• Title/Summary/Keyword: dependent random variables

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Estimation of P(X > Y) when X and Y are dependent random variables using different bivariate sampling schemes

  • Samawi, Hani M.;Helu, Amal;Rochani, Haresh D.;Yin, Jingjing;Linder, Daniel
    • Communications for Statistical Applications and Methods
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    • v.23 no.5
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    • pp.385-397
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    • 2016
  • The stress-strength models have been intensively investigated in the literature in regards of estimating the reliability ${\theta}$ = P(X > Y) using parametric and nonparametric approaches under different sampling schemes when X and Y are independent random variables. In this paper, we consider the problem of estimating ${\theta}$ when (X, Y) are dependent random variables with a bivariate underlying distribution. The empirical and kernel estimates of ${\theta}$ = P(X > Y), based on bivariate ranked set sampling (BVRSS) are considered, when (X, Y) are paired dependent continuous random variables. The estimators obtained are compared to their counterpart, bivariate simple random sampling (BVSRS), via the bias and mean square error (MSE). We demonstrate that the suggested estimators based on BVRSS are more efficient than those based on BVSRS. A simulation study is conducted to gain insight into the performance of the proposed estimators. A real data example is provided to illustrate the process.

THE COMPLETE MOMENT CONVERGENCE FOR ARRAY OF ROWWISE ENOD RANDOM VARIABLES

  • Ryu, Dae-Hee
    • Honam Mathematical Journal
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    • v.33 no.3
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    • pp.393-405
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    • 2011
  • In this paper we obtain the complete moment convergence for an array of rowwise extended negative orthant dependent random variables. By using the result we can prove the complete moment convergence for some positively orthant dependent sequence satisfying the extended negative orthant dependence.

On the weak law of large numbers for weighted sums of airwise negative quadrant dependent random variables

  • Kim, Tae-Sung;Beak, Jong-Il
    • Journal of the Korean Statistical Society
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    • v.29 no.3
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    • pp.261-268
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    • 2000
  • Let {Xn,n$\geq$1} be a sequence of pairwise negative quadrant dependent(NQD) random variables and let {an,n$\geq$1} and {bn,n$\geq$1} be sequencesof constants such that an$\neq$0 and 0$\infty$. In this note, for pairwise NQD random varibles, a general weak law of alrge numbers of the form(∑│aj│Xj-$\upsilon$n)/bnlongrightarrow0) is established, where {νn,n$\geq$1} is a suitable sequence. AMS 2000 subject classifications ; 60F05

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STRONG LAW OF LARGE NUMBERS FOR ASYMPTOTICALLY NEGATIVE DEPENDENT RANDOM VARIABLES WITH APPLICATIONS

  • Kim, Hyun-Chull
    • Journal of applied mathematics & informatics
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    • v.29 no.1_2
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    • pp.201-210
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    • 2011
  • In this paper, we obtain the H$\`{a}$jeck-R$\`{e}$nyi type inequality and the strong law of large numbers for asymptotically linear negative quadrant dependent random variables by using this inequality. We also give the strong law of large numbers for the linear process under asymptotically linear negative quadrant dependence assumption.

Time-dependent analysis of cable trusses -Part II. Simulation-based reliability assessment

  • Kmet, S.;Tomko, M.;J., Brda
    • Structural Engineering and Mechanics
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    • v.38 no.2
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    • pp.171-193
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    • 2011
  • One of the possible alternatives of simulation-based time-dependent reliability assessment of pre-stressed biconcave and biconvex cable trusses, the Monte Carlo method, is applied in this paper. The influence of an excessive deflection of cable truss (caused by creep of cables and rheologic changes) on its time-dependent serviceability is investigated. Attention is given to the definition of the basic random variables and their statistical functions (basic, mutually dependent random variables such as the pre-stressing forces of the bottom and top cable, structural geometry, the Young's modulus of elasticity of the cables, and the independent variables, such as permanent load, wind, snow and thermal actions). Then, the determination of the response of the cable truss to the loading effects, and the definition of the limiting values considering serviceability of the structure are performed. The potential of the method, using direct Monte Carlo technique for simulation-based time-dependent reliability assessment as a powerful tool, is emphasized. Results obtained by the First order reliability method (FORM) are compared with those obtained by the Monte Carlo simulation technique.

ON COMPLETE CONVERGENCE AND COMPLETE MOMENT CONVERGENCE FOR A CLASS OF RANDOM VARIABLES

  • Wang, Xuejun;Wu, Yi
    • Journal of the Korean Mathematical Society
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    • v.54 no.3
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    • pp.877-896
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    • 2017
  • In this paper, the complete convergence and complete moment convergence for a class of random variables satisfying the Rosenthal type inequality are investigated. The sufficient and necessary conditions for the complete convergence and complete moment convergence are provided. As applications, the Baum-Katz type result and the Marcinkiewicz-Zygmund type strong law of large numbers for a class of random variables satisfying the Rosenthal type inequality are established. The results obtained in the paper extend the corresponding ones for some dependent random variables.

SOME CONVERGENCE THEOREM FOR AND RANDOM VARIABLES IN A HILBERT SPACE WITH APPLICATION

  • Han, Kwang-Hee
    • Honam Mathematical Journal
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    • v.36 no.3
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    • pp.679-688
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    • 2014
  • The notion of asymptotically negative dependence for collection of random variables is generalized to a Hilbert space and the almost sure convergence for these H-valued random variables is obtained. The result is also applied to a linear process generated by H-valued asymptotically negatively dependent random variables.

COMPLETE f-MOMENT CONVERGENCE FOR EXTENDED NEGATIVELY DEPENDENT RANDOM VARIABLES UNDER SUB-LINEAR EXPECTATIONS

  • Lu, Chao;Wang, Rui;Wang, Xuejun;Wu, Yi
    • Journal of the Korean Mathematical Society
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    • v.57 no.6
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    • pp.1485-1508
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    • 2020
  • In this paper, we investigate the complete f-moment convergence for extended negatively dependent (END, for short) random variables under sub-linear expectations. We extend some results on complete f-moment convergence from the classical probability space to the sub-linear expectation space. As applications, we present some corollaries on complete moment convergence for END random variables under sub-linear expectations.