• 제목/요약/키워드: Truncated Regression Model

검색결과 19건 처리시간 0.02초

Simplicial Regression Depth with Censored and Truncated Data

  • Park, Jinho
    • Communications for Statistical Applications and Methods
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    • 제10권1호
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    • pp.167-175
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    • 2003
  • In this paper we develop a robust procedure to estimate regression coefficients for a linear model with censored and truncated data based on simplicial regression depth. Simplicial depth of a point is defined as the proportion of data simplices containing it. This simplicial depth can be extended to regression problem with censored and truncated data. Any line can be given a depth and the deepest regression line is the line with the maximum simplicial regression depth. We show how the proposed regression performs through analyzing AIDS incubation data.

Bayesian Analysis for a Functional Regression Model with Truncated Errors in Variables

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제31권1호
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    • pp.77-91
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    • 2002
  • This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

토빗모형을 이용한 가로구간 보행자 사고모형 개발 (Developing the Pedestrian Accident Models Using Tobit Model)

  • 이승주;김윤환;박병호
    • 한국도로학회논문집
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    • 제16권3호
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    • pp.101-107
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    • 2014
  • PURPOSES : This study deals with the pedestrian accidents in case of Cheongju. The goals are to develop the pedestrian accident model. METHODS : To analyze the accident, count data models, truncated count data models and Tobit regression models are utilized in this study. The dependent variable is the number of accident. Independent variables are traffic volume, intersection geometric structure and the transportation facility. RESULTS : The main results are as follows. First, Tobit model was judged to be more appropriate model than other models. Also, these models were analyzed to be statistically significant. Second, such the main variables related to accidents as traffic volume, pedestrian volume, number of Entry/exit, number of crosswalk and bus stop were adopted in the above model. CONCLUSIONS : The optimal model for pedestrian accidents is evaluated to be Tobit model.

Efficient Score Estimation and Adaptive Rank and M-estimators from Left-Truncated and Right-Censored Data

  • Chul-Ki Kim
    • Communications for Statistical Applications and Methods
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    • 제3권3호
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    • pp.113-123
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    • 1996
  • Data-dependent (adaptive) choice of asymptotically efficient score functions for rank estimators and M-estimators of regression parameters in a linear regression model with left-truncated and right-censored data are developed herein. The locally adaptive smoothing techniques of Muller and Wang (1990) and Uzunogullari and Wang (1992) provide good estimates of the hazard function h and its derivative h' from left-truncated and right-censored data. However, since we need to estimate h'/h for the asymptotically optimal choice of score functions, the naive estimator, which is just a ratio of estimated h' and h, turns out to have a few drawbacks. An altermative method to overcome these shortcomings and also to speed up the algorithms is developed. In particular, we use a subroutine of the PPR (Projection Pursuit Regression) method coded by Friedman and Stuetzle (1981) to find the nonparametric derivative of log(h) for the problem of estimating h'/h.

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가산자료모형을 이용한 송정 해수욕장의 경제적 가치추정: - 비수기 해수욕장의 가치추정 - (Estimating the Economic Value of the Songieong Beach Using A Count Data Model: - Off-season Estimating Value of the Beach -)

  • 허윤정;이승래
    • 수산경영론집
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    • 제38권2호
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    • pp.79-101
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    • 2007
  • The purpose of this study is to estimate the economic value of the Songieong Beach in Off-season, using a Individual Travel Cost Model(ITCM). Songieong Beach is located in Busan but far away from city. These days, however, the increased rate of traffic inflow to the Songieong beach and the five-day working week are reflected in the trend analysis. Moreover, people have changed psychological value. For that reason, visitors are on the increase on the beach in off-season. The ITCM is applied to estimate non-market value or environmental Good like a Contingent Valuation Method and Hedonic Price Model etc. The ITCM was derived from the Count Data Model(i.e. Poisson and Negative Binomial model). So this paper compares Poisson and negative binomial count data models to measure the tourism demands. The data for the study were collected from the Songjeong Beach on visitors over the a week from November 1 through November 23, 2006. Interviewers were instructed to interview only individuals. So the sample was taken in 113. A dependent variable that is defined on the non-negative integers and subject to sampling truncation is the result of a truncated count data process. This paper analyzes the effects of determinants on visitors' demand for exhibition using a class of maximum-likelihood regression estimators for count data from truncated samples, The count data and truncated models are used primarily to explain non-negative integer and truncation properties of tourist trips as suggested by the economic valuation literature. The results suggest that the truncated negative binomial model is improved overdispersion problem and more preferred than the other models in the study. This paper is not the same as the others. One thing is that Estimating Value of the Beach in off-season. The other thing is this study emphasizes in particular 'travel cost' that is not only monetary cost but also including opportunity cost of 'travel time'. According to the truncated negative binomial model, estimates the Consumer Surplus(CS) values per trip of about 199,754 Korean won and the total economic value was estimated to be 1,288,680 Korean won.

