• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.163-168
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• 2011

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.97-108
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• 2020

This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.841-849
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• 2009

Let {$X_n$, n ${\ge}$ 1} be a negatively associated sequence of identically distributed random variables with mean zeros and positive finite variances. Set $S_n$ = ${\Sigma}^n_{i=1}\;X_i$. Suppose that 0 < ${\sigma}^2=EX^2_1+2{\Sigma}^{\infty}_{i=2}\;Cov(X_1,\;X_i)$ < ${\infty}$. We prove that, if $EX^2_1(log^+{\mid}X_1{\mid})^{\delta}$ < ${\infty}$ for any 0< ${\delta}{\le}1$, then $\lim_{{\epsilon}\downarrow0}{\epsilon}^{2{\delta}}\sum_{{n=2}}^{\infty}\frac{(logn)^{\delta-1}}{n^2}ES^2_nI({\mid}S_n{\mid}\geq{\epsilon}{\sigma}\sqrt{nlogn}=\frac{E{\mid}N{\mid}^{2\delta+2}}{\delta}$, where N is the standard normal random variable. We also prove that if $S_n$ is replaced by $M_n=max_{1{\le}k{\le}n}{\mid}S_k{\mid}$ then the precise rate still holds. Some results in Fu and Zhang (2007) are improved to the complete moment case.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.377-383
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• 2020

The likelihood-based inference in a nonlinear generalized mixed model often requires computing moments of truncated multivariate normal random variables. Many methods have been proposed for the computation using a recurrence relation or the moment generating function; however, these methods rely on high dimensional numerical integrations. The numerical method is known to be inefficient for high dimensional integral in accuracy. Besides the accuracy, the methods demand too much computing time to use them in practical analyses. In this note, a moment calculation method is proposed under an assumption of a certain covariance structure that occurred mostly in generalized mixed models. The method needs only low dimensional numerical integrations.

• KCI Concrete Journal
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• v.12 no.2
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• pp.11-21
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• 2000

It is not easy to correctly predict deflections of reinforced concrete beams and one-way slabs due to the variability of parameters involved in the calculation of deflections. Monte Carlo simulation is used to assess the variability of deflections with known statistical data and probability distributions of variables. A deterministic deflection value is obtained using the layered beam model based on the finite element approach in which a finite element is divided into a number of layers over the depth. The model takes into account nonlinear effects such as cracking, creep and shrinkage. Statistical parameters were obtained from the literature. For the assessment of variability of deflections, 12 cases of one-way slabs and T-beams are designed on the basis of ultimate moment capacity. Several results of a probabilistic study are presented to indicate general trends indicated by results and demonstrate the effect of certain design parameters on the variability of deflections. From simulation results, the variability of deflections relies primarily on the ratio of applied moment to cracking moment and the corre-sponding reinforcement ratio.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.147-161
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• 2016

This paper introduces the four parameter new generalized inverse Weibull distribution and investigates the potential usefulness of this model with application to reliability data from engineering studies. The new extended model has upside-down hazard rate function and provides an alternative to existing lifetime distributions. Various structural properties of the new distribution are derived that include explicit expressions for the moments, moment generating function, quantile function and the moments of order statistics. The estimation of model parameters are performed by the method of maximum likelihood and evaluate the performance of maximum likelihood estimation using simulation.

• Korean Journal of Applied Biomechanics
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• v.15 no.2
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• pp.31-39
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• 2005

The Purpose of this study was to reveal the biomechanical difference of two soccer footwear(soft ground footwear and hard ground footwear). Secondly, the purpose of this study was to clarify how each type of soccer footwear effects soccer players, which will provide scientific data to coaches and players, to further prevent injuries and to improve each players capacity. The result of comparative analysis of two soccer footwear can be summarized as below. The comparison of the very first braking force at walking found distinctive factors in the statistical data(t=3.092, p<.05). Braking impulse of two difference footwear showed distinctive factors in the statistical data(t=2.542, p<.05). In comparing GRFz max(N), the result showed a statistically significant difference in the two soccer footwear at running(t=2.784, p<.05). In the maximum braking impulse(t=2.774, p<.05) and propulsive impulse for antero-posterior direction, there was a statistically significant difference between the two soccer footwear at running. In the maximum braking force(t=3.270, p<.05) and propulsive force(t=4.956, p<.05) for antero-posterior direction, there was a statistically significant difference between the two soccer footwear at running. Significant differences were not found in moment(rotational friction) with two difference soccer footwear(moment max; t=2.231, moment min; t=1.784).

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.353-365
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• 2017

In this article, we proposed a new three-parameter distribution called generalized half-logistic Poisson distribution with a failure rate function that can be increasing, decreasing or upside-down bathtub-shaped depending on its parameters. The new model extends the half-logistic Poisson distribution and has exponentiated half-logistic as its limiting distribution. A comprehensive mathematical and statistical treatment of the new distribution is provided. We provide an explicit expression for the $r^{th}$ moment, moment generating function, Shannon entropy and $R{\acute{e}}nyi$ entropy. The model parameter estimation was conducted via a maximum likelihood method; in addition, the existence and uniqueness of maximum likelihood estimations are analyzed under potential conditions. Finally, an application of the new distribution to a real dataset shows the flexibility and potentiality of the proposed distribution.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.76-81
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• 1999

The degree of relative similarity between 2D patterns is obtained using Open-Ball Scheme. Open-Ball Scheme employs a method of transforming the geometrical information on 3D objects or 2D patterns into the features to measure the relative similarity for object(patten) recognition, with invariance on scale, rotation, and translation. The feature of an object is used to obtain the relative similarity and mapped into [0, 1] the interval of real line. For decades, Moment-Invariant Method has been used as one of the excellent methods for pattern classification and object recognition. Open-Ball Scheme uses the geometrical structure of patterns while Moment Invariant Method uses the statistical characteristics. Open-Ball Scheme is compared to Moment Invariant Method with respect to the way that it interprets two-dimensional patten classification, especially the paradigms are compared by the degree of closeness to human's intuitive understanding. Finally the effectiveness of the proposed Open-Ball Scheme is illustrated through simulations.

• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.73-80
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• 1984

In this paper, likelihood-ratio test has been derived for testing multisample sphericity in complex multivariate Gaussian populations. The $h^{th}$ moment of the test statistic is given and its exact distribution has been derived using inverse Mellin transform. Asymptotic distribution of the statistic is also given.