Statistical Tools for Estimation of Threshold Values at Data Classification Task Solution

Affiliation(s)

Department of Statistics, Novosibirsk State University of Economics and Management, Novosibirsk, Russian Federation.

Department of Statistics, Novosibirsk State University of Economics and Management, Novosibirsk, Russian Federation.

ABSTRACT

The paper contains a summary of some results of original research total aggregates. The main idea is determining the boundaries of the groups for classification of fuzzy and threshold aggregates using the method of decomposing a mixture of probability distributions. The article presents the experience of partitions of a real aggregate as a finite mixture of probability distributions on private aggregates. Threshold value defined by the boundaries of private aggregates, will match the value of the phenomenon at the intersection of the curves of probability distributions, which extracted from the mixture. The proposed scheme of identification threshold aggregates has found practical application in the research of aggregate of Russian employees by level of payroll and establishing the optimal minimum value monthly wage. The official data of the Federal State Statistics Service were used.

The paper contains a summary of some results of original research total aggregates. The main idea is determining the boundaries of the groups for classification of fuzzy and threshold aggregates using the method of decomposing a mixture of probability distributions. The article presents the experience of partitions of a real aggregate as a finite mixture of probability distributions on private aggregates. Threshold value defined by the boundaries of private aggregates, will match the value of the phenomenon at the intersection of the curves of probability distributions, which extracted from the mixture. The proposed scheme of identification threshold aggregates has found practical application in the research of aggregate of Russian employees by level of payroll and establishing the optimal minimum value monthly wage. The official data of the Federal State Statistics Service were used.

Cite this paper

Glinskiy, V. , Serga, L. , Chemezova, E. and Zaykov, K. (2014) Statistical Tools for Estimation of Threshold Values at Data Classification Task Solution.*Open Journal of Statistics*, **4**, 736-741. doi: 10.4236/ojs.2014.49068.

Glinskiy, V. , Serga, L. , Chemezova, E. and Zaykov, K. (2014) Statistical Tools for Estimation of Threshold Values at Data Classification Task Solution.

References

[1] Glinskiy, V.V. (2008) Statistical Methods to Support Management Decisions: Monograph. Publishing NSUEM, Novosibirsk. (Statisticheskie metody podderzhki upravlencheskih reshenij).

[2] Glinskiy, V.V. (2008) Mystical Small Business Statistics. Problems of Statistical Study of Turbulent Sets. ECO, 9, 51-61.

[3] Glinskiy, V.V. and Serga, L.K. (2011) Statistics of the XXI Century. Vector of Development. Vestnik NSUEM, 1, 108-118.

[4] Glinskiy, V.V. and Serga, L.K. (2011) On State Regulation of Small Business in Russia National Interests: Priorities and Safety, 19, 2-8.

[5] Serga, L.K. (2013) Research of Innovation Activity of Small and Medium-Sized Business. Vestnik NSUEM, 1, 112-140.

[6] Chemezova, E.Yu. (2010) Typology of RF Subjects by Level of Social and Economic Development. Vestnik NSUEM, 1, 171-176.

[7] Glinskiy, V.V. and Serga, L.K. (2009) Nonstable Aggregates: Conceptual Foundation of Statistical Study Methodology. Vestnik NSUEM, 2, 137-142.

[8] Glinskiy, V.V. and Chemezova, E.Yu. (2012) On Convergence of Main Concepts of Typology of Social-Economic Studies Data. Vestnik NSUEM, 2, 67-73.

[9] Serga, L.K. (2012) On the Approach to the Definition of the Threshold Values in the Solution of Classification. Vestnik NSUEM, 1, 54-60.

[10] Serga, L.K., Nikiforova, M.I., Rumynskaya, E.S. and Khvan, M.S. (2012) Applied Use of Portfolio Analysis Methods. Vestnik NSUEM, 3, 146-158.

[11] Serga, L.K. (2013) On Approaches to Solution of the Problem of Fuzzy Aggregations. Vestnik NSUEM, 3, 83-91.

[12] (2014) Labor Code of the Russian. Art. 133. In: Consultant plus Version Professional. (Trudovoj kodeks Rossijskoj Federacii).

http://www.consultant.ru/popular/tkrf/

[13] Orlov, A.I. (2004) Non-Numeric Statistics. MZ-Press, Moscow. (Neparametricheskaja statistika).

[14] Tu, J. and Gonzalez, C. (1978) Principles of Pattern Recognition: Monograph. Mir, Moscow.

