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The Strong Consistency of Regression Quantiles Estimators in Nonlinear Censored Regression Models

  • Choi, Seung-Hoe
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.1
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    • pp.157-164
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    • 2002
  • In this paper, we consider the strong consistency of the regression quantiles estimators for the nonlinear regression models when dependent variables are subject to censoring, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. one example is given to illustrate the application of the main result.

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Test of the Hypothesis based on Nonlinear Regression Quantiles Estimators

  • Choi, Seung-Hoe
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.153-165
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    • 2003
  • This paper considers the likelihood ratio test statistic based on nonlinear regression quantiles estimators in order to test of hypothesis about the regression parameter $\theta_o$ and derives asymptotic distribution of proposed test statistic under the null hypothesis and a sequence of local alternative hypothesis. The paper also investigates asymptotic relative efficiency of the proposed test to the test based on the least squares estimators or the least absolute deviation estimators and gives some examples to illustrate the application of the main result.

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Separate Fuzzy Regression with Fuzzy Input and Output

  • Choi, Seung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.183-193
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    • 2007
  • This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).

HIGH-DEGREE INTERPOLATION RULES GENERATED BY A LINEAR FUNCTIONAL

  • Kim, Kyung-Joong
    • Communications of the Korean Mathematical Society
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    • v.22 no.3
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    • pp.475-485
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    • 2007
  • We construct high-degree interpolation rules using not only pointwise values of a function but also of its derivatives up to the p-th order at equally spaced nodes on a closed and bounded interval of interest by introducing a linear functional from which we produce systems of linear equations. The linear systems will lead to a conclusion that the rules are uniquely determined for the nodes. An example is provided to compare the rules with the classical interpolating polynomials.

Meshless local collocation method for natural frequencies and mode shapes of laminated composite shells

  • Xiang, Song;Chen, Ying-Tao
    • Structural Engineering and Mechanics
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    • v.51 no.6
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    • pp.893-907
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    • 2014
  • Meshless local collocation method produces much better conditioned matrices than meshless global collocation methods. In this paper, the meshless local collocation method based on thin plate spline radial basis function and first-order shear deformation theory are used to calculate the natural frequencies and mode shapes of laminated composite shells. Through numerical experiments, the accuracy and efficiency of present method are demonstrated.

THE CENSORED REGRESSION QUANTILE ESTIMATORS FOR NONLINEAR REGRESSION MODEL

  • Park, Seung-Hoe
    • Journal of applied mathematics & informatics
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    • v.13 no.1_2
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    • pp.373-384
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    • 2003
  • In this paper, we consider the asymptotic properties of regression quantile estimators for the nonlinear regression model when dependent variables are subject to censoring time, and propose the sufficient conditions which ensure consistency and asymptotic normality for regression quantile estimators in censored nonlinear regression model. Also, we drive the asymptotic relative efficiency of the censored regression model with respect to the ordinary regression model.

Nonlinear Regression Quantile Estimators

  • Park, Seung-Hoe;Kim, Hae kyung;Park, Kyung-Ok
    • Journal of the Korean Statistical Society
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    • v.30 no.4
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    • pp.551-561
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    • 2001
  • This paper deals with the asymptotic properties for statistical inferences of the parameters in nonlinear regression models. As an optimal criterion for robust estimators of the regression parameters, the regression quantile method is proposed. This paper defines the regression quintile estimators in the nonlinear models and provides simple and practical sufficient conditions for the asymptotic normality of the proposed estimators when the parameter space is compact. The efficiency of the proposed estimator is especially well compared with least squares estimator, least absolute deviation estimator under asymmetric error distribution.

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Asymptotic Properties of LAD Esimators of a Nonlinear Time Series Regression Model

  • Kim, Tae-Soo;Kim, Hae-Kyung;Park, Seung-Hoe
    • Journal of the Korean Statistical Society
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    • v.29 no.2
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    • pp.187-199
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    • 2000
  • In this paper, we deal with the asymptotic properties of the least absolute deviation estimators in the nonlinear time series regression model. For the sinusodial model which frequently appears in a time series analysis, we study the strong consistency and asymptotic normality of least absolute deviation estimators. And using the derived limiting distributions we show that the least absolute deviation estimators is more efficient than the least squared estimators when the error distribution of the model has heavy tails.

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Interval Regression Models Using Variable Selection

  • Choi Seung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.13 no.1
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    • pp.125-134
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    • 2006
  • This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

Asymptotics Properties of LAD Estimators in Censored Nonlinear Regression Model

  • Park, Seung-Hoe;Kim, Hae-Kyung
    • Journal of the Korean Statistical Society
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    • v.27 no.1
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    • pp.101-112
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    • 1998
  • This paper is concerned with the asymptotic properties of the least absolute deviation estimators for the nonlinear regression model when dependent variables are subject to censoring time, and proposed the simple and practical sufficient conditions for the strong consistency and asymptotic normality of the least absolute deviation estimators in censored regression model. Some desirable asymptotic properties including the asymptotic relative efficiency of proposed model with respect to standard model are given.

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