Non-Parametric Estimation of a Distribution Function with Interval Censored Data

Samuel Zapata and Carlos Carpio

No 229802, 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas from Southern Agricultural Economics Association

Abstract: Disjoint interval-censored (DIC) observations are found in a variety of applications including survey responses, contingent valuation studies and grouped data. Despite being a recurrent type of data, little attention has been given to their analysis in the nonparametric literature. In this study, we develop an alternative approach for the estimation of the empirical distribution function of DIC data by optimizing their nonparametric maximum likelihood (ML) function. In contrast to Turnbull’s standard nonparametric method, our estimation approach does not require iterative numerical algorithms or the use of advanced statistical software packages. In fact, we demonstrate the existence of a simple closed-form solution to the nonparametric ML problem, where the empirical distribution, its variance, and measures of central tendency can be estimated by using only the frequency distribution of observations. The advantages of our estimation approach are illustrated using two empirical datasets.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Pages: 21
Date: 2016
New Economics Papers: this item is included in nep-ecm and nep-pr~
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Persistent link: https://EconPapers.repec.org/RePEc:ags:saea16:229802

DOI: 10.22004/ag.econ.229802

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