IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v173y2022ics0167947322001001.html
   My bibliography  Save this article

Complexity reduction and approximation of multidomain systems of partially ordered data

Author

Listed:
  • Arcagni, Alberto
  • Avellone, Alessandro
  • Fattore, Marco

Abstract

Two greedy algorithms for the synthesis and approximation of multidomain systems of partially ordered data are proposed. Given k input partially ordered sets (posets) on the same elements, the algorithms search for the optimally approximating partial orders, minimizing the dissimilarity between the generated and input posets, based on their matrices of mutual ranking probabilities. A general approximation algorithm is developed, together with a specific procedure for approximation over bucket orders, which are the natural choice when the goal is to “condense” the inputs into rankings, possibly with ties. Different loss functions are also employed, and their outputs are compared. A real example pertaining to regional well-being in Italy motivates the algorithms and shows them in action.

Suggested Citation

  • Arcagni, Alberto & Avellone, Alessandro & Fattore, Marco, 2022. "Complexity reduction and approximation of multidomain systems of partially ordered data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  • Handle: RePEc:eee:csdana:v:173:y:2022:i:c:s0167947322001001
    DOI: 10.1016/j.csda.2022.107520
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947322001001
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2022.107520?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Katia Iglesias & Christian Suter & Tugce Beycan & B. P. Vani, 2017. "Exploring Multidimensional Well-Being in Switzerland: Comparing Three Synthesizing Approaches," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(3), pages 847-875, December.
    2. Enrico di Bella & Luca Gandullia & Lucia Leporatti & Marcello Montefiori & Patrizia Orcamo, 2018. "Ranking and Prioritization of Emergency Departments Based on Multi-indicator Systems," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 1089-1107, April.
    3. Kai Puolamäki & Mikael Fortelius & Heikki Mannila, 2006. "Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods," PLOS Computational Biology, Public Library of Science, vol. 2(2), pages 1-9, February.
    4. David Madden, 2010. "Ordinal and cardinal measures of health inequality: an empirical comparison," Health Economics, John Wiley & Sons, Ltd., vol. 19(2), pages 243-250, February.
    5. Jacques, Julien & Biernacki, Christophe, 2018. "Model-based co-clustering for ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 101-115.
    6. Korhonen, Pekka & Siljamaki, Aapo, 1998. "Ordinal principal component analysis theory and an application," Computational Statistics & Data Analysis, Elsevier, vol. 26(4), pages 411-424, February.
    7. Flavio Comim, 2021. "A Poset-Generalizability Method for Human Development Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(3), pages 1179-1198, December.
    8. Marco Fattore, 2016. "Partially Ordered Sets and the Measurement of Multidimensional Ordinal Deprivation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(2), pages 835-858, September.
    9. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
    10. Marco Fattore & Alberto Arcagni, 2019. "F-FOD: Fuzzy First Order Dominance Analysis and Populations Ranking Over Ordinal Multi-Indicator Systems," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 1-29, July.
    11. Alberto Arcagni & Elisa Barbiano di Belgiojoso & Marco Fattore & Stefania M. L. Rimoldi, 2019. "Multidimensional Analysis of Deprivation and Fragility Patterns of Migrants in Lombardy, Using Partially Ordered Sets and Self-Organizing Maps," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(2), pages 551-579, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fattore, Marco & Alaimo, Leonardo Salvatore, 2023. "A partial order toolbox for building synthetic indicators of sustainability with ordinal data," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fattore, Marco & Alaimo, Leonardo Salvatore, 2023. "A partial order toolbox for building synthetic indicators of sustainability with ordinal data," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Stefania M. L. Rimoldi & Alberto Arcagni & Marco Fattore & Laura Terzera, 2022. "Social and Material Vulnerability of the Italian Municipalities: Comparing Alternative Approaches," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 523-540, June.
