• Structural Monitoring and Maintenance
• /
• v.5 no.3
• /
• pp.379-390
• /
• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.4
• /
• pp.121-131
• /
• 2020

This study aims to investigate the interplay between education and local roads on Foreign Direct Investment (FDI) in the Philippines, using economic growth as an instrument. The study used the quantitative research design applying both descriptive and inferential statistics. A combination of Two Stage Least Square Regression Model and three approaches in Panel Regression Model such as Pooled Least Square, Fixed Effect Model, and Random Effect Model were utilized in order to study the effects of education and local roads on foreign direct investment of the Philippines. Based on Fixed Effect regression results, higher education graduates and local road investments, as conditioned by economic growth, were significant factors in order to increase the foreign direct investment in the Philippines. Accordingly, a unit increase in higher education graduates, as conditioned by economic growth, leads to 8.758 unit increases in the foreign direct investment. While, a unit increased in local road investments, as conditioned by economic growth, leads to a 0.002 decrease in foreign direct investment. The regression results of the study suggest that the Foreign Direct Investment in the regions such as CAR, I, II, IV-B, V, VIII, IX, X, XI, XII, XIII, and ARMM are higher compared to Region IV-A.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.5
• /
• pp.1153-1165
• /
• 2017

Structural equation modeling (SEM) is a basic tool for testing theories in a variety of disciplines. A maximum likelihood (ML) method for parameter estimation is by far the most widely used in SEM. Alternatively, two-stage least squares (2SLS) estimator has been proposed as a more robust procedure to address model misspecification. A regularized extension of 2SLS, two-stage ridge least squares (2SRLS) has recently been introduced as an alternative to ML to effectively handle the small-sample-size issue. However, it is unclear whether and when misspecification and small sample sizes may pose problems in theory testing with 2SLS, 2SRLS, and ML. The purpose of this article is to evaluate the three estimation methods in terms of inferences errors as well as parameter recovery under two experimental conditions. We find that: 1) when the model is misspecified, 2SRLS tends to recover parameters better than the other two estimation methods; 2) Regardless of specification errors, 2SRLS produces small or relatively acceptable Type II error rates for the small sample sizes.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.647-656
• /
• 2020

This study empirically examines the effect of ownership structure on bank diversification and risk-taking behavior. The population of this study is based on all commercial banks listed in Bangladesh. Thirty-two conventional commercial banks were randomly selected from thirty-three conventional banks for this study. Data was collected from the annual reports of the concerned banks from 2000 to 2017. To analyze the data, we had applied the two-stage least squares (2SLS) estimator. The results of the analysis show that ownership structure i.e. managerial ownership, institutional ownership, general public ownership, and ownership concentration have a significant negative impact on bank diversification. On the other hand, institutional ownership, managerial ownership, and general public ownership have a significant positive impact on Z-score, and ownership concentration has an insignificant but positive impact on the Z-score of banks in Bangladesh. Therefore, the study opposes the benefits of diversification and promotes ownership structure which is capable of ensuring better financial stability by reducing the probability of risk. The policy-makers especially, Bangladesh banks should evaluate the fact of this study to issue guidelines on corporate governance, bank diversification, and risk-taking behavior of commercial banks.

• Journal of KIISE:Software and Applications
• /
• v.30 no.7_8
• /
• pp.744-751
• /
• 2003

A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.

• Journal of Korean Port Research
• /
• v.12 no.1
• /
• pp.55-64
• /
• 1998

Directed graphic algorithm was applied to an empirical analysis of traffic occupant fatalities based on a model by Crandall. In this paper, Crandall's data on U.S. traffic fatalities for the period 1947-1981 are focused and extended to include 1982-1993. Based on the 1947-1981 annual data, the directed graph algorithms reveal that occupant traffic deaths are directly caused by income, vehicle miles, and safety devices. Vehicle mileage is caused by income and rural driving. The estimation is conducted using three stage least squares regression. Those results show a difference between the traditional regression methodology and causal graphical analysis. It is also found that forecasts from the directed graph based model outperform forecasts from the regression-based models, in terms of mean squared forecasts error. Furthermore, it is demonstrates that there exists some latent variables between all explanatory variables and occupant deaths.

• Proceedings of the KOR-KST Conference
• /
• 1998.09a
• /
• pp.160-167
• /
• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.265-272
• /
• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.15 no.6
• /
• pp.533-542
• /
• 2003

This study aims to propose a simplified mathematical model for calculating vertical air temperature distribution in a four-sided atrium. In the first stage of the mathematical modeling, the computer model combined zonal model and solar radiation model using Monte Carlo method and Ray tracing technique went through a computer simulation with architectural variables applied to a four-sided atrium in summer. In the next stage, Curve Expert, a computer program that gets the most suitable solution ac-cording to the least squares method, is used to analyze the results of the computer simulation and to derive the mathematical model. The accuracy of the mathematical model was evaluated through a comparison of calculation results from a mathematical model and computer simulation. In this validation step using the least square method, the R2 value of the Zones 1, 2 and 3 showed higher than 0.945. Zone 4 has an R2 value of 0.911, lower than the previous three zones. However the relative error was below 0.5%, which is considered very small.

• Korea Trade Review
• /
• v.44 no.2
• /
• pp.1-19
• /
• 2019

This paper investigates the effects of TPP on Korean domestic employment. Using data from 1995-2011 obtained from the world input-output database (WIOD) and firm-level data, this paper attempts to identify changes in global value chain (GVC) structures involving Korea and TPP member countries in the Asia-Pacific region. Three stage least squares estimation is employed, and empirical findings show that there exists a statistically positive and significant causal relationship between GVC and domestic manufacturing employment. The positive impacts of TPP on Korean domestic employment suggest that Korea actively encourage TPP negotiation. TPP will bring positive domestic employment effects and opportunities for structural transformation in the manufacturing and services industries in Korea.