General conditional specification models

Arnold, Barry C.; Castillo, Enrique; Sarabia, José Marı́a

doi:10.1080/03610929508831471

Cited by 37 publications

(48 citation statements)

References 3 publications

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“…This method, used in the construction of numerous multivariate probability distributions, was extensively developed mostly since around 1987. See, for example, Arnold, Castillo and Sarabia [9] with citations. Also consider Castillo and Galambos [10].…”

Section: Notice On Similar Investigations In the Literaturementioning

confidence: 99%

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Parameter Dependence in Stochastic Modeling—Multivariate Distributions

Filus¹

2014

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We start with analyzing stochastic dependence in a classic bivariate normal density framework. We focus on the way the conditional density of one of the random variables depends on realizations of the other. In the bivariate normal case this dependence takes the form of a parameter (here the "expected value") of one probability density depending continuously (here linearly) on realizations of the other random variable. The point is, that such a pattern does not need to be restricted to that classical case of the bivariate normal. We show that this paradigm can be generalized and viewed in ways that allows one to extend it far beyond the bivariate or multivariate normal probability distributions class.

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Section: Notice On Similar Investigations In the Literaturementioning

confidence: 99%

“…However, in most of the cases they are not directly related to the problem of construction of multivariate probability distributions (so, also are different from the "conditioning" procedures in [9] [10]; see above, Section 5). There are two such subjects that we discuss in the following.…”

Section: Other Parameter Dependence Paradigms In the Literaturementioning

confidence: 99%

Parameter Dependence in Stochastic Modeling—Multivariate Distributions

Filus¹

2014

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“…Now, we define r 1 (λ) = λ −1 , r 2 (θ) = θ −1 (1 − θ) −1 and u λ and v θ as (15) and (16), respectively. Since both conditional distributions belong to the exponential family, and using Theorem 4.1 in Arnold, Castillo and Sarabia (1999), the most general bivariate distribution with conditionals (12) and (13) is given by,…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…For the elicitation of the hyperparameters in the general case we can use the methods proposed in Arnold, Castillo and Sarabia (1999) and Sarabia et al (2005). Assume that we have information about the mean and variance of the two conditional distributions λ|θ and θ|λ, i.e.,…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Bayesian estimation of incomplete data using conditionally specified priors

Sarabia

Shahtahmassebi

2015

Communications in Statistics - Simulation and Computation

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In this paper, a class of conjugate prior for estimating incomplete count data based on a broad class of conjugate prior distributions is presented. The new class of prior distributions arises from a conditional perspective, making use of the conditional specification methodology and can be considered as the generalisation of the form of prior distributions that have been used previously in the estimation of incomplete count data well. Finally, some examples of simulated and real data are given.

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Bivariate income distributions with lognormal conditionals

et al. 2007

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In this paper, the most general bivariate distribution with Lognormal conditionals is fully characterized, using the methodology proposed by Arnold, Castillo and Sarabia (1999). The properties of the new family are studied in detail, including marginal and conditional distributions, regression functions, dependence measures, moments and inequality measures. The new distribution is very wide, and contains as particular case the bivariate Lognormal distribution. Several subfamilies are studied. Some extensions and variations of the basic model are discussed. Finally, we present an empirical application. We estimate and compare the models proposed in the paper, using data from the European Community Household Panel (ECHP) in different periods of time.

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General conditional specification models

Cited by 37 publications

References 3 publications

Parameter Dependence in Stochastic Modeling—Multivariate Distributions

Parameter Dependence in Stochastic Modeling—Multivariate Distributions

Bayesian estimation of incomplete data using conditionally specified priors

Bivariate income distributions with lognormal conditionals

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