• Journal of Korea Multimedia Society
• /
• v.16 no.12
• /
• pp.1465-1474
• /
• 2013

In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.

• 한국데이터정보과학회:학술대회논문집
• /
• 2005.04a
• /
• pp.127-134
• /
• 2005

A suitable representation for the conceptual simplicity of the data in statistics and signal processing is essential for a subsequent analysis such as prediction, pattern recognition, and spatial analysis. Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.

• Journal of the Korean Data and Information Science Society
• /
• v.3 no.1
• /
• pp.65-78
• /
• 1992

In this paper, we obtain the natural estimators of the coefficient parameters and propose strongly consistent estimators of the parameter in the autoregressive model of order three with non-negative innovations. It is shown that the natural estimators are also strongly consistent for the parameters. We also compare the proposed estimators with the natural estimators and the least square estimators via Monte Carlo simulation studies.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2006.04a
• /
• pp.211-216
• /
• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.273-289
• /
• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• The Korean Journal of Applied Statistics
• /
• v.19 no.1
• /
• pp.135-148
• /
• 2006

Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.12
• /
• pp.85-93
• /
• 2020

This study aimed to explore the impact of macroeconomic (Real GDP growth (GDPG), Inflation rate (INF)) and bank -specific variables (profitability (ROA), capital adequacy (CADEQ), non-performing loans (NPL), deposit growth (DEPG)) on the liquidity (lIQ) of 13 listed Jordanian commercial banks for the period 2011-2018. Panel data analysis, Pooled least square, fixed effects model and random effects model, Lagrange multiplier test, and Hausman test were used. The random effects model output shows that, macroeconomic variables have a significant impact on Jordanian commercial banks liquidity since inflation has a positive impact while GDPG has a negative impact on banks (LIQ). On the other hand among the bank-specific variables capital adequacy and deposit growth have a positive significant impact on banks (LIQ), while (NPL) and (SIZE) have a negative significant impact on Jordanian commercial banks liquidity. But ROA has a negative insignificant impact on (LIQ). The findings of the study suggest that commercial banks departments need to pay attention to the economic and internal variables of banks in order to maintain acceptable levels of liquidity.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.9
• /
• pp.19-30
• /
• 2020

This paper investigates, in a single equation framework, the effect of bank-specific and macroeconomic determinants on micro-, small- and medium-sized loans by commercial banks in Indonesia. This study uses a sample of 790 observations from 79 commercial banks in Indonesia over the years 2006-2015. This study uses two estimation methods for our panel regressions: static and dynamic generalized method of moments (GMM) panel estimator. In static relationships, the literature usually uses the least square methods on fixed effects (FE) or random effects (RE). I found evidence that all banks, bank profitability and size are positively and significantly related to micro-, small- and medium-sized loans, while the coefficients of liquidity are significantly positive in all specifications, except government banks which is significantly negative. The relationship between risk and credit growth is negative for non-government banks. All estimated equations show that the effect of the capital variable on lending banks to MSMEs is not important in government banks and non-government banks. Finally, macroeconomic variables, such as inflation and gross domestic product, clearly affect the lending of the banking sector particularly non-state banks. The findings have several policy implications to Indonesia government, regulatory authority and bank managers in order to improve bank profitability through bank lending.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.4
• /
• pp.97-105
• /
• 2020

This paper explores the impact of capital structure on firm performance in the context of Vietnam. The paper investigates the different effect of capital structure on firm performance in state-owned and non-state enterprises listed on the Vietnam stock market. The panel data of research sample includes 488 non-financial listed companies on the Vietnam stock market for a period of six years, from 2013 to 2018. The Generalized Least Square (GLS) is employed to address econometric issues and to improve the accuracy of the regression coefficients. In this research, firm performance is measured by return on equity (ROE), return on assets (ROA), and earnings per share (EPS). The ratios of short-term liabilities, long-term liabilities, and total liabilities to total assets are proxy for capital structure. Firm sizes, growth rate, liquidity, and ratio of fixed assets to total assets are control variables in the study. The empirical results show that capital structure has a statistically significant negative effect on the firm performance. The result also shows this effect is stronger in state-owned enterprises than non-state enterprises in Vietnam. These evidences provide a new insight to managers of both state-owned and non-state enterprises on how to improve the firm's performance with capital structure.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.953-965
• /
• 2021

This study aims to analyze the benefits of corporate social responsibility (CSR) performance on corporate risk in controversial and non-controversial industries. The hypothesis of this study is based on the conflicting effects of industry type on CSR and firm risk. The research sample consisted of 927 companies listed on the Indonesia Stock Exchange from 2016 to 2019. The main method for data processing was the ordinary least square method and subgroup analysis as a robustness test. The findings suggest that the performance of CSR can reduce corporate risk. However, the impact was only significant for non-controversial firms and weakened for controversial industries. These results support risk management and signaling theory. Firm risk in this study reflects the company's total risk, further research can categorize it into systematic and idiosyncratic risk. Besides, the number of samples of controversial industry research is not as much as non-controversial; further research can use paired samples. Regulators can use the results to create a new policy regarding CSR implementation. This study contributes to the existing literature by showing that the ability of social responsibility to reduce corporate risk only works in non-controversial industries. This result may be due to the controversial industry receiving negative stigma from its stakeholders.