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  1. Perceptive risk clusters of European citizens and NPI compliance in face of the Covid-19 pandemics
    Autor*in: Bughin, Jacques; Cincera, Michele; Reykowska, Dorota; Żyszkiewicz, Marcin; Ohme, Rafal
    Erschienen: December 2020
    Verlag:  ECARES, Brussels, Belgium

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    Medientyp: Buch (Monographie)
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    hdl: 2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/316040
    Schriftenreihe: ECARES working paper ; 2020, 51
    Schlagworte: Covid-19 pandemics; Clustering; Non-pharmaceutical interventions; Europe
    Umfang: 1 Online-Ressource (circa 33 Seiten)
  2. Perceptive risk clusters of European citizens and NPI compliance in face of the Covid-19 pandemics
    Autor*in: Bughin, Jacques; Cincera, Michele; Reykowska, Dorota; Żyszkiewicz, Marcin; Ohme, Rafal
    Erschienen: [2020]
    Verlag:  iCite - International Centre for Innovation, Technology and Education Studies, Université libre de Bruxelles, Brussels - Belgium

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    hdl: 2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/316087
    Schriftenreihe: iCite working paper ; 042 (2020)
    Schlagworte: Covid-19 pandemics; Clustering; Non-pharmaceutical interventions; Europe
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  3. Updating Variational Bayes
    fast sequential posterior inference
    Autor*in: Tomasetti, Nathaniel; Forbes, Catherine Scipione; Panagiotelis, Anastasios
    Erschienen: May 2019
    Verlag:  Monash University, Department of Econometrics and Business Statistics, [Victoria, Australia]

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    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 19, 13
    Schlagworte: Importance Sampling; Forecasting; Clustering; Dirichlet Process Mixture; Variational Inference
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  4. Exploring dual information in distance metric learning for clustering
    Autor*in: Randel, Rodrigo; Aloise, Daniel; Hertz, Alain
    Erschienen: [2021]
    Verlag:  GERAD, HÉC Montréal, Montréal (Québec), Canada

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    Schriftenreihe: Les cahiers du GERAD ; G-2021, 32 (May 2021)
    Schlagworte: Clustering; distance metric learning; pairwise constraints; Lagrangian relaxation; duality theory
    Umfang: 1 Online-Ressource (circa 22 Seiten), Illustrationen
  5. Semantic Support in Multilingual Text Retrieval
    Autor*in: De Luca, Ernesto William
    Erschienen: 2008
    Verlag:  Shaker, Aachen

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    Sprache: Englisch
    Medientyp: Dissertation
    Format: Online
    ISBN: 9783832274894
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    Auflage/Ausgabe: 1. Auflage
    Schriftenreihe: Berichte aus der Informatik
    Schlagworte: Text Mining; Mehrsprachigkeit; Semantische Analyse; Disambiguierung; Ontologie <Wissensverarbeitung>
    Weitere Schlagworte: (Produktform)Electronic book text; (Zielgruppe)Fachpublikum/ Wissenschaft; (DDC-Sachgruppen der Deutschen Nationalbibliografie)004: Informatik; Clustering; Computational Linguistics; Lexical Resources; Multilingual Text Retrieval; Ontologies; Semantic Retrieval; Word Sense Disambiguation; (VLB-WN)1630: Hardcover, Softcover / Informatik, EDV
    Umfang: Online-Ressource, 276 Seiten, 29 Illustrationen, 16 Illustrationen
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    Dissertation, Otto-von-Guericke-Universität Magdeburg, 2008
  6. Updating variational Bayes
    fast sequential posterior inference
    Autor*in: Tomasetti, Nathaniel; Forbes, Catherine Scipione; Panagiotelis, Anastasios
    Erschienen: August 2020
    Verlag:  Monash University, Department of Econometrics and Business Statistics, [Victoria, Australia]