    3. Leonardo Salvatore Alaimo & Alberto Arcagni & Marco Fattore & Filomena Maggino, 2021. "Synthesis of Multi-indicator System Over Time: A Poset-based Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(1), pages 77-99, August.
    4. Filippo Damiani & Paula Rodríguez-Modroño, 2024. "Measuring the digital inclusion of women: a poset-based approach to the women in digital scoreboard," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 705-722, February.
    5. Stefania M. L. Rimoldi & Alberto Arcagni & Marco Fattore & Elisa Barbiano di Belgiojoso, 2021. "Targeting Policies for Multidimensional Poverty and Social Fragility Relief Among Migrants in Italy, Using F-FOD Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(1), pages 57-75, August.
    6. Alaimo, Leonardo Salvatore & Fiore, Mariantonietta & Galati, Antonino, 2022. "Measuring consumers’ level of satisfaction for online food shopping during COVID-19 in Italy using POSETs," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    7. Alaimo, Leonardo Salvatore & Ivaldi, Enrico & Landi, Stefano & Maggino, Filomena, 2022. "Measuring and evaluating socio-economic inequality in small areas: An application to the urban units of the Municipality of Genoa," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    8. Alberto Arcagni & Laura Cavalli & Marco Fattore, 2021. "Partial Order Algorithms for the Assessment of Italian Cities Sustainability," Working Papers 2021.01, Fondazione Eni Enrico Mattei.
    9. Arcagni, Alberto & Cavalli, Laura & Fattore, Marco, 2021. "Partial Order Algorithms for the Assessment of Italian Cities Sustainability," FEEM Working Papers 309036, Fondazione Eni Enrico Mattei (FEEM).
    10. Marco Fattore & Filomena Maggino, 2018. "Some Considerations on Well-Being Evaluation Procedures, Taking the Cue from “Exploring Multidimensional Well-Being in Switzerland: Comparing Three Synthesizing Approaches”," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 83-91, May.
    11. Di Zhou & Kuangyuan Cai & Shaojun Zhong, 2021. "A Statistical Measurement of Poverty Reduction Effectiveness: Using China as an Example," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(1), pages 39-64, January.
    12. Nita Handastya & Gianni Betti, 2023. "The ‘Double Fuzzy Set’ Approach to Multidimensional Poverty Measurement: With a Focus on the Health Dimension," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(1), pages 201-217, February.
    13. Desirée Campagna & Giulio Caperna & Valentina Montalto, 2020. "Does Culture Make a Better Citizen? Exploring the Relationship Between Cultural and Civic Participation in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 657-686, June.
    14. Marco Fattore & Stefania M.L. Rimoldi, 2023. "Effects of the Covid Pandemic on the Economic Vulnerability of Italian Society," Hacienda Pública Española / Review of Public Economics, IEF, vol. 247(4), pages 37-68, December.
    15. Suman Seth & Maria Emma Santos, 2019. "On the Interaction Between Focus and Distributional Properties in Multidimensional Poverty Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 503-521, September.
    16. Flavio Comim, 2021. "A Poset-Generalizability Method for Human Development Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(3), pages 1179-1198, December.
    17. Alaimo, Leonardo Salvatore & Galli, Emma & Rizzo, Ilde & Scaglioni, Carla, 2023. "A new index of transparency: Evidence for the Italian municipalities," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    18. Alberto Arcagni & Laura Cavalli & Marco Fattore, 2021. "Partial Order Algorithms for the Assessment of Italian Cities Sustainability," Working Papers 2021.01, Fondazione Eni Enrico Mattei.
    19. Kateryna Tkach & Chiara Gigliarano, 2022. "Multidimensional Poverty Index with Dependence-Based Weights," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 843-872, June.
    20. Comim, Flavio & Hirai, Tadashi, 2022. "Sustainability and Human Development Indicators: A Poset Analysis," Ecological Economics, Elsevier, vol. 198(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:173:y:2022:i:c:s0167947322001001. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.