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ABSTRACT In this article we prove a central limit theorem for the Lp distance where μ is a weight function and fn is the kernel density estimator proposed by Jones… Click to show full abstract
ABSTRACT In this article we prove a central limit theorem for the Lp distance where μ is a weight function and fn is the kernel density estimator proposed by Jones (1991) for length-biased data. The approach is based on the invariance principle for the empirical processes proved by Horváth (1985). We study the difference In(p) with its approximation in terms of its rates of convergence to zero. We subsequently present a central limit theorem for approximation of In(p).
Keywords:
kernel density;
distance;
length biased;
density estimator;
biased data
Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2017
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Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2017
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!