Webinar in Statistics

[michael tsagris]

• The Department of Economics at the University of Crete welcomes you to attend a webinar on Wednesday 30 April 2025 at 15:00 GREEK time.

  
  

  Title: Geospatial Data Science for Public Health Surveillance
  

  
  

  Presenter: Moraga Paula, King Abdullah University of Science and Technology
  

  
  

  Abstract: Geospatial health data are essential to inform public health and policy. These data can be used to understand geographic and temporal patterns, identify risk factors, measure inequalities, and quickly detect outbreaks. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance. Using dengue surveillance in Brazil as a case study, I will discuss data biases and availability issues in surveillance systems. I will also present modeling advancements to integrate complex health, climate, and digital data from different sources and resolutions to predict disease risk and detect outbreaks. Finally, I will discuss the importance of effective communication and dissemination to inform policymaking and improve global population health.
  

  
  

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[michael tsagris]

The Department of Economics at the University of Crete welcomes you to attend a webinar on Wednesday 7 May 2025 at 15:00 GREEK time.

Title: Detecting Influential Observations in Single-Index Fréchet Regression

Presenter: Soale Abdul-Nasah, Case Western Reserve University, Cleveland, USA

Abstract: Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook's distance extending the original Cook's distances to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook's distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.

[michael tsagris]

The Department of Economics at the University of Crete welcomes you to attend a webinar on Wednesday 14 May 2025 at 15:00 GREEK time.

Title: A comparative analysis of count data models

Presenter: Hussain Abid , PMAS-Arid Agriculture University, Rawalpindi, Pakistan

Abstract: Count data, representing the number of occurrences of an event within a specific time or space, are prevalent across various disciplines, including ecology, epidemiology, economics, and social sciences. Unlike continuous data, count data are non-negative integers and often exhibit characteristics such as overdispersion (variance exceeding the mean) or zero-inflation (an excess of zero counts). Consequently, standard regression models designed for continuous outcomes are often inappropriate for analyzing count data. In this talk, we introduce the fundamental principles of count data analysis, highlighting the Poisson and negative binomial regression models as primary analytical tools. It discusses the assumptions underlying these models, methods for assessing model fit, and strategies for addressing common issues like overdispersion and zero-inflation through extensions such as the zero-inflated Poisson and zero-inflated negative binomial models. Furthermore, this talk emphasizes the importance of appropriate model selection and interpretation in drawing meaningful inferences from count data. By employing suitable statistical techniques, researchers can effectively model and understand the factors influencing the frequency of events in diverse real-world applications.