This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.

The bank of Lithuania in its supervisory functions evaluates how credit unions carries out the prudential norms (capital adequacy, liquidity, maximum open position in foreign currency, the maximum loan amount per borrower, large loan requirements), examine the overall financial condition of the credit union, is watching whether the credit union's activities comply with laws, regulations, statutes and internal union document requirements. However, the steady growth of the credit union's capital, loan number and size of credit unions operating regulations limiting still does not measure the credit union's business risk profile and operational efficiency, protect against failures, errors or loss. The analysis of scientific literature also shows that there is no unanimous and undisputed methodology for enterprises performance evaluation. Moreover, there is no company's evaluation models adapted to specifically interested persons group and one to of the financial institutions, i.e. credit unions. Therefore, performance evaluation of credit union and prediction research of their financial parameters in this area is valuable, relevant and new, in both theoretical and practical terms. The aim - to perform Lithuanian credit unions comparative performance evaluation. The scientific literature suggests that credit unions' assessment can be carried out both traditional (dynamics, structure, relative indicators, business continuity) and the innovative modern methods. It should be emphasized that the evaluation of the activities is individual for each business enterprise, because it depends on many factors, such as: object of assessment, the assessment purpose; the assessment period; assessment of the required information and the availability of data, the evaluation period, the users of evaluation results and other. It should be noted that the main users of credit unions performance assessment results are the owners (partners), creditors and future owners and lenders and borrowers. According to the Securities stock exchange NASDAQ OMX Vilnius (2010) proposed financial ratios calculation methodology were calculated and compared 76 credit unions operating in Lithuania profitability, efficiency, and financial leverage ratios. By using linear and exponential trend methodology analysed the mean trends of Lithuanian credit unions key performance indicators. It should be noted that setup of complex financial indicators, tailored specifically to the financial institution with the opportunity to compare it with other relevant authorities of the credit unions' assessment and performance expected.

Spatial time series model for wind speed data is proposed. Based on few similar papers, first at each location univariate time series model, containing seasonal component and higher order autoregressive component is fitted. After eliminating time dependence in time series, empirical semivariogram based on time independent residuals is fitted and spatial weights for new location are calculated.