Adjusting Published Estimates for Exploratory Biases Using the Truncated Normal Distribution

Loux, Travis; Davy, Orlando

doi:10.1080/00031305.2020.1775700

Cited by 2 publications

(1 citation statement)

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“…with the MGF valid for all t ∈ R. Following on from [17], numerous authors present results for the moments of the truncated normal distribution and generalizations thereof. These include [18][19][20][21][22] and, recently, [23], among others. For values of τ that are less than zero, the asymptotic expansion of Φ(τ) from page 932 of [24] is…”

Section: The Truncated Normal Distribution and Its Approximationsmentioning

confidence: 99%

Properties and Limiting Forms of the Multivariate Extended Skew-Normal and Skew-Student Distributions

Adcock

2022

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This paper is concerned with the multivariate extended skew-normal [MESN] and multivariate extended skew-Student [MEST] distributions, that is, distributions in which the location parameters of the underlying truncated distributions are not zero. The extra parameter leads to greater variability in the moments and critical values, thus providing greater flexibility for empirical work. It is reported in this paper that various theoretical properties of the extended distributions, notably the limiting forms as the magnitude of the extension parameter, denoted t in this paper, increases without limit. In particular, it is shown that as t ! 􀀀¥, the limiting forms of the MESN and MEST distributions are different. The effect of the difference is exemplified by a study of stock market crashes. A second example is a short study of the extent to which the extended skew-normal distribution can be approximated by the skew-Student.

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Section: The Truncated Normal Distribution and Its Approximationsmentioning

confidence: 99%

Properties and Limiting Forms of the Multivariate Extended Skew-Normal and Skew-Student Distributions

Adcock

2022

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Evaluating Embedded Monte Carlo vs. Total Monte Carlo for Nuclear Data Uncertainty Quantification

Biot,

Rochman,

Ducru

et al. 2024

EPJ Web Conf.

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The purpose of this paper is to compare a new method called Embedded Monte Carlo (EMC) to the well-known Total Monte Carlo (TMC) method for nuclear data uncertainty propagation. Indeed, the TMC methodology is based on the use of a large number of random samples of nuclear data libraries and performing separate Monte Carlo calculations for each random sample. Then, the computation of nuclear data uncertainty is based on the difference between the total uncertainty and the statistical uncertainty of each Monte Carlo simulation. This method can either be applied to MC and deterministic codes where there are no statistical uncertainties. The goal of EMC is to compute statistical uncertainty for each random sample by utilizing historical statistics instead of the batch statistics employed in TMC. Consequently, one large Monte Carlo simulation can be conducted where each batch represents a new random sample, thereby embedding the propagation of uncertainties within a single calculation and reducing computational expenses. This approach allows for the calculation of nuclear data uncertainty using history statistics in fixed source and eigenvalue calculations. This paper demonstrates the capability of this new method using OpenMC. The analysis will be performed on a Godiva sphere benchmark by propagating the uncertainty on two input parameters: the average neutron multiplicity v and 235U density.

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Adjusting Published Estimates for Exploratory Biases Using the Truncated Normal Distribution

Cited by 2 publications

References 20 publications

Properties and Limiting Forms of the Multivariate Extended Skew-Normal and Skew-Student Distributions

Properties and Limiting Forms of the Multivariate Extended Skew-Normal and Skew-Student Distributions

Evaluating Embedded Monte Carlo vs. Total Monte Carlo for Nuclear Data Uncertainty Quantification

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