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Sparse Composite Likelihood Inference in Count Time Series

Total Cost €


EC-Contrib. €






Project "SCouT" data sheet

The following table provides information about the project.


Organization address
address: DORSODURO 3246
postcode: 30123

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Project website
 Total cost 168˙277 €
 EC max contribution 168˙277 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-06-01   to  2018-05-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITA CA' FOSCARI VENEZIA IT (VENEZIA) coordinator 168˙277.00


 Project objective

The availability of many interesting datasets consisting of count time series has motivated a steadily increasing research activity towards the development of appropriate statistical models. Nowadays, a variety of time series models constructed on the basis of the integer-valued property of count data is available. However, their practical usefulness is often limited because of difficulties to implement efficient estimation procedures. These difficulties grow significantly when higher-order autoregressive and moving average terms are included in the model or when multivariate time series data are considered. The SCouT project aims to enhance the flexibility of time series models for counts and facilitate their application to data characterized by complex dependence structures. To this end, the SCouT project proposes an innovative methodological approach that integrates two powerful statistical tools, that is sparsity techniques and composite likelihood methods. The project will provide a unified framework for simultaneous order selection and estimation of autoregressive and moving average terms in count time series models, reducing considerably the computational burden of traditional model selection approaches and improving the predictive performance of the fitted model. The potential of the suggested methodology will be further highlighted by investigating its extensions to spatial and spatio-temporal data that are usually characterized by complex dependence structures. Such data are often met in the field of temporal and spatial analysis of public health surveillance data that will be the main application field of the project. The simultaneous order selection and estimation procedure established by the SCouT project will offer great support to statistical methods for public health surveillance and significantly contribute to the achievement of the effective and timely detection of disease outbreaks.


year authors and title journal last update
List of publications.
2018 Xanthi Pedeli and Cristiano Varin
lacm: Latent Autoregressive Count Models
published pages: , ISSN: , DOI:
2017 Xanthi Pedeli and Cristiano Varin
Pairwise Likelihood Inference for Parameter-Driven Models
published pages: 773-777, ISSN: , DOI:
SIS 2017 Statistics and Data Science: new challenges, new generations - Proceedings of the Conference of the Italian Statistical Society 28–30 June 2017 2019-06-13
2018 Xanthi Pedeli and Cristiano Varin
Pairwise likelihood estimation of latent autoregressive count models
published pages: , ISSN: , DOI:
2017 Xanthi Pedeli and Cristiano Varin
The Pairwise Expectation Maximization Algorithm for Fitting Parameter-Driven Models
published pages: 196-199, ISSN: , DOI:
Proceedings of the 32nd International Workshop on Statistical Modelling Volume I 3-7 July, 2017 2019-06-13
2018 Xanthi Pedeli and Dimitris Karlis
An integer-valued time series model for multivariate surveillance
published pages: , ISSN: , DOI:

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The information about "SCOUT" are provided by the European Opendata Portal: CORDIS opendata.

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