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

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.6
• /
• pp.374-380
• /
• 2010

It is known that the problem of FIR filtering with noisy input and output data can be solved by a total least squares (TLS) estimation. It is also known that the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose a convex combination algorithm between the ordinary recursive LS based TLS (RTLS) and the ordinary recursive LS (RLS). This combined algorithm is robust to the noise variance ratio and has almost the same complexity as the RTLS. Simulation results show that the proposed algorithm performs near TLS in noise variance ratio ${\gamma}{\approx}1$ and that it outperforms TLS and LS in the rage of 2 < $\gamma$ < 20. Consequently, the practical workability of the TLS method applied to noisy data has been significantly broadened.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.651-659
• /
• 2001

Preference map is a widely used graphical method for the preference data set which is frequently encountered in the field of marketing research. This provides joint configuration usually in two dimensional space between "products" and their "attributes". Whereas the classical preference map adopts the ordinary least squares method in deriving map, the present article suggests the weighted least squares approach providing the better graphical display and interpretation compared to the classical one. Internet search engine data in Korea are analysed for illustration.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.2E
• /
• pp.63-68
• /
• 2007

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, we applied this neural network model to channel equalization. Simulations show that the neural network based DLS outperforms ordinary least squares in channel equalization problems.

• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.615-624
• /
• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

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

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.2
• /
• pp.401-409
• /
• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1235-1243
• /
• 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 Mechanical Science and Technology
• /
• v.20 no.10
• /
• pp.1567-1575
• /
• 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.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.2C
• /
• pp.255-261
• /
• 2004

The problem of geolocation using multiple satellites is to determine the position of a transmitter located on the Earth by processing received signals. The specific problem addressed in this paper is that of estimating the position of a stationary transmitter located on or above the Earth's surface from measured time difference of arrivals (TDOA) by a geostationary orbiting (GSO) satellite and a low earth orbiting (LEO) satellite. The proposed geolocation method is based on the total least squares (TLS) algorithm. Under erroneous positions of the satellites together with noisy TDOA measurements, the TLS algorithm provides a better solution. By running Monte-Carlo simulations, the proposed method is compared with the ordinary least squares (LS) approach.