• 제목/요약/키워드: asymptotic function

검색결과 395건 처리시간 0.026초

GLOBAL ASYMPTOTIC STABILITY FOR A DIFFUSION LOTKA-VOLTERRA COMPETITION SYSTEM WITH TIME DELAYS

  • Zhang, Jia-Fang;Zhang, Ping-An
    • Bulletin of the Korean Mathematical Society
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    • 제49권6호
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    • pp.1255-1262
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    • 2012
  • A type of delayed Lotka-Volterra competition reaction-diffusion system is considered. By constructing a new Lyapunov function, we prove that the unique positive steady-state solution is globally asymptotically stable when interspecies competition is weaker than intraspecies competition. Moreover, we show that the stability property does not depend on the diffusion coefficients and time delays.

AN ASYMPTOTIC FORMULA FOR exp(x/1-x)

  • Song, Jun-Ho;Lee, Chang-Woo
    • Communications of the Korean Mathematical Society
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    • 제17권2호
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    • pp.363-370
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    • 2002
  • We show that G(x) = $e^{(x}$(1-x))/ -1 is the exponential generating function for the labeled digraphs whose weak components are transitive tournaments and derive both a recursive formula and an explicit formula for the number of them on n vertices. Moreover, we investigate the asymptotic behavior for the coefficients of G(x) using Hayman's method.d.

The Estimation of Mean Residual Life Function under Left Truncation and Right Censoring Model

  • Moon, Gyoung-Ae;Shin, Im-Hee;Chae, Hyeon-Suk
    • Journal of the Korean Data and Information Science Society
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    • 제6권2호
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    • pp.65-76
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    • 1995
  • The importance of left truncated and right censoring cases has considered for better information in medical follow-up and engineering life testing studies. We propose some estimation procedure for the mean residual life function with consistency and asymptotic normality on the left truncated and right censoring model. And then, the comparision with Kaplan-Meier estimator ignoring the left truncated effect and the small sample properities are investigated by asymptotic biases and M.S.E.'s thresh Monte Carlo study.

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Sequential Confidence Interval with $\beta$-protection for a Linear Function of Two Normal Means

  • Kim, Sung-Lai
    • Journal of the Korean Statistical Society
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    • 제26권3호
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    • pp.309-317
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    • 1997
  • A sequential procedure for estimating a linear function of two normal means which satisfies the two requirements, i.e. one is a condition of coverage probability, the other is a condition of $\beta$-protection, is proposed when the variances are unknown and not necessarily equal. We give asymptotic behaviors of the proposed stopping time.

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INVERSION OF L-FUNCTIONS, GENERAL KLOOSTERMAN SUMS WEIGHTED BY INCOMPLETE CHARACTER SUMS

  • Zhang, Xiaobeng;Liu, Huaning
    • Journal of the Korean Mathematical Society
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    • 제47권5호
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    • pp.947-965
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    • 2010
  • The main purpose of this paper is using estimates for character sums and analytic methods to study the mean value involving the incomplete character sums, 2-th power mean of the inversion of Dirichlet L-function and general Kloosterman sums, and give four interesting asymptotic formulae for it.

New Dispersion Function in the Rank Regression

  • Choi, Young-Hun
    • Communications for Statistical Applications and Methods
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    • 제9권1호
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    • pp.101-113
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    • 2002
  • In this paper we introduce a new score generating (unction for the rank regression in the linear regression model. The score function compares the $\gamma$'th and s\`th power of the tail probabilities of the underlying probability distribution. We show that the rank estimate asymptotically converges to a multivariate normal. further we derive the asymptotic Pitman relative efficiencies and the most efficient values of $\gamma$ and s under the symmetric distribution such as uniform, normal, cauchy and double exponential distributions and the asymmetric distribution such as exponential and lognormal distributions respectively.

A Note on Bootstrapping M-estimators in TAR Models

  • Kim, Sahmyeong
    • Communications for Statistical Applications and Methods
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    • 제7권3호
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    • pp.837-843
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    • 2000
  • Kreiss and Franke(192) and Allen and Datta(1999) proposed bootstrapping the M-estimators in ARMA models. In this paper, we introduce the robust estimating function and investigate the bootstrap approximations of the M-estimators which are solutions of the estimating equations in TAR models. A number of simulation results are presented to estimate the sampling distribution of the M-estimators, and asymptotic validity of the bootstrap for the M-estimators is established.

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ROBUST ESTIMATION USING QUASI-SCORE ESTIMATING FUNCTIONS FOR NONLINEAR TIME SERIES MODELS

  • Cha, Kyung-Yup;Kim, Sah-Myeong;Lee, Sung-Duck
    • Journal of the Korean Statistical Society
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    • 제32권4호
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    • pp.385-399
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    • 2003
  • We first introduce the quasi-score estimating function and applied the quasi-score estimating function to nonlinear time series models. We proposed the M quasi-score estimating functions bounded functions for the quasi-score estimating functions. Also, we investigated the asymptotic properties of quasi-likelihood estimators and M quasi-likelihood estimators. Simulation results show that the M quasi-likelihood estimators work better than the least squares estimators under the heavy-tailed distributions

Simple Estimate of the Relative Risk under the Proportional Hazards Model

  • Lee, Sung-Won;Kim, Ju-Sung;Park, Jung-Sub
    • Journal of the Korean Data and Information Science Society
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    • 제15권2호
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    • pp.347-353
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    • 2004
  • We propose a simple nonparametric estimator of relative risk in the two sample case of the proportional hazards model for complete data. The asymptotic distribution of this estimator is derived using a functional equation. We obtain the asymptotic normality of the proposed estimator and compare with Begun's estimator by confidence interval through simulations.

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Sequential Estimation with $\beta$-Protection of the Difference of Two Normal Means When an Imprecision Function Is Variable

  • Kim, Sung-Lai;Kim, Sung-Kyun
    • Journal of the Korean Statistical Society
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    • 제31권3호
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    • pp.379-389
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    • 2002
  • For two normal distribution with unknown means and unknown variances, a sequential procedure for estimating the difference of two normal means which satisfies both the coverage probability condition and the $\beta$-protection is proposed under some smoothness of variable imprecision function, and the asymptotic normality of the proposed stopping time after some centering and scaling is given.