• Title, Summary, Keyword: Difference-based estimator

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First Order Difference-Based Error Variance Estimator in Nonparametric Regression with a Single Outlier

  • Park, Chun-Gun
    • Communications for Statistical Applications and Methods
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    • v.19 no.3
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    • pp.333-344
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    • 2012
  • We consider some statistical properties of the first order difference-based error variance estimator in nonparametric regression models with a single outlier. So far under an outlier(s) such difference-based estimators has been rarely discussed. We propose the first order difference-based estimator using the leave-one-out method to detect a single outlier and simulate the outlier detection in a nonparametric regression model with the single outlier. Moreover, the outlier detection works well. The results are promising even in nonparametric regression models with many outliers using some difference based estimators.

A Robust Estimator in Multivariate Regression Using Least Quartile Difference

  • Jung Kang-Mo
    • Communications for Statistical Applications and Methods
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    • v.12 no.1
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    • pp.39-46
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    • 2005
  • We propose an equivariant and robust estimator in multivariate regression model based on the least quartile difference (LQD) estimator in univariate regression. We call this estimator as the multivariate least quartile difference (MLQD) estimator. The MLQD estimator considers correlations among response variables and it can be shown that the proposed estimator has the appropriate equivariance properties defined in multivariate regressions. The MLQD estimator has high breakdown point as does the univariate LQD estimator. We develop an algorithm for MLQD estimate. Simulations are performed to compare the efficiencies of MLQD estimate with coordinatewise LQD estimate and the multivariate least trimmed squares estimate.

A Modi ed Entropy-Based Goodness-of-Fit Tes for Inverse Gaussian Distribution (역가우스분포에 대한 변형된 엔트로피 기반 적합도 검정)

  • Choi, Byung-Jin
    • The Korean Journal of Applied Statistics
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    • v.24 no.2
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    • pp.383-391
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    • 2011
  • This paper presents a modified entropy-based test of fit for the inverse Gaussian distribution. The test is based on the entropy difference of the unknown data-generating distribution and the inverse Gaussian distribution. The entropy difference estimator used as the test statistic is obtained by employing Vasicek's sample entropy as an entropy estimator for the data-generating distribution and the uniformly minimum variance unbiased estimator as an entropy estimator for the inverse Gaussian distribution. The critical values of the test statistic empirically determined are provided in a tabular form. Monte Carlo simulations are performed to compare the proposed test with the previous entropy-based test in terms of power.

On study for change point regression problems using a difference-based regression model

  • Park, Jong Suk;Park, Chun Gun;Lee, Kyeong Eun
    • Communications for Statistical Applications and Methods
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    • v.26 no.6
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    • pp.539-556
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    • 2019
  • This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

The Efficiency of the Cochrane-Orcutt Estimation Procedure in Autocorrelated Regression Models

  • Song, Seuck-Heun;Myoungshic Jhun;Jung, Byoung-Cheol
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.319-329
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    • 1998
  • In the linear regression model with an autocorrelated disturbances, the Cochrane-Orcutt estimator (COE) is a well known alternative to the Generalized Least Squares estimator (GLSE). The efficiency of COE has been studied empirically in a Monte Carlo study when the unknown parameters are estimated by maximum likelihood method. In this paper, it is theoretically proved that the COE is shown to be inferior to the GLSE. The comparisons are based on the difference of corresponding information matrices or the ratio of their determinants.

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A SIMPLE VARIANCE ESTIMATOR IN NONPARAMETRIC REGRESSION MODELS WITH MULTIVARIATE PREDICTORS

  • Lee Young-Kyung;Kim Tae-Yoon;Park Byeong-U.
    • Journal of the Korean Statistical Society
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    • v.35 no.1
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    • pp.105-114
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    • 2006
  • In this paper we propose a simple and computationally attractive difference-based variance estimator in nonparametric regression models with multivariate predictors. We show that the estimator achieves $n^{-1/2}$ rate of convergence for regression functions with only a first derivative when d, the dimension of the predictor, is less than or equal to 4. When d > 4, the rate turns out to be $n^{-4/(d+4)}$ under the first derivative condition for the regression functions. A numerical study suggests that the proposed estimator has a good finite sample performance.

