Load Frequency Control (LFC) has received considerable attention during last decades. This paper proposes a new method for designing decentralized interaction estimators for interconnected large-scale systems and utilizes it to multi-area power systems. For each local area, a local estimator is designed to estimate the interactions of this area using only the local output measurements. In fact,...

 Minimax estimation problems with restricted parameter space reached increasing interest within the last two decades Some authors derived minimax and admissible estimators of bounded parameters under squared error loss and scale invariant squared error loss In some truncated estimation problems the most natural estimator to be considered is the truncated version of a classic...

Let X be a random variable from a normal distribution with unknown mean θ and known variance σ2. In many practical situations, θ is known in advance to lie in an interval, say [−m,m], for some m > 0. As the usual estimator of θ, i.e., X under the LINEX loss function is inadmissible, finding some competitors for X becomes worthwhile. The only study in the literature considered the problem of min...

The growth incidence curve of Ravallion and Chen (2003) is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in tails. authors propose a series parametric models Bayesian framework. A first solution consists modeling underlying income distribution using simple densities for which function has closed analytical form. T...

We consider the problem of minimizing a high-dimensional objective function, which may include regularization term, using only (possibly noisy) evaluations function. Such optimization is also called derivative-free, zeroth-order, or black-box optimization. propose new zeroth-order regularized method, dubbed ZORO. When underlying gradient approximately sparse at an iterate, ZORO needs very few f...

In this study, a robust adaptive unscented Kalman filter (RAUKF) is developed to mitigate the unfavorable effects derived from uncertainties in noise and model. To address these issues, M-estimator first utilized update measurement covariance. Next, deal with of model parameter errors while considering computational complexity real-time requirements dynamic state estimation, an method produced....

Human-based surveys such as medical and social science are often characterized by non-response or missing observations. In this study, a new class of regression-type mean imputation method that uses X̄n an estimate X̄ was suggested. Using partial derivative approach, the MSEs estimators presented were derived up to first order approximation under two cases. Case I: when secondary sample S2 size n...

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...

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...