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    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 20, 27
    Schlagworte: Importance Sampling; Forecasting; Clustering; Dirichlet Process Mixture; Variational Inference
    Umfang: 1 Online-Ressource (circa 37 Seiten), Illustrationen
  7. The jury is still out on the performance of naïve diversification (1/N rule)
    Autor*in: Elkamhi, Redouane; Salerno, Marco
    Erschienen: [2020]
    Verlag:  [University of Toronto - Rotman School of Management], [Toronto]

    DeMiguel et. al. (2009b) made a compelling case that estimation error dwarfs diversification benefits resulting in naive diversification (1/N) dominating mean-variance portfolios. We illustrate the necessary and sufficient conditions for risk-based... mehr

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    DeMiguel et. al. (2009b) made a compelling case that estimation error dwarfs diversification benefits resulting in naive diversification (1/N) dominating mean-variance portfolios. We illustrate the necessary and sufficient conditions for risk-based allocation rules to be optimal in a mean-variance framework. We show empirically that many common datasets satisfy such conditions, making these rules preferred to mean-variance in the presence of estimation error. Our out-of-sample tests show that these rules outperform both mean-variance and 1/N. Further, we show that clustering the data using machine learning enhances the diversification benefits of these rules by making the data closer to the required conditions for optimality

     
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    Schriftenreihe: [Rotman School of Management working paper ; no. 3638713]
    Schlagworte: Portfolio Choice; Asset Allocation; Machine Learning; Clustering
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 67 Seiten), Illustrationen
  8. The role of education and income inequality on environmental quality
    a panel data analysis of the EKC hypothesis on OECD
    Autor*in: Maranzano, Paolo; Bento, João Paulo Cerdeira; Manera, Matteo
    Erschienen: March 2021
    Verlag:  Fondazione Eni Enrico Mattei, Milano, Italia

    We study the impact of human capital and the level of education on the pollution-income relationship controlling for income inequality in 17 OECD countries. By applying an innovative approach to country grouping, based on the temporal evolution of... mehr

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    We study the impact of human capital and the level of education on the pollution-income relationship controlling for income inequality in 17 OECD countries. By applying an innovative approach to country grouping, based on the temporal evolution of income inequality and clustering techniques to feature the annual value of the Gini Index on disposable income from 1987 to 2015, we have estimated panel data models by distinguishing between low and high levels of income inequality country clusters. Robustness checks and endogeneity tests are further performed considering as the discriminant factor the income inequality affecting the countries in the sample. The findings highlight the role of the educational level and years of schooling in validating the EKC hypothesis. We recommend that this variable should not be neglected in future EKC studies. Therefore, any EKC theory should also acknowledge a new EKC model specification that we named the Educational EKC.

     
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    hdl: 10419/233093
    Schriftenreihe: Working paper / Fondazione Eni Enrico Mattei ; 2021, 008
    Schlagworte: Pollution-Income; Environmental Kunzets Curve; Education; Income-Inequality; Panel Data; Clustering
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  9. Bayesian modeling via discrete nonparametric priors
    Autor*in: Catalano, Marta; Lijoi, Antonio; Prünster, Igor; Rigon, Tommaso
    Erschienen: [2023]
    Verlag:  CCA, Fondazione Collegio Carlo Alberto, Torino

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    Schriftenreihe: Carlo Alberto notebooks ; no. 696 (July 2023)
    Schlagworte: Clustering; Density estimation; Dependence; Dirichlet process; Exchangeability; Mixture model; Partial Exchangeability; Pitman-Yor process; Species discovery
    Umfang: 1 Online-Ressource (circa 23 Seiten), Illustrationen
  10. Clustering the Swiss Pension Register
    Autor*in: Lettry, Layal Christine
    Erschienen: February 27, 2023
    Verlag:  University of Fribourg, Switzerland, Faculty of Management, Economics and Social Sciences, Fribourg