A New VLSI Architecture of a Hierarchical Motion Estimator for Low Bit-rate Video Coding (저전송률 동영상 압축을 위한 새로운 계층적 움직임 추정기의 VLSI 구조)

  • 이재헌;나종범
    • Proceedings of the IEEK Conference
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    • pp.601-604
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    • 1999
  • We propose a new hierarchical motion estimator architecture that supports the advanced prediction mode of recent low bit-rate video coders such as H.263 and MPEG-4. In the proposed VLSI architecture, a basic searching unit (BSU) is commonly utilized for all hierarchical levels to make a systematic and small sized motion estimator. Since the memory bank of the proposed architecture provides scheduled data flow for calculating 8$\times$8 block-based sum of absolute difference (SAD), both a macroblock-based motion vector (MV) and four block-based MVs are simultaneously obtained for each macroblock in the advanced prediction mode. The proposed motion estimator gives similar coding performance compared with full search block matching algorithm (FSBMA) while achieving small size and satisfying the advanced prediction mode.

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Output-Feedback Input-Output Linearizing Controller for Nonlinear System Using Backward-Difference State Estimator (후방차분 상태 추정기를 이용한 비선형 계통의 입출력 궤환 선형화 제어기)

  • Kim, Seong-Hwan;Park, Jang-Hyun
    • Journal of IKEEE
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    • v.9 no.1
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    • pp.72-78
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    • 2005
  • This paper describes the design of a robust output-feedback controller for a single-input single-output nonlinear dynamical system with a full relative degree. While all the previous research works on the output-feedback control are based on dynamic observers, a new state estimator which uses the past values of the measurable system output is proposed. We name it backward-difference state estimator since the derivatives of the output are estimated simply by backward difference of the present and past values of the output. The disturbance generated due to the error between the estimated and real state variables is compensated using an additional robustifying control law whose gain is tuned adaptively. Overall control system guarantees that the tracking error is asymptotically convergent and that all signals involved are uniformly bounded. Theoretical results are illustrated through a simulation example of inverted pendulum.

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A Control Chart of the Deviation Based on the Gini′s Mean Difference (지니(Gini)의 평균차이를 이용한 산포관리도)

  • 남호수;강중철
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.24 no.67
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    • pp.11-18
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    • 2001
  • The efficiency and robustness of the scale estimator based on the Gini's mean difference are well known in Nam et al.(2000). In this paper we propose use of robust control limits based on the Gini's mean difference for the control of the process deviation. To compare the performances of the proposed control chart with the existing R-chart or S-chart, some Monte Carlo simulations are performed. The simulation results show that the use of the Gini's mean difference in construction of the control limits has good performance.

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A Sensorless Vector Controller for Induction Motors using an Adaptive Fuzzy Logic

  • Huh, Sung-Hoe;Park, Jang-Hyun;Ick Choy;Park, Gwi-Tae
    • 제어로봇시스템학회:학술대회논문집
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    • pp.162.5-162
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    • 2001
  • This paper presents a indirect vector control system for induction motors using an adaptive fuzzy logic(AFL) speed estimator. The proposed speed estimator is based on the MRAS(Mode Referece Adaptive System) scheme. In general, the MRAS speed estimation approaches are more simple than any other strategies. However, there are some difficulties in the scheme, which are strong sensitivity to the motor parameters variations and necessity to detune the estimator gains caused by different speed area. In this paper, the AFL speed estimator is proposed to solve the problems. The structure of the proposed AFL is very simple. The input of the AFL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed. Moreover, the back propagation algorithm is combined to adjust the parameters of the fuzzy logic to the most appropriate values during the operating the system. Finally, the validity of the ...

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