Results 21 to 30 of about 279,412 (303)
Revstat Statistical Journal, 2013 In this work we derive the exact joint distribution of a linear combination of concomitants of order statistics and linear combinations of their order statistics in a multivariate normal distribution.
Ayyub Sheikhi , Mahbanoo Tata
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Comparison of Risk Ratios of Shrinkage Estimators in High Dimensions
Mathematics, 2021 In this paper, we analyze the risk ratios of several shrinkage estimators using a balanced loss function. The James–Stein estimator is one of a group of shrinkage estimators that has been proposed in the existing literature.
Abdenour Hamdaoui +3 more
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Kullback–Leibler Divergence Measure for Multivariate Skew-Normal Distributions
Entropy, 2012 The aim of this work is to provide the tools to compute the well-known Kullback–Leibler divergence measure for the flexible family of multivariate skew-normal distributions.
Reinaldo B. Arellano-Valle +1 more
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On shrinkage estimators improving the positive part of James-Stein estimator
Demonstratio Mathematica, 2021 In this work, we study the estimation of the multivariate normal mean by different classes of shrinkage estimators. The risk associated with the quadratic loss function is used to compare two estimators. We start by considering a class of estimators that
Hamdaoui Abdenour
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Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
Machines, 2021 Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities.
Riku-Pekka Nikula +4 more
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Open Mathematics, 2022 One of the most common challenges in multivariate statistical analysis is estimating the mean parameters. A well-known approach of estimating the mean parameters is the maximum likelihood estimator (MLE).
Benkhaled Abdelkader +4 more
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On the conditional distribution of a multivariate Normal given a transformation – the linear case
Heliyon, 2019 We show that the orthogonal projection operator onto the range of the adjoint T⁎ of a linear operator T can be represented as UT, where U is an invertible linear operator. Given a Normal random vector Y and a linear operator T, we use this representation
Rajeshwari Majumdar, Suman Majumdar
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A Characterization of the Multivariate Normal Distribution
The Annals of Mathematical Statistics, 1971 In this paper we characterize the multivariate normal distribution through the mean vector of the distribution. In Theorem 3, we find that the multivariate normal distribution can also be characterized through its variance, even though unknown, provided the variance-covariance matrix is free of the parameter involved.
openaire +2 more sources
Molecular Oncology, EarlyView.We analyze cisplatin–DNA adducts (CDAs) and double‐strand breaks (DSBs) in a cell‐cycle‐dependent manner. We find that CDAs form similarly across all cell cycle phases. DSBs arise only in S‐phase. CDAs might not directly impair DSB repair, but S‐phase DSB lesions evolve in the presence of CDAs and disrupt repair in G2, also causing radiosensitization ...
Ye Qiu +10 more
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Note on the moment generating function of the multivariate normal distribution
Miskolc Mathematical NotesWe present a streamlined proof of a formula for the derivatives of the moment generating function of the multivariate normal distribution. We formulate it in terms of the summation of the contractions by pairings, which encodes a combinatorial ...
Kenichi Hirose
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