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부트스트랩 DEA를 이용한 공기업 효율성 분석 (An Efficiency Analysis of Public Enterprises Using Bootstrap DEA)

  • 박만희
    • 한국콘텐츠학회논문지
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    • 제15권5호
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    • pp.475-487
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    • 2015
  • 본 연구에서는 부트스트랩 DEA를 이용하여 2013년 14개 공기업의 경영 효율성을 분석하였다. 또한 단절회귀분석을 이용하여 부트스트랩 편의조정 효율성에 영향을 미치는 요인을 분석하였다. 본 연구를 통해 도출된 결과와 시사점을 정리하면 다음과 같다. 첫째, 부트스트랩 DEA모형을 적용한 효율성 분석결과에 따르면 평균 기술효율성이 0.3182, 평균 순수기술효율성이 0.4994, 평균 규모효율성이 0.6585로 나타났으며, 기술비효율성의 주된 원인이 순수기술비효율성에 기인하는 것으로 분석되었다. 둘째, CRS 가정과 VRS 가정하의 일반 DEA모형을 적용한 TE와 부트스트랩 DEA모형을 적용한 TE 순위간에는 유의한 차이가 없는 것으로 나타났다. 셋째, 비효율원인 분석에서는 평가대상 14개 기관 중 11개 기관의 비효율원인이 순수기술에 더 크게 기인하고 있고, 3개 기관이 규모효율에 의해 비효율이 나타나는 것으로 분석되었다. 넷째, 단절회귀분석에서는 규모효율성에 영향을 미치는 요인으로 유의수준 10%에서 인건비, 영업이익, 매출액, 자기자본수익률, 종원원수 순으로 나타났다.

The skew-t censored regression model: parameter estimation via an EM-type algorithm

  • Lachos, Victor H.;Bazan, Jorge L.;Castro, Luis M.;Park, Jiwon
    • Communications for Statistical Applications and Methods
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    • 제29권3호
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    • pp.333-351
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    • 2022
  • The skew-t distribution is an attractive family of asymmetrical heavy-tailed densities that includes the normal, skew-normal and Student's-t distributions as special cases. In this work, we propose an EM-type algorithm for computing the maximum likelihood estimates for skew-t linear regression models with censored response. In contrast with previous proposals, this algorithm uses analytical expressions at the E-step, as opposed to Monte Carlo simulations. These expressions rely on formulas for the mean and variance of a truncated skew-t distribution, and can be computed using the R library MomTrunc. The standard errors, the prediction of unobserved values of the response and the log-likelihood function are obtained as a by-product. The proposed methodology is illustrated through the analyses of simulated and a real data application on Letter-Name Fluency test in Peruvian students.

A Bayesian Method for Narrowing the Scope fo Variable Selection in Binary Response t-Link Regression

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제29권4호
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    • pp.407-422
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    • 2000
  • This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

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자료포락분석(DEA)를 이용한 우리나라 서비스산업의 효율성과 결정요인 분석 (A Study on the Efficiency and Its Determinants in Korea's Service Sectors Using DEA)

  • 배세영
    • 디지털융복합연구
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    • 제19권10호
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    • pp.339-348
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    • 2021
  • 본 연구의 목적은 2010년부터 2019년까지 우리나라 10개의 서비스산업을 중심으로 자료포락분석 (DEA)을 이용하여 생산효율성을 측정하고 단절토빗회귀모형과 중도절단토빗회귀모형을 이용하여 그 결정요인을 분석하는 것이다. 분석결과, 첫째, 추정된 서비스산업의 효율성은 대체적으로 매우 낮았고 양극화되어 있었으며, '하수·폐기물 처리, 원료재생업'의 경우 기술적 비효율성이 규모의 비효율성에 기인하는 것을 알 수 있었다. 둘째, 결정요인 분석에서는 투자와 연구개발비는 기술효율성에 정(+)의 관계에 있고, 해외직접투자와 접대비는 부(-)의 영향을 주는 것으로 측정되었다. 또한, 규모가 큰 산업일수록 효율성도 높은 것으로 나타났다. 따라서, 우리나라 미래의 산업인 서비스산업의 발전을 위한 정부의 경제정책은 투자와 연구개발활동을 통한 거시정책과 해외직접투자와 인적자본의 융합 등 미시적인 부문이 동시에 고려되는 보다 융합적 정책이 필요하다.

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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    • 제26권4호
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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.