• 제목/요약/키워드: generalized least squares

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일반화된 이동최소자승법과 이를 이용한 얇은 보의 무요소 해석 (Generalized Moving Least Squares Method and its use in Meshless Analysis of Thin Beam)

  • 조진연
    • 한국전산구조공학회:학술대회논문집
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    • 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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    • pp.497-504
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    • 2002
  • In meshless methods, the moving least squares approximation technique is widely used to approximate a solution space because of its useful numerical characters such as non-element approximation, easily controllable smoothness, and others. In this work, a generalized version of the moving least squares method Is introduced to enhance the approximation performance through the Information converning to the derivative of the field variable. The results of numerical tests for approximation verify the improved accuracy of the generalized meshless approximation procedure compared to the conventional moving least squares method. By using this generalized moving least squares method, meshless analysis of thin beam is carried out, and its performance is investigated.

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Another Look at Combined Intrablock and Interblock Estimation in Block Designs

  • Paik, U.B.
    • Journal of the Korean Statistical Society
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    • 제15권2호
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    • pp.118-126
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    • 1986
  • The relationships between combined estimators and generalized least squares estimators in block designs are reviewed. Here combined estimators mean the best linear combination of intrablock and interblock estimaters. It is well known that only for balanced incomplete block designs the combined estimators of Yates and of the generalized least squares estimators give the same result. In this paper, a general form of the combined estimators for treatment effects is derived and it can be seen that such estimators are equivalent to the generalized least squares estimators.

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Estimation of Seasonal Cointegration under Conditional Heteroskedasticity

  • Seong, Byeongchan
    • Communications for Statistical Applications and Methods
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    • 제22권6호
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    • pp.615-624
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    • 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.

On the generalized truncated least squares adaptive algorithm and two-stage design method with application to adaptive control

  • Yamamoto, Yoshihiro;Nikiforuk, Peter-N.;Gupta, Madam-M.
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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    • pp.7-12
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    • 1993
  • This paper presents a generalized truncated least, squares adaptive algorithm and a two-stage design method. The proposed algorithm is directly derived from the normal equation of the generalized truncated least squares method (GTLSM). The special case of the GTLSM, the truncated least squares (TLS) adaptive algorithm, has a distinct features which includes the case of minimum steps estimator. This algorithm seemed to be best in the deterministic case. For real applications in the presence of disturbances, the GTLS adaptive algorithm is more effective. The two-stage design method proposed here combines the adaptive control system design with a conventional control design method and each can be treated independently. Using this method, the validity of the presented algorithms are examined by the simulation studies of an indirect adaptive control.

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MOMENTS OF VARIOGRAM ESTIMATOR FOR A GENERALIZED SKEW t DISTRIBUTION

  • KIM HYOUNG-MOON
    • Journal of the Korean Statistical Society
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    • 제34권2호
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    • pp.109-123
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    • 2005
  • Variogram estimation is an important step of spatial statistics since it determines the kriging weights. Matheron's variogram estimator can be written as a quadratic form of the observed data. In this paper, we extend a skew t distribution to a generalized skew t distribution and moments of the variogram estimator for a generalized skew t distribution are derived in closed forms. After calculating the correlation structure of the variogram estimator, variogram fitting by generalized least squares is discussed.

A transductive least squares support vector machine with the difference convex algorithm

  • Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • 제25권2호
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    • pp.455-464
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    • 2014
  • Unlabeled examples are easier and less expensive to obtain than labeled examples. Semisupervised approaches are used to utilize such examples in an eort to boost the predictive performance. This paper proposes a novel semisupervised classication method named transductive least squares support vector machine (TLS-SVM), which is based on the least squares support vector machine. The proposed method utilizes the dierence convex algorithm to derive nonconvex minimization solutions for the TLS-SVM. A generalized cross validation method is also developed to choose the hyperparameters that aect the performance of the TLS-SVM. The experimental results conrm the successful performance of the proposed TLS-SVM.

A Method of Obtaning Least Squares Estimators of Estimable Functions in Classification Linear Models

  • Kim, Byung-Hwee;Chang, In-Hong;Dong, Kyung-Hwa
    • Journal of the Korean Statistical Society
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    • 제28권2호
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    • pp.183-193
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    • 1999
  • In the problem of estimating estimable functions in classification linear models, we propose a method of obtaining least squares estimators of estimable functions. This method is based on the hierarchical Bayesian approach for estimating a vector of unknown parameters. Also, we verify that estimators obtained by our method are identical to least squares estimators of estimable functions obtained by using either generalized inverses or full rank reparametrization of the models. Some examples are given which illustrate our results.

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Least Squares Approach for Structural Reanalysis

  • Kyung-Joon Cha;Ho-Jong Jang;Dal-Sun Yoon
    • Journal of the Korean Statistical Society
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    • 제25권3호
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    • pp.369-379
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    • 1996
  • A study is made of approximate technique for structural reanalysis based on the force method. Perturbntion analysis of generalized least squares problem is adopted to reanalyze a damaged structure, and related results are presented.

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LEAST SQUARES SOLUTIONS OF THE MATRIX EQUATION AXB = D OVER GENERALIZED REFLEXIVE X

  • Yuan, Yongxin
    • Journal of applied mathematics & informatics
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    • 제26권3_4호
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    • pp.471-479
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    • 2008
  • Let $R\;{\in}\;C^{m{\times}m}$ and $S\;{\in}\;C^{n{\times}n}$ be nontrivial unitary involutions, i.e., $R^*\;=\;R\;=\;R^{-1}\;{\neq}\;I_m$ and $S^*\;=\;S\;=\;S^{-1}\;{\neq}\;I_m$. We say that $G\;{\in}\;C^{m{\times}n}$ is a generalized reflexive matrix if RGS = G. The set of all m ${\times}$ n generalized reflexive matrices is denoted by $GRC^{m{\times}n}$. In this paper, an efficient method for the least squares solution $X\;{\in}\;GRC^{m{\times}n}$ of the matrix equation AXB = D with arbitrary coefficient matrices $A\;{\in}\;C^{p{\times}m}$, $B\;{\in}\;C^{n{\times}q}$and the right-hand side $D\;{\in}\;C^{p{\times}q}$ is developed based on the canonical correlation decomposition(CCD) and, an explicit formula for the general solution is presented.

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Resistant GPA algorithms based on the M and LMS estimation

  • Hyun, Geehong;Lee, Bo-Hui;Choi, Yong-Seok
    • Communications for Statistical Applications and Methods
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    • 제25권6호
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    • pp.673-685
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    • 2018
  • Procrustes analysis is a useful technique useful to measure, compare shape differences and estimate a mean shape for objects; however it is based on a least squares criterion and is affected by some outliers. Therefore, we propose two generalized Procrustes analysis methods based on M-estimation and least median of squares estimation that are resistant to object outliers. In addition, two algorithms are given for practical implementation. A simulation study and some examples are used to examine and compared the performances of the algorithms with the least square method. Moreover since these resistant GPA methods are available for higher dimensions, we need some methods to visualize the objects and mean shape effectively. Also since we have concentrated on resistant fitting methods without considering shape distributions, we wish to shape analysis not be sensitive to particular model.