• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.113-116
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• 2007

In this paper, our proposed Nonlinear Autoregressive Exogenous (NARX) based on Least Square-Support Vector Regression (LS-SVR) is introduced and tested for producing natural sounds. This nonlinear synthesizer perfectly reproduce voiced sounds, and also conserve the naturalness such as jitter and shimmer, compared to LPC does not keep these naturalness. However, the results of some phonation are quite different from the original sounds. These results are assumed that single-band model can not afford to control and decompose the high frequency components. Therefore multi-band model with wavelet filterbank is adopted for substituting single band model. As a results, multi-band model results in improved stability. Finally, nonlinear speech modeling using NARX based on LS-SVR can successfully reconstruct synthesized sounds nearly similar to original voiced sounds.

• Korean Journal of Social Welfare Studies
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• v.48 no.3
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• pp.29-55
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• 2017

This study aims to examine how children's life satisfaction is interrelated with parents' life satisfaction longitudinally. Autoregressive cross-lagged model was employed to analyze the interrelationship between children's and parents' life satisfaction. This study used 6 waves (Elementary school 4th year to Middle school 3rd year) from the Korean Children and Youth Panel Study (KCYPS). The sample size was 1,963 families with child and parent. Key findings are as follows: First, children's and parents' life satisfaction, respectively, was found to influence each other during the study period. Second, parents' life satisfaction were found to have positive and significant cross-lagged impacts on children's life satisfaction. Third, similarly, children's life satisfaction was positively related to parents' life satisfaction longitudinally. The findings suggest that children's and parents' life satisfaction are interrelated in an autoregressive cross-lagged model. This study concludes with implications for enhancing children's and parents' life satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.177-190
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• 2022

In this study, using data from the second wave of Panel Survey of Employment for The Disabled[PESD] (2016) to the sixth year (2021), the longitudinal changes between disability acceptance and job satisfaction of adult wage workers aged 20 or older with disabilities and the correlation An autoregressive cross-lagged model was applied to analyze the causal relationship. As a result of the analysis, first, the disability acceptance and job satisfaction at the previous time point of the disabled workers were stably significant to the disability acceptance(𝛽=.556~.610) and job satisfaction(𝛽=.554~.585) at the later time point. Second, disability acceptance at the previous point in time for wage workers with disabilities crosses the job satisfaction at the later time point(𝛽=.077~.090), and the job satisfaction at the previous point crosses the disability acceptance at the later time point(𝛽=.087~.092). Third, as a result of model fit analysis according to the gender group of wage workers with disabilities, the difference in the autoregressive effect between disability acceptance and job satisfaction and the cross-lagged effect between disability acceptance and job satisfaction according to the passage of time was not significant.

• Journal of the Ergonomics Society of Korea
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• v.17 no.1
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• pp.67-77
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• 1998

The objective of this study is to extract information from electroencephalogram(EEG) signals with which we can discriminate mental states. Seven university students were participated in this study. Ten stimuli based on IAPS (International Affective Picture Systems) Were presented at random according to the experimental schedule. 8-channel ($O_1$, $O_2$, $F_3$, $F_4$, $F_7$, $F_8$, $FP_1$, and $FP_2$)EEG signals were recorded at a sampling rate of 204.8 Hz for visual stimuli and analyzed. After random ten sequential stimuli presentation, the subject subjectively assessed the stimulus by scaling from -5 to 5. If the stimulus was the best and the worst, it was scored 5 and -5, respectively. Only maximum and minimum scored-EEG signals within each subject were selected on the basis of subjectively assessment for analysis. EEG signals were transformed into feature objects based on scalar autoregressive model coefficients. They were classified with Discriminant Analysis for each channel. The features produced results with the best classification accuracy of 85.7 % in $O_1$ and $O_2$ for visual stimuli. This study could be extended to establish an algorithm which quantify and classify emotions evoked by visual stimulus using autoregressive models.

• Journal of Child Welfare and Development
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• v.16 no.2
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• pp.109-135
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• 2018

The purpose of this study was to analyze the longitudinal causal relationship between caregiver relations and peer relations of children in out-of-home care. We analyzed the three years(2011-2013) of longitudinal data from the Panel Study on Korean Children in Out-of-Home Care. The autoregressive cross-lagged model (ARCL) was used to measure the longitudinal causal relationship between caregiver relations and peer relations. As a result, first, caregiver relations and peer relations showed stability over time. In other words, the results of the measurement at three time points showed that the caregiver relations and peer relations at the previous time had a significant effect on the caregiver relations and peer relations at the later time point. Second, the previous caregiver relations had a significant effect on the subsequent peer relations over time. Third, the previous peer relations had a significant effect on the subsequent caregiver relations over time. This study confirmed the interrelationships of caregiver relations and peer relations of children in care by examining the longitudinal data using the longitudinal analysis method.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.55-67
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• 1998

Recent work by Shin and Sarkar (1996) examines model misspecification problems for nonstationary time series. Shin and Sarkar introduce a general regression model with integrated errors and one system of integrated regressors and discuss the limiting distributions of the OLS estimators and the usual OLS statistics such as $\hat{\sigma^2}$t, DW and $R^2$. We analyze three different model misspecification problems through a Monte Carlo study and investigate each model misspecification problem. Our Monte Carlo experiments show that DW and $R^2$ can be in general used as diagnostic tools to detect spurious regression, misspecification of nonstationary autoregressive and polynomial regression models.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.37-46
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• 2020

The paper examines the dynamic relationship of domestic credit and stock market liquidity on the economic growth of the Philippines from 1995 to 2018 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration, together with Granger causality test based on vector error correction model (VECM). The ARDL model indicated a long-run relationship of domestic credit and stock market liquidity on GDP growth. When the GDP per capita is the dependent variable there is weak cointegration. Also, the Johansen cointegration test confirmed the existence of long-run relationship of domestic credit and stock market liquidity both on GDP growth and GDP per capita. The VECM concludes a long-run causality running from domestic credit and stock market liquidity to GDP growth. At levels, domestic credit has significant short-run causal relationship with GDP growth. As for stock market liquidity at first lag, has significant short-run causal relationship with GDP growth. With regards to VECM for GDP per capita, domestic credit and stock market liquidity indicates no significant dynamic adjustment to a new equilibrium if a disturbance occurs in the whole system. At levels, the results indicated the presence of short-run causality from stock market liquidity and GDP per capita. The CUSUMSQ plot complements the findings of the CUSUM plot that the estimated models for GDP growth and GDP per capita were stable.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.323-334
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• 2017

We note that the dynamics of realized volatilities (RVs) are near the boundary between stationarity and non-stationarity because RVs have persistent long-memory and are often subject to fairly large outlying values. To forecast realized volatility, we consider a new method that adaptively use models with and without unit root according to the abnormality of observed RV: heterogeneous autoregressive (HAR) model and the Integrated HAR (IHAR) model. The resulting method is called the IHAR-O-HAR method. In an out-of-sample forecast comparison for the realized volatility datasets of the 3 major indexes of the S&P 500, the NASDAQ, and the Nikkei 225, the new IHAR-O-HAR method is shown superior to the existing HAR and IHAR method.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.265-275
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• 2009

This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.