• Title/Summary/Keyword: Asymptotic

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A reliability-based criterion of structural performance for structures with linear damping

  • Kovaleva, Agnessa
    • Smart Structures and Systems
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    • v.2 no.4
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    • pp.313-320
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    • 2006
  • The reliability analysis of structures subjected to stochastic loading involves evaluation of time and probability of the system's residence in a reference domain. In this paper, we derive an asymptotic estimate of exit time for multi-degrees-of-freedom structural systems. The system's dynamics is governed by the Lagrangian equations with linear dissipation and fast additive noise. The logarithmic asymptotic of exit time is found explicitly as a sum of two terms dependent on kinetic and potential energy of the system, respectively. As an example, we estimate exit time and an associated structural performance for a rocking structure.

Asymptotic Results for a Class of Fourth Order Quasilinear Difference Equations

  • Thandapani, Ethiraju;Pandian, Subbiah;Dhanasekaran, Rajamannar
    • Kyungpook Mathematical Journal
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    • v.46 no.4
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    • pp.477-488
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    • 2006
  • In this paper, the authors first classify all nonoscillatory solutions of equation (1) $${\Delta}^2|{\Delta}^2{_{y_n}}|^{{\alpha}-1}{\Delta}^2{_{y_n}}+q_n|y_{{\sigma}(n)}|^{{\beta}-1}y_{{\sigma}(n)}=o,\;n{\in}\mathbb{N}$$ into six disjoint classes according to their asymptotic behavior, and then they obtain necessary and sufficient conditions for the existence of solutions in these classes. Examples are inserted to illustrate the results.

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On Delay-Dependent Stability of Neutral Systems with Mixed Time-Varying Delay Arguments

  • Park, H.J.
    • KIEE International Transaction on Systems and Control
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    • v.12D no.1
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    • pp.39-42
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    • 2002
  • This paper focuses on the asymptotic stability of a class of neutral linear systems with mixed time-varying delay arguments. Using the Lyapunov method, a delay-dependent stability criterion to guarantee the asymptotic stability for the systems is derived in terms of linear matrix inequalities (LMIs). The LMIs can be easily solved by various convex optimization algorithms. Two numerical examples are given to illustrate the proposed methods.

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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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    • v.18 no.3
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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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One-step Least Squares Fitting of Variogram

  • Choi, Hye-Mi
    • Communications for Statistical Applications and Methods
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    • v.12 no.2
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    • pp.539-544
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    • 2005
  • In this paper, we propose the one-step least squares method based on the squared differences to estimate the parameters of the variogram used for spatial data modelling, and discuss its asymptotic efficiency. The proposed method does not require to specify lags of interest and partition lags, so that we can delete the subjectiveness and ambiguity originated from the lag selection in estimating spatial dependence.

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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    • v.31 no.3
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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.

ON THE EMPIRICAL MEAN LIFE PROCESSES FOR RIGHT CENSORED DATA

  • Park, Hyo-Il
    • Journal of the Korean Statistical Society
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    • v.32 no.1
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    • pp.25-32
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    • 2003
  • In this paper, we define the mean life process for the right censored data and show the asymptotic equivalence between two kinds of the mean life processes. We use the Kaplan-Meier and Susarla-Van Ryzin estimates as the estimates of survival function for the construction of the mean life processes. Also we show the asymptotic equivalence between two mean residual life processes as an application and finally discuss some difficulties caused by the censoring mechanism.

Asymptotics of the Variance Ratio Test for MA Unit Root Processes

  • Lee, Jin
    • Communications for Statistical Applications and Methods
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    • v.17 no.2
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    • pp.223-229
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    • 2010
  • We consider the asymptotic results of the variance ratio statistic when the underlying processes have moving average(MA) unit roots. This degenerate situation of zero spectral density near the origin cause the limit of the variance ratio to become zero. Its asymptotic behaviors are different from non-degenerating case, where the convergence rate of the variance ratio statistic is formally derived.

Asymptotic Properties of the Disturbance Variance Estimator in a Spatial Panel Data Regression Model with a Measurement Error Component

  • Lee, Jae-Jun
    • Communications for Statistical Applications and Methods
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    • v.17 no.3
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    • pp.349-356
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    • 2010
  • The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

The Confidence Regions for the Logistic Response Surface Model

  • Cho, Tae-Kyoung
    • Journal of Korean Society for Quality Management
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    • v.25 no.2
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    • pp.102-111
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    • 1997
  • In this paper I discuss a method of constructing the confidence region for the logistic response surface model. The construction involves a, pp.ication of a general fitting procedure because the log odds is linear in its parameters. Estimation of parameters of the logistic response surface model can be accomplished by maximum likelihood, although this requires iterative computational method. Using the asymptotic results, asymptotic covariance of the estimators can be obtained. This can be used in the construction of confidence regions for the parameters and for the logistic response surface model.

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