[15] Zaykov, K.A. (2013) Research of the Threshold Aggregates by Decomposition of Mixtures Distributions. Scientific Works of the Free Economic Society of Russia, 172, 192-202.

[16] Everitt, B.S. (2010) Large Dictionary of Statistics. 3rd Edition, Prospect, Moscow. (Bol’shoj slovar’ po statistike).

[17] Venetskiy, I.G. and Venetskiy, V.I. (1974) Basic Mathematical and Statistical Concepts and Formulas in the Economic Analysis. Statistics, Moscow. (Osnovnye matematiko-statisticheskie ponjatija i formuly v jekonomicheskom analize).

[18] Wentzel, E.S. and Ovcharov, L.A. (2000) Probability Theory and Its Engineering Applications. Textbook. Manual for Technical Schools. 2nd Edition, Higher School, Moscow. (Teorija verojatnosti i ee inzhenernye prilozhenija).

[19] Official Website of the Federal State Statistics Service.

http://www.gks.ru

[1] Glinskiy, V.V. (2008) Statistical Methods to Support Management Decisions: Monograph. Publishing NSUEM, Novosibirsk. (Statisticheskie metody podderzhki upravlencheskih reshenij).

[2] Glinskiy, V.V. (2008) Mystical Small Business Statistics. Problems of Statistical Study of Turbulent Sets. ECO, 9, 51-61.

[3] Glinskiy, V.V. and Serga, L.K. (2011) Statistics of the XXI Century. Vector of Development. Vestnik NSUEM, 1, 108-118.

[4] Glinskiy, V.V. and Serga, L.K. (2011) On State Regulation of Small Business in Russia National Interests: Priorities and Safety, 19, 2-8.

[5] Serga, L.K. (2013) Research of Innovation Activity of Small and Medium-Sized Business. Vestnik NSUEM, 1, 112-140.

[6] Chemezova, E.Yu. (2010) Typology of RF Subjects by Level of Social and Economic Development. Vestnik NSUEM, 1, 171-176.

[7] Glinskiy, V.V. and Serga, L.K. (2009) Nonstable Aggregates: Conceptual Foundation of Statistical Study Methodology. Vestnik NSUEM, 2, 137-142.

[8] Glinskiy, V.V. and Chemezova, E.Yu. (2012) On Convergence of Main Concepts of Typology of Social-Economic Studies Data. Vestnik NSUEM, 2, 67-73.

[9] Serga, L.K. (2012) On the Approach to the Definition of the Threshold Values in the Solution of Classification. Vestnik NSUEM, 1, 54-60.

[10] Serga, L.K., Nikiforova, M.I., Rumynskaya, E.S. and Khvan, M.S. (2012) Applied Use of Portfolio Analysis Methods. Vestnik NSUEM, 3, 146-158.

[11] Serga, L.K. (2013) On Approaches to Solution of the Problem of Fuzzy Aggregations. Vestnik NSUEM, 3, 83-91.

[12] (2014) Labor Code of the Russian. Art. 133. In: Consultant plus Version Professional. (Trudovoj kodeks Rossijskoj Federacii).

http://www.consultant.ru/popular/tkrf/

[13] Orlov, A.I. (2004) Non-Numeric Statistics. MZ-Press, Moscow. (Neparametricheskaja statistika).

[14] Tu, J. and Gonzalez, C. (1978) Principles of Pattern Recognition: Monograph. Mir, Moscow.

[15] Zaykov, K.A. (2013) Research of the Threshold Aggregates by Decomposition of Mixtures Distributions. Scientific Works of the Free Economic Society of Russia, 172, 192-202.

[16] Everitt, B.S. (2010) Large Dictionary of Statistics. 3rd Edition, Prospect, Moscow. (Bol’shoj slovar’ po statistike).

[17] Venetskiy, I.G. and Venetskiy, V.I. (1974) Basic Mathematical and Statistical Concepts and Formulas in the Economic Analysis. Statistics, Moscow. (Osnovnye matematiko-statisticheskie ponjatija i formuly v jekonomicheskom analize).

[18] Wentzel, E.S. and Ovcharov, L.A. (2000) Probability Theory and Its Engineering Applications. Textbook. Manual for Technical Schools. 2nd Edition, Higher School, Moscow. (Teorija verojatnosti i ee inzhenernye prilozhenija).

[19] Official Website of the Federal State Statistics Service.

http://www.gks.ru