools have common underpinnings but are often expressed with different terminology. H Friedman. Clarity rating: 5 The author has explained concepts very well.

The book's coverage is broad, from supervised learning prediction to unsupervised learning. Many examples are given, with a liberal use of color graphics. Free shipping for individuals worldwide Online orders shipping within days. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.

Hand, Short Book Reviews, Vol. Description "During the past decade there has been an explosion in computation and information technology. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.

They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book.

This is the first book of its kind to treat data mining from a statistical perspective that is comprehensive and up-to-date on the statistical methods…I found the book to be both innovative and fresh.

I learned a lot from this well written book and recommend it highly. It can be used for an advanced special topics course in statistics for graduate students. New York: Springer. It was a pleasure to read.The go-to bible for this data scientist and many others is The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

Each of the authors is an expert in machine learning / prediction, and in some cases invented the techniques we turn to today to make sense of big data: ensemble learning methods, penalized regression. This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.

While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics.

Download: Statistical Inference.pdf

The Elements of Statistical Learning: Elements of statistical inference. book Mining, Inference, and Prediction. Second Edition February Pdf Elements of Statistical Learning: Data Pdf, Inference, and Prediction - Ebook written by Trevor Hastie, Robert Tibshirani, Jerome Friedman.

Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read The Elements of Statistical Learning: Data Mining, Inference, and Prediction.5/5(2).Find books like The Elements of Statistical Learning: Data Mining, Inference, and Prediction from the world’s largest community of readers.

Goodreads mem.About the Book. Ebook is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics college student, and covers the topics typically covered in the first semester of such a course.4/4(2).