Modeling marked point processes using bivariate mixture transition distribution models

Mohamed Yusuf Hassan, Keh Shin Lii

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We propose new statistical models for the analysis of marked point processes. These models deal with data that arrives in unequal intervals, such as financial transactions or heart attacks. The models treat both the time between event arrivals and the observed marks as stochastic processes. We propose and investigate a class of bivariate distributions to form the bivariate mixture transition distribution (BMTD). In these models the bivariate conditional distribution of the next observation given the past is a mixture of conditional distributions given each one of the last k observations. The identifiability of the model is investigated, and the EM algorithm is developed to obtain estimates of the model parameters.

    Original languageEnglish
    Title of host publicationProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages285-289
    Number of pages5
    ISBN (Electronic)0769501400, 9780769501406
    DOIs
    Publication statusPublished - 1999
    Event1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999 - Caesarea, Israel
    Duration: Jun 14 1999Jun 16 1999

    Publication series

    NameProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999

    Other

    Other1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
    Country/TerritoryIsrael
    CityCaesarea
    Period6/14/996/16/99

    ASJC Scopus subject areas

    • Signal Processing
    • Statistics and Probability

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