Conditionally Specified Distributions
The concept of conditional specification is not new. It is likely that earlier investigators in this area were deterred by computational difficulties encountered in the analysis of data following con ditionally specified models. Readily available computing power has swept away that roadblock. A bro...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1992, 1992
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| Edition: | 1st ed. 1992 |
| Series: | Lecture Notes in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 11.7 Characterizations involving conditional moments
- 11.8 Multivariate extensions
- 11.9 Parameter estimation in conditionally specified models
- 11.10 Simulations
- 8.1 Extension by underlining
- 8.2 Compatibility in 3 dimensions
- 8.3 Conditionals in prescribed families
- 8.4 Conditionals in exponential families
- 8.5 Examples
- 8.6 Further extension by underlining
- 9 Parameter estimation in conditionally specified models
- 9.1 The ubiquitous norming constant
- 9.2 Maximum likelihood
- 9.3 Pseudolikelihood involving conditional densities
- 9.4 Marginal likelihood
- 9.5 An efficiency comparison
- 9.6 Method of moments estimates
- 9.7 Bayesian estimates
- 10 Simulations
- 10.1 Introduction
- 10.2 The rejection method
- 10.3 Application to models with conditionals in exponential families
- 10.4 Other conditionally specified models
- 10.5 A direct approach not involving rejection
- 11 Bibliographic Notes
- 11.1 Introduction
- 11.2 Basic theorems
- 11.3 Distributions with normal conditionals
- 11.4 Conditionals in exponential families
- 11.5 Other conditionally specified Families
- 11.6 Impossible models
- 1 Conditional Specification
- 1.1 Why?
- 1.2 How may one specify a bivariate distribution?
- 1.3 Early work on conditional specification
- 1.4 Organization of this monograph
- 2 Basic Theorems
- 2.1 Compatible conditionals: The finite discrete case
- 2.2Compatibility in more general settings
- 2.3Uniqueness
- 2.4 Conditionals in prescribed families
- 2.5 An example
- 3 Distributions with normal conditionals
- 3.1 Variations on the classical bivariate normal theme
- 3.2 Normal conditionals
- 3.3 Properties of the normal conditionals distribution
- 3.4 The centered model
- 4 Conditionals in Exponential Families
- 4.1 Introduction
- 4.2 Distributions with conditionals in given exponential families
- 4.3 Dependence in CEF distributions
- 4.4 Examples
- 5 Other conditionally specified families
- 5.1 Introduction
- 5.2 Bivariate Distributions with Pareto conditionals
- 5.3 Some extensions of the Pareto case
- 5.4 Bivariate distributions with Cauchy conditionals
- 5.5 Bivariate distributions with uniform conditionals
- 5.6 Possibly translated exponential conditionals
- 5.7 Bivariate distributions with scaled beta conditionals
- 5.8 Weibull and logistic conditionals
- 5.9 Mixtures
- 6 Impossible Models
- 6.1 Introduction
- 6.2 Logistic Regression
- 6.3 Uniform conditionals
- 6.4 Exponential and Weibull conditionals
- 6.5 Measurement error models
- 6.6 Stochastic processes and Wohler fields
- 6.6.1 The Gumbel-Gumbel model
- 6.6.2 The Wei bull-Weibull model
- 7 Characterizations involving conditional moments
- 7.1 Introduction
- 7.2 Mardia’s bivariate Pareto distribution
- 7.3Linear regressions with conditionals in exponential families
- 7.4Linear regressions with conditionals in location families
- 7.5Specified regressions with conditionals in scale families
- 7.6 Conditionalsin location-scale families with specified moments
- 8 Multivariate extensions