References of "Journal of Statistical Planning and Inference"
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See detailInvariant density adaptive estimation for ergodic jump diffusion processes over anisotropic classes
Amorino, Chiara UL; Gloter, Arnaud

in Journal of Statistical Planning and Inference (in press)

We consider the solution of a multivariate stochastic differential equation with Levy-type jumps and with unique invariant probability measure with density μ. We assume that a continuous record of ... [more ▼]

We consider the solution of a multivariate stochastic differential equation with Levy-type jumps and with unique invariant probability measure with density μ. We assume that a continuous record of observations is available. In the case without jumps, Reiss and Dalalyan [7] and Strauch [24] have found convergence rates of invariant density estimators, under respectively isotropic and anisotropic H ̈older smoothness constraints, which are considerably faster than those known from standard multivariate density estimation. We extend the previous works by obtaining, in presence of jumps, some estimators which have the same convergence rates they had in the case without jumps for d ≥ 2 and a rate which depends on the degree of the jumps in the one-dimensional setting. We propose moreover a data driven bandwidth selection procedure based on the Goldenshluger and Lepski method [11] which leads us to an adaptive non-parametric kernel estimator of the stationary density μ of the jump diffusion X. [less ▲]

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See detailEfficient inference about the tail weight in multivariate Student t distributions
Neven, Anouk UL; Ley, Christophe

in Journal of Statistical Planning and Inference (2015)

We propose a new testing procedure about the tail weight parameter of multivariate Student t distributions by having recourse to the Le Cam methodology. Our test is asymptotically as efficient as the ... [more ▼]

We propose a new testing procedure about the tail weight parameter of multivariate Student t distributions by having recourse to the Le Cam methodology. Our test is asymptotically as efficient as the classical likelihood ratio test, but outperforms the latter by its flexibility and simplicity: indeed, our approach allows to estimate the location and scatter nuisance parameters by any root-n consistent estimators, hereby avoiding numerically complex maximum likelihood estimation. The finite-sample properties of our test are analyzed in a Monte Carlo simulation study, and we apply our method on a financial data set. We conclude the paper by indicating how to use this framework for efficient point estimation. Keywords and Phrases: efficient testing procedures; likelihood ratio test; local asymptotic normality; Student t distribution; tail weight [less ▲]

Detailed reference viewed: 39 (2 UL)