• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1169-1180
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• 2006

Partial least squares regression (PLS) is a biased, non-least squares regression method and is an alternative to the ordinary least squares regression (OLS) when predictors are highly collinear or predictors outnumber observations. One way to understand the properties of biased regression methods is to know how the estimators shrink the OLS estimator. In this paper, we introduce an expression for the shrinkage factor of PLS and develop a new shrinkage expression, and then prove the equivalence of the two representations. We use two near-infrared (NIR) data sets to show general behavior of the shrinkage and in particular for what eigendirections PLS expands the OLS coefficients.

• Economic and Environmental Geology
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• v.50 no.6
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• pp.517-523
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• 2017

A linear relationship between two stable water isotopes, oxygen and hydrogen, has been used to understand the water cycle as a basic tool. A slope and intercept from the linear relationship indicates what kind of physical processes occur during movement of water. Traditionally, ordinary least squares (OLS) method has been utilized for the linear relationship, but total least squares (TLS) method provides more accurate slope and intercept theoretically because isotopic compositions of both oxygen and hydrogen have uncertainties. In this work, OLS and TLS were compared with isotopic compositions of snow and snowmelt collected from the King Sejong Station, Antarctica and isotopic compositions of water vapor observed by Lee et al. (2013) in the western part of Korea. The slopes from the linear relationship of isotopic compositions of snow and snowmelt at the King Sejong Station were estimated to be 7.00 (OLS) and 7.16(TLS) and the slopes of stable water vapor isotopes were 7.75(OLS) and 7.87(TLS). There was a melting process in the snow near the King Sejong Station and the water vapor was directly transported from the ocean to the study area based on the slope calculations. There is no significant difference in two slopes to interpret the physical processes. However, it is necessary to evaluate the slope differences from the two methods for studies for example, groundwater recharge processes, using the absolute slope values.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.355-366
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• 2003

A unified procedure for principal component regression (PCR), partial least squares (PLS) and ordinary least squares (OLS) is proposed. The process gives solutions for PCR, PLS and OLS in a unified and non-iterative way. This enables us to see the interrelationships among the three regression coefficient vectors, and it is seen that the so-called E-matrix in the solution expression plays the key role in differentiating the methods. In addition to setting out the procedure, the paper also supplies a robust numerical algorithm for its implementation, which is used to show how the procedure performs on a real world data set.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.733-739
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• 2011

The linear absolute shrinkage and selection operator(Lasso) method improves the low prediction accuracy and poor interpretation of the ordinary least squares(OLS) estimate through the use of $L_1$ regularization on the regression coefficients. However, the Lasso is not robust to outliers, because the Lasso method minimizes the sum of squared residual errors. Even though the least absolute deviation(LAD) estimator is an alternative to the OLS estimate, it is sensitive to leverage points. We propose a robust Lasso estimator that is not sensitive to outliers, heavy-tailed errors or leverage points.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1235-1243
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• 2015

Increased data volume in the ICT area has increased the importance of forecasting accuracy for internet traffic. Forecasting results may have paper plans for traffic management and control. In this paper, we propose combined forecasts based on several time series models such as Seasonal ARIMA and Taylor's adjusted Holt-Winters and Fractional ARIMA(FARIMA). In combined forecasting methods, we use simple-combined method, MSE based method (Armstrong, 2001), Ordinary Least Squares (OLS) method and Equality Restricted Least Squares (ERLS) method. The results show that the Seasonal ARIMA model outperforms in 3 hours ahead forecasts and that combined forecasts outperform in longer periods.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.1
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• pp.163-170
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• 1996

An errors-in-variables model (EVM) differs from the classical regression model in that in the former the independent variable is also subject to error. This paper shows that to assess the applicability of the ordinary least squares (OLS) estimation procedure to the EVM, the relative dispersion of the independent variable to its error variance must be also considered in addition to Mandel's criterion. The effect of physically reducing the variance of errors in the independent variable on the performance of the OLS slope estimator is also discussed.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.23-31
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• 2023

Predicting forest productivity is essential to evaluate sustainable forest management or to enhance forest ecosystem services. Ordinary least squares (OLS) and partial least squares (PLS) regression models were used to develop predictive models for forest productivity (site index) from the site characteristics and soil profile, along with soil physical and chemical properties, of 112 Quercus mongolica stands. The adjusted coefficients of determination (adjusted R²) in the regression models were higher for the site characteristics and soil profile of B horizon (R²=0.32) and of A horizon (R²=0.29) than for the soil physical and chemical properties of B horizon (R²=0.21) and A horizon (R²=0.09). The PLS models (R²=0.20-0.32) were better predictors of site index than the OLS models (R²=0.09-0.31). These results suggest that the regression models for Q. mongolica can be applied to predict the forest productivity, but new variables may need to be developed to enhance the explanatory power of regression models.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1567-1575
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• 2006

In an automatic combination weigher, the line feeders distribute the product to several weighing hoppers. The ability to supply appropriate amount of product to the weighing hoppers for each combination operation is crucial for the overall performance. Determining the right duration of operating a line feeder to supply a given amount of product becomes very challenging in case of products which are irregular in volume or specific gravity such as granular secondary processed foods. In this research, several schemes were investigated to determine the best way for a line feeder to approximate the next operating time in order to supply a set amount of irregular goods to the corresponding weighing hopper. Results obtained show that a weighted least squares method (WLS) employing 10 data points is the most effective in determining the operating times of line feeders.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.5
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• pp.105-111
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• 2004

This paper shows that active and reactive power load have significant positive relationship and there exist two types of relationship between them using Test Statistics. In investigating the cross plots at every hour, we found out that from 0 to 8 hours, there relationships are linear, while from 9 to 23 hours, they are two piece-wise linear. Also, reactive power loads was estimated and forecasted using active power load as the explanary variable with OLS (Ordinary Least Squares) regression methods. MAPE (Mean Absolute Percentage Error) for each model is calculated for one-hour ahead forecasting.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.229-234
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• 2022

The study aims to investigate whether Musharakah management has an impact on Chinese and Malaysian business partnerships. To estimate the relationship between Musharakah and the Sino-Malaysian partnership, this study uses a panel econometric technique namely pooled ordinary least squares. Ordinary Least Squares regression (OLS) is a common technique for estimating coefficients of linear regression equations which describe the relationship between one or more independent quantitative variables and a dependent variable. Data was retrieved from the annual reports (from 2009 to 2019) of non-financial firms listed on the stock exchange of China and Malaysia. Four partnership measures (i.e., Musharakah, Mudarabah, Tawuruq, and Kafalah) were used to estimate the impact of Musharakah on the Sino-Malaysian partnership. Empirical results reveal that Musharakah and Mudarabah are positively related to Kafalah but the relationship is statistically insignificant. Alternatively, Musharakah is positively and significantly related to Mudarabah. Musharakah and Mudarabah have a positive but insignificant relationship. The findings of this study suggest that management of partnership has a positive impact on firm partnership. Furthermore, it supports the hypothesis that improving partnership enhances Musharakah, which has a positive impact on the firm's partnership.