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    Beteiligt: Donzé, Laurent (MitwirkendeR)
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Working papers SES / Université de Fribourg, Faculté des sciences économiques et sociales et du management ; n. 529 (II. 2023)
    Schlagworte: Kamila; Clustering; R; AVS; AHV; OASI; Swiss Pension Register; FSIO; prediction strength criterion; classification; RAMD; AADR; UniFr
    Umfang: 1 Online-Ressource (circa 132 Seiten), Illustrationen
  11. A panel clustering approach to analyzing bubble behavior
    Autor*in: Liu, Yanbo; Phillips, Peter C. B.; Yu, Jun
    Erschienen: Feb 2022
    Verlag:  Singapore Management University, Singapore

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    Schriftenreihe: SMU economics and statistics working paper series ; paper no. 2022, 01
    Schlagworte: Bubbles; Clustering; Mildly explosive behavior; k-means; Latent membership detection
    Umfang: 1 Online-Ressource (circa 57 Seiten), Illustrationen
  12. A panel clustering approach to analyzing bubble behavior
    Autor*in: Liu, Yanbo; Phillips, Peter C. B.; Yu, Jun
    Erschienen: [2022]
    Verlag:  Cowles Foundation for Research in Economics, Yale University, New Haven, Connecticut

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    Schriftenreihe: Cowles Foundation discussion paper ; no. 2323 (February 2022)
    Schlagworte: Bubbles; Clustering; Mildly explosive behavior; k-means; Latent membership detection
    Umfang: 1 Online-Ressource (circa 56 Seiten), Illustrationen
  13. Heterogeneous consumer preferences for product quality and uncertainty
    Autor*in: Maennig, Wolfgang; Mueller, Steffen Q.
    Erschienen: [2021]
    Verlag:  Universität Hamburg, Faculty of Business, Economics and Social Sciences, Chair for Economic Policy, Hamburg, Germany

    We provide evidence for heterogeneous consumer preferences for product quality and game outcome uncertainty (GOU) in Major League Baseball. Using attendance data from 2013 to 2019, we explore functional data clustering techniques to detect common... mehr

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    We provide evidence for heterogeneous consumer preferences for product quality and game outcome uncertainty (GOU) in Major League Baseball. Using attendance data from 2013 to 2019, we explore functional data clustering techniques to detect common patterns in predictive margins of team-specific winning probability. As a central result, we identify five groups of teams with similar GOU effects. However, only a few teams' fans show GOU preferences that resemble the typical hump-shape that is postulated by the uncertainty of outcome hypothesis; the largest cluster is comprised of teams with fans whose attendance behavior is relatively insensitive to differences in GOU.

     
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    Medientyp: Ebook
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    ISBN: 9783942820554
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    hdl: 10419/248657
    Schriftenreihe: Hamburg contemporary economic discussions ; no. 70
    Schlagworte: Consumer demand; Heterogeneous preferences; Product quality; Uncertainty of outcome hypothesis; Clustering; Functional data analysis
    Umfang: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  14. Clustering-based sector investing
    Autor*in: Bagnara, Matteo; Goodarzi, Milad
    Erschienen: [2023]
    Verlag:  Leibniz Institute for Financial Research SAFE, Sustainable Architecture for Finance in Europe, [Frankfurt am Main]

    Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple... mehr

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    Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.

     
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    hdl: 10419/273735
    Schriftenreihe: SAFE working paper ; no. 397 (August 2023)
    Schlagworte: Empirical Asset Pricing; Risk Premium; Machine Learning; Industry Classification; Clustering
    Umfang: 1 Online-Ressource (circa 63 Seiten), Illustrationen
  15. Error-in-variables jump regression using local clustering
    Autor*in: Kang, Yicheng; Gong, Xiaodong; Gao, Jiti; Qiu, Peihua
    Erschienen: July 2016
    Verlag:  Monash University, Department of Econometrics and Business Statistics, Victoria

