• Title/Summary/Keyword: Linear Regression Fit

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Estimation of the Number of Change-Points with Local Linear Fit

  • Kim, Jong-Tae;Choi, Hey-Mi
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.2
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    • pp.251-260
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    • 2002
  • The aim of this paper is to consider of detecting the location, the jump size and the number of change-points in regression functions by using the local linear fit which is one of nonparametric regression techniques. It is obtained the asymptotic properties of the change points and the jump sizes. and the correspondin grates of convergence for change-point estimators.

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Change-Points with Jump in Nonparametric Regression Functions

  • Kim, Jong-Tae
    • 한국데이터정보과학회:학술대회논문집
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    • 2005.04a
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    • pp.193-199
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    • 2005
  • A simple method is proposed to detect the number of change points with jump discontinuities in nonparamteric regression functions. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Also, the proposed methodology is suggested as the test statistic for detecting of change points and the direction of jump discontinuities.

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Detection of Change-Points by Local Linear Regression Fit;

  • Kim, Jong Tae;Choi, Hyemi;Huh, Jib
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.31-38
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    • 2003
  • A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

Fuzzy Local Linear Regression Analysis

  • Hong, Dug-Hun;Kim, Jong-Tae
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.2
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    • pp.515-524
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    • 2007
  • This paper deals with local linear estimation of fuzzy regression models based on Diamond(1998) as a new class of non-linear fuzzy regression. The purpose of this paper is to introduce a use of smoothing in testing for lack of fit of parametric fuzzy regression models.

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Some Results on the Log-linear Regression Diagnostics

  • Yang, Mi-Young;Choi, Ji-Min;Kim, Choong-Rak
    • Communications for Statistical Applications and Methods
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    • v.14 no.2
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    • pp.401-411
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    • 2007
  • In this paper we propose an influence measure for detecting potentially influential observations using the infinitesimal perturbation and the local influence in the log-linear regression model. Also, we propose a goodness-of-fit measure for variable selection. A real data set are used for illustration.

A Technique to Improve the Fit of Linear Regression Models for Successive Sets of Data

  • Park, Sung H.
    • Journal of the Korean Statistical Society
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    • v.5 no.1
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    • pp.19-28
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    • 1976
  • In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

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Comparison of Powers in Goodness of Fit Test of Quadratic Measurement Error Model

  • Moon, Myung-Sang
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.229-240
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    • 2002
  • Whether to use linear or quadratic model in the analysis of regression data is one of the important problems in classical regression model and measurement error model (MEM). In MEM, four goodness of fit test statistics are available In solving that problem. Two are from the derivation of estimators of quadratic MEM, and one is from that of the general $k^{th}$-order polynomial MEM. The fourth one is derived as a variation of goodness of fit test statistic used in linear MEM. The purpose of this paper is to find the most powerful test statistic among them through the small-scale simulation.

A Flexible Statistical Growth Model for Describing Plant Disease Progress (식물병(植物病) 진전(進展)의 한 유연적(柔軟的)인 통계적(統計的) 생장(生長) 모델)

  • Kim, Choong-Hoe
    • Korean journal of applied entomology
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    • v.26 no.1 s.70
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    • pp.31-36
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    • 1987
  • A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

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Hidden Truncation Normal Regression

  • Kim, Sungsu
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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    • pp.793-798
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    • 2012
  • In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

FUZZY REGRESSION MODEL WITH MONOTONIC RESPONSE FUNCTION

  • Choi, Seung Hoe;Jung, Hye-Young;Lee, Woo-Joo;Yoon, Jin Hee
    • Communications of the Korean Mathematical Society
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    • v.33 no.3
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    • pp.973-983
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    • 2018
  • Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.