• 제목/요약/키워드: conditional expectations

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CONDITIONAL EXPECTATIONS GENERATING THE COMMUTANTS OF SUBALGEBRAS OF $L^{\infty}$

  • Lambert, Alan
    • 대한수학회지
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    • 제36권4호
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    • pp.699-705
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    • 1999
  • Given a probability space and a subsigma algebra A, each measure equivalent to the probability measure generates a different conditional expectation operator. We characterize those which act boundedly on the original $L^2$ space, and show there are sufficiently many such conditional expectations to generate the commutant of $L^{\infty}$ (A).

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ON CHARACTERIZATIONS OF CONTINUOUS DISTRIBUTIONS BY CONDITIONAL EXPECTATIONS OF UPPER RECORD VALUES

  • Jin, Hyun-Woo;Lee, Min-Young
    • 충청수학회지
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    • 제25권3호
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    • pp.501-505
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    • 2012
  • In this paper, general classes of continuous distributions are characterized by considering the conditional expectations of functions of upper record statistics. The specific distribution considered as a particular case of the general class of distribution are Exponential, Exponential Power(EP), Inverse Weibull, Beta Gumbel, Modified Weibull(MW), Weibull, Pareto, Power, Singh-Maddala, Gumbel, Rayleigh, Gompertz, Extream value 1, Beta of the first kind, Beta of the second kind and Lomax.

ON CHARACTERIZATIONS OF PARETO AND WEIBULL DISTRIBUTIONS BY CONSIDERING CONDITIONAL EXPECTATIONS OF UPPER RECORD VALUES

  • Jin, Hyun-Woo;Lee, Min-Young
    • 충청수학회지
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    • 제27권2호
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    • pp.243-247
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    • 2014
  • Let {$X_n$, $n{\geq}1$} be a sequence of i.i.d. random variables with absolutely continuous cumulative distribution function(cdf) F(x) and the corresponding probability density function(pdf) f(x). In this paper, we give characterizations of Pareto and Weibull distribution by considering conditional expectations of record values.

A CONDITIONAL FOURIER-FEYNMAN TRANSFORM AND CONDITIONAL CONVOLUTION PRODUCT WITH CHANGE OF SCALES ON A FUNCTION SPACE I

  • Cho, Dong Hyun
    • 대한수학회보
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    • 제54권2호
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    • pp.687-704
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    • 2017
  • Using a simple formula for conditional expectations over an analogue of Wiener space, we calculate a generalized analytic conditional Fourier-Feynman transform and convolution product of generalized cylinder functions which play important roles in Feynman integration theories and quantum mechanics. We then investigate their relationships, that is, the conditional Fourier-Feynman transform of the convolution product can be expressed in terms of the product of the conditional FourierFeynman transforms of each function. Finally we establish change of scale formulas for the generalized analytic conditional Fourier-Feynman transform and the conditional convolution product. In this evaluation formulas and change of scale formulas we use multivariate normal distributions so that the orthonormalization process of projection vectors which are essential to establish the conditional expectations, can be removed in the existing conditional Fourier-Feynman transforms, conditional convolution products and change of scale formulas.

OPERATOR-VALUED FUNCTION SPACE INTEGRALS VIA CONDITIONAL INTEGRALS ON AN ANALOGUE WIENER SPACE II

  • Cho, Dong Hyun
    • 대한수학회보
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    • 제53권3호
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    • pp.903-924
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    • 2016
  • In the present paper, using a simple formula for the conditional expectations given a generalized conditioning function over an analogue of vector-valued Wiener space, we prove that the analytic operator-valued Feynman integrals of certain classes of functions over the space can be expressed by the conditional analytic Feynman integrals of the functions. We then provide the conditional analytic Feynman integrals of several functions which are the kernels of the analytic operator-valued Feynman integrals.

Random vibration analysis of structures by a time-domain explicit formulation method

  • Su, Cheng;Xu, Rui
    • Structural Engineering and Mechanics
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    • 제52권2호
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    • pp.239-260
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    • 2014
  • Non-stationary random vibration of linear structures with uncertain parameters is investigated in this paper. A time-domain explicit formulation method is first presented for dynamic response analysis of deterministic structures subjected to non-stationary random excitations. The method is then employed to predict the random responses of a structure with given values of structural parameters, which are used to fit the conditional expectations of responses with relation to the structural random parameters by the response surface technique. Based on the total expectation theorem, the known conditional expectations are averaged to yield the random responses of stochastic structures as the total expectations. A numerical example involving a frame structure is investigated to illustrate the effectiveness of the present approach by comparison with the power spectrum method and the Monte Carlo simulation method. The proposed method is also applied to non-stationary random seismic analysis of a practical arch bridge with structural uncertainties, indicating the feasibility of the present approach for analysis of complex structures.

CHARACTERIZATIONS OF THE LOMAX, EXPONENTIAL AND PARETO DISTRIBUTIONS BY CONDITIONAL EXPECTATIONS OF RECORD VALUES

  • Lee, Min-Young;Lim, Eun-Hyuk
    • 충청수학회지
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    • 제22권2호
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    • pp.149-153
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    • 2009
  • Let {$X_{n},\;n\;\geq\;1$} be a sequence of independent and identically distributed random variables with absolutely continuous cumulative distribution function (cdf) F(x) and probability density function (pdf) f(x). Suppose $X_{U(m)},\;m = 1,\;2,\;{\cdots}$ be the upper record values of {$X_{n},\;n\;\geq\;1$}. It is shown that the linearity of the conditional expectation of $X_{U(n+2)}$ given $X_{U(n)}$ characterizes the lomax, exponential and pareto distributions.

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CHARACTERIZATION OF CONTINUOUS DISTRIBUTIONS THROUGH RECORD STATISTICS

  • Khan, Abdul Hamid;Faizan, Mohd.;Haque, Ziaul
    • 대한수학회논문집
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    • 제25권3호
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    • pp.485-489
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    • 2010
  • A family of continuous probability distribution has been characterized through the difference of two conditional expectations, conditioned on a non-adjacent record statistic. Also, a result based on the unconditional expectation and a conditional expectation is used to characterize a family of distributions. Further, some of its deductions are also discussed.