• Journal of the Korean Statistical Society
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• 제34권1호
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• pp.61-71
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• 2005

A class of asymmetric ARCH processes is proposed via binary random power transformations. This class accommodates traditional nonlinear models such as threshold ARCH (Rabemanjara and Zacoian (1993)) and Box-Cox type ARCH models(Higgins and Bera (1992)). Stationarity condition of the model is addressed. Iterative least squares(ILS) and pseudo maximum like-lihood(PML) methods are discussed for estimating parameters and related algorithms are presented. Illustrative analysis for Korea Stock Prices Index (KOSPI) data is conducted.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.905-912
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• 2006

Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• 전자공학회논문지B
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• 제29B권1호
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• pp.34-42
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• 1992

This paper presents two fast least-squares lattice algorithms for adaptive nonlinear filters equipped with bilinear system models. The lattice filters perform a Gram-Schmidt orthogonalization of the input data and have very good numerical properties. Furthermore, the computational complexity of the algorithms is an order of magnitude snaller than previously algorithm is an order of magnitude smaller than previously available methods. The first of the two approaches is an equation error algorithm that uses the measured desired response signal directly to comprte the adaptive filter outputs. This method is conceptually very simple`however, it will result in biased system models in the presence of measurement noise. The second approach is an approximate least-squares output error solution. In this case, the past samples of the output of the adaptive system itself are used to produce the filter output at the current time. Results of several experiments that demonstrate and compare the properties of the adaptive bilinear filters are also presented in this paper. These results indicate that the output error algorithm is less sensitive to output measurement noise than the squation error method.

• Journal of applied mathematics & informatics
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• 제42권1호
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• pp.149-158
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• 2024

A least squares problem without correspondences is expressed as the following optimization: Π∈P^min_{m, x∈ℝn} ║Ax-Πy║, where A ∈ ℝ^m×n and y ∈ ℝ^m are given. In general, solving such an optimization problem is highly challenging. In this paper we use the rearrangement inequalities to find the closed form of solutions for certain cases. Moreover, despite the stringent constraints, we successfully tackle the nonlinear least squares problem without correspondences by leveraging rearrangement inequalities.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.879-886
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• 2009

본 논문에서는 시장점유율을 추정할 때 최소제곱 서포트벡터기계를 적용하여 보통최소제곱과 최소제곱 서포트벡터기계의 성능을 비교하고자 한다. 최소제곱 서포트벡터기계는 커널 함수를 사용함으로 고차원의 특징 공간에서 선형회귀로 재구성함으로 비선형 회귀문제까지도 해결할 수 있는 장점을 가지고 있다. 그래서 본 논문에서는 비모수 기법인 최소제곱 서포트벡터기계를 이용하여 시장점유율 모형을 추정하고자 한다. 최소제곱 서포트벡터기계를 기반으로 한 모형 추정은 시장점유율 유인모형을 해결하기 위한 좋은 대안이 된다. 최소제곱 서포트벡터기계의 성능을 평가하기 위해 비교 실험에서는 한국 자동차 시장에서 차량 판매량을 이용하여 브랜드별 시장점유율 모형을 추정하였다.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.288-295
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• 1995

In this paper a hypotheses test procedure based on the least absolute deviation estimators for the unknown parameters in nonlinear regression models is investigated. The asymptotic distribution of the proposed likelihood ratio test statistic are established voth under the null hypotheses and a sequence of local alternative hypotheses. The asymptotic relative efficiency of the proposed test with classical test based on the least squares estimator is also discussed.

• 전기학회논문지
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• 제63권9호
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• pp.1266-1272
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• 2014

We define the mobile robot navigation problem as an optimization problem to minimize the cost function with the pose error between the goal position and the position of a mobile robot. Using Gauss-Newton method for the optimization, the optimal speeds of the left and right wheels can be found as the solution of the optimization problem. Especially, the rotational speed of wheels of a mobile robot can be directly related to the overall speed of a mobile robot using the Jacobian derived from the kinematic model. When the robot detects the obstacle using sensors, the sub-goal switching method is adopted for the efficient obstacle avoidance during the navigation. The performance was evaluated using the simulation and the simulation results show the validity of the proposed method.

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.625-636
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• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.319-322
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• 2016

We propose a parametric blind deconvolution method for bi-level images with unknown intensity levels that estimates unknown parameters for point spread functions and images by minimizing a penalized nonlinear least squares objective function based on normalized correlation coefficients and two regularization functions. Unlike conventional methods, the proposed method does not require knowledge about true intensity values. Moreover, the objective function of the proposed method can be effectively minimized, since it has the special structure of nonlinear least squares. We demonstrate the effectiveness of the proposed method through simulations and experiments.

• 한국정밀공학회지
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• 제16권8호
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• pp.130-137
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• 1999

The criteria for determining the elements are the minimum zone method(MZM) and the least squares method(LSM). The LSM is deterministic and simple but is limited at the measurements whose errors are significant compared with form errors. For the precise condition, minimum zone method(MZM) has been selected to determine the elements. It is not deterministic and nonlinear so that a optimizing procedure is needed. The Straightness is the fundamental problem in the evaluating form error. In this paper, a new approach adapting the genius education concept is proposed to obtain an accurate results for the minimum zone problem of the straightness. Its computational algorithm is studied on a set of randomly generated data. To be of almost no account of the specification(the number and the standard devistion etc.) of the sample data, the results shows excellent reliability and high accuracy in estimating the straightness.