• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.301-309
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• 2019

A methodology to assess seismic fragility of a nuclear power plant (NPP) using a conditional mean spectrum is proposed as an alternative to using a uniform hazard response spectrum. Rather than the single-scenario conditional mean spectrum, which is the conventional conditional mean spectrum based on a single scenario, a multi-scenario conditional mean spectrum is proposed for the case in which no single scenario is dominant. The multi-scenario conditional mean spectrum is defined as the weighted average of different conditional mean spectra, each one of which corresponds to an individual scenario. The weighting factors for scenarios are obtained from a deaggregation of seismic hazards. As a validation example, a seismic fragility assessment of an NPP containment structure is performed using a uniform hazard response spectrum and different single-scenario conditional mean spectra and multi-scenario conditional mean spectra. In the example, the number of scenarios primarily influences the median capacity of the evaluated structure. Meanwhile, the control frequency, a key parameter of a conditional mean spectrum, plays an important role in reducing logarithmic standard deviation of the corresponding fragility curves and corresponding high confidence of low probability of failure (HCLPF) capacity.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.783-791
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• 2004

In this paper we review recent developments in nonlinear time series modeling on both conditional mean and conditional variance. Traditional linear model in conditional mean is referred to as ARMA(autoregressive moving average) process investigated by Box and Jenkins(1976). Nonlinear mean models such as threshold, exponential and random coefficient models are reviewed and their characteristics are explained. In terms of conditional variances, ARCH(autoregressive conditional heteroscedasticity) class is considered as typical linear models. As nonlinear variants of ARCH, diverse nonlinear models appearing in recent literature including threshold ARCH, beta-ARCH and Box-Cox ARCH models are remarked. Also, a class of unified nonlinear models are considered and parameter estimation for that class is briefly discussed.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.831-839
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• 2010

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.563-568
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• 1995

It is well-known that the sample mean and the sample variance are jointly sufficient under normality assumption. In this paper a proof of the joint sufficiency is given without using the factorization criterion. It is related to a finite Sanov-type conditional theorem, i.e., the conditional probability density of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are independently and identically distributed (i.i.d.) normal random variables with mean m and variance $\delta^2$, equals that of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are i.i.d. normal random variables with mean $\mu$ and variance $\sigma^2$.

• Earthquakes and Structures
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• v.7 no.2
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• pp.141-157
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• 2014

The Conditional Mean Spectrum represents a powerful link between the seismic hazard information and the selection of strong ground motion records at a particular site. The scope of the paper is to apply for the city of Bucharest for the first time the method to obtain the Conditional Mean Spectrum (CMS) presented by Baker (2011) and to select, on the basis of the CMS, a suite of strong ground motions for performing elastic and inelastic dynamic analyses of buildings and structures with fundamental periods of vibration in the vicinity of 1.0 s. The major seismic hazard for Bucharest and for most of Southern and Eastern Romania is dominated by the Vrancea subcrustal seismic source. The ground motion prediction equation developed for subduction-type earthquakes and soil conditions by Youngs et al. (1997) is used for the computation of the Uniform Hazard Spectrum (UHS) and the CMS. The disaggregation of seismic hazard is then performed in order to determine the mean causal values of magnitude and source-to-site distance for a particular spectral ordinate (for a spectral period T = 1.0 s in this study). The spectral period of 1.0 s is considered to be representative for the new stock of residential and office reinforced concrete (RC) buildings in Bucharest. The differences between the Uniform Hazard Spectrum (UHS) and the Conditional Mean Spectrum (CMS) are discussed taking into account the scarcity of ground motions recorded in the region of Bucharest and the frequency content characteristics of the recorded data. Moreover, a record selection based on the criteria proposed by Baker and Cornell (2006) and Baker (2011) is performed using a dataset consisting of strong ground motions recorded during seven Vrancea seismic events.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.107-115
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• 2012

Yoo and Cook (2007) developed an optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression and it is known that their method is asymptotically optimal and its test statistic has a chi-squared distribution asymptotically under the null hypothesis. To check the effect of dimension used in estimation on regression coefficients and the explanatory power of the conditional mean model in multivariate regression, we applied their method to several simulated data sets with various dimensions. A small simulation study showed that it is quite helpful to search for an appropriate dimension for a given data set if we use the asymptotic test for the dimension as well as results from the estimation with several dimensions simultaneously.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.533-542
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• 2021

The main objective of this article is to investigate the existence of the lunar effect during the full moon period (FM period) and the new moon period (NM period) on the selected Islamic stock market returns and volatilities. For this purpose, the Ordinary Least Squares model, Autoregressive Conditional Heteroscedasticity model, Generalised Autoregressive Conditional Heteroscedasticity model and Generalised Autoregressive Conditional Heteroscedasticity-in-Mean model are employed using the mean daily returns data between January 2010 and December 2019. Next, the log-likelihood, Akaike Information Criterion and Schwarz Information Criterion value are analyzed to determine the best models for explaining the returns and volatility of returns. The empirical results have deduced that, during the NM period, excluding Malaysia, the total mean daily returns for all of the selected countries have increased mean daily returns in contrast to the mean daily returns during the FM period. The volatility shocks are intense and conditional volatility is persistent in all countries. Subsequently, the volatility behavior tends to have lower volatility during the FM period and NM period in the Islamic stock market, except Malaysia. This article also concluded that the ARCH (1) model is the preferred model for stock returns whereas GARCH-M (1, 1) is preferred for the volatility of returns.

• 한국연소학회:학술대회논문집
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• 2000.12a
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• pp.24-32
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• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OH in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• Journal of the Korean Society of Combustion
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• v.5 no.2
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• pp.9-17
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• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OR in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• International Journal of Automotive Technology
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• v.6 no.6
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• pp.571-577
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• 2005

Combustion of turbulent sprays in a direct injection diesel engine is modeled by the conditional moment closure (CMC) model. The CMC routines are combined with the KIVA code to provide conditional flame structures to determine mean state variables, instead of mean reaction rates. An independent transport equation is solved for each flame group with equal mass of sequentially evaporating fuel vapor. CMC calculation begins as the fuel mass for each flame group begins to evaporate with corresponding initialization conditions. Comparison is made with measured pressure traces for four operating conditions at different rpm's and injection conditions. Results show that the CMC model with multiple flame histories can successfully be applied to ignition and mixing-controlled combustion phases of a diesel engine.