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Journal ArticleDOI

Teaching Singular Distributions to Undergraduates

L. H. Koopmans
- 01 Nov 1983 - 
- Vol. 37, Iss: 4, pp 313-316
TLDR
In this article, a representation for singular distributions in two dimensions is given, which is used to characterize the types of marginal distributions that members of this class can have, and a representation is given that makes the construction of a class of singular distributions of two dimensions simple to carry out.
Abstract
Singular distributions are seldom covered in undergraduate probability courses, although they are of interest in statistics and, as is shown by example, can easily arise through extending mixed discrete and continuous distributions to two or more dimensions. A representation is given that makes the construction of a class of singular distributions in two dimensions simple to carry out. This representation is also used to characterize the types of marginal distributions that members of this class can have.

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Citations
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Proceedings ArticleDOI

Adaptive population importance samplers: A general perspective

TL;DR: The underlying motivation for the different adaptation schemes is interpreted, opening the door for novel and more efficient algorithms.
Journal ArticleDOI

Mixed Discrete-Continuous Bayesian Inference: Censored Measurements of Sparse Signals

TL;DR: The result provides a unified Bayesian-inference framework and leads to closed-form solutions for the inference problem, numerical results, and the analysis of the probability of censorship.
References
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Book

The theory of functions

TL;DR: Alfaro et al. as mentioned in this paper conservado en la Biblioteca del Campus de Mostoles de la Universidad Rey Juan Carlos (sign. 517.5 TIT THE).
Book ChapterDOI

A Class of Statistics with Asymptotically Normal Distribution

TL;DR: In this article, the authors considered the problem of estimating a U-statistic of the population characteristic of a regular functional function, where the sum ∑″ is extended over all permutations (α 1, α m ) of different integers, 1 α≤ (αi≤ n, n).
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