The presence of endogenous variables in the statistical models leads to inconsistent and bias estimators for the parameters. In this case, several approaches have been proposed which are able to tackle the biase and inconsistency problems only in large sample situations. One of these methods is biased on instrumental variables which causes removing endogenous variables. The method of two-stage least squares is another approach in this case that it has more accurate than ordinary least squares.This paper aims to enhance the accuracy of three methods of estimation based upon least square methodology called, two-stage iterative least squares, two-stage Jackknife least squares and also two-stage calibration least squares. In order to evaluate the performance of each method, a simulation study is conducted. Also, using data collected in 1390 related to the cost and revenue in Iran, those methods to estimate parameters are compared.

In this paper, we obtain the generalized least square, restricted generalized least square and shrinkage estimators for the regression vector parameter assuming that the errors have multivariate t distribution. Also we calculate their quadratic risks and propose the dominance order of the underlying estimators.

Increasing precision and stability in the online estimation of a spacecraft's model, due to the uncertainty and noise is one of the challenges in the attitude control of the space systems. The least squares error method in combination with the Kalman filter is one of the effective methods for estimating these types of dynamic models. Based on the development of the GMRES (generalized minimal residual) methods, in order to increase the performance of the estimation method, a newonline meta-heuristic algorithm is proposed. The algorithm is an iterative-based method that uses previous step information based on user experience, or a new online meta-heuristic method which determines the number of steps to solve the matrix equations system in the Krylov subspace and improves overall convergence to the response. In order to evaluate the accuracy of this estimation method, the GMRES, Bi-CG (Bi Conjugate Gradient), CGS (Conjugate Gradients Squared), BI-CGSTAB (Bi Conjugate Gradient Stabilized) methods are compared that the online meta-heuristic GMRES method shows the highest accuracy and stability in the estimation.

Memristor, as a fundamental element of SRAM and DRAM memories, can effectively reduce startup time and power consumption of the circuits. Non-volatility, high density of the final circuit, and reduction of power delay product (PDP) are some of the significant facts of memristor circuits, which has led to the suggestion of a memory cell including and four transistors and two memristors (4T2M) in this paper. In order to simulate the proposed memory cell, the length of memristors has been selected 10 nm, and their on/off state resistors have been selected 250 Ω and 10 KΩ respectively. In addition, the proposed memory cell MOS transistors are simulated by the 32 nm CMOS PTM model. Simulation in the HSPICE software with 1V supply voltage and comparison with two conventional six-transistor (6T) and two transistors-two memory (2T2M) cells show that the use of memristors has made the proposed memory cell and 2T2M cell non-volatile. Moreover, the power consumption of the proposed circuit has decreased by 99. 8% and 57. 2%, compared to the previous two circuits respectively, and the power average delay product has also improved by 99. 4% and 26. 7%, respectively; however, the writing delay of this cell and 2T2M cell increased by 400% and 218% compared to 6T cell, respectively.

In this paper a novel method for the solution of parabolic equations is presented. The solution of parabolic equation is expanded by the 2nd order spline basis function. Then the expansion coefficients are computed by the least square method (LSM). The minimization of least square functional directly leads to the solution of the parabolic equation. It will be seen that the proposed LSM is more reliable than the split step method, because it can be applied over impedance boundaries in a simple manner. In comparison with the finite difference method, the proposed LSM is very fast.