The improving investment climate objective requires a comprehensive approach to the regulatory framework enhancement. Policy Framework for Investment (PFI) is a significant OECD’s investment tool which makes possible to identify the key obstacles to the inflow foreign direct investment and to determine the main measures to overcome them. Using PFI by Russian authorities would allow a systematic monitoring of the national investment policy and also take steps to improve the effectiveness of sustainable development promotion regulations.
The paper approaches the problem of private fixed capital underinvestment in Russia. The author uses empirical studies of the Russian economy and cases of successful technological modernization to outline several groups of disincentives for private companies to perform fixed capital investment in Russia. To counter these constraints, a certain incentive-based economic policy framework is developed.
The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.
Classification spanning correlated data streams
