JOURNAL ARTICLE

Asymptotic normality of the mixture density estimator in a disaggregation scheme

Dmitrij CelovRemigijus LeipusAnne Philippe

Year: 2010 Journal:   Journal of nonparametric statistics Vol: 22 (4)Pages: 425-442   Publisher: Taylor & Francis

Abstract

The paper concerns the asymptotic distribution of the mixture density estimator, proposed by Oppenheim et al 2006, 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 2007. The moving average representation of the aggregated process is investigated. A small simulation study illustrates the result.

Keywords:
Estimator Mathematics Applied mathematics Asymptotic distribution Limit (mathematics) Quadratic equation Central limit theorem Density estimation Series (stratigraphy) Stochastic process Random variable Statistical inference Statistics Mathematical analysis

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18
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0.90
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Citation History

Topics

Bayesian Methods and Mixture Models
Physical Sciences →  Computer Science →  Artificial Intelligence
Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Financial Risk and Volatility Modeling
Social Sciences →  Economics, Econometrics and Finance →  Finance

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