• 제목/요약/키워드: asymptotic distribution

검색결과 427건 처리시간 0.021초

Asymptotic Distribution of the LM Test Statistic for the Nested Error Component Regression Model

  • Jung, Byoung-Cheol;Myoungshic Jhun;Song, Seuck-Heun
    • Journal of the Korean Statistical Society
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    • 제28권4호
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    • pp.489-501
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    • 1999
  • In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.

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Notes on the Comparative Study of the Reliability Estimation for Standby System with Exponential Lifetime Distribution

  • Kim, Hee-Jae
    • Journal of the Korean Data and Information Science Society
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    • 제14권4호
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    • pp.1055-1065
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    • 2003
  • We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with exponential lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

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Notes on the Comparative Study of the Reliability Estimation for Standby System with Rayleigh Lifetime Distribution

  • Kim, Hee-Jae
    • Journal of the Korean Data and Information Science Society
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    • 제15권1호
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    • pp.239-250
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    • 2004
  • We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with Rayleigh lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

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SEQUENTIAL CONFIDENCE INTERVALS WITH ${\beta}-PROTECTION$ IN A NORMAL DISTRIBUTION HAVING EQUAL MEAN AND VARIANCE

  • Kim, Sung-Kyun;Kim, Sung-Lai;Lee, Young-Whan
    • Journal of applied mathematics & informatics
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    • 제23권1_2호
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    • pp.479-488
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    • 2007
  • A sequential procedure is proposed in order to construct one-sided confidence intervals for a normal mean with guaranteed coverage probability and ${\beta}-protection$ when the normal mean and variance are identical. First-order asymptotic properties on the sequential sample size are found. The derived results hold with uniformity in the total parameter space or its subsets.

Nonparametric detection algorithm of discontinuity points in the variance function

  • Huh, Jib
    • Journal of the Korean Data and Information Science Society
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    • 제18권3호
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    • pp.669-678
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    • 2007
  • An algorithm to detect the number of discontinuity points of the variance function in regression model is proposed. The proposed algorithm is based on the left and right one-sided kernel estimators of the second moment function and test statistics of the existence of a discontinuity point coming from the asymptotic distribution of the estimated jump size. The finite sample performance is illustrated by simulated example.

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THE ZERO-DISTRIBUTION AND THE ASYMPTOTIC BEHAVIOR OF A FOURIER INTEGRAL

  • Ki, Ha-Seo;Kim, Young-One
    • 대한수학회지
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    • 제44권2호
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    • pp.455-466
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    • 2007
  • The zero-distribution of the Fourier integral $${\int}^{\infty}_{-{\infty}}\;Q(u)e^{p(u)+^{izu}du$$, where P is a polynomial with leading term $-u^{2m}(m\;{\geq}\;1)$ and Q an arbitrary polynomial, is described. To this end, an asymptotic formula for the integral is established by applying the saddle point method.

Test of the Hypothesis based on Nonlinear Regression Quantiles Estimators

  • Choi, Seung-Hoe
    • Journal of the Korean Data and Information Science Society
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    • 제14권2호
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    • pp.153-165
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    • 2003
  • This paper considers the likelihood ratio test statistic based on nonlinear regression quantiles estimators in order to test of hypothesis about the regression parameter $\theta_o$ and derives asymptotic distribution of proposed test statistic under the null hypothesis and a sequence of local alternative hypothesis. The paper also investigates asymptotic relative efficiency of the proposed test to the test based on the least squares estimators or the least absolute deviation estimators and gives some examples to illustrate the application of the main result.

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Asymptotic Approximation of Kernel-Type Estimators with Its Application

  • 장유선;김성래;김성균
    • 한국전산응용수학회:학술대회논문집
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    • 한국전산응용수학회 2003년도 KSCAM 학술발표회 프로그램 및 초록집
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    • pp.12.1-12
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    • 2003
  • Sufficient conditions are given under which a generalized class of kernel-type estimators allows asymptotic approximation On the modulus of continuity This generalized class includes sample distribution function, kernel-type estimator of density function, and an estimator that may apply to the censored case. In addition, an application is given to asymptotic normality of recursive density estimators of density function at an unknown point.

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Asymptotic Gaussian Structures in a Critical Generalized Curie-Wiss Mean Field Model : Large Deviation Approach

  • Kim, Chi-Yong;Jeon, Jong-Woo
    • Journal of the Korean Statistical Society
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    • 제25권4호
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    • pp.515-527
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    • 1996
  • It has been known for mean field models that the limiting distribution reflecting the asymptotic behavior of the system is non-Gaussian at the critical state. Recently, however, Papangelow showed for the critical Curie-Weiss mean field model that there exist Gaussian structures in the asymptotic behavior of the total magnetization. We construct Gaussian structures existing in the internal fluctuation of the system for the critical case of a generalized Curie-Weiss mean field model.

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ASYMPTOTIC NORMALITY OF ESTIMATOR IN NON-PARAMETRIC MODEL UNDER CENSORED SAMPLES

  • Niu, Si-Li;Li, Qlan-Ru
    • 대한수학회지
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    • 제44권3호
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    • pp.525-539
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    • 2007
  • Consider the regression model $Y_i=g(x_i)+e_i\;for\;i=1,\;2,\;{\ldots},\;n$, where: (1) $x_i$ are fixed design points, (2) $e_i$ are independent random errors with mean zero, (3) g($\cdot$) is unknown regression function defined on [0, 1]. Under $Y_i$ are censored randomly, we discuss the asymptotic normality of the weighted kernel estimators of g when the censored distribution function is known or unknown.