A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

Caudill, Steven B.

doi:10.1007/s10260-011-0182-z

Cited by 25 publications

(22 citation statements)

References 33 publications

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“…Finite mixtures of normal distributions also presents the problem of an unbounded log-likelihood function (McLachlan and Peel 2000;Caudill 2012), which is made possible by variances not bounded away from zero. The suggested way of avoiding this problem is to restrict the parameter space for the variances.…”

Section: Estimationmentioning

confidence: 99%

“…The simulation also includes the partially adaptive estimator for the censored regression model (PAM), suggested by Caudill (2012). In terms of the FMT estimator, the PAM estimator equals the FMT with the restriction of equal slope coefficients over mixture components, i.e., if β s k denotes the slope coefficients in…”

Section: Simulationmentioning

confidence: 99%

“…The data set contains data on hours worked outside the home for 753 married women, of whom 325 worked zero hours, and data on several other characteristics of the women. In Caudill (2012), the PAM estimator and the Tobit estimator are both applied to the Mroz data. However, the results presented below and in Caudill (2012) are not directly comparable because in the data set in Ecdat there is no data on the "nonwife income", i.e., family income exclusive of the wifes income, as was included in the model of Caudill (2012).…”

Section: An Empirical Example: the Mroz Datamentioning

confidence: 99%

“…Here this model is extended to also include a mixture of slope coefficients. This paper presents results from Monte Carlo simulations comparing estimates derived from a finite mixture of Tobit models with those derived from estimators compared by Moon (1989) and Honoré and Powell (1994), and the partially adaptive estimator of Caudill (2012). The simulation study also includes comparisons of estimators derived from finite mixture models extended with skedastic functions to address potential heteroskedasticity.…”

mentioning

confidence: 99%

“…The mixture is utilized as a means for modeling an unknown distribution, and is not primarily motivated by data containing observations from different populations. Adapting the model by Jedidi et al (1993) for estimation of censored regression models extends the partially adaptive estimator suggested by Caudill (2012), where the distribution of the disturbance term, with the constant term added, is modeled by a mixture of normal distributions. Here this model is extended to also include a mixture of slope coefficients.…”

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confidence: 99%

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Finite mixture modeling of censored regression models

Karlsson

Laitila

2013

Stat Papers

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A finite mixture of Tobit models is suggested for estimation of regression models with a censored response variable. A mixture of models is not primarily adapted due to a true component structure in the population; the flexibility of the mixture is suggested as a way of avoiding non-robust parametrically specified models. The new estimator has several interesting features. One is its potential to yield valid estimates in cases with a high degree of censoring. The estimator is in a Monte Carlo simulation compared with earlier suggestions of estimators based on semi-parametric censored regression models. Simulation results are partly in favor of the proposed estimator and indicate potentials for further improvements.

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Section: Estimationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Section: An Empirical Example: the Mroz Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Finite mixture modeling of censored regression models

Karlsson

Laitila

2013

Stat Papers

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Two‐parameter estimation forTobitmodel: An application to national health and nutrition examination survey dataset

Özbay

Şiray

Toker

et al. 2022

Concurrency and Computation

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Fixed censorship is usually encountered in medical fields, as well as biological, econometric, and engineering researches. It is quite possible to come across censored data with multicollinearity in real-life studies. The multicollinearity affects traditional Tobit maximum likelihood estimation, which is often used in presence of censored data.Biased estimators such as Tobit Liu estimator with one biasing parameter can be used for solving the multicollinearity. In this study, Tobit two-parameter estimator with two biasing parameters is proposed as an alternative to the classical Tobit maximum likelihood and Tobit Liu estimators for the censored data. This new estimator provides advantages from two different aspects thanks to having two biasing parameters. With a simulation study consisting of several scenarios where various levels of factors like the number of independent variables, sample size, correlation degree, variance, and censorship are examined, an in-depth experimental analysis is conducted. In these scenarios, two different biasing parameters' selection methods where one of the biasing parameters is fixed to determine the other one are used and their corresponding outputs are obtained. In addition, a real-life application is carried out with the National Health and Nutrition Examination Survey public-use dataset which includes some demographic and health-related laboratory measurements. The simulation study and data analysis results show that the proposed estimator is more advantageous to its competitors.

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