Influence diagnostics in the tobit censored response model

Barros, Michelli; Galea, Manuel; Rodríguez, Manuel González; Leiva, Víctor

doi:10.1007/s10260-010-0135-y

Cited by 41 publications

(27 citation statements)

References 32 publications

(26 reference statements)

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“…It is well known that the validity of the Tobit estimator depends on the assumption of normality (Barros, Galea, Gonzalez and Leiva 2010). Several potential misspecifications in the form of heteroskedasticity and incorrect normal assumption imply inconsistency for the Tobit estimation (Brannas and Laitila 1989).…”

Section: Resultsmentioning

confidence: 99%

Injury-related absenteeism and job satisfaction: insights from Greek and UK data

Drakopoulos

Grimani

2013

The International Journal of Human Resource Management

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Most of the literature on absenteeism suggest that absence from work is a complex issue influenced by multiple causes, both of personal and of organizational nature. Job satisfaction has also been identified as one of the factors affecting an employee's motivation to work attendance. There is no universal agreement concerning the relationship between absenteeism and job satisfaction. Some research have found no correlation between these two variables, whereas other studies indicate a weak relationship between these two variables. It has also been suggested that absence and job satisfaction might be more strongly related under some conditions, for instance in the case of blue-collar workers. There is a lack of attention in the literature, however, to injury-related absenteeism, which is a particular type of absenteeism. This paper attempts to fill this gap and examines the effect of job satisfaction on injury-related absenteeism by using Greek and UK data. The empirical results suggest that there is a negative relationship between injury-related absenteeism and job satisfaction. The paper also discusses possible policy measures towards reducing injury-related absenteeism.

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Section: Resultsmentioning

confidence: 99%

Injury-related absenteeism and job satisfaction: insights from Greek and UK data

Drakopoulos

Grimani

2013

The International Journal of Human Resource Management

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“…. , n, we obtain the Tobit censored response model studied by Barros et al (2010). In addition, if U i ∼ Gamma(ν/2, ν/2) we obtain the Student-t censored regression model developed by Massuia et al (2014).…”

Section: The Modelmentioning

confidence: 99%

“…In order to identify atypical observations and/or model misspecification, we analyzed the transformation of the martingale residual, r MT i , proposed by Barros et al (2010). These residuals are defined by…”

Section: Applicationmentioning

confidence: 99%

Linear censored regression models with scale mixtures of normal distributions

et al. 2015

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In the framework of censored regression models the random errors are routinely assumed to have a normal distribution, mainly for mathematical convenience. However, this method has been criticized in the literature because of its sensitivity to deviations from the normality assumption. Here, we first establish a new link between the censored regression model and a recently studied class of symmetric distributions, which extend the normal one by the inclusion of kurtosis, called scale mixtures of normal (SMN) distributions. The Student-t, Pearson type VII, slash, contaminated normal, among others distributions, are contained in this class. A member of this class can be a good alternative to model this kind of data, because they have been shown its flexibility in several applications. In this work, we develop an analytically simple and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closedform expressions at the E-step, that rely on formulas for the mean and variance of certain truncated SMN distributions. The proposed algorithm is implemented in the R package SMNCensReg. Applications with simulated and a real data set are reported, illustrating the usefulness of the new methodology.

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“…The local influence method is employed in several areas of applied econometrics and statistics. For example, there are a number of applications and studies in regression modelling and time series analysis; see Cook (1986), Galea et al (1997), Liu (2000Liu ( , 2002Liu ( , 2004, Díaz-García et al (2003), Galea et al (2008) and Shi and Chen (2008) for studies in linear regression and time series models, de Castro et al (2007) and Galea and de Castro (2012) for heteroskedastic errors-in-variables models, Leiva et al (2007Leiva et al ( , 2014 for influence diagnostics with censored and uncensored data, Barros et al (2010) for a Tobit model and Paula et al (2012) for robust modelling applied to insurance data. In particular, the local influence method can play an important role in regression models involving restrictions.…”

Section: Introductionmentioning

confidence: 99%

Influence diagnostic analysis in the possibly heteroskedastic linear model with exact restrictions

Liu

Leiva

et al. 2015

Stat Methods Appl

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The local influence method has proven to be a useful and powerful tool for detecting influential observations on the estimation of model parameters. This method has been widely applied in different studies related to econometric and statistical modelling. We propose a methodology based on the Lagrange multiplier method with a linear penalty function to assess local influence in the possibly heteroskedastic linear regression model with exact restrictions. The restricted maximum likelihood estimators and information matrices are presented for the postulated model. Several perturbation schemes for the local influence method are investigated to identify potentially influential observations. Three real-world examples are included to illustrate and validate our methodology.

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Influence diagnostics in the tobit censored response model

Abstract: Cook distance, Likelihood methods, Local influence, Residual analysis,

Cited by 41 publications

References 32 publications

Injury-related absenteeism and job satisfaction: insights from Greek and UK data

Injury-related absenteeism and job satisfaction: insights from Greek and UK data

Linear censored regression models with scale mixtures of normal distributions

Influence diagnostic analysis in the possibly heteroskedastic linear model with exact restrictions

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