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Relevant bibliographies by topics / Asymptotic distribution

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Journal articles

Academic literature on the topic 'Asymptotic distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Asymptotic distribution"

1

Miyazawa, Masakiyo. "Martingale approach for tail asymptotic problems in the generalized Jackson network." Probability and Mathematical Statistics 37, no. 2 (2018): 395–430. http://dx.doi.org/10.19195/0208-4147.37.2.11.

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MARTINGALE APPROACH FOR TAIL ASYMPTOTIC PROBLEMS IN THE GENERALIZED JACKSON NETWORKWe study the tail asymptotic of the stationary joint queue length distribution for a generalized Jackson network GJN for short, assumingits stability. For the two-station case, this problem has recently been solved in the logarithmic sense for the marginal stationary distributions under the setting that arrival processes and service times are of phase-type. In this paper, we study similar tail asymptotic problems on the stationary distribution, but problems and assumptions are different. First, the asymptotics a

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2

WANG, Frank Xuyan. "Shape Factor Asymptotic Analysis I." Journal of Advanced Studies in Finance 11, no. 2 (2020): 108. http://dx.doi.org/10.14505//jasf.v11.2(22).05.

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We proposed using shape factor to distinguish probability distributions, and using relative minimum or maximum values of shape factor to locate distribution parameter allowable ranges for distribution fitting in our previous study. In this paper, the shape factor asymptotic analysis is employed to study such conditional minimum or maximum, to cross validate results found from numerical study and empirical formula we obtained and published earlier. The shape factor defined as kurtosis divided by skewness squared is characterized as the unique maximum choice of among all factors that is greater

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3

KUMAR, C. SATHEESH, and G. V. ANILA. "Asymptotic curved normal distribution." Journal of Statistical Research 52, no. 2 (2019): 173–86. http://dx.doi.org/10.47302/2018520204.

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Here we introduce a new class of skew normal distribution as a generalization of the extended skew curved normal distribution of Kumar and Anusree (J. Statist. Res., 2017) and investigate some of its important statistical properties. The location-scale extension of the proposed class of distribution is also defined and discussed the estimation of its parameters by method of maximum likelihood. Further, a real life data set is considered for illustrating the usefulness of the model and a brief simulation study is attempted for assessing the performance of the estimators.

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4

Shimizu, Eiji, and Hiroshi Shiraishi. "An asymptotic distribution of compound Poisson distribution." Cogent Mathematics 3, no. 1 (2016): 1221614. http://dx.doi.org/10.1080/23311835.2016.1221614.

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5

Tanaka, Katsuto. "Asymptotic expansions for time series statistics." Journal of Applied Probability 23, A (1986): 211–27. http://dx.doi.org/10.2307/3214354.

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Asymptotic expansions for the distributions of estimators and test statistics are derived in connection with time series models. The expansions relate to marginal and joint distributions together with the percentiles of marginal distributions. We also consider transforming a statistic so that the transformed statistic has a distribution that coincides with its asymptotic distribution up to a higher order.

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6

Tanaka, Katsuto. "Asymptotic expansions for time series statistics." Journal of Applied Probability 23, A (1986): 211–27. http://dx.doi.org/10.1017/s0021900200117097.

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Asymptotic expansions for the distributions of estimators and test statistics are derived in connection with time series models. The expansions relate to marginal and joint distributions together with the percentiles of marginal distributions. We also consider transforming a statistic so that the transformed statistic has a distribution that coincides with its asymptotic distribution up to a higher order.

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7

Kahounová, Jana. "Asymptotic Probability Distribution of Sample Maximum." Acta Oeconomica Pragensia 16, no. 3 (2008): 40–46. http://dx.doi.org/10.18267/j.aop.103.

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8

Lyons, Russell. "Mixing and asymptotic distribution modulo 1." Ergodic Theory and Dynamical Systems 8, no. 4 (1988): 597–619. http://dx.doi.org/10.1017/s0143385700004715.

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AbstractIf μ is a probability measure which is invariant and ergodic with respect to the transformationx↦qxon the circle ℝ/ℤ, then according to the ergodic theorem, {qnx} has the asymptotic distribution μ for μ-a.e.x. On the other hand, Weyl showed that when μ is Lebesgue measure, λ, and {mj} is an arbitrary sequence of integers increasing strictly to ∞, the asymptotic distribution of {mjx} is λ for λ-a.e.x. Here, we investigate the asymptotic distributions of {mjx} μ-a.e. for fairly arbitrary {mj} under some strong mixing conditions on μ. The result is a kind of stable ergodicity: the distrib

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9

Coffman, Donna L., Alberto Maydeu-Olivares, and Jaume Arnau. "Asymptotic Distribution Free Interval Estimation." Methodology 4, no. 1 (2008): 4–9. http://dx.doi.org/10.1027/1614-2241.4.1.4.

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Abstract. Confidence intervals for the intraclass correlation coefficient (ICC) have been proposed under the assumption of multivariate normality. We propose confidence intervals which do not require distributional assumptions. We performed a simulation study to assess the coverage rates of normal theory (NT) and asymptotically distribution free (ADF) intervals. We found that the ADF intervals performed better than the NT intervals when kurtosis was greater than 4. When violations of distributional assumptions were not too severe, both the intervals performed about the same. The point estimate

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10

Barral, Julien, and Yan-Hui Qu. "Multifractals in Weyl asymptotic distribution." Nonlinearity 24, no. 10 (2011): 2785–811. http://dx.doi.org/10.1088/0951-7715/24/10/008.

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