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Comment on "On some statistical paradoxes and non-conglomerability" by Bruce Hill.

Isaac Levi (1981)

Trabajos de Estadística e Investigación Operativa

Those who follow Harold Jeffreys in using improper priors together with likelihoods to determine posteriors have thought of the improper measures as probability measures of a deviant sort. This is a mistake. Probability measures are finite measures. Improper distributions generate σ-finite measures. (...)

Commuting functions and simultaneous Abel equations

W. Jarczyk, K. Łoskot, M. C. Zdun (1994)

Annales Polonici Mathematici

The system of Abel equations α(ft(x)) = α(x) + λ(t), t ∈ T, is studied under the general assumption that f t are pairwise commuting homeomorphisms of a real interval and have no fixed points (T is an arbitrary non-empty set). A result concerning embeddability of rational iteration groups in continuous groups is proved as a simple consequence of the obtained theorems.

Construction of multivariate copulas in n -boxes

José M. González-Barrios, María M. Hernández-Cedillo (2013)

Kybernetika

In this paper we give an alternative proof of the construction of n -dimensional ordinal sums given in Mesiar and Sempi [17], we also provide a new methodology to construct n -copulas extending the patchwork methodology of Durante, Saminger-Platz and Sarkoci in [6] and [7]. Finally, we use the gluing method of Siburg and Stoimenov [20] and its generalization in Mesiar et al. [15] to give an alternative method of patchwork construction of n -copulas, which can be also used in composition with our patchwork...

Convergence of conditional expectations for unbounded closed convex random sets

Charles Castaing, Fatima Ezzaki, Christian Hess (1997)

Studia Mathematica

We discuss here several types of convergence of conditional expectations for unbounded closed convex random sets of the form E n X n where ( n ) is a decreasing sequence of sub-σ-algebras and ( X n ) is a sequence of closed convex random sets in a separable Banach space.

Dependence Measuring from Conditional Variances

Noppadon Kamnitui, Tippawan Santiwipanont, Songkiat Sumetkijakan (2015)

Dependence Modeling

A conditional variance is an indicator of the level of independence between two random variables. We exploit this intuitive relationship and define a measure v which is almost a measure of mutual complete dependence. Unsurprisingly, the measure attains its minimum value for many pairs of non-independent ran- dom variables. Adjusting the measure so as to make it invariant under all Borel measurable injective trans- formations, we obtain a copula-based measure of dependence v* satisfying A. Rényi’s...

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