Kernel density estimation with Berkson error
نویسندگان
چکیده
منابع مشابه
Density estimation with heteroscedastic error
It is common, in deconvolution problems, to assume that the measurement errors are identically distributed. In many real life applications however, this condition is not satisfied and the deconvolution estimators developed for homoscedastic errors become inconsistent. In this paper, we introduce a kernel estimator of a density in the case of heteroscedastic contamination. We establish consisten...
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Preface The following diploma thesis is thought to be a diploma thesis in applied statistics. I declare this in the first paragraph of my work, because you can treat this subject either from a theoretic or an applied view, although the borders between these two areas of statistics cannot be drawn exactly. The reason why I got the idea to treat this subject, is that on the one hand density estim...
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Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is no...
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ژورنال
عنوان ژورنال: Canadian Journal of Statistics
سال: 2016
ISSN: 0319-5724
DOI: 10.1002/cjs.11281