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    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 16, 13
    Schlagworte: Clustering; Demand for private health insurance; Kernel smoothing; Local regression; Measurement errors; Price elasticity
    Umfang: 1 Online-Ressource (circa 50 Seiten), Illustrationen
  16. The knowledge complexity of the European metropolitan areas
    selecting and clustering their hidden features
    Autor*in: Bottai, Carlo; Iori, Martina
    Erschienen: [2022]
    Verlag:  LEM, Laboratory of Economics and Management, Institute of Economics, Scuola Superiore Sant'Anna, Pisa, Italy

    Cities are key places of economic activity, as they produce an enormous amount of wealth compared to the land they cover. Their study is, therefore, of primary importance in understanding the success of nations. Given the many interactions among... mehr

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    Cities are key places of economic activity, as they produce an enormous amount of wealth compared to the land they cover. Their study is, therefore, of primary importance in understanding the success of nations. Given the many interactions among people that happen within them, cities are well described as complex evolving systems, and a thorough analysis of their economy should be able to deal with this complexity. A likely candidate to grasp the reality of complex evolving systems, such the economy of cities, is the Economic Complexity framework (Hidalgo and Hausmann, 2009), given its capacity to synthesize a large amount of informa- tion into a single index. We use patent data to compute the knowledge complexity index (KCI) of European metropolitan areas and describe their economy in terms of their innovative potential. Interpreted as a dimensionality-reduction algorithm, as proposed by Mealy et al. (2019), KCI helps to filter out the background noise from the abundant information produced by the interactions that happen within cities. By extending the work by van Dam et al. (2021), we highlight the relevance of going beyond the first leading eigenvector, to the analysis of which the rest of the literature is limited. We define clusters of similar cities, based on the additional dimensions obtained through this dimensionality-reduction procedure. The introduction of clusters dramatically increases the predicting power of KCI. Under this lens, the Economic Complexity framework is more than a single index: it is a powerful methodology to reveal the organized complexity hidden behind the large amount of chaotic information produced by out-of-equilibrium economic systems such as cities.

     
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    hdl: 10419/297107
    Schriftenreihe: LEM working paper series ; 2022, 38 (December 2022)
    Schlagworte: Economic Complexity; Labor Productivity; Metropolitan Areas; Dimensionality Reduction; Clustering
    Umfang: 1 Online-Ressource (circa 22 Seiten), Illustrationen
  17. Latent heterogeneity in the marginal propensity to consume
    Autor*in: Lewis, Daniel J.; Melcangi, Davide; Pilossoph, Laura
    Erschienen: [2024]
    Verlag:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using... mehr

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    We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using interactions with observables, we can recover the full distribution of MPCs. We find households spent between 4 and 133% of the rebate within a quarter, and individual households used rebates for different goods. While many observable characteristics correlate individually with our estimated MPCs, these relationships disappear when tested jointly, except for income and the average propensity to consume. Household observable characteristics explain only 8% of MPC variation, highlighting the role of latent heterogeneity.

     
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    Schriftenreihe: Cemmap working paper ; CWP24, 13
    Schlagworte: Marginal Propensity to Consume; Consumption; Tax Rebate; Clustering
    Umfang: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  18. Sparse portfolio selection via topological data analysis based clustering
    Autor*in: Goel, Anubha; Filipović, Damir; Pasricha, Puneet
    Erschienen: [2024]
    Verlag:  Swiss Finance Institute, Geneva

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    Schriftenreihe: Swiss Finance Institute research paper series ; no 24, 07
    Schlagworte: Topological Data Analysis; Clustering; Index Tracking; Mean-Variance Portfolio; Global Minimum Variance Portfolio; Sparse Portfolios
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  19. Clustering for multi-dimensional heterogeneity with an application to production function estimation
    Autor*in: Cheng, Xu; Schorfheide, Frank; Shao, Peng
    Erschienen: September 19, 2023
    Verlag:  Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, Philadelphia, PA

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    Auflage/Ausgabe: This version: September 19, 2023
    Schriftenreihe: PIER working paper ; 23, 016
    Schlagworte: Clustering; GMM; K-mean; Panel Data; Production Function Estimation
    Umfang: 1 Online-Ressource (circa 53 Seiten), Illustrationen