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Approximation of density functions by orthogonal series with grouped data
Authors:Lucio Barabesi  Lorenzo Fattorini
Institution:(1) Dipartimento di Metodi Quantitativi, Università di Siena, P.zza S. Francesco 17, 53100 Siena, Italy
Abstract:Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕj} be a complete orthonormal sequence of functions onL 2(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that 
$$f(x) = g(x)\left\{ {1 + \mathop \Sigma \limits_{j = 1}^\infty  \vartheta _j \varphi _j (x)} \right\}$$
over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑj’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.
Keywords:Non-parametric density estimation  grouped data  orthogonal series  M-estimators  epiconverge  asymptotic normality
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