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Showing results 1 to 20 of 150
   Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 dataSchiltz, Jang  ; Scientific Conference (2023, July 04) Detailed reference viewed: 83 (0 UL)Multiple Trajectory Analysis in Finite Mixture Modeling; Schiltz, Jang Scientific Conference (2023, June 08) Detailed reference viewed: 80 (0 UL)A new R package for Finite Mixture Models with an application to clustering countries with respect to COVID data; Schiltz, Jang Scientific Conference (2023, June 02) Detailed reference viewed: 83 (0 UL)New results in finite mixture modelingSchiltz, Jang ; Scientific Conference (2023, January 05) Detailed reference viewed: 51 (0 UL)Modèles de mélanges finis pour une distribution de loi BETA sous-jacente avec une application à des données sur la COVID-19Noel, Cédric ; Schiltz, Jang Scientific Conference (2022, September 14) Detailed reference viewed: 51 (2 UL)A new R package for Finite Mixture Models with an application to clustering countries with respect to COVID dataNoel, Cédric ; Schiltz, Jang Scientific Conference (2022, June 10) Detailed reference viewed: 77 (0 UL)A new R package for Finite Mixture Models with an application to pension systemsSchiltz, Jang ; ; Guigou, Jean-Daniel Scientific Conference (2022, April 20) Detailed reference viewed: 84 (3 UL) Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 dataSchiltz, Jang ; Noel, Cédric E-print/Working paper (2022) Detailed reference viewed: 76 (3 UL)trajeR, une nouvelle librairie R pour les modèles de mélange pour données longitundinalesNoel, Cédric ; Schiltz, Jang Scientific Conference (2022, April 04) Detailed reference viewed: 54 (0 UL)trajeR, an R package for cluster analysis of time seriesNoel, Cédric ; Schiltz, Jang E-print/Working paper (2022) Detailed reference viewed: 74 (0 UL)Multiple Trajectory Analysis in Finite Mixture ModelingNoel, Cédric ; Schiltz, Jang Scientific Conference (2021, June 02) Detailed reference viewed: 83 (0 UL)trajeR - une nouvelle librairie R pour les modèles de mélanges pour données longitudinales.Noel, Cédric ; Schiltz, Jang in CNRIUT' 2021 - Recueil des Publications (2021, June) Detailed reference viewed: 93 (0 UL)Luxembourg Fund Data Repository; ; Schiltz, Jang in Data (2020), 5(3), 1-15 In this paper, we introduce the Luxembourg Fund Data Repository, a novel database of investment funds available for academic research that was created at the Department of Finance of the University of ... [more ▼] In this paper, we introduce the Luxembourg Fund Data Repository, a novel database of investment funds available for academic research that was created at the Department of Finance of the University of Luxembourg. The database contains the population of Undertakings for Collective Investment in Transferable Securities funds domiciled in Luxembourg from the starting month of their existence (March 1988) to October 2016. The fund characteristics are organized in a comprehensive database architecture encompassing static and dynamic data over the entire life of the funds. The characteristics include fund identifiers, official name, status information, management company and other service providers, daily and monthly performance time-series, portfolio holdings, classification of investment objective, fees, dividends, and cash flows. The database was constructed after collecting and assembling complementary historical information from three data providers. Importantly, funds no longer in existence due to liquidation or mergers are included in the database, preventing survivorship bias. The database has been constructed to serve as a research dataset of high accuracy due to the maximization of population coverage, the maximization of historical coverage, and validation by using information acquired from the supervisory authority of the financial sector of Luxembourg. License currently available to researchers of the Department of Finance of the University of Luxembourg. Future plans for extending accessibility to the global academic community. [less ▲] Detailed reference viewed: 152 (3 UL)TrajeR an R package for the clustering of longitudinal data; Schiltz, Jang Scientific Conference (2020, June 04) Detailed reference viewed: 103 (0 UL)Identifiability of Finite Mixture Models; Schiltz, Jang Scientific Conference (2020, June 04) Detailed reference viewed: 61 (0 UL)Identifiability of Finite Mixture Models with underlying Normal Distribution; Schiltz, Jang E-print/Working paper (2020) In this paper, we show under which conditions generalized finite mixture with underlying normal distribution are identifiable in the sense that a given dataset leads to a uniquely determined set of model ... [more ▼] In this paper, we show under which conditions generalized finite mixture with underlying normal distribution are identifiable in the sense that a given dataset leads to a uniquely determined set of model parameter estimations up to a permuta-tion of the clusters. [less ▲] Detailed reference viewed: 74 (1 UL)A performance evaluation of weight-constrained conditioned portfolio optimizationSchiltz, Jang ; Boissaux, Marc Scientific Conference (2019, December 20) Detailed reference viewed: 86 (0 UL)A new model selection criterion for finite mixture modelsSchiltz, Jang in Proceedings of the 62nd ISI World Statistics Congress (2019, August 20) Detailed reference viewed: 72 (0 UL)Financial markets dependence modeling using vine copulae - Vine copulae estimation of asset decompositionPetitjean, Simon Paul ; Schiltz, Jang Scientific Conference (2019, July 12) Detailed reference viewed: 106 (1 UL)Financial markets dependence modeling using vine copulae - Preliminary work for vine copula modeling in financePetitjean, Simon Paul ; Schiltz, Jang Scientific Conference (2019, July 08) Detailed reference viewed: 95 (2 UL) |
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