Generalized Linear Models: A Bayesian Perspective

Hardcover (Print)
Used and New from Other Sellers
Used and New from Other Sellers
from $141.58
Usually ships in 1-2 business days
Other sellers (Hardcover)
  • All (2) from $141.58   
  • New (1) from $188.92   
  • Used (1) from $141.58   
Close
Sort by
Page 1 of 1
Showing All
Note: Marketplace items are not eligible for any BN.com coupons and promotions
$188.92
Seller since 2011

Feedback rating:

(867)

Condition:

New — never opened or used in original packaging.

Like New — packaging may have been opened. A "Like New" item is suitable to give as a gift.

Very Good — may have minor signs of wear on packaging but item works perfectly and has no damage.

Good — item is in good condition but packaging may have signs of shelf wear/aging or torn packaging. All specific defects should be noted in the Comments section associated with each item.

Acceptable — item is in working order but may show signs of wear such as scratches or torn packaging. All specific defects should be noted in the Comments section associated with each item.

Used — An item that has been opened and may show signs of wear. All specific defects should be noted in the Comments section associated with each item.

Refurbished — A used item that has been renewed or updated and verified to be in proper working condition. Not necessarily completed by the original manufacturer.

New
Brand new and unread! Join our growing list of satisfied customers!

Ships from: Phoenix, MD

Usually ships in 1-2 business days

  • Standard, 48 States
  • Standard (AK, HI)
Page 1 of 1
Showing All
Close
Sort by

Overview

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Read More Show Less

Editorial Reviews

Booknews
Describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation, covering random effects in generalized linear mixed models (GLMMs) with explained examples. Considers parametric and semiparametric approaches to overdispersed GLMs, applies Bayesian GLMs to US mortality data, and presents methods of analyzing correlated binary data using latent variables. Describes and analyzes item response modeling for categorical data, and provides variable selection methods using the Gibbs sampler for Cox models. Dey is professor and head of the department of statistics at the University of Connecticut-Storrs. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Read More Show Less

Product Details

Table of Contents

I General  Overview                                                                                                                              1
Generalized Linear Models: A Bayesian View 3
Random Effects in Generalized Linear Mixed Models 23
Prior Elicitation and Variables Selection for Generalized Linear Mixed Models 41
II Extending the GLMs 55
Dynamic Generalized Linear Models 57
Bayesian Approaches for Overdispersion in Generalized Linear Models 73
Bayesian Generalized Linear Models for Inference About Small Areas 89
III Categorical and Longitudinal Data 111
Bayesian Methods for Correlated Binary Data 113
Bayesian Analysis for Correlated Ordinal Data Models 133
Bayesian Methods for Time Series Count Data Item 159
Response Modeling 173
Developing and Applying Medical Practice Guidelines Following Acute Myocardial Infarction: A Case Study Using Bayesian Probit and Logit Models 195
IV Semiparametric Approaches 215
Semiparametric Generalized Linear Models: Bayesian Approaches 217
Binary Response Regression with Normal Scale Mixture Links 231
Binary Regression Using Data Adaptive Robust Link Functions 243
A Mixture-Model Approaches to the Analysis of Survival Data 255
V Model Diagnostics and Variable Selection in GLMs 271
Bayesian Variable Selection Using the Gibbs Sampler 273
Bayesian Methods for Variables Selection in the Cox Model 287
Bayesian Model Diagnostics for Correlated Binary Data 313
VI Challenging Approaches in GLMs 329
Bayesian Errors-in-Variables Modeling 331
Bayesian Analysis of Compositional Data 349
Classification Trees 365
Modeling and Inference for Point-Referenced Binary Spatial Data 373
Bayesian Graphical Models and Software for GLMs 387
Index 407
Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)