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Mixture Poisson point process [electronic resource] : assessing heterogeneity in EMA analysis / by Nat Kulvanich.

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  • Additional Information
    • Abstract:
      Summary: The times of repeated behavioral events can be viewed as a realization of a temporal point process. Rathbun, Shi man, and Gwaltney (2007) used a Poisson process (Cox 1972) for modeling repeated behavioral events impacted by time-varying covariates. Taking an inspiration from the techniques of generalized linear mixed models, and the EM algorithm (Dempster et al. 1977) for nite mixture model estimation, we will further extend models to handle data arising from a heterogeneous population. In Chapter 2, we present a nite mixture model for Poisson point processes, classifying subjects into clusters sharing identical responses to time-varying covariates within clusters. In Chapter 3, a mixture mixed-e ect model is presented which accommodates variation among subjects within clusters with respect to their responses to the time-varying covariates. In Chapter 4, we discuss some issues we encountered in the research and point out potential topics for future research. All the approaches in this dissertation are illustrated using data from an ecological momentary assessment of smoking.
    • Notes:
      Directed by Stephen L. Rathbun.
      Thesis (Ph. D.)--University of Georgia, 2013.
      Includes bibliographical references (leaves 80-89).
      Electronic reproduction. [Athens, Ga. : University of Georgia Libraries, 2013]. Mode of access: World Wide Web. System requirements: Adobe Acrobat reader. s2013 guan s
    • Accession Number:
      ocn858269295
    • Accession Number:
      d.uga.4133102
  • Citations
    • ABNT:
      KULVANICH, N. Mixture Poisson point process. [electronic resource] : assessing heterogeneity in EMA analysis. [S. l.: s. n.]. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102. Acesso em: 24 set. 2020.
    • AMA:
      Kulvanich N. Mixture Poisson Point Process. [Electronic Resource] : Assessing Heterogeneity in EMA Analysis.; 2013. Accessed September 24, 2020. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102
    • APA:
      Kulvanich, N. (2013). Mixture Poisson point process. [electronic resource] : assessing heterogeneity in EMA analysis.
    • Chicago/Turabian: Author-Date:
      Kulvanich, Nat. 2013. Mixture Poisson Point Process. [Electronic Resource] : Assessing Heterogeneity in EMA Analysis. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102.
    • Harvard:
      Kulvanich, N. (2013) Mixture Poisson point process. [electronic resource] : assessing heterogeneity in EMA analysis. Available at: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102 (Accessed: 24 September 2020).
    • Harvard: Australian:
      Kulvanich, N 2013, Mixture Poisson point process. [electronic resource] : assessing heterogeneity in EMA analysis, viewed 24 September 2020, .
    • MLA:
      Kulvanich, Nat. Mixture Poisson Point Process. [Electronic Resource] : Assessing Heterogeneity in EMA Analysis. 2013. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102.
    • Chicago/Turabian: Humanities:
      Kulvanich, Nat. Mixture Poisson Point Process. [Electronic Resource] : Assessing Heterogeneity in EMA Analysis, 2013. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102.
    • Vancouver/ICMJE:
      Kulvanich N. Mixture Poisson point process. [electronic resource] : assessing heterogeneity in EMA analysis [Internet]. 2013 [cited 2020 Sep 24]. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4133102