• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.95-104
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• 2020

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to non-stationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea's stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market's volatility than the positive shock in the case of South Korea and Tokyo.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.185-190
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• 2000

The sound absorption coefficient of glass wool (bulk density of 48 kg/$m^3$ and 32 kg/$m^3$) was measured by reverberation room method as varying their cross-sectional area. The results show that the absorption is larger for smaller samples because of edge effect. The absorption coefficient with two different kinds of sources, 1/3-octave band and white noise, gives similar values.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.9
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• pp.82-88
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• 1999

The object of this study is to find new evaluation index for in-cylinder flow chracteristics istead of current swirl, tumble coefficient using steady flow test rig on intake port system. To this end, port flow system. To this end, port flow rig test was conducted on DOHC head varying intake valve lift respectively. Finally combination angular coefficient and inclination angle were introduced as new evaluation index for in-cylinder angularflow characteristics instead of swirl and tumble coefficient.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.263-271
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• 2013

This study presents a fuzzy varying coefficient regression model after deleting the outliers to improve the feasibility and effectiveness of the fuzzy regression model. The objective of our methodology is to allow the fuzzy regression coefficients to vary with a covariate, and simultaneously avoid the impact of data contaminated by outliers. In this paper, fuzzy regression coefficients are represented by Gaussian fuzzy numbers. We also formulate suitable goodness of fit to evaluate the performance of the proposed methodology. An example is given to demonstrate the effectiveness of our methodology.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1027-1041
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• 2016

Different incident angles of ground motions have been considered to evaluate the relationship between floor rotation and torsional irregularity coefficient. The issues specifically addressed are (1) variability in torsional irregularity coefficient and floor rotations with varying incident angles of ground motion (2) contradictory relationship between floor rotation and torsional irregularity coefficient. To explore the stated issues, an evaluation based on relative variation in seismic response quantities of linear asymmetric structure under the influence of horizontal bi-directional excitation with varying seismic orientations has been carried out using response history analysis. Several typical earthquake records are applied to the structure to demonstrate the relative variations of floor rotation and torsional irregularity coefficient for different seismic orientations. It is demonstrated that (1) Torsional irregularity coefficient (TIC) increases as the story number decreases when the ground motion is considered along reference axes of the structure. For incident angles other than structure's reference axes, TIC either decreases as the story number decreases or there is no specific trend for TIC. Floor rotation increases in proportion to the story number when the ground motion is considered along reference axes of structure. For incident angles other than structure's reference axes, floor rotation either decreases as the story number increases or there is no specific trend for floor rotation and (2) TIC and floor rotation seems to be approximately inversely proportional to each other when the ground motion is considered along reference axes of the structure. For incident angles other than structure's reference axes, the relationship can even become directly proportional instead of inversely proportional.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.76-82
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• 1994

The characteristics of bottom sediment may be able to vary within a few meters of depth in shallow water. Since bottom attenuation coefficient as well as sound velocity in the bottom layer is determined by the composition and characteristics of sediment itself, it is reasonable to assume that the bottom attenuation coefficient is accordingly variable with depth. In this study, we use a parabolic equation scheme to examine the effects of depth-varying compressional wave attenuation on acoustic wave propagation in the low frequency ranging from 100 to 805 Hz. The sea floor under consideration is sandy bottom where the water and the sediment depths are 40 meters and 10 meters, respectively. Depending on the assumption that attenuation coefficient is constant or depth-varying, the propagation loss difference is as large as 10dB within 15 km. The predicted propagation loss is very much comparable to the measured one when we employ a depth-varying attenuation coefficient.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.63-71
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• 2020

In this paper, we propose the new time-varying coefficient GARCH-in-Mean model. The benefit of our model is to allow the risk-return parameter in the mean equation to vary over time. At the end of 2019 to the beginning of 2020, the world witnessed two shocking events: COVID-19 pandemic and 2020 oil price war. So, we decide to use the daily data from December 2, 2019 to May 29, 2020, which cover these two major events. The purpose of this study is to find the dynamic movement between risk and return in four major oil markets: Brent, West Texas Intermediate, Dubai, and Singapore Exchange, during COVID-19 pandemic and 2020 oil price war. For the European oil market, our model found a significant and positive risk-return relationship in Brent during March 26-April 21, 2020. For the North America oil market, our model found a significant positive risk return relationship in West Texas Intermediate (WTI) during March 12-May 8, 2020. For the Middle East oil market, we found a significant and positive risk-return relationship in Dubai during March 12-April 14, 2020. Lastly, for the South East Asia oil market, we found a significant positive risk return relationship in Singapore Exchange (SGX) from March 9-May 29, 2020.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.77-83
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• 1996

This paper is concerned with an estimation problem for the AR(1) process $Y_t, t=0, {\pm}1, {\cdots}$with time carying autoregressive coefficient, where coefficient itself is also stochastic process. Attention is directed to the problem of finding a consistent estimator of ${\Phi}$, the mean level of autoregressive coefficient. The asymptotic distribution of the resulting consistent estimator of ${\Phi}$, is them discussed. We do not assume any time series model for the time varying autoregressive coefficient.