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- W1575390316 abstract "In this paper, we propose two types of estimator (one of histogram type, the other a kernel estimate) of the quantile density (or sparsity) function αmapsto [f(F-1(α ))]-1 associated with the innovation density f of an autoregressive model of order p. Our estimators are based on autoregression quantiles. Contrary to more classical estimators based on estimated residuals, they are autoregression-invariant and scale-equivariant. Their asymptotic behaviour is derived from a uniform Bahadur representation for autoregression quantiles - a result of independent interest. Simulations are carried out to illustrate their performance." @default.
- W1575390316 created "2016-06-24" @default.
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- W1575390316 date "2002-04-01" @default.
- W1575390316 modified "2023-09-23" @default.
- W1575390316 title "Estimation of the innovation quantile density function of an AR(p) process based on autoregression quantiles" @default.
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