3-Days Workshop 

June 2025

Basic Course Offering (Virtual, Group Format):

Standard fees : 780  Euros

Discounted fees (Participants from M&S Research Hub list of developing countries): 420 Euros

Early-bird discount (1 Month in advance): 20% discount

This intensive 3-day course introduces finite mixture models and their applications across fields such as biostatistics, finance, and social sciences. Participants will learn the fundamental theory and estimation techniques for mixture models, with an emphasis on practical aspects and real-world case studies. Topics will cover classification methods, identification of latent populations, and applications using skewed-normal distributions.

Here’s a summary of its main uses and applications:

• Market Segmentation: Identify distinct customer segments based on purchasing behavior, preferences, or demographics, allowing businesses to tailor marketing strategies effectively.
• Biomedical and Genetic Research: Detect hidden subgroups within patient or population data, supporting precision medicine and targeted treatments.
• Finance and Risk Analysis: Model heterogeneous risks within investment portfolios, distinguishing between varying levels of risk exposure among assets or client groups.
• Structural Break Detection in Time Series: Identify structural changes over time within economic or financial data, aiding in policy analysis and investment strategies.
• Behavioral and Social Sciences: Analyze diverse behavioral patterns in social or psychological research, helping to identify and model different population subgroups.
• Statistical and Machine Learning Applications: Use finite mixture models as a basis for clustering, anomaly detection, and probabilistic classification in complex datasets.

Day 1: Introduction to Finite Mixture Models, Fundamental Concepts, and Maximum Likelihood Estimation

• Overview of Finite Mixture Models
• Basic Components: Discuss the core structure of finite mixture models, covering essential aspects like mixture distributions, component distributions, and the mixing proportions.
• Parameter Estimation: Explanation of maximum likelihood estimation (MLE) for finite mixture models. This includes:
  • How MLE can be applied in the context of mixtures.
  • Challenges in estimation, such as the issue of local optima.
• Introduction to the Expectation-Maximization (EM) Algorithm
• Example Cases: Examples and exercises to illustrate basic finite mixture models, 

Day 2: Practical Applications and Identification of Heterogeneous Populations

• Real-world Applications: Detailed look at where finite mixture models can be applied practically, such as in finance, marketing, and biology.
• Identifying Sub-populations: Hands-on training on how to interpret the results of mixture models to identify distinct sub-populations.
• Statistical Software (R and MATLAB): Introduction to practical tools for implementation:
  • R: Covering packages and functions (e.g., mixtools, mclust) and providing code examples.
  • MATLAB: Practical examples using MATLAB’s statistics toolbox.
• Case Studies and Exercises: Participants work with datasets to apply finite mixture models in R and MATLAB, interpreting the output and fine-tuning models for improved accuracy.

Day 3: Extension to Skewed-Normal Distributions and Structural Break Detection

• Skewed-Normal Distributions
  
• Structural Break Detection
• Advanced Case Studies: Participants analyze more complex datasets, incorporating skewed-normal distributions and identifying structural breaks to refine their mixture model applications.
• Final Q&A and Model Troubleshooting: Open session for participants to ask questions, discuss challenges faced during the training, and receive guidance on common troubleshooting tips for mixture modeling.

Highlights

Get in Touch 

WhatsApp

+49 017686387586

Chat

Mo-Fri: 9 AM-4 PM