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An Analysis of Vectorised Automatic Differentiation for Statistical Applications
Published 2025-05-01Get full text
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Statistical Analysis of the Labor Force of the Arkhangelsk Oblast
Published 2024-12-01“…The methodological basis of the study is general scientific statistical methods of data analysis: absolute and relative statistical indicators, series of dynamics, correlation and regression analysis. …”
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Statistical Analysis of the Possibility of Hail in Northern Zagros
Published 2024-12-01Get full text
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STATISTICAL ANALYSIS OF ACCELEROGRAMS OF REAL STRONG EARTHQUAKES
Published 2018-03-01“…Abstract. Objectives The statistical analysis of real accelerograms is considered. …”
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Statistical Analysis of the Model Parameters of the Epidemic Situation
Published 2015-10-01Get full text
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To be(t) or not to be(t): A Bayesian approach to statistical data analysis
Published 2025-01-01“…Statistics aims at disciplining the process of answering to the latter question. …”
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Teaching application of statistics methods to bachelors of technological fields in case of a competence approach
Published 2023-03-01Subjects: Get full text
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Method of Calculation of Integrated Performance Indicators of Defense Industry Organizations
Published 2021-12-01“…A method has been developed for processing data on the activities of enterprises and reducing them to the optimal set of observed indicators without losing awareness of the monitoring object; possibilities of using non-parametric methods of statistics for analysis of complex systems, in particular, holdings of defense industry, have been researched.Conclusion. …”
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A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
Published 2025-04-01“…Although statistics-based methods have lower data requirements and machine learning methods have faster calculation speeds, this paper concludes that deep learning methods have the best overall effect after a comprehensive analysis of the principles, advantages, limitations, and practical applications of each method. …”
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PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing
Published 2024-11-01“…Results We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. …”
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