• Title/Summary/Keyword: Statistical moments

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Nonlinear damage detection using higher statistical moments of structural responses

  • Yu, Ling;Zhu, Jun-Hua
    • Structural Engineering and Mechanics
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    • v.54 no.2
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    • pp.221-237
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    • 2015
  • An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

Moments of Probability Distributions Derived Using Differential Operators

  • Kwan-Joong Kang
    • Communications for Statistical Applications and Methods
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    • v.3 no.1
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    • pp.189-193
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    • 1996
  • In 1992. Boullion obtained the method of the calculus of the moments of discrete probability distributions using differential operator, and he published the method of calculus of the moments. The purpose of this paper is to introduce an idea that this method can be applied to calculate the moments of continuous probability distributions.

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On Bounds for Moments of Unimodal Distributions

  • Sharma, R.;Bhandaria, R.
    • Communications for Statistical Applications and Methods
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    • v.21 no.3
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    • pp.201-212
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    • 2014
  • We provide a simple basic method to find bounds for higher order moments of unimodal distributions in terms of lower order moments when the random variable takes value in a given finite real interval. The bounds for moments in terms of the geometric mean of the distribution are also derived. Both continuous and discrete cases are considered. The bounds for the ratio and difference of moments are obtained. The special cases provide refinements of several well-known inequalities, such as Kantorovich inequality and Krasnosel'skii and Krein inequality.

Moments of Order Statistics from Doubly Truncated Linear-Exponential Distribution

  • Saran, Jagdish;Pushkarna, Narinder
    • Journal of the Korean Statistical Society
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    • v.28 no.3
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    • pp.279-296
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    • 1999
  • In this paper we establish some recurrence relations for both single and product moments of order statistics from a doubly truncated linear- exponential distribution with increasing hazard rate. These recurrence relations would enable one to compute all the higher order moments of order statistics for all sample sizes from those of the lower order in a simple recursive way. In addition, percentage points of order statistics are also discussed. These generalize the corresponding results for the linear- exponential distribution with increasing hazard rate derived by Balakrishnan and Malik(1986)

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Cubic normal distribution and its significance in structural reliability

  • Zhao, Yan-Gang;Lu, Zhao-Hui
    • Structural Engineering and Mechanics
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    • v.28 no.3
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    • pp.263-280
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    • 2008
  • Information on the distribution of the basic random variable is essential for the accurate analysis of structural reliability. The usual method for determining the distributions is to fit a candidate distribution to the histogram of available statistical data of the variable and perform approximate goodness-of-fit tests. Generally, such candidate distribution would have parameters that may be evaluated from the statistical moments of the statistical data. In the present paper, a cubic normal distribution, whose parameters are determined using the first four moments of available sample data, is investigated. A parameter table based on the first four moments, which simplifies parameter estimation, is given. The simplicity, generality, flexibility and advantages of this distribution in statistical data analysis and its significance in structural reliability evaluation are discussed. Numerical examples are presented to demonstrate these advantages.

CHARACTERIZATION OF SOME CONTINUOUS DISTRIBUTIONS BY PROPERTIES OF PARTIAL MOMENTS

  • Abraham, B.;Nair, N. Unnikrishnan;Sankaran, P.G.
    • Journal of the Korean Statistical Society
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    • v.36 no.3
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    • pp.357-365
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    • 2007
  • In this paper we present characterizations of the Pareto, Lomax, exponential and beta models by some properties of their $r^{th}$ partial moment defined as ${\alpha}_r(t)=E[(X-t)^+]^r$, where $(X-t)^+ = max(X-t,0)$. Given the partial moments at a few truncation points, these results enable us to calculate the moments at many other points.

RS-based method for estimating statistical moments and its application to reliability analysis (반응표면을 활용한 통계적 모멘트 추정 방법과 신뢰도해석에 적용)

  • Huh, Jae-Sung;Kwak, Byung-Man
    • Proceedings of the KSME Conference
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    • 2004.11a
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    • pp.852-857
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    • 2004
  • A new and efficient method for estimating the statistical moments of a system performance function has been developed. The method consists of two steps: (1) An approximate response surface is generated by a quadratic regression model, and (2) the statistical moments of the regression model are then calculated by experimental design techniques proposed by Seo and $Kwak^{(4)}$. In this approach, the size of experimental region affects the accuracy of the statistical moments. Therefore, the region size should be selected suitably. The D-optimal design and the central composite design are adopted over the selected experimental region for the regression model. Finally, the Pearson system is adopted to decide the distribution type of the system performance function and to analyze structural reliability.

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LH-Moments of Some Distributions Useful in Hydrology

  • Murshed, Md. Sharwar;Park, Byung-Jun;Jeong, Bo-Yoon;Park, Jeong-Soo
    • Communications for Statistical Applications and Methods
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    • v.16 no.4
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    • pp.647-658
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    • 2009
  • It is already known from the previous study that flood seems to have heavier tail. Therefore, to make prediction of future extreme label, some agreement of tail behavior of extreme data is highly required. The LH-moments estimation method, the generalized form of L-moments is an useful method of characterizing the upper part of the distribution. LH-moments are based on linear combination of higher order statistics. In this study, we have formulated LH-moments of five distributions useful in hydrology such as, two types of three parameter kappa distributions, beta-${\kappa}$ distribution, beta-p distribution and a generalized Gumbel distribution. Using LH-moments reduces the undue influences that small sample may have on the estimation of large return period events.

Moments of a Class of Internally Truncated Normal Distributions

  • Kim, Hea-Jung
    • Communications for Statistical Applications and Methods
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    • v.14 no.3
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    • pp.679-686
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    • 2007
  • Moment expressions are derived for the internally truncated normal distributions commonly applied to screening and constrained problems. They are obtained from using a recursive relation between the moments of the normal distribution whose distribution is truncated in its internal part. Closed form formulae for the moments can be presented up to $N^{th}$ order under the internally truncated case. Necessary theories and two applications are provided.

Recurrence Relations Between Product Moments of Order Statistics for Truncated Distributions and Their Applications

  • Saran, Jagdish;Pushkarna, Narinder
    • Journal of the Korean Statistical Society
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    • v.31 no.3
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    • pp.391-403
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    • 2002
  • In this paper, some general results for obtaining recurrence relations between product moments of order statistics for doubly truncated distributions are established. These results are then applied to some specific doubly truncated distributions, viz. doubly truncated Weibull, Exponential, Pareto, power function, Cauchy, Lomax and Rayleigh.