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  1. 141

    Asymptotic Distribution of the Number for Error in the Time of Random Observation by Lu Chuanlai

    Published 1986-01-01
    “…In this paper, it is discussed that the distribution of sum for errors in the random time of observation is approached to a normal distribution, when the sequence {X1} of errors in an unit time is a m-dependent.…”
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  2. 142

    Kidney damage on fertility and pregnancy: A Mendelian randomization. by Jin Ren, Qiuyan Huang, Xiaowei Lie, Xingli Tong, Qi Yao, Ge Zhou

    Published 2023-01-01
    “…Hence, this study aimed to investigate the causal effect of kidney damage on fertility and pregnancy using Mendelian randomization (MR).<h4>Methods</h4>We first used two-sample MR to examine the effects of kidney damage on fertility and pregnancy. …”
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  3. 143
  4. 144

    A Markov Chain Approach to Randomly Grown Graphs by Michael Knudsen, Carsten Wiuf

    Published 2008-01-01
    “…A Markov chain approach to the study of randomly grown graphs is proposed and applied to some popular models that have found use in biology and elsewhere. …”
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  5. 145

    Optimizing Circuit Reusing and its Application in Randomized Benchmarking by Zhuo Chen, Guoding Liu, Xiongfeng Ma

    Published 2025-01-01
    “…Quantum learning tasks often leverage randomly sampled quantum circuits to characterize unknown systems. …”
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  8. 148

    On the Efficiency of the Newly Developed Composite Randomized Response Technique by Senani P. Dlamini, Wilford B. Molefe, Olusegun S. Ewemooje

    Published 2024-01-01
    “…To address this, the Composite Randomized Response Technique (CRRT) was introduced in estimating the proportion of respondents possessing sensitive attributes. …”
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  9. 149

    Determining the First Probability Density Function of Linear Random Initial Value Problems by the Random Variable Transformation (RVT) Technique: A Comprehensive Study by M.-C. Casabán, J.-C. Cortés, J.-V. Romero, M.-D. Roselló

    Published 2014-01-01
    “…The consideration of uncertainty into their formulation leads to random differential equations. Solving a random differential equation means computing not only its solution stochastic process but also its main statistical functions such as the expectation and standard deviation. …”
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  10. 150
  11. 151

    Fundamental J. Hajek lema for samples from finite population of vectors by Algimantas Bikelis, Jurgita Turkuvienė

    Published 2004-12-01
    Subjects: “…series of independent random vectors…”
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  12. 152
  13. 153

    An edgeworth expansion for a sum of M-Dependent random variables by Wan Soo Rhee

    Published 1985-01-01
    “…Given a sequence X1,X2,…,Xn of m-dependent random variables with moments of order 3+α (0<α≦1), we give an Edgeworth expansion of the distribution of Sσ−1(S=X1+X2+…+Xn, σ2=ES2) under the assumption that E[exp(it Sσ1)] is small away from the origin. …”
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  14. 154
  15. 155

    Geometric Models for Isotropic Random Porous Media: A Review by Helmut Hermann, Antje Elsner

    Published 2014-01-01
    “…Models for random porous media are considered. The models are isotropic both from the local and the macroscopic point of view; that is, the pores have spherical shape or their surface shows piecewise spherical curvature, and there is no macroscopic gradient of any geometrical feature. …”
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  16. 156
  17. 157

    High Efficiency Computation of the Variances of Structural Evolutionary Random Responses by J.H. Lin, D.K. Sun, W.X. Zhong, W.S. Zhang

    Published 2000-01-01
    “…For structures subjected to stationary or evolutionary white/colored random noise, their various response variances satisfy algebraic or differential Lyapunov equations. …”
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  18. 158

    On the Unambiguous Distance of Multicarrier Phase Ranging With Random Hopped Frequencies by Peng Liu, Wangdong Qi, Yue Zhang, Li Wei

    Published 2017-01-01
    “…Under the RSF configuration, the UD becomes a random variable. We try to depict its statistical property with a deterministic value and find that the upper bound of the random UD plays an important role. …”
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