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ICAMSM · Registering as Listener

International Conference on Applied Multivariate Statistical Methods

5 - 6 Oct 2026 Geneva, Switzerland Standard / Physical Participation
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$135
virtual · $195 in person
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Registration summary

ConferenceICAMSM
ModeVirtual
ParticipationListener
Registration fee$135.00
Bank charges (5.8%)$7.83
Total payable$142.83
Includes all bank processing charges — the amount above is exactly what will be charged.

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• Conference Session Tracks •
SDGs
SDG-Aligned Research Themes

ICAMSM conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.

SDG 3 - Good Health and Well-being SDG 4 - Quality Education SDG 8 - Decent Work and Economic Growth SDG 9 - Industry, Innovation and Infrastructure
01 Advanced Multivariate Statistical Techniques +
This track focuses on the latest advancements in multivariate statistical methods, including novel approaches to principal component analysis and factor analysis. Researchers are encouraged to present innovative applications and theoretical developments in this area.
02 Discriminant Analysis and Its Applications +
This session will explore the methodologies and applications of discriminant analysis in various fields, highlighting its effectiveness in classification problems. Contributions that showcase real-world applications and methodological enhancements are particularly welcome.
03 Canonical Correlation Analysis: Theory and Practice +
This track aims to delve into canonical correlation analysis, emphasizing both theoretical frameworks and practical implementations. Papers that demonstrate the utility of this technique in complex data scenarios are encouraged.
04 Cluster Analysis in Big Data +
This session will address the challenges and methodologies of cluster analysis in the context of big data. Researchers are invited to share insights on scalable algorithms and their applications in diverse domains.
05 Multivariate Regression Models +
This track will cover the development and application of multivariate regression models, focusing on both traditional and contemporary approaches. Contributions that enhance understanding of model selection and interpretation are highly encouraged.
06 Structural Equation Modeling: Innovations and Applications +
This session will explore the latest innovations in structural equation modeling, including advancements in estimation techniques and model evaluation. Papers that illustrate practical applications in social sciences and health research are particularly sought after.
07 Data Science and Multivariate Analysis +
This track will examine the intersection of data science and multivariate analysis, highlighting the role of statistical methods in data-driven decision making. Contributions that showcase the integration of machine learning techniques with traditional statistical approaches are encouraged.
08 Applied Statistics in Industry +
This session will focus on the application of multivariate statistical methods in various industrial contexts. Researchers are invited to present case studies that demonstrate the impact of statistical analysis on operational efficiency and decision-making.
09 Machine Learning and Multivariate Techniques +
This track will investigate the synergy between machine learning and multivariate statistical techniques, exploring how these fields can enhance each other. Papers that propose new methodologies or frameworks integrating both domains are welcome.
10 Statistical Methods for High-Dimensional Data +
This session will address statistical methodologies designed for high-dimensional data analysis, focusing on challenges such as overfitting and variable selection. Contributions that propose innovative solutions to these challenges are encouraged.
11 Ethics and Best Practices in Statistical Research +
This track will discuss the ethical considerations and best practices in the conduct of statistical research, particularly in the context of multivariate analysis. Papers that address issues of transparency, reproducibility, and ethical data use are highly encouraged.