Skip to content

Booksellers & Trade Customers: Sign up for online bulk buying at trade.atlanticbooks.com for wholesale discounts

Booksellers: Create Account on our B2B Portal for wholesale discounts

Bayesian Predictive Inference for Some Linear Models under Student-t Errors

by Azizur Rahman
Save 12% Save 12%
Current price ₹4,512.00
Original price ₹5,145.00
Original price ₹5,145.00
Original price ₹5,145.00
(-12%)
₹4,512.00
Current price ₹4,512.00

Imported Edition - Ships in 18-21 Days

Free Shipping in India on orders above Rs. 500

Request Bulk Quantity Quote
+91
Book cover type: Paperback
  • ISBN13: 9783639040869
  • Binding: Paperback
  • Subject: N/A
  • Publisher: VDM Verlag Dr. Mueller E.K.
  • Publisher Imprint: VDM Verlag Dr. Mueller E.K.
  • Publication Date:
  • Pages: 88
  • Original Price: GBP 40.67
  • Language: English
  • Edition: N/A
  • Item Weight: 127 grams
  • BISAC Subject(s): Statistics

In real life often we need to make inferences about the behaviour of the unobserved responses for a model based on the observed responses from the model. Regression models with normal errors are commonly considered in prediction problems. However, when the underlying distributions have heavier tails, the normal errors assumption fails to allow sufficient probability in the tail areas to make allowance for any extreme value or outliers. As well, it cannot deal with the uncorrelated but not independent observations which are common in time series and econometric studies. In such situations, the Student-t errors assumption is appropriate. Traditionally, a number of statis-tical methods such as the classical, structural distribution and structural relations approaches can lead to prediction distributions, the Bayesian approach is more sound in statistical theory. This book, therefore, deals with the derivation problems of prediction distri-butions for some widely used linear models having Student-t errors under the Bayesian approach. Results reveal that our models are robust and the Baye-sian approach is competitive with traditional methods. In perturbation ana-lysis, process control, optimization, classification, discordancy testing, interim analysis, speech recognition, online environmental learning and sampling cur-tailment studies predictive inferences are successfully used.

Trusted for over 49 years

Family Owned Company

Secure Payment

All Major Credit Cards/Debit Cards/UPI & More Accepted

New & Authentic Products

India's Largest Distributor

Need Support?

Whatsapp Us