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Regression Variable Selection: Cross-Validation, Group Method of Data Handling, Akaike Information Criterion, Stepwise Regression

Regression Variable Selection: Cross-Validation, Group Method of Data Handling, Akaike Information Criterion, Stepwise Regression


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About the Book

Chapters: Cross-Validation, Group Method of Data Handling, Akaike Information Criterion, Stepwise Regression, Bayesian Information Criterion, Deviance Information Criterion, Model Selection, Least-Angle Regression, Mallows' Cp, Hannan-quinn Information Criterion. Source: Wikipedia. Pages: 47. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: Cross-validation, sometimes called rotation estimation, is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. Cross-validation is important in guarding against testing hypotheses suggested by the data (called "Type III errors"), especially where further samples are hazardous, costly or impossible to collect (see uncomfortable science). Suppose we have a model with one or more unknown parameters, and a data set to which the model can be fit (the training data set). The fitting process optimizes the model parameters to make the model fit the training data as well as possible. If we then take an independent sample of validation data from the same population as the training data, it will generally turn out that the model does not fit the validation data as well as it fits the training data. This is called overfitting, and is particularly likely to hap...More: http: //booksllc.net/?id=41661


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Product Details
  • ISBN-13: 9781155754154
  • Publisher: Books LLC
  • Publisher Imprint: Books LLC
  • Height: 152 mm
  • Sub Title: Cross-Validation, Group Method of Data Handling, Akaike Information Criterion, Stepwise Regression
  • Width: 229 mm
  • ISBN-10: 1155754158
  • Publisher Date: 15 Sep 2010
  • Binding: Paperback
  • Spine Width: 3 mm
  • Weight: 82 gr


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Regression Variable Selection: Cross-Validation, Group Method of Data Handling, Akaike Information Criterion, Stepwise Regression
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