Course Leader | Rosanna Verde |
| Target Group | Staff involved in preparing quality reports; a good knowledge of quality management is a prerequisite for attending the course. |
| Entry Qualifications | Sound command of English. Participants should be able to make short interventions and to actively participate in discussion. A good knowledge of quality management is a prerequisite for attending the course
|
| Objective(s) | The course aims to enable participants to: Understand the fundamental concepts of symbolic data analysis (SDA) and the extension of classical statistical approaches to such types of data Pre-processing and representing different types of symbolic data, including ranges, distributions, and multivalued variables Apply basic SDA techniques for exploratory data analysis Explore and implement advanced methods such as clustering, classification, regression modelling, and factorial methods Examine the use of SDA in official statistics, particularly when working with aggregated or complex data sets Analyse case studies and assess the relevance and applicability of SDA within their own areas of work
|
| Contents | In the recent years symbolic data analysis (SDA) has assumed an important role in the analysis of aggregated data. SDA has provided the generalization of the main exploratory techniques of multivariate data analysis for the study of no-standard data, like symbolic data. The course will focus on: Introduction to symbolic data analysis: data types, data preprocessing, basic analysis techniques Advanced symbolic data analysis techniques: clustering, classification, regression, factorial methods Applications of symbolic data analysis and case studies
|
| Expected Outcome | The course will introduce NSI staff to symbolic data analysis, providing them with innovative tools for multivariate data analysis and thus expanding their statistical capabilities to effectively manage and analyse modern and complex types of data. |
| Training Methods | - Presentations and lectures, practical examples
- Exchange of views/experiences on national practices
- Exercises
|
Required Reading | None |
| Suggested Reading | - Bock, Hans-Hermann. (2009). Analyzing Symbolic Data. 10.1007/978-3-642-00668-5_1. (free downloading (1))
- Rosanna Verde, Edwin Diday - Symbolic Data Analysis: A Factorial Approach Based on Fuzzy Coded Data (free downloading (2))
- Rosanna Verde, Antonio Irpino, Antonio Balzanella - Dimension Reduction Techniques for Distributional Symbolic Data (available upon request to the authors (3))
- Rosanna Verde, Clustering Methods in Symbolic Data Analysis (available upon request to the authors (4))
- Irpino, A., Verde, R. Basic statistics for distributional symbolic variables: a new metric-based approach (available upon request to the authors (5)
|
| Required Preparation | None |
Trainer(s)/
Lecturer(s) | Rosanna VERDE (Independent expert) Antonio IRPINO (Independent expert) |
