• Title, Summary, Keyword: censored dependent variable

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CENSORED FUZZY REGRESSION MODEL

  • Choi, Seung-Hoe;Kim, Kyung-Joong
    • Journal of the Korean Mathematical Society
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    • v.43 no.3
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    • pp.623-634
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    • 2006
  • Various methods have been studied to construct a fuzzy regression model in order to present a fuzzy relation between a dependent variable and an independent variable. However, in the fuzzy regression analysis the value of the center point of estimated fuzzy output may be either greater than the value of the right endpoint or smaller than the value of the left endpoint. In the case, we cannot predict the fuzzy output properly. This paper presents sufficient conditions to construct the fuzzy regression model using several methods investigated by some authors and then introduces the censored fuzzy regression model using the censored samples to manipulate the problem of crossing of the center and the end points of the estimated fuzzy number. Examples show that the censored fuzzy regression model is an extension of the fuzzy regression model and also it improves the problem of crossing.

The restricted maximum likelihood estimation of a censored regression model

  • Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.24 no.3
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    • pp.291-301
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    • 2017
  • It is well known in a small sample that the maximum likelihood (ML) approach for variance components in the general linear model yields estimates that are biased downward. The ML estimate of residual variance tends to be downwardly biased. The underestimation of residual variance, which has implications for the estimation of marginal effects and asymptotic standard error of estimates, seems to be more serious in some limited dependent variable models, as shown by some researchers. An alternative frequentist's approach may be restricted or residual maximum likelihood (REML), which accounts for the loss in degrees of freedom and gives an unbiased estimate of residual variance. In this situation, the REML estimator is derived in a censored regression model. A small sample the REML is shown to provide proper inference on regression coefficients.

Restricted maximum likelihood estimation of a censored random effects panel regression model

  • Lee, Minah;Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.26 no.4
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    • pp.371-383
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    • 2019
  • Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

Bayesian Inference for Censored Panel Regression Model

  • Lee, Seung-Chun;Choi, Byongsu
    • Communications for Statistical Applications and Methods
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    • v.21 no.2
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    • pp.193-200
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    • 2014
  • It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.

Estimation on Modified Proportional Hazards Model

  • Lee, Kwang-Ho;Lee, Mi-Sook
    • Journal of the Korean Data and Information Science Society
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    • v.5 no.1
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    • pp.59-66
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    • 1994
  • Heller and Simonoff(1990) compared several methods of estimating the regression coefficient in a modified proportional hazards model, when the response variable is subject to censoring. We give another method of estimating the parameters in the model which also allows the dependent variable to be censored and the error distribution to be unspecified. The proposed method differs from that of Miller(1976) and that of Buckely and James(1979). We also obtain the variance estimator of the coefficient estimator and compare that with the Buckely-James Variance estimator studied by Hillis(1993).

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The determinants of the youth employment rate using panel tobit model (패널 토빗모형을 이용한 청년채용비율 결정요인 분석)

  • Park, Sungik;Ryu, Jangsoo;Kim, Jonghan;Cho, Jangsik
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.4
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    • pp.853-862
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    • 2017
  • In this study, we analyse the determinants of the youth employment rate of public agencies and local public enterprises. On the other hand the youth employment rate contains information of the youth employment rate and the size of the youth employment. We use pooled tobit model and panel tobit model since dependent variable is a censored form observed only in a certain area. The results of the analysis are summarized as follows. First, the panel tobit model is more statistically significant as compared to the combined tobit model. Second, the youth employment rate is more statistically significantly higher in 2014 and 2015 than in 2011. Third, the youth employment rate in public enterprises is more statistically significantly higher than that in local public agencies. Finally, the higher the average wage is, the lower the youth employment ratio is.