A NOTE ON MONOTONE COMPARATIVE STATICS FOR MONETARY DIRECTED SEARCH MODELS

This note uses monotone methods to derive two sets of comparative statics results for monetary directed search models. First, it characterizes the impact of a higher inflation rate or a higher cost of using credit on market outcomes, regardless of the choice of matching function. Second, the seller-to-buyer ratio, output level, and money demand increase as the matching function becomes more efficient in a log-supermodular sense. I also consider an extension with endogenous search intensity and show that search intensity and trade volume always decrease in the nominal interest rate.

On renormalized solutions for thermomechanical problems in perfect plasticity with damping forces

We consider the quasi-static evolution of the thermo-plasticity model in which the evolution equation law for the inelastic strain is given by the Prandtl–Reuss flow rule. The thermal part of the Cauchy stress tensor is not linearized in the neighbourhood of a reference temperature. This nonlinear thermal part is imposed to add a damping term to the balance of the momentum, which can be interpreted as external forces acting on the material. In general, the dissipation term occurring in the heat equation is an integrable function only and the standard methods can not be applied. Combining truncation techniques and Boccardo-Gallouët approach with monotone methods, we prove an existence of renormalized solutions.

A Nonmonotone Line Search Filter Algorithm for the System of Nonlinear Equations

We present a new iterative method based on the line search filter method with the nonmonotone strategy to solve the system of nonlinear equations. The equations are divided into two groups; some equations are treated as constraints and the others act as the objective function, and the two groups are just updated at the iterations where it is needed indeed. We employ the nonmonotone idea to the sufficient reduction conditions and filter technique which leads to a flexibility and acceptance behavior comparable to monotone methods. The new algorithm is shown to be globally convergent and numerical experiments demonstrate its effectiveness.

تقدير دالة الأنحدار اللامعلمي باستخدام بعض الطرائق اللامعلمية الرتيبة

This research was concerning to study monotone nonparametric methods for estimating the nonparametric regression function (i.e treatment outlier) to achieve a monotone function (increasing or decreasing). So we will use the monotone methods to treatment outlier but after estimate the regression function with use kernel estimator (Nadarya - Watson) these methods are:- 1- Mukerjee method takes averages of maximums and minimum of subsets of the data was used to adjust the initial kernel regression estimates and use the researcher special case when . 2- Algorithm least square isotonic regression. In the experimental aspect comparison was done of which is the best methods through the simulation procedure using Mote Carlo method using five models. While in the application aspect practical application was done on data represent the measurements for blood pressure patients. In both aspects we use two of the important statistical measures which are Mean square error (MSE) and efficiency. We find through the application that the best method is Mukerjee method for general case as it has minimum Mean square error and maximum efficiency.