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115 results on '"Piersimoni, Federica"'

1. The identification of spatially constrained homogeneous clusters of Covid-19 transmission

Author

Benedetti, Roberto, Piersimoni, Federica, Pignataro, Giacomo, and Vidoli, Francesco

Subjects
Statistics - Applications, Physics - Physics and Society
Abstract

The paper introduces an approach to identify a set of spatially constrained homogeneous areas maximally homogeneous in terms of epidemic trends. The proposed hierarchical algorithm is based on the Dynamic TimeWarping distances between epidemic time trends where units are constrained by a spatial proximity graph. The paper includes two different applications of this approach to Italy, based on different data (number of positive test and number of differential deaths, with respect to the previous years) and on different observational units (provinces and Labour Market Areas). Both applications, above all the one related to Labour Market Areas, show the existence of well-defined areas, where the dynamics of growth of the infection have been strongly differentiated. The adoption of the same lock-down policy throughout the entire national territory has been therefore sub-optimal, showing once again the urgent need for local data-driven policies., Comment: Preliminary version

Published
2020

4. Fast Selection of Spatially Balanced Samples

Author

Benedetti, Roberto and Piersimoni, Federica

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

Sampling from very large spatial populations is challenging. The solutions suggested in recent literature on this subject often require that the randomly selected units are well distributed across the study region by using complex algorithms that have the feature, essential in a design-based framework, to respect the fixed first-order inclusion probabilities for every unit of the population. The size of the frame, $N$, often causes some problems to these algorithms since, being based on the distance matrix between the units of the population, have at least a computational cost of order $N^2$. In this paper we propose a draw-by-draw algorithm that randomly selects a sample of size $n$ in exactly $n$ steps, updating at each step the selection probability of not-selected units depending on their distance from the units already selected in the previous steps. The performance of this solution is compared with those of other methods derived from the {\it spatially balanced sampling} literature in terms of their root mean squared error (RMSE) using the simple random sampling (SRS) without replacement as benchmark. The fundamental interest is not only to evaluate the efficiency of a such different procedure, but also to understand if similar results can be obtained even with a notable reduction in the computational burden needed to obtain more efficient sampling designs. Repeated sample selections on real and simulated populations support this perspective. An application to the Land Use and Land Cover Survey (LUCAS) 2012 data-set in an Italian region is presented as a concrete and practical illustration of the capabilities of the proposed sample selection method.

Published
2017

5. Handling Out‐of‐Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy.

Author

Benedetti, Roberto, Piersimoni, Federica, Pratesi, Monica, Salvati, Nicola, and Suesse, Thomas

Subjects
*SMALL area statistics, *UNEMPLOYMENT statistics, *LABOR supply, *LABOR market, *MISSING data (Statistics)
Abstract

Summary Unemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in‐sample as well as out‐of‐sample areas. Extensive simulation experiments are used to assess the impact of the auto‐regression parameter and of the rate of out‐of‐sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Basic Concepts

Author

Postiglione, Paolo, primary, Benedett, Roberto, additional, and Piersimoni, Federica, additional

Published
2021
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13. Spatial Sampling Designs

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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14. Survey Data Collection and Processing

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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15. Sampling Designs

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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16. An Introduction to Remotely Sensed Data Analysis

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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17. Sample Size and Sample Allocation

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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18. Setting Up the Frame

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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19. Overview and Brief History

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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21. GIS: The Essentials

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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22. Small Area Estimation

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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23. Advances in Sampling Estimation

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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24. Spatial Survey Data Modeling

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Fischer, Manfred M, Series editor, Thill, Jean-Claude, Series editor, van Dijk, Jouke, Series editor, Westlund, Hans, Series editor, Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015
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26. Advanced Methods to Design Samples for Land Use/Land Cover Surveys

Author

Benedetti, Roberto, Piersimoni, Federica, Postiglione, Paolo, Vichi, Maurizio, Editor-in-chief, French Statistical Society (SFdS), Series editor, Italian Statistical Society (SIS), Series editor, Portugese Statistical Society (SPE), Series editor, Spanisch Statistical Society (SEIO), Series editor, Di Battista, Tonio, editor, Moreno, Elías, editor, and Racugno, Walter, editor

Published
2016
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41. Multivariate boundaries of a self representing stratum of large units in agricultural survey design

Author

Benedetti, Roberto, Piersimoni, Federica, Benedetti, Roberto, and Piersimoni, Federica

Abstract

"In business surveys in general, and in multipurpose agricultural surveys in particular, the problem of designing a sample from a list frame usually consists of two different aspects. The first is concerned with the choice of a rule for stratifying the population when several size variables are available and the second is devoted to sample size determination and sample allocation to a given set of strata. The main property that is required of the sample design is that it delivers a specified level of precision for a set of variables of interest using as few sampling units as possible. This article examines how this can be achieved via a basic partition into two strata, one completely enumerated and the other sampled, defined in such a way as to achieve both these objectives. The procedure was used to design the Italian Milk Products Monthly Survey on the basis of a set of auxiliary variables obtained from an annual census of the same target population. Given the combinatorial optimization nature of the problem, the authors use stochastic relaxation theory, and in particular, they use simulated annealing because of its flexibility. Their results indicate that in this situation the multivariate partition obtained by using this random search strategy is a suitable solution as it permits identification of boundaries of any shape. Furthermore, numerical comparisons between sampling designs obtained by using these procedures and some simple extensions of univariate stratification rules are made. The gain from using the proposed strategy is nontrivial as it achieves the required precision using a sample size that is notably smaller than that required by simple extensions to univariate stratification rules." (author's abstract)

Published
2015

42. FrontMatter.

Author

Benedetti, Roberto, Piersimoni, Federica, and Postiglione, Paolo

Published
2015

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