Improving Your NCAA® Bracket with Statistics, Adams

Автор: Adams
Название: Improving Your NCAA® Bracket with Statistics
ISBN: 1138597740 ISBN-13(EAN): 9781138597747
Издательство: Taylor&Francis
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Цена: 4592.00 р.
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Описание: Improving Your NCAA (R) Bracket with Statistics is both an easy-to-use tip sheet to improve your winning odds and an intellectual history of how statistical reasoning has been applied to the bracket pool using methods standard and innovative.

Автор: Gruber, Marvin
Название: Improving Efficiency by Shrinkage
ISBN: 0367579367 ISBN-13(EAN): 9780367579364
Издательство: Taylor&Francis
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Цена: 7348.00 р.
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Автор: Rand R. Wilcox
Название: Fundamentals of Modern Statistical Methods
ISBN: 1489984704 ISBN-13(EAN): 9781489984708
Издательство: Springer
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Цена: 8384.00 р.
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Описание: This book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. The book includes a number of new advances and insights.

Автор: Gruber, Marvin
Название: Improving Efficiency by Shrinkage
ISBN: 0824701569 ISBN-13(EAN): 9780824701567
Издательство: Taylor&Francis
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Цена: 56654.00 р.
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Автор: Woo, Seong-woo,
Название: Design of mechanical systems based on statistics :
ISBN: 0367076268 ISBN-13(EAN): 9780367076269
Издательство: Taylor&Francis
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Цена: 24499.00 р.
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Описание: This book covers both a descriptive and inferential approach of reliability design in the development process of mechanical products, along with a focus on parametric accelerated life testing and case studies. This new reliability methodology enables the engineer to uncover the faulty design of the product and avoid recalls in the marketplace.

Автор: Timothy Masters
Название: Assessing and Improving Prediction and Classification
ISBN: 1484233352 ISBN-13(EAN): 9781484233351
Издательство: Springer
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Цена: 7685.00 р.
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Описание: Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application.
Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics.
All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program.

What You'll Learn

• Compute entropy to detect problematic predictors.
  
• Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions.
  
• Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing.
  
• Improve classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling.
  
• Use information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising.
  
• Use Monte-Carlo permutation methods to assess the role of good luck in performance results.