• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.135-141
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• 2003

For the analysis of machined surface topography and machine-tool chatter, the cutting system is considered to be a single degree of freedom system. This paper presents a modeling method of equivalent vibratory system for precision cutting in end-milling using an impact test, an Autoregressive Moving Average (ARMA) mode] and a bisection method It has been shown that the proposed modeling method provides a good identification of the cutting system. The advantages of the proposed method in comparison to the existing method are that it is very easy and accurate.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.3
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• pp.55-62
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• 1993

Autoregressive moving average(ARMA) model which is a time domain parametric modeling method is implemented for modeling and reproducing characteristics of exhaust noise of an automobile in various RPM range. Experiments have been carried out using 9 set of exhaust noise signals measured at 1,000-3,000 RPM range. Characteristics of sampled signals were estimated using ARMA modeling and Akaike's FPE(final prediction error) criterion to define exact model structure and for model validation. The digital filter consisted of the esitmated ARMA(70,1) model parameters was programed to reproduce exhaust noise. The spectral analysis of reproduced noise is very close to original. The results show that our approaching technique for reproducing acoustic characteristics is valid and feasible to apply in the field of noise quality control.

• Journal of the Korean Society of Child Welfare
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• no.32
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• pp.7-32
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• 2010

The purpose of this research is to identify the longitudinal reciprocal relationship between family social capital and self-esteem of adolescents. Five waves of panel data from the Korea Youth Panel Survey were employed for this research. Korean Adolescents Policy Research Institute collected the first wave data in 2004 from elementary school children in 4th grade. Autoregressive cross-lagged modeling was performed to analysis the longitudinal reciprocal relationship between family social capital and self-esteem. The major findings were as follows. First, stability coefficient of family social capital and self-esteem showed that both variables were significantly stable over time. Secondly family social capital(t) had statistically significant effect on self-esteem(t+1), whereas self-esteem did not predict family social capital at a statistically significant level. These findings suggest that family social capital and self-esteem is stable overtime, and that obtaining family social capital and establishing positive self-esteem is important during this period in childhood. In addition, the results show that family social capital affects self-esteem, which highlights the importance of family social capital accumulation on the development of adolescent self-esteem.

• Korean Journal of Culture and Social Issue
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• v.22 no.4
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• pp.567-593
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• 2016

This study examined longitudinal mediated effects of informal labeling on the relationship between adolescent abuse and academic achievement using autoregressive cross-lagged modeling. Data were obtained from the second, third, and fourth waves of the middle school student cohort (N=3,168) of the Korean Youth Panel Survey. The major longitudinal findings of this study are as follows: First, adolescent abuse was found to have a positive association with future informal labeling. Second, informal labeling was found to have a negative association with future academic achievement. Finally, the longitudinal relationship between adolescent abuse and academic achievement was partially mediated by informal labeling. Based on these results, this study suggests directions for adolescent abuse prevention. The need for education and prevention of informal labeling was discussed, as well as the direction of intervention programs for adolescents with experience of informal labeling. Furthermore, this study may provide empirical evidence for labeling theory and contribute to increasing awareness on the longitudinal influence of adolescent abuse and informal labeling.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.3-9
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• 2002

In financial time series, the autoregressive conditional heteroscedastic (ARCH) models have been widely used for modeling conditional variances. In many cases, non-normality or heavy-tailed distributions of the data have influenced the estimation methods under normality assumption. To solve this problem, a robust function for the conditional variances of the errors is proposed and compared the relative efficiencies of the estimators with other conventional models.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• Korean Journal of Social Welfare
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• v.62 no.2
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• pp.161-185
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• 2010

The purpose of this study is to verify the causal relationship between depression and aggressiveness among adolescents. The 4-year longitudinal data collected from 2,670 4th grade elementary school students by the Korean Youth Panel study was used in this study. From the analysis result using the Autoregressive Cross-Lagged Model, the depression and aggressiveness in adolescents were continued from elementary school 4th grade to middle school 7th grade in significant stability. In addition, the previous aggressiveness turned out to have significant positive effect on the later period depression. Similarly, the previous depression had significant effect on the later aggressiveness, but the direction was negative. This means that the adolescents's depression increases as their aggressiveness increases, but as the depression increases, the later aggressiveness of the adolescents decreases. There were no differences between girls and boys within the relationship of these two variables. Finally, the implication derived from the results, the limitation of this study, and suggestion for following studies were presented.

• Human Ecology Research
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• v.52 no.3
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• pp.301-312
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• 2014

The purpose of this study was to examine whether a temporal relationship would develop between the overuse/addictive use of mobile phones and depression in adolescents. For this study, we used the 4-year longitudinal data (2004-2007, Study 1, which measured the overuse of mobile phones and depression) and the 2-year longitudinal data (2010-2011, Study 2, which measured the addictive use of mobile phones and depression) of the Korea Youth Panel study. In addition, the study explored gender differences with respect to the above mentioned relationship. Autoregressive cross-lagged modeling was carried out, along with a multiple group analysis across genders. The findings showed that the overuse/addictive use of mobile phones and depression in adolescents had a significant effect on the future selves of these adolescents over time. Moreover, the overuse/addictive use of mobile phones had a significant influence on subsequent depression, rather than vice versa. This means that as the overuse/addictive use of mobile phones by adolescents increases, their depression intensifies later on; however, as depression among adolescents intensifies, the overuse/addictive use of mobile phones by adolescents' does not increase. Further, the study showed there were significant gender differences in the longitudinal relationship between the overuse/addictive use of mobile phones and depression. Study 1 shows that, prior to the release of smartphones, the overuse of mobile phones had a definite effect on the depression of only males. However, Study 2 shows that, after the release of smartphones, the effect of the addictive use of mobile phones on depression in females was greater than that in males.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.295-307
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• 2023

The heterogeneous autoregressive (HAR) model is a simple linear model that is commonly used to explain long memory in the realized volatility. However, as realized volatility has more complicated features such as conditional heteroscedasticity, leverage effect, and volatility clustering, it is necessary to extend the simple HAR model. Therefore, to better incorporate the stylized facts, we propose a threshold HAR model with GARCH errors, namely the THAR-GARCH model. That is, the THAR-GARCH model is a nonlinear model whose coefficients vary according to a threshold value, and the conditional heteroscedasticity is explained through the GARCH errors. Model parameters are estimated using an iterative weighted least squares estimation method. Our simulation study supports the consistency of the iterative estimation method. In addition, we show that the proposed THAR-GARCH model has better forecasting power by applying to the realized volatility of major 21 stock indices around the world.

• ETRI Journal
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• v.39 no.5
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• pp.718-728
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• 2017

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.