• 제목/요약/키워드: linear estimator

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Piecewise Continuous Linear Density Estimator

  • Jang, Dae-Heung
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.959-968
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    • 2005
  • The piecewise linear histogram can be used as a simple and efficient tool for the density estimator. But, this piecewise linear histogram is discontinuous function. We suppose the piecewise continuous linear histogram as a simple and efficient tool for the density estimator and the alternative of the piecewise linear histogram.

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국소선형 준가능도 추정량의 자료 희박성 문제 해결방안 (Sparse Design Problem in Local Linear Quasi-likelihood Estimator)

  • 박동련
    • 응용통계연구
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    • 제20권1호
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    • pp.133-145
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    • 2007
  • 국소선형 추정량은 여러 면에서 바람직한 특성을 많이 갖고 있는 좋은 추정량이다. 그러나 자료가 희박한 부분에서는 매우 불안정한 추정값을 갖게 되는 문제가 있음이 밝혀졌으며, 이 문제를 해결하기 위한 여러 방안이 많이 연구되었다. 그러나 이항반응변수를 위한 국소선형 추정량의 변형이라고 할 수 있는 국소선형 준가능도 추정량에 대해서는 아직 자료의 희박성 문제가 다루어지지 않고 있었다. 이 논문에서는 국소선형 준가능도 추정량이 갖고 있는 자료의 희박성 문제를 인식하고, 몇 가지 해결방안을 제시하였으며, 모의 실험을 통하여 가장 효과적인 방안을 선택하였다.

A POSTERIORI ERROR ESTIMATOR FOR LINEAR ELASTICITY BASED ON NONSYMMETRIC STRESS TENSOR APPROXIMATION

  • Kim, Kwang-Yeon
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • 제16권1호
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    • pp.1-13
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    • 2012
  • In this paper we present an a posteriori error estimator for the stabilized P1 nonconforming finite element method of the linear elasticity problem based on a nonsymmetric H(div)-conforming approximation of the stress tensor in the first-order Raviart-Thomas space. By combining the equilibrated residual method and the hypercircle method, it is shown that the error estimator gives a fully computable upper bound on the actual error. Numerical results are provided to confirm the theory and illustrate the effectiveness of our error estimator.

Shifted Nadaraya Watson Estimator

  • Chung, Sung-S.
    • Communications for Statistical Applications and Methods
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    • 제4권3호
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    • pp.881-890
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    • 1997
  • The local linear estimator usually has more attractive properties than Nadaraya-Watson estimator. But the local linear estimator gives bad performance where data are sparse. Muller and Song proposed Shifted Nadaraya Watson estimator which has treated data sparsity well. We show that Shifted Nadaraya Watson estimator has good performance not only in the sparse region but also in the dense region, through the simulation study. Ans we suggest the boundary treatment of Shifted Nadaraya Watson estimator.

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The local influence of LIU type estimator in linear mixed model

  • Zhang, Lili;Baek, Jangsun
    • Journal of the Korean Data and Information Science Society
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    • 제26권2호
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    • pp.465-474
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    • 2015
  • In this paper, we study the local influence analysis of LIU type estimator in the linear mixed models. Using the method proposed by Shi (1997), the local influence of LIU type estimator in three disturbance models are investigated respectively. Furthermore, we give the generalized Cook's distance to assess the influence, and illustrate the efficiency of the proposed method by example.

위상지연필터를 이용한 리니어 모터 피스톤 진폭 추정기의 구현 (Implementation of Linear Motor Piston Amplitude Estimator Using Phase Lag Filter)

  • 오준태;김규식
    • 전자공학회논문지
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    • 제50권4호
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    • pp.212-218
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    • 2013
  • 본 논문에서는 위상지연필터를 이용한 리니어 모터 피스톤 진폭 추정기를 구현하였다. 리니어 컴프레서가 적용된 냉장고나 에어컨의 냉각능력을 제어하기 위해서는 단위시간동안 피스톤의 움직인 거리, 즉 피스톤의 속도를 제어해야 하는데 이때 리니어 모터의 주파수나 스트로크를 조정함으로써 가능하다. 이때, 주파수를 고정하고 스트로크를 변화시키는 것이 일반적이다. 스트로크, 즉 피스톤 진폭을 정확하게 추정하는 것이 리니어 컴프레서의 동특성을 좌우하는데, 본 연구에서는 위상지연필터를 이용한 피스톤 진폭 추정기를 제안하고 성능이 우수함을 모의실험을 통해 확인하였다.

