throughout the current study, the appropriate function for a description of egg production curve in a commercial broiler dam line was determined through fittings of a number of six mathematical models, namely: incomplete gamma function (wm): modified incomplete gamma function (mwm): compartmental function (cm): modified compartmental function )mcm): polynomial regression function of ali and sch...

The term “empirical predictor” refers to a two-stage predictor of a linear combination of fixed and random effects. In the first stage, a predictor is obtained but it involves unknown parameters; thus, in the second stage, the unknown parameters are replaced by their estimators. In this paper, we consider mean squared errors (MSE) of empirical predictors under a general setup, where ML or REML ...

In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is a striking observation that the ensemble mean often out-performs at least half of the ensemble members in mean squared error (measured with respect to observations). In fact, as demonstrated in the most recent IPCC report, the ensemble mean often out-performs all...

In this paper, we model complex signals by approximating the phase and the logarithm of the time-varying amplitude of the signal as a finite-order polynomial. We refer to a signal that has this form as an exponential polynomial signal (EPS). We derive an iterative maximum-likelihood (ML) estimation algorithm to estimate the unknown parameters of the EPS model. The initialization of the ML algor...

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 continuation to an earlier work, we further develop bounds on the mean-squared error (MSE) when estimating a deterministic parameter vector 0 in a given estimation problem, as well as estimators that achieve the optimal performance. The traditional Cramér–Rao (CR) type bounds provide benchmarks on the variance of any estimator of 0 under suitable regularity conditions, while requiring a prio...

Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number sources. To that end, the receiver estimates autocorrelation matrix a larger virtual uniform linear array (coarray), by applying selection or averaging to physical array's estimates, followed spatial-smoothing. Both and have been designed under no optimality criterion attain arbitrary (suboptimal) Mean-Squared-Er...