0000000000461958

AUTHOR

Juris Krasts

showing 3 related works from this author

Opportunities for the Use of Business Data Analysis Technologies

2016

Abstract The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

0209 industrial biotechnologyEngineeringHF5001-6182Big dataonline analytical processing02 engineering and technologyAnalytics platformsbusiness intelligenceTerminologyBusiness data020901 industrial engineering & automationBusiness analytics0502 economics and businessanalytics platformsBusinessHB71-74business.industryManagement scienceOnline analytical processing05 social sciencesbusiness analyticsdata miningpredictive modelling.Data scienceEconomics as a scienceAnalyticsBusiness intelligencebusinesspredictive modelling050203 business & managementPredictive modelling
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Technique of Statistical Validation of Rival Models for Fatigue Crack Growth Process and Its Identification

2011

The development of suitable models of stochastic crack growth process is important for the reliability analysis of fatigued structures as well as the scheduling of inspection and repair/replacement maintenance. Based on modifications of the solution of the deterministic differential equation for the crack growth rate, where a stochastic nature of this rate is expressed by a random disturbance embedded in the solution of the differential equation, the simple stochastic models are presented for practical applications. Each of these models represents a stochastic version of the solution of the Paris-Erdogan law equation. The models take into account the random disturbance parameters while main…

Identification (information)Stochastic modellingSimple (abstract algebra)Differential equationComputer scienceProcess (computing)Scheduling (production processes)Applied mathematicsParis' lawReliability (statistics)Simulation
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Invariant Embedding Technique and Its Applications for Improvement or Optimization of Statistical Decisions

2010

In the present paper, for improvement or optimization of statistical decisions under parametric uncertainty, a new technique of invariant embedding of sample statistics in a performance index is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. It allows one to eliminate unknown parameters from the problem and to find the best invariant decision rule, which has smaller risk than any of the well-known decision rules. To illustrate the proposed technique, applica…

Mathematical optimizationSimple (abstract algebra)Mathematical statisticsPrior probabilityBayesian probabilityDecision ruleInvariant (mathematics)ConstructiveMathematicsParametric statistics
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