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   Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 dataSchiltz, Jang  ; Scientific Conference (2023, July 04) Detailed reference viewed: 101 (0 UL)Multiple Trajectory Analysis in Finite Mixture Modeling; Schiltz, Jang Scientific Conference (2023, June 08) Detailed reference viewed: 134 (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: 99 (0 UL)New results in finite mixture modelingSchiltz, Jang ; Scientific Conference (2023, January 05) Detailed reference viewed: 76 (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: 99 (3 UL)TrajeR an R package for the clustering of longitudinal data; Schiltz, Jang Scientific Conference (2020, June 04) Detailed reference viewed: 114 (0 UL)Identifiability of Finite Mixture Models; Schiltz, Jang Scientific Conference (2020, June 04) Detailed reference viewed: 79 (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: 81 (1 UL) |
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