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The SCouT project aims to improve statistical models for analyzing count time series data, particularly in public health surveillance. By integrating sparsity techniques and composite likelihood methods, it seeks to enhance model selection and estimation for complex data structures.
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 auto…
UNIVERSITA CA' FOSCARI VENEZIA
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