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Barry C. Arnold
Researcher at University of California, Riverside
Publications - 180
Citations - 5967
Barry C. Arnold is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Joint probability distribution & Conditional probability distribution. The author has an hindex of 31, co-authored 180 publications receiving 5609 citations. Previous affiliations of Barry C. Arnold include University of Cantabria.
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Book ChapterDOI
On Parameter Dependence and Related Topics: The Impact of Jerzy Filus from Genesis to Recent Developments
Jerzy K. Filus,Lidia Z. Filus,Barry C. Arnold,Pavlina K. Jordanova,Ludy Núñez Soza,Ying Lu,Silvia Stehlíková,Milan Stehlík +7 more
TL;DR: In this paper, the authors discuss the genesis and development of multivariate pseudonormal distributions, parameter dependence, and finally stochastic dependence in general, and discuss the recent developments on this topic and propose an interesting new model.
Journal ArticleDOI
Conditional specification of statistical models: Classical models, new developments and challenges
TL;DR: Some of the main aspects of models with conditional specification of distributions, including the most relevant aspects of these models are summarized and some challenges that they will face in the coming years are established.
Journal ArticleDOI
Alternative approaches to conditional specification of bivariate distributions
Barry C. Arnold,Ramesh C. Gupta +1 more
TL;DR: In this article, the authors discuss approaches to the determination of a bivariate distribution by specifying the two conditional density functions, two conditional survival functions, the two hazard components, and two conditional hazard functions of one variable given the value of the other.
Journal ArticleDOI
On bivariate pseudo-exponential distributions
TL;DR: A bivariate conditionally specified distribution is one in which the dependence relationship between the two random variables is accomplished by defining the distribution of one of the random varia....
Journal ArticleDOI
On the inconsistency of Bayesian non-parametric estimators in competing risks/multiple decrement models
TL;DR: It is shown that in dimensions ⩾ 2, the posterior mean yields an inconsistent estimator of the joint probability law, contrary to the common assumption that the prior law ‘washes out’ with large samples.