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We consider the statistical experiment given by a sample y(1), . . . , y(n) of a stationary Gaussian process with an unknown smooth spectral density f. Asymptotic equivalence, in the sense of Le Cam’s...
We study extremal problems related to nonparametric maximum likelihood estimation (MLE) of a signal in white noise.The aim is to reduce these to standard problems of optimal control which can be solve...
We develop the exact constant of the risk asymptotics in the uniform norm for density estimation. This constant has first been found for nonparametric regression and for signal estimation in Gaussian ...
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
We propose nonparametric methods for estimating the support curve of a bivariate density, when the density decreases at a rate which might vary along the curve. Attention is focussed on cases where th...
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
A method is presented for parameter set estimation of open-loop stable systems with uncertain parameters and quasi-stationary disturbances. The system model is assumed to contain both parametric and n...
Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary non-parametric maximum likelihood density estimators are shown to...
A common practice in obtaining a semiparametric efficient esti-mate is through iteratively maximizing the (penalized) log-likelihood w.r.t.

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