Model choice in nonnested families
By: Pereira, Basilio de Bragança.
Contributor(s): Pereira, Carlos Alberto de Braganca.
Series: Springer breifs in statistics. Publisher: Germany Springer 2016Description: x, 96p.ISBN: 9783662537350.Subject(s): Mathematical statistics | Bayesian statistical decision theory | StatisticsDDC classification: 519.5 | P414m Summary: his book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Books
|
PK Kelkar Library, IIT Kanpur | General Stacks | 519.5 P414m (Browse shelf) | Available | A183390 |
Total holds: 0
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
| 519.5 Og9r Random phenomena | 519.5 Op7 OPTIMAL DESIGN AND ANALYSIS OF EXPERIMENTS | 519.5 P193s Statistical inference | 519.5 P414m Model choice in nonnested families | 519.5 P448d Dynamic linear models with R | 519.5 P544 STATISTICAL THINKING | 519.5 P683s Some basic theory for statistical inference |
his book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
There are no comments for this item.