Asymptotic normality of the mixture density estimator in a disaggregation scheme

Dmitrij Celov, Remigijus Leipus and Anne Philippe

Journal of Nonparametric Statistics, 2010, vol. 22, issue 4, 425-442

Abstract: The paper concerns the asymptotic distribution of the mixture density estimator, proposed by Leipus et al. [Leipus, R., Oppenheim, G., Philippe, A., and Viano, M.-C. (2006), ‘Orthogonal Series Density Estimation in a Disaggregation Scheme’, Journal of Statistical Planning and Inference, 136, 2547–2571], in the aggregation/disaggregation problem of random parameter AR(1) process. We prove that, under mild conditions on the (semiparametric) form of the mixture density, the estimator is asymptotically normal. The proof is based on the limit theory for the quadratic form in linear random variables developed by Bhansali et al. [Bhansali, R.J., Giraitis, L., and Kokoszka, P.S. (2007), Approximations and Limit Theory for Quadratic Forms of Linear Processes’, Stochastic Processes and their Applications, 117, 71–95]. The moving average representation of the aggregated process is investigated. A simulation study illustrates the result.

Date: 2010
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/10485250903045528

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