Z
Zhanna Tsaurkubule
Researcher at Baltic International Academy
Publications - 7
Citations - 35
Zhanna Tsaurkubule is an academic researcher from Baltic International Academy. The author has contributed to research in topics: Human resources & Prior probability. The author has an hindex of 3, co-authored 7 publications receiving 29 citations.
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Journal ArticleDOI
Assessment of competitiveness of regions of the Republic of Kazakhstan
Zhanna Tsaurkubule,Zhaxat Kenzhin,Dana Saylauovna Bekniyazova,Gulmira Bayandina,Gulsara Saparbekovna Dyussembekova +4 more
TL;DR: In this paper, the authors proposed a methodology for ranking the regions of Kazakhstan based on an assessment of the development of their human resources that affect the competitiveness of the region, which includes an analysis of demographic, labor and social and economic indicators reflecting the state of human resources.
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Evaluating the Efficiency of State Socio-economic Policy of Latvia
TL;DR: In this paper, the authors defined the essence of the concept of social policy in the context of its relation to the economy and developed a system of indicators to measure the impact of social policies and proposed a technique for assessing its efficiency on the basis of a comparative analysis of the dynamics of changes in key indicators of socioeconomic development of Latvian socio-economic development.
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Influence of quality of life on the state and development of human capital in Latvia
TL;DR: In this paper, the authors studied the relationship between quality of life and human capital and made recommendations to improve the socioeconomic policy in ways that improve the welfare of the population of Latvia.
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A Novel Unified Computational Approach to Constructing Shortest-Length or Equal Tails Confidence Intervals in Terms of Pivotal Quantities and Quantile Functions
TL;DR: In this article, a new simple computational method is proposed for simultaneous constructing and comparing confidence intervals of shortest length and equal tails in order to make efficient decisions under parametric uncertainty, which are developed from either maximum likelihood estimates or sufficient statistics.
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Intelligent Constructing Efficient Statistical Decisions via Pivot-Based Elimination of Unknown (Nuisance) Parameters from Underlying Models
TL;DR: In this article, a new method is proposed to eliminate the unknown (nuisance) parameter from the underlying model, which is independent of the choice of priors and represents a novelty in the theory of statistical decisions.