We investigate the general strategy of measuring cosmologically induced polarization by analyzing the polarization signature in the small scale limit. Polarization induced by cosmological scalar perturbations leads to a typical anisotropy pattern in the power spectrum, which can best be analyzed in Fourier domain. This allows one to unambiguously distinguish cosmological signal of polarization ...

We propose a nonparametric discrimination method based on a nonparametric Nadaray-Watson kernel regression type-estimator of the posterior probability that an incoming observed vector is a given class. To overcome the curse of dimensionality of the multivariate kernel density estimate, we introduce a variance stabilizing approach which constructs independent predictor variables. Then, the multi...

We prove a stochastic expansion for a residual-based estimator of the error distribution function in a partly linear regression model. It implies a functional central limit theorem. As special cases we cover nonparametric, nonlinear and linear regression models.

Recently Peres and Shields discovered a new method for estimating the order of a stationary fixed order Markov chain [15]. They showed that the estimator is consistent by proving a threshold result. While this threshold is valid asymptotically in the limit, it is not very useful for DNA sequence analysis where data sizes are moderate. In this paper we give a novel interpretation of the Peres-Sh...

Abstract. We present an error estimator based on firstand second-order derivatives recovery for finite element adaptive analysis. At first, we briefly discuss the abstract framework of the adopted error estimation techniques. Some possibilities of derivatives recovery are considered, including the proposal of a directional error estimator. Using the directional error estimator proposed, an adap...

A nonlinear regression model with correlated, normally distributed errors with non zero means is investigated. The limit properties of bias and the mean square error matrix of the approximate least squares estimator of regression parameters are studied.

A nonlinear regression model with correlated, normally distributed errors is investigated. The bias and the mean square error matrix of the approximate least squares estimator of regression parameters are derived and their limit properties are studied.

A fast and efficient estimation method is proposed that compensates the distortion in nonlinear transformation models. A likelihood-based estimator is developed that can be computed by an EM-type algorithm. The consistency of the estimator is shown and its limit distribution is provided. The new estimator is particularly well suited for fluorescence lifetime measurements, where only the shortes...

When estimating high-frequency covariance (quadratic covariation) of two arbitrary assets observed asynchronously, simple assumptions, such as independence, are usually imposed on the relationship between the prices process and the observation times. In this paper, we introduce a general endogenous two-dimensional nonparametric model. Because an observation is generated whenever an auxiliary pr...

Process capability of a process is defined as inherent variability of a process which is running under chance cause of variation only. Process capability index is measuring the ability of a process to meet the product specification limit. Generally process capability is measured by 6 assuming that the product characteristic follows Normal distribution. In many practical situations the product ...