• Title/Summary/Keyword: Regression Model

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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.

A Study on Change of Logistics in the region of Seoul, Incheon, Kyunggi (물류예측모형에 관한 연구 -수도권 물동량 예측을 중심으로-)

  • Roh Kyung-Ho
    • Management & Information Systems Review
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    • v.7
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    • pp.427-450
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    • 2001
  • This research suggests the estimation methodology of Logistics. This paper elucidates the main problems associated with estimation in the regression model. We review the methods for estimating the parameters in the model and introduce a modified procedure in which all models are fitted and combined to construct a combination of estimates. The resulting estimators are found to be as efficient as the maximum likelihood (ML) estimators in various cases. Our method requires more computations but has an advantage for large data sets. Also, it enables to detect particular features in the data structure. Examples of real data are used to illustrate the properties of the estimators. The backgrounds of estimation of logistic regression model is the increasing logistic environment importance today. In the first phase, we conduct an exploratory study to discuss 9 independent variables. In the second phase, we try to find the fittest logistic regression model. In the third phase, we calculate the logistic estimation using logistic regression model. The parameters of logistic regression model were estimated using ordinary least squares regression. The standard assumptions of OLS estimation were tested. The calculated value of the F-statistics for the logistic regression model is significant at the 5% level. The logistic regression model also explains a significant amount of variance in the dependent variable. The parameter estimates of the logistic regression model with t-statistics in parentheses are presented in Table. The object of this paper is to find the best logistic regression model to estimate the comparative accurate logistics.

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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.

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).

Analysis of health-related quality of life using Beta regression (베타회귀분석 방법을 이용한 건강 관련 삶의 질 자료 분석)

  • Jang, Eun Jin
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.3
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    • pp.547-557
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    • 2017
  • The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.

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.

An application to Multivariate Zero-Inflated Poisson Regression Model

  • Kim, Kyung-Moo
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.177-186
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    • 2003
  • The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the correlated response variables are intrested, we have to extend the univariate zero-inflated regression model to multivariate model. In this paper, we study and simulate the multivariate zero-inflated regression model. A real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of multivariate zero-inflated Poisson regression model with the decision tree model.

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The Distributions of Variance Components in Two Stage Regression Model

  • Park, Dong-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.1
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    • pp.87-92
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    • 1996
  • A regression model with nested erroe structure is considered. The regression model includes two error terms that are independent and normally distributed with zero means and constant variances. This error structure of the model gives correlated response variables. The distributions of variance components in the regression model with nested error structure are dervied by using theorems for quadratic forms.

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Estimation of Asymmetric Bell Shaped Probability Curve using Logistic Regression (로지스틱 회귀모형을 이용한 비대칭 종형 확률곡선의 추정)

  • 박성현;김기호;이소형
    • The Korean Journal of Applied Statistics
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    • v.14 no.1
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    • pp.71-80
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    • 2001
  • Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.

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