• Title/Summary/Keyword: random components

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Variance components for two-way nested design data

  • Choi, Jaesung
    • Communications for Statistical Applications and Methods
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    • v.25 no.3
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    • pp.275-282
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    • 2018
  • This paper discusses the use of projections for the sums of squares in the analyses of variance for two-way nested design data. The model for this data is assumed to only have random effects. Two different sizes of experimental units are required for a given experimental situation, since nesting is assumed to occur both in the treatment structure and in the design structure. So, variance components are coming from the sources of random effects of treatment factors and error terms in different sizes of experimental units. The model for this type of experimental situation is a random effects model with more than one error terms and therefore estimation of variance components are concerned. A projection method is used for the calculation of sums of squares due to random components. Squared distances of projections instead of using the usual reductions in sums of squares that show how to use projections to estimate the variance components associated with the random components in the assumed model. Expectations of quadratic forms are obtained by the Hartley's synthesis as a means of calculation.

Large Sample Tests for Independence and Symmetry in the Bivariate Weibull Model under Random Censorship

  • Cho, Jang-Sik;Ko, Jeong-Hwan;Kang, Sang-Kil
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.405-412
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    • 2003
  • In this paper, we consider two components system which the lifetimes have a bivariate weibull distribution with random censored data. Here the censoring time is independent of the lifetimes of the components. We construct large sample tests for independence and symmetry between two-components based on maximum likelihood estimators and the natural estimators. Also we present a numerical study.

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Variance components in one-factor random model by projections (사영을 이용한 일원 분산성분)

  • Choi, Jae-Sung
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.3
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    • pp.381-387
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    • 2011
  • This paper suggests a method for estimating components of variance in one-factor random model. Estimates of variance components are given by the method of moments. Sums of squares due to variance sources are obtained by projections. This paper also shows how to use eigenvalues for getting the coefficients of variance components in the expression of the expectations of the mean squares. The suggested method shows easier and faster than the method of Harley's synthesis.

Random Vibration Analysis of Thick Composite Laminated Plate Using Mixed Finite Element Model (1) (혼합유한요소모델을 이용한 두꺼운 복합적층판의 불규칙 진동해석(1)-이론적 고찰)

  • Seok, Keun-Yung;Kang, Joo-Won
    • 한국공간정보시스템학회:학술대회논문집
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    • 2004.05a
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    • pp.190-196
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    • 2004
  • Thick composite laminated plates is considered in 3D finite-element. To consider continuity of transverse stresses and displacement field, mixed finite-element has been developed by using layerwise theory and the minimum potential energy principle. Mixed finite-element has been enforced through the thick direction, Z, of a laminated plate by considering six degree-of-freedoms per node. Six degree-of-freedoms are three displacement components in the coordinate axes directions and three transverse stress components ${\sigma}_z,\;{\tau}_{xz},\;{\tau}_{yz}$. The model maintain the fundamental elasticity relations that are stress-strain relation and displacement-strain relation, because the transverse stress components invoked as nodal degrees of freedom by using the fundamental elasticity relationship between th components of stress and displacement. Random vibration analysis of the model is performed by computing consistent mass matrix and computing covariance in frequency domain technique.

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System Reliability Estimation in Bivariate Pareto Model Affected by Common Stress : Bivariate Random Censored Data Case

  • Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.791-799
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    • 2005
  • We consider two components parallel system in which the lifetimes have the bivariate Pareto model with bivariate random censored data. We assume that bivariate Pareto model is affected by common stress which is independent of the lifetimes of the components. We obtain estimators for the system reliability based on likelihood function and relative frequency. Also we construct approximated confidence intervals for the reliability based on maximum likelihood estimator and relative frequency estimator, respectively. Finally we present a numerical study.

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Large Sample Test for Independence in the Bivariate Pareto Model with Censored Data

  • Cho, Jang-Sik;Lee, Jea-Man;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.377-383
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    • 2003
  • In this paper, we consider two components system in which the lifetimes follow the bivariate Pareto model with random censored data. We assume that the censoring time is independent of the lifetimes of the two components. We develop large sample tests for testing independence between two components. Also we present simulated study which is the test based on asymptotic normal distribution in testing independence.

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Test for Independence in Bivariate Pareto Model with Bivariate Random Censored Data

  • Cho, Jang-Sik;Kwon, Yong-Man;Choi, Seung-Bae
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.1
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    • pp.31-39
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    • 2004
  • In this paper, we consider two components system which the lifetimes follow bivariate pareto model with bivariate random censored data. We assume that the censoring times are independent of the lifetimes of the two components. We develop large sample test for testing independence between two components. Also we present a simulation study which is the test based on asymptotic normal distribution in testing independence.

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Comparison of Detection Probability for Conventional and Time-Reversal (TR) Radar Systems

  • Yoo, Hyung-Ha;Koh, Il-Suek
    • Journal of electromagnetic engineering and science
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    • v.12 no.1
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    • pp.70-76
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    • 2012
  • We compare the detection probabilities of the time-reversal(TR) detection system and the conventional radar system. The target is assumed to be hidden inside a random medium such as a forest. We propose a TR detection system based on the SAR(Synthetic Aperture Radar) algorithm. Unlike the conventional SAR images, the proposed TR-SAR system has an interesting property. Specifically, the target-related signal components due to the time-reversal refocusing characteristics, as well as some of clutter-related signal components are concentrated at the time-reversal reference point. The remaining clutter-related signal components are scattered around that reference point. In this paper, we model the random media as a collection of point scatterers to avoid unnecessary complexities. We calculate the detection probability of the TR radar system based on the proposed simple random media model.

Nonnegative estimates of variance components in a two-way random model

  • Choi, Jaesung
    • Communications for Statistical Applications and Methods
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    • v.26 no.4
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    • pp.337-346
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    • 2019
  • This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.