Semiparametric econometric estimators for a truncated regression model: a review with an extension

Lee, M.-J.; Kim, H.

doi:10.1111/1467-9574.00078

Cited by 20 publications

(15 citation statements)

References 16 publications

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“…Moreover, for any fixed value of δ n , the estimators obtained by maximizing (4) can be understood as members of the class of M-estimators introduced by Huber (1973), which aim to ''give a good fit to the bulk of the data without being perturbed by a small proportion of outliers'' (Maronna et al, 2006, p. 88). The link between mode regression and robust M-estimators was noted by Lee (1989Lee ( , 1993 and by Lee and Kim (1998), who also noted the close relation between mode regression and the least median of squares estimator of Rousseeuw (1984).…”

Section: Implementation Issuesmentioning

confidence: 72%

Regression towards the mode

Kemp

Silva

2012

Journal of Econometrics

116

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Section: Implementation Issuesmentioning

confidence: 72%

Regression towards the mode

Kemp

Silva

2012

Journal of Econometrics

116

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“…To resolve this issue, we use a model truncated at zero. Lee and Kim (1998) provides detail review and a comparison of properties of estimators for regression models under truncated data.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data

Adegboye

Leung

Wang

2017

Preprint

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This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

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“…Past research has mostly focused on estimators for data which are either censored or truncated but not both. Reviews of estimators for truncated and censored regression models are found in Lee and Kim (1998) and Honoré and Powell (1994), respectively. It is, however, desirable to develop a new, alternative estimator for LTRC data as well.…”

Section: Introductionmentioning

confidence: 99%