Foto del docente

Christian Martin Hennig

Full Professor

Department of Statistical Sciences "Paolo Fortunati"

Academic discipline: SECS-S/01 Statistics

Teaching

Recent dissertations supervised by the teacher.

First cycle degree programmes dissertations

  • Contextual Analysis of the Central Limit Theorem: Exploring Inspirations, Significance, and Mathematical Developments
  • Fast Clustering Algorithms for Optimizing the Average Silhouette Width
  • Gun Law Strength and Firearm Mortality in the United States: A Statistical Analysis
  • How to build an index? Unwrapping the construction process of the HDI index
  • kmeansreg: An R package for k-means for regression prediction
  • Predictive Modeling of NBA Game Outcomes
  • Sentiment Analysis Applied to Google Trends Data in Periods of Financial Instability
  • Stationarity and Outlier Detection for Multivariate Time Series: A study of brain scans in Neuroscience

Second cycle degree programmes dissertations

  • Ackerman and Ben-David's Characterization of CQMs
  • Climate evolution in Italy: An empirical study on temperature time series 1973-2022 through a cluster analysis
  • Clustering central attackers from the five top leagues in male football.
  • Clustering methods for classifying common bottlenose dolphin whistles
  • Defining New Basketball Positions Using Cluster Analysis
  • Detecting psychiatric taxonomy combining unsupervised learning techniques.
  • Experiment on the validation of clustering results
  • Football Players’ Goal Predictions: Webscraping, Data Management and Data Analysis
  • italian climate regions: a clustering analysis on italian meteorological data
  • Italian tourists’ patterns: a cluster analysis based on mixed-type data
  • Multiclass prediction of imbalanced and high dimensional data using co-data: an anal cancer case study
  • Predicting the selling price of used cars: application of clustering and regression methods to a real-world data set.
  • Prediction of energy consumption related to the production capacity for a foundry using machine learning algorithms

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