This paper considers delta estimators of the Radon-Nikodym derivative of a probability function with respect to a a-finite measure. We provide sufficient conditions for universal consistency, which are checked for some wide classes of non-parametric estimators. Let P be a probability measure in the Borel space (]l~d,~d), absolutely continuous with respect to the a-finite measure # and f = dP/d#...

An important component of any spatial temporal gradient motion estimation algorithm is the accuracy by which spatial gradients are calculated. When an image sequence is corrupted by noise, the problem of determining these spatial gradients becomes extremely difficult. This is immediately apparent, since the magnitude response of the derivative operator is j!j. In other words, the components of ...

We consider linear differential equations with bounded time delay driven by additive white noise. Our aim is the estimation of the maximal delay time from observations of one realisation of the solution process X under nonparametric drift assumptions. In the stationarity case the covariance function has a jump in the third derivative according to the location of the delay time. Based on this re...

We introduce a simple and linear SNR (strictly speaking, periodic to random power ratio) estimator (0 dB to 80 dB without additional calibration/linearization) for providing reliable descriptions of aperiodicity in speech corpus. The main idea of this method is to estimate the background random noise level without directly extracting the background noise. The proposed method is applicable to a ...

We present a simple but effective fully automated framework for estimating derivatives nonparametrically based on weighted difference sequences. Although regression estimation is often studied more, derivative estimation is of equal importance. For example in the study of exploration of structures in curves, comparison of regression curves, analysis of human growth data, etc. Via the introduced...

Nonparametric regression estimator based on locally weighted least squares fitting has been studied by Fan and Ruppert and Wand. The latter paper also studies, in the univariate case, nonparametric derivative estimators given by a locally weighted polynomial fitting. Compared with traditional kernel estimators, these estimators are often of simpler form and possess some better properties. In th...

Countable-state, continuous-time Markov chains are often analyzed through simulation when simple analytical expressions are unavailable. Simulation is typically used to estimate costs or performance measures associated with the chain and also characteristics like state probabilities and mean passage times. Here we consider the problem of estimating derivatives of these types of quantities with ...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

In this paper we propose two techniques for the estimation of the fundamental frequency of speech signals. The rst technique is based on the Autoregressive Harmonic Excitation (ARHE) speech model. ARHE model consists of an autoregressive process driven simultaneously by white noise and a periodic excitation. The second technique is based on the estimation of a complex sinusoid in white Gaussian...