• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.146-146
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• 2000

This paper presents field test results of the GPS precise positioning using carrier phase observable. The Least-squares AMBiguity Decorrelation Adjustment(LAMBDA) method is implemented to resolve integer ambiguity problem for two epoch Ll carrier phase measurement data. Field test results show that the GPS precise positioning of cm-level accuracy is obtainable with conventional low cost, single frequency C/A code GPS receivers.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.401-410
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.207-214
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• 1999

Optimal estimating functions for a class of autoregressive models with GARCH(1,1) error are discussed. The asymptotic properties of the estimator as the solution of the optimal estimating equation are investigated for the models. We have also some simulation results which suggest that the proposed optimal estimators have smaller sample variances than those of the Conditional least-squares estimators under the heavy-tailed error distributions.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.307-316
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• 2004

Principal Component Regression(PCR) and Partial Least Squares Regression(PLSR) are the two most popular regression techniques in chemometrics. In the field of chemometrics usually the number of regressor variables greatly exceeds the number of observation. So we have to reduce the number of regressors to avoid the identifiability problem. In this paper we compare PCR and PLSR techniques combined with various robust regression methods including regression depth estimation. We compare the efficiency, goodness-of-fit and robustness of each estimators under several contamination schemes.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.421-429
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• 2008

We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.32-37
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• 2020

This paper proposes a new l₁-norm-Recursive Least Squares (RLS) algorithm which is numerically more robust than the conventional l₁-norm-RLS. The l₁-norm-RLS was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm has numerical instability in the inverse matrix calculation. In this paper, we propose a new algorithm which is robust against the numerical instability. We show that the proposed method improves stability under several numerically erroneous situations.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2345-2352
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• 1998

The hyper-geometric distribution software reliability growth model was recently developed and successfully applied Due to mathematical difficultv of the maximum likclihmd method, the least squares method has hem suggested for parameter estimation by the previous studies. We first summarize and compare the minimization criteria adopted by the previous studies. It is theo shown that the weighted least squares method is more appropriate hecause of the nonhomogeneous variability of the number of newly detected faults. The adequacy of the weighted least squares method is illustrated by two numerical examples. Finally, we propose a new method fur predicting the number of faults newly discovered by next test instances. The new prediction method can be used for determining the time to stop testing.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.370-375
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• 2004

This paper proposes an algorithm that estimates three dimensional array shape calibration about the bottom-mounted sensor array. under the assumption that the active sources are in the far-field with unknown positions. Under some assumptions. we calculate the sensor positions via an algebraic solutions of a least squares problem that the linear equations are related to the sensor positions and directions or arrival. We give examples of algorithm performance from both computer simulations and sea test. We also illustrate the performance of sensor positions estimation as a function of time delay estimation variance and the distribution of the localizing sources.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.383-388
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• 2016

We develop two methods to select a constant in the RLS (Recursive Least Squares) with the convex regularization. The RLS with the convex regularization was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm uses the regularization constant which needs the information about the true channel response for the best performance. In this paper, we propose two methods to select the regularization constant which don't need the information about the true channel response. We show that the estimation performance using the proposed methods is comparable with the Eksioglu and Tanc's algorithm.

• Journal of Sport and Applied Science
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• v.2 no.2
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• pp.1-10
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• 2018

Prior literature has posited that the sport industry has been effective method to drive the economic growth. Given the rationale, this study sets China as a research object with a quarterly data from the first quarter of 2003 to the fourth quarter of 2017 to explore how the sport industry affects economic growth. This study employed Johansen cointegration test and dynamic ordinary least squares as methods for an empirical analysis. The input of sport industry, the labor input, the capital input, and the economic growth are used as research variables. The results show that there is a long-run relationship among them. Johansen cointegration test's estimation indicated that 1% increase in the input of sport industry will lead to 0.064% increase in economic growth. Dynamic ordinary least squares' estimation showed that whenever in the one lead, in the one lag and in the present period, the input of sport industry always poses a positive effect on economic growth. Labor input also has a positive effect on economic growth. The capital input has a negative effect on economic growth. Finally, even though the input of sport industry has a positive effect on economic growth, its impact on economic growth is relative weak.