In various statistical model, such as density estimation and estimation of regression curves or hazard rates, monotonicity constraints can arise naturally. A frequently encountered problem in nonparametric statistics is to estimate a monotone density function f on a compact interval. A known estimator for density function of f under the restriction that f is decreasing, is Grenander estimator, ...

S. Wright suggested an estimator, m, of the number of loci, m, contributing to the difference in a quantitative character between two differentiated populations, which is calculated from the phenotypic means and variances in the two parental populations and their F1 and F2 hybrids. The same method can also be used to estimate m contributing to the genetic variance within a single population, by...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

We present a new model-based monaural speech separation technique for separating two speech signals from a single recording of their mixture. This work is an attempt to solve a fundamental limitation in current model-based monaural speech separation techniques in which it is assumed that the data used in the training and test phases of the separation model have the same energy level. To overcom...

Robustness is an important performance index of power system state estimation, which defined as the estimator’s capability to resist interference. However, improving robustness estimation often reduces accuracy. To solve this problem, paper proposes a method for generalized M-estimation optimized parameters based on sampling. Compared with traditional robust estimator, M-estimator projection st...

This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive conditional heteroskedastic ~GARCH! model+ The class of estimators includes least absolute deviation and Huber’s estimator in addition to the well-known quasi maximum likelihood estimator+ For some estimators, the asymptotic normality results are obtained only under the existence of fractional u...

A state estimator design is presented for a Mars rover prototype. Odometry estimates are obtained by utilizing the f u l l kinematics of the vehicle including the nonlinear internal kinematics of the rover rocker-bogey mechanism as well as the contact kinematics between the wheels and the ground. Additional sewing using gyroscopes, acclerometers and visual sensors allows for robust rover motion...

The nonparametric estimation of the m-fold convolution power of an unknown function f is considered. We introduce an estimator based on a plug-in approach and a wavelet hard thresholding estimator. We explore its theoretical asymptotic performances via the mean integrated squared error assuming that f has a certain degree of smoothness. Applications and numerical examples are given for the stan...

The discrete reliability states that the difference of the discrete solutions on two arbitrary levels u` and u`+m with respect to triangulations T` and T`+m is bounded by the contributions of the residual-based error estimator on the refined simplices T` \ T`+m only. After some natural split of the error, the additional difficulty for the nonconforming FEMs is to bound min v`+m∈CR(T`+m) ‖∇NC(u`...