Power Search
Low Price Guarantee
We Take School POs
Fuzzy Statistics
Contributor(s): Buckley, James J. (Author)

View larger image

 ISBN: 3642059244     ISBN-13: 9783642059247
Publisher: Springer
OUR PRICE: $103.55  

Binding Type: Paperback - See All Available Formats & Editions
Published: December 2010
Qty:

Click for more in this series: Studies in Fuzziness and Soft Computing
Additional Information
BISAC Categories:
- Mathematics | Applied
- Computers | Intelligence (ai) & Semantics
- Mathematics | Probability & Statistics - General
Dewey: 519.5
Series: Studies in Fuzziness and Soft Computing
Physical Information: 0.38" H x 6.14" W x 9.21" L (0.57 lbs) 168 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
1. 1 Introduction This book is written in four major divisions. The first part is the introductory chapters consisting of Chapters 1 and 2. In part two, Chapters 3-11, we develop fuzzy estimation. For example, in Chapter 3 we construct a fuzzy estimator for the mean of a normal distribution assuming the variance is known. More details on fuzzy estimation are in Chapter 3 and then after Chapter 3, Chapters 4-11 can be read independently. Part three, Chapters 12- 20, are on fuzzy hypothesis testing. For example, in Chapter 12 we consider the test Ho: /1 = /10 verses HI: /1 f=- /10 where /1 is the mean of a normal distribution with known variance, but we use a fuzzy number (from Chapter 3) estimator of /1 in the test statistic. More details on fuzzy hypothesis testing are in Chapter 12 and then after Chapter 12 Chapters 13-20 may be read independently. Part four, Chapters 21-27, are on fuzzy regression and fuzzy prediction. We start with fuzzy correlation in Chapter 21. Simple linear regression is the topic in Chapters 22-24 and Chapters 25-27 concentrate on multiple linear regression. Part two (fuzzy estimation) is used in Chapters 22 and 25; and part 3 (fuzzy hypothesis testing) is employed in Chapters 24 and 27. Fuzzy prediction is contained in Chapters 23 and 26. A most important part of our models in fuzzy statistics is that we always start with a random sample producing crisp (non-fuzzy) data.
 
Customer ReviewsSubmit your own review
 
To tell a friend about this book, you must Sign In First!