The resistivity method is frequently used in fields of engineering geology and exploration of mineral resources. The simplicity of the equipment, the low cost of the survey in comparison with other methods, and the abundance of interpretation methods makes it as a popular geophysical method. There are many methods for inversion of resistivity data. Validation of inverted models is an important step in modeling. In general, validation of the resistivity results is performed by calculating the difference between observed and estimated values, but in this study, a validation technique based on data resolution matrix and model resolution matrix is proposed. The applied method for validation of the results has not been used so far, in this research work, resistivity and induced polarization data have been collected using dipole-dipole electrode array in Hamyj copper deposit located near the city of Birjand. The results of data resolution matrix and model resolution matrix have shown that generalized inversion is a suitable method for processing of resistivity data, because both data and model resolution matrix have been close to an identity matrix. ...

Let S be the matrix of residual sum of square in linear modelY=Ab+e, where the matrix of errors is distributed as elliptically contoured with unknown scale matrix S. For Stein loss function, L1 (S ,S) =tr (SˆS-1) -log |(SˆS-1|-p, and squared loss function, L2 ((SˆS-1) =tr ((SˆS-1-I) 2, we offer empirical Bayes estimators of S, which dominate any scalar multiple of S, i.e., aS, by an effective amount. In fact, this study somehow shows that improvement of the empirical Bayes estimators obtained under the normality assumption remains robust under elliptically contoured model.

Capital portfolio management is considered an important issue in the field of economics and its main subject is about the scientific management of combination choice of assets that meet the specific investment objectives. Maximizing returns and minimizing asset risk are the most important goals in the management of the portfolio of capital. This paper proposes two novel risk measures based on the MLP neural networks and prediction intervals (PI). The MLP based risk is constant and assumes that the uncertainty is uniform in the dataset. The second one is a time varying risk measure that doesn’ t assume uniformity condition. After introducing two novel risk measures, a new cost function is presented to consider the expected returns and the involving risk at the same time. Finally, the covariance matrix adaptation evolution strategy (CMA-ES) algorithm is used to obtain the optimal portfolio. The validity of the proposed selection process (including risk measures, cost function, and the optimization method) is tested using the dataset of the 18 shares of the Tehran Stock Exchange, and the results are compared with the obtained portfolio using the conditional value at risk (CVaR) criterion as a well-known benchmark.

Multivariate reward processes with reward functions of constant rates, defined  on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process s Z(t) = (Z1 (t),..., Z p (t)), t ≥ 0 , where Z1(t),...,Zp (t) are reward processes with nonlinear reward functions ρ1 ,...,ρ p respectively. The Laplace transform of the covariance matrix, Σ(t), is specified for given ρ1 ,...,ρ p , and if they are real analytic functions, then the covariance matrix is fully specified. This result in particular provides an explicit formula for the variances of univariate reward processes. We also view Σ(t) as a solution of a renewal equation.

Deviation of financial markets behavior in reality from which the Efficient Market Hypothesis suggests is a serious challenge. This fact is led to emerging of new fields in economics, like behavioral economics/finance and recently Econophysics/finance. These new fields aim to provide new ways of thinking about financial time series as the result of human factors and idealized rationality. They may shed some light on the inner workings of the market. In this study, we want to improve optimizing portfolio selection through denoising the empirical covariance matrix by using Random Matrix Theory.We extract stable relations of stocks from empirical covariance matrix and compare the results of optimized portfolios in noisy and denoised states.We use daily returns of 70 stocks of Tehran Stock Exchange from March-2004 to May-2008. We show that denoised data significantly reduces the real risk of optimized portfolios and is beneficial for risk management and we recommend it to portfolio managers and financial analysts in real market.

In this paper, the problem of covariance matrix design to increase signal-to-interference-plus-noise ratio (SINR) in receiver for multiple-input multiple-output (MIMO) radars is considered. Our goal is to design a covariance matrix which can suppress more interferers compared to phased array radar and recent covariance matrix design methods. It can also result in a better SINR level compared to conventional MIMO radars. In this paper, maximum SINR of the proposed covariance matrix is calculated in closed form. Simulation results show that our proposed covariance matrix in addition to achieve better SINR performance, can suppress more interferers compared to phased array radar and recent covariance matrix design methods, by using waveform diversity with the same number of antennas. Simulation results also validate analytical achievements that presented in this paper.