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Tuesday, September 18, 2018

Predicting? Then optimize prediction not modeling | Cambridge University Press


Prediction, one of the most important practical applications of statistical analysis, has rarely been treated as anything more than an afterthought in most formal treatments of statistical inference.

Predictive Statistics
Analysis and Inference beyond Models
This important book aims to counter this neglect by a wholehearted emphasis on prediction as the primary purpose of the analysis. The authors cut a broad swathe through the statistical landscape, conducting thorough analyses of numerous traditional, recent, and novel techniques, to show how these are illuminated by taking the predictive perspective.' 
Philip Dawid, University of Cambridge.



  • Connects statistical theory directly to the goals of machine learning, data mining, and modern applied science
  • Positions statisticians to cope with emerging, non-traditional data types
  • Well-documented R code in a Github repository allows readers to replicate examples
  •  
    Connects statistical theory directly to the goals of machine learning, data mining, and modern applied science Positions statisticians to cope with emerging, non-traditional data types Well-documented R code in a Github repository allows readers to replicate examples Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and 'black box' settings, with computed examples.  
    Read more... 

    Enjoy your reading!

    Source: Cambridge University Press