Abstract. We consider the problem of estimating a vector x in the linear model Ax ≈ y, where A is a block circulant (BC) matrix with N blocks and x is assumed to have a weighted norm bound. In the case where both A and y are subjected to noise, we propose a minimax mean-squared error (MSE) approach in which we seek the linear estimator that minimizes the worst-case MSE over a BC structured unce...

in this paper, drift change point estimation in the mean of polynomial profiles is considered. for this purpose, the proposed change point estimator is computed using maximum likelihood approach. performance of the proposed estimator is evaluated using monte carlo simulations when t2 control chart issues an out-of-control signal. simulation results show that the performance of the proposed esti...

This paper considers the design of robust`1 estimators based on multiplier theory (which is intimately related to the mixed structured singular value). Speciically, the Popov-Tsypkin multiplier is used to develop an upper bound on an`1 cost function over an uncertainty set. The robust`1 estimation problem is formulated as a parameter optimization problem in which the upper bound is minimized su...

We study parameter estimation and asymptotic inference for sparse nonlinear regression. More specifically, we assume the data are given by y = f(x>β∗) + , where f is nonlinear. To recover β∗, we propose an `1-regularized least-squares estimator. Unlike classical linear regression, the corresponding optimization problem is nonconvex because of the nonlinearity of f . In spite of the nonconvexity...

The ability to quantify uncertainty in information extracted from spectroscopic measurements is important in numerous fields. The traditional approach of repetitive measurements may be impractical or impossible in some measurements scenarios, while chi-squared analysis does not provide insight into the sources of uncertainty. As such, a need exists for analytical expressions for estimating unce...

In this paper, we consider performance output tracking for a boundary controlled one-dimensional wave equation with possibly unknown internal nonlinear uncertainty and external disturbance. We first show that the open-loop system is well-posed and then propose a disturbance estimator. It is shown that the disturbance estimator can estimate successfully the total disturbance that consists of int...

One of the major sources of uncertainty associated with geographical data in GIS arises when they are the outcome of a sampling process. It is well known that when sampling from a spatially autocorrelated homogeneous surface, stratification reduces the error variance of the estimator of the population mean. In this study, we evaluate the efficiency of different spatial sampling strategies when ...

The uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function network (RBF network) as an uncertainty estimator. The ...

Corresponding Author: Joseph Gladwin Sekar Department of ECE, SSN College of Engineering, Kalavakkam, India Email: [email protected] Abstract: This paper, deals with H-infinity channel estimation technique for Orthogonal Frequency Division Multiplexing (OFDM) systems, in fading channel. Channel estimation is important for wireless applications because, in practical transmission scenario, chann...

In this paper we present a novel method to obtain both improved estimates and reliable stopping rules for stochastic optimization algorithms such as the Monte Carlo EM (MCEM) algorithm. By characterizing a stationary point, θ∗, of the algorithm as the solution to a fixed point equation, we provide a parameter estimation procedure by solving for the fixed point of the update mapping. We investig...