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Sri Haryatmi Kartiko
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics

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NONPARAMETRIC ESTIMATION FOR REGRESSION FUNCTION IN TRUNCATED SAMPLE Sri Haryatmi Kartiko
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.902

Abstract

Truncated sample arise when one do not observe a certain segment of a population. This typically happens when a surveytargets a particular subset of a population and, perhaps due to the cost considerations ignores the other part of thepopulation. This paper focus on the estimation of regression function in samples which are truncated or censored. Severalstudy on the subject has focused on the estimation of a parametric regression function with a certain distribution of error.Others made the similar research for the unknown form of the error distribution. Here an estimator is proposed for theproblem of nonparametric regression when the sample is truncated above or below some known threshold of the dependentvariable. We specify the error distribution while estimating the regression function without assuming a parametric form.Nadaraya Watson estimator is employed, a Monte Carlo study is performed to ascertain the finite sample properties of theestimator.
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