Finite-Sample, Small-Dispersion Asymptotic Optimality of the Non-Linear Least Squares Estimator

  • So, Beong-Soo
    • Journal of the Korean Statistical Society
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    • 제24권2호
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    • pp.303-312
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    • 1995
  • We consider the following type of general semi-parametric non-linear regression model : $y_i = f_i(\theta) + \epsilon_i, i=1, \cdots, n$ where ${f_i(\cdot)}$ represents the set of non-linear functions of the unknown parameter vector $\theta' = (\theta_1, \cdots, \theta_p)$ and ${\epsilon_i}$ represents the set of measurement errors with unknown distribution. Under suitable finite-sample, small-dispersion asymptotic framework, we derive a general lower bound for the asymptotic mean squared error (AMSE) matrix of the Gauss-consistent estimator of $\theta$. We then prove the fundamental result that the general non-linear least squares estimator (NLSE) is an optimal estimator within the class of all regular Gauss-consistent estimators irrespective of the type of the distribution of the measurement errors.

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선형 홀센서 기반의 모터 회전속도 측정을 위한 평균 최소 자승 추정기 (Least Mean Square Estimator for Motor Frequency Measurement Based on Linear Hall Sensor)

  • 최가형;나원상;곽기석;윤태성;박진배
    • 전기학회논문지
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    • 제57권5호
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    • pp.866-874
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    • 2008
  • Motor frequency can be measured by a hall sensor. Among the many hall sensors, a linear type hall sensor is good at high accuracy frequency measuring problem. However, in general, this linear type hall sensor has DC offset which can vary along sensor's operating voltage change. Therefore, In motor frequency measurement problem using the linear hall sensor, it needs an estimator that can estimate frequency and DC offset simultaneously. In this paper, we propose the least mean square estimator to estimate motor frequency. To verify its performance, we compare the LMS estimator with a commercial analog tachometer. Experimental results shows the proposed LMS estimator works well in varying frequency and stationary DC offset.

Bayes Estimation in a Hierarchical Linear Model

  • Park, Kuey-Chung;Chang, In-Hong;Kim, Byung-Hwee
    • Journal of the Korean Statistical Society
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    • 제27권1호
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    • pp.1-10
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    • 1998
  • In the problem of estimating a vector of unknown regression coefficients under the sum of squared error losses in a hierarchical linear model, we propose the hierarchical Bayes estimator of a vector of unknown regression coefficients in a hierarchical linear model, and then prove the admissibility of this estimator using Blyth's (196\51) method.

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SOME PROPERTIES OF SIMEX ESTIMATOR IN PARTIALLY LINEAR MEASUREMENT ERROR MODEL

  • Meeseon Jeong;Kim, Choongrak
    • Journal of the Korean Statistical Society
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    • 제32권1호
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    • pp.85-92
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    • 2003
  • We consider the partially linear model E(Y) : X$^{t}$ $\beta$+η(Z) when the X's are measured with additive error. The semiparametric likelihood estimation ignoring the measurement error gives inconsistent estimator for both $\beta$ and η(.). In this paper we suggest the SIMEX estimator for f to correct the bias induced by measurement error, and explore its properties. We show that the rational linear extrapolant is proper in extrapolation step in the sense that the SIMEX method under this extrapolant gives consistent estimator It is also shown that the SIMEX estimator is asymptotically equivalent to the semiparametric version of the usual parametric correction for attenuation suggested by Liang et al. (1999) A simulation study is given to compare two variance estimating methods for SIMEX estimator.