High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters...

the best quadratic unbiased estimation (bque) of variance components in the gauss-helmert model is used to combine adjustment of gps/levelling and geoid to determine the individual variance components for each of the three height types. through the research, different reasons for achievement of the negative variance components were discussed and a new modified version of the best quadratic unbi...

A unifying theoretical and algorithmic framework for diffusion tensor estimation is presented. Theoretical connections among the least squares (LS) methods, (linear least squares (LLS), weighted linear least squares (WLLS), nonlinear least squares (NLS) and their constrained counterparts), are established through their respective objective functions, and higher order derivatives of these object...

This paper is concerned with the variance-constrained state estimation problem for a class of networked multi-rate systems (NMSs) with network-induced probabilistic sensor failures and measurement quantization. The stochastic characteristics of the sensor failures are governed by mutually independent random variables over the interval [0, 1]. By applying the lifting technique, an augmented syst...

This paper deals with the state estimation of a strongly nonlinear system. In a noisy state space representation setting, Central Difference Kalman Filter, Ensemble Kalman Filter and Particle Filter are tested on a second order system. The choice of estimators parameters is then discussed, and their behaviour in relation to noise is studied, in order to compare estimation quality according to n...

In this paper, we propose a new methodology to build latent variables that are optimal if a nonlinear model is used afterward. This method is based on Nonparametric Noise Estimation (NNE). NNE is providing an estimate of the variance of the noise between input and output variables. The linear projection that builds latent variables is optimized in order to minimize the NNE. We successfully test...

Taylor linearization is a popular method of variance estimation for complex statistics such as ratio and regression estimators and logistic regression coefficient estimators. It is generally applicable to any sampling design that permits unbiased variance estimation for linear estimators, and it is computationally simpler than a resampling method such as the jackknife. However, it can lead to m...

The method of using Global Positioning System-leveling data to obtain orthometric heights has been well studied. A simple formulation for the weighted least squares problem has been presented in an earlier work. This formulation allows one directly employing the errors-in-variables models which completely descript the covariance matrices of the observables. However, an important question that w...

Today, in the rangeland management science and determination of rangecapacity, accurate and true information about range production is crucial. In fact, rangeproduction is considered as a basis for range management. The aim of this study was tocompare different methods for the estimation of forage production with four samplingmethods in the rangelands of Kurdistan province, Iran. The sampling m...