Results 31 to 40 of about 558,384 (291)

Model Selection Test for the Heavy-Tailed Distributions under Censored Samples with Application in Financial Data

International Journal of Financial Studies, 2016
Numerous heavy-tailed distributions are used for modeling financial data and in problems related to the modeling of economics processes. These distributions have higher peaks and heavier tails than normal distributions.
Hanieh Panahi
doaj   +1 more source

The number of distinct adjacent pairs in geometrically distributed words: a probabilistic and combinatorial analysis [PDF]

Discrete Mathematics & Theoretical Computer Science, 2023
The analysis of strings of $n$ random variables with geometric distribution has recently attracted renewed interest: Archibald et al. consider the number of distinct adjacent pairs in geometrically distributed words.
Guy Louchard, Werner Schachinger, Mark Daniel Ward   +2 more
doaj   +1 more source

Parametric Estimation in the Vasicek-Type Model Driven by Sub-Fractional Brownian Motion

Algorithms, 2018
In the paper, we tackle the least squares estimators of the Vasicek-type model driven by sub-fractional Brownian motion: d X t = ( μ + θ X t ) d t + d S t H , t ≥ 0 with X 0 = 0 , where S H is a sub-fractional Brownian ...
Shengfeng Li, Yi Dong
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Asymptotic analysis of Bragg fibers [PDF]

, 2000
Using an asymptotic analysis, we obtain an eigenvalue equation for the general mode dispersion in Bragg fibers. The asymptotic analysis is applied to calculate the dispersion relation and the field distribution of TE modes in a Bragg fiber.
Lee, Reginald K., Xu, Yong, Yariv, Amnon
core   +1 more source

Asymptotic independence for unimodal densities [PDF]

, 2009
Asymptotic independence of the components of random vectors is a concept used in many applications. The standard criteria for checking asymptotic independence are given in terms of distribution functions (dfs).
Balkema, Guus, Nolde, Natalia
core   +1 more source

Rank-Based Family of Probability Laws for Testing Homogeneity of Variable Grouping

Mathematics
In order to test within-group homogeneity for numerical or ordinal variable groupings, we have introduced a family of discrete probability distributions, related to the Gini mean difference, that we now study in a deeper way. A member of such a family is
Manuel L. Esquível, Nadezhda P. Krasii, Célia Nunes, Kwaku Opoku-Ameyaw, Pedro P. Mota   +4 more
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Asymptotic Capture-Number and Island-Size Distributions for One-Dimensional Irreversible Submonolayer Growth

, 2003
Using a set of evolution equations [J.G. Amar {\it et al}, Phys. Rev. Lett. {\bf 86}, 3092 (2001)] for the average gap-size between islands, we calculate analytically the asymptotic scaled capture-number distribution (CND) for one-dimensional ...
A.D. Gates, A.D. Gates, A.D. Gates, C. Ratsch, D.D. Vvedensky, D.D. Vvedensky, F.G. Gibou, J. G. Amar, J.A. Blackman, J.A. Venables, J.A. Venables, J.G. Amar, J.G. Amar, J.G. Amar, J.G. Amar, J.G. Amar, J.W. Evans, J.W. Evans, M. N. Popescu, M. von Smoluchowski, M. von Smoluchowski, M.C. Bartelt, M.C. Bartelt, M.C. Bartelt, M.N. Popescu, P.A. Mulheran, P.A. Mulheran, P.A. Mulheran, T.R. Linderoth, Y.W. Mo   +29 more
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Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions

Mathematics, 2023
The class of natural exponential families (NEFs) of distributions having power variance functions (NEF-PVFs) is huge (uncountable), with enormous applications in various fields.
Shaul K. Bar-Lev, Apostolos Batsidis, Jochen Einbeck, Xu Liu, Panpan Ren   +4 more
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Random intersection graph process

, 2013
We introduce a random intersection graph process aimed at modeling sparse evolving affiliation networks that admit tunable (power law) degree distribution and assortativity and clustering coefficients.
Bloznelis, Mindaugas, Karonski, Michal
core   +1 more source

An adaptive lack of fit test for big data

Statistical Theory and Related Fields, 2017
New technological advancements combined with powerful computer hardware and high-speed network make big data available. The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods.
Yanyan Zhao, Changliang Zou, Zhaojun Wang   +2 more
doaj   +1 more source