Product Description

Data Driven Statistical Methods is designed for use either as a text book at the undergraduate level, as a source book providing material and suggestions for teachers wishing to incorporate some of its features into more general courses, and also as a self-instruction manual for applied statisticians seeking a simple introduction to many important practical concepts that use the 'data driven' rather than the 'model driven' approach.

Product Details

• Amazon Sales Rank: #3074567 in Books
• Published on: 1997-12-01
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 406 pages

Review
This scholarly book brings together a vast literature on methods for analyzing and modeling rank data...it is a mathematical statistics book in the best sense of the word...
- Short Books Reviews of the ISI

Customer Reviews

covers the computer-intensive methods among others
In this computer age, statistical methods like the bootstrap, that can be based solely on the data at hand become very practical. Sprent focusses on the bootstrap, permutation methods and cross-validation. He also covers with other nonparametric techniques. When dealing with modeling techniques he emphasizes robust methods outlier detection and model diagnostics. These are all important tools when applying statistics in the "real world". Many of the techniques are computer-intensive. With the speed of computing increasing at such an amazing rate, methods considered impractical 20 or 25 years ago are now commonplace. This is a very well-written intermediate level statistical text.

Well written text on an important subject
In this computer age, statistical methods like the bootstrap, that can be based solely on the data at hand become very practical. Sprent focusses on the bootstrap, permutation methods and cross-validation. He also covers with other nonparametric techniques. When dealing with modeling techniques he emphasizes robust methods outlier detection and model diagnostics. These are all important tools when applying statistics in the "real world". Many of the techniques are computer-intensive. With the speed of computing increasing at such an amazing rate, methods considered impractical 20 or 25 years ago are now commonplace. This is a very well-written intermediate level statistical text.