English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109968/140903 (78%)
Visitors : 46382578      Online Users : 976
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/141009
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141009


    Title: 基於Sarmanov分佈的二元縱向計數資料模型
    A Sarmanov Distribution Based Model for Bivariate Panel Count Data
    Authors: 陳盈諳
    Chen, Ying-An
    Contributors: 黃佳慧
    Huang, Chia-Hui
    陳盈諳
    Chen, Ying-An
    Keywords: 非齊次Poisson過程
    縱向計數資料
    比例均值迴歸模型
    隨機效應
    Sarmanov分佈
    Nonhomogeneous Poisson process
    Panel count data
    Proportional mean regression model
    Random effect
    Sarmanov distribution
    Date: 2022
    Issue Date: 2022-08-01 17:16:07 (UTC+8)
    Abstract: 在本文中,我們為二元縱向計數資料建立聯合模型,此類資料只能在特定時間點上被蒐集。在模型的架構中,我們假設每一種事件類型的計數服從一個非齊次Poisson過程,並使用比例均值迴歸模型建構事件發生率。為了將二元資料內的關聯性納入模型,我們考慮在每一個事件類型的均值函數內存在隨機效應,而提出的模型允許此二元縱向計數資料可以透過包含於均值函數內的隨機效應使其建立相依性,這些隨機效應服從一個邊際分佈為Gamma分佈的Sarmanov分佈。在此隨機模型假設下,我們推導出二元縱向計數資料的聯合機率分佈,並利用最大概似估計法取得參數估計。我們使用模擬比較兩種估計方法下所得之估計量的表現,從模擬的結果中可以觀察到兩者的表現相似。最後,本文所提出之模型套用在內政部警政署的交通資料,估計協變量與季節對於車禍發生率的影響。
    In this work, we consider a joint model for panel count data with bivariate event types, which are only collected at particular time points. We assume that the counts follow a nonhomogeneous Poisson process for each event type, and a proportional mean regression model is specified. To account for the association, we further impose a positive random effect on each of the mean functions. The proposed model allows for the dependence of event types through random effects that follow the bivariate Sarmanov distribution with gamma marginals. The estimations of the parameters are based on the maximum likelihood method. We use two estimation methods and compare the performance of the estimators based on several simulation studies, which result in similar performance. An application to traffic accident data is presented.
    Reference: Abdallah, A., Boucher, J.-P., and Cossette, H. (2016). Sarmanov family of multivariate distributions for bivariate dynamic claim counts model. Insurance: Mathematics and Economics, 68:120–133.

    Bahraoui, Z., Bolancé, C., Pelican, E., and Vernic, R. (2015a). On the bivariate sarmanov distribution and copula. an application on insurance data using truncated marginal dis- tributi. SORT, 39(2):209–230.

    Bahraoui, Z., Bolancé, C., Pelican, E., and Vernic, R. (2015b). On the bivariate sarmanov distribution and copula. an application on insurance data using truncated marginal dis- tributions. Statistics and Operations Research Transactions, SORT, 39(2):209–230.

    Bairamov, I., Altinsoy, B., and Kerns, G. J. (2011). On generalized sarmanov bivariate distributions. TWMS Journal of Applied and Engineering Mathematics, 1(1):86–97.

    Bairamov, I., Kotz, S., and Gebizlioglu, O. L. (2001). The sarmanov family and its gen- eralization: theory and methods. South African Statistical Journal, 35(2):205–224.

    Bolancé, C. and Vernic, R. (2019). Multivariate count data generalized linear models: Three approaches based on the sarmanov distribution. Insurance: Mathematics and Economics, 85:89–103.

    Dias, A., Embrechts, P., et al. (2004). Dynamic copula models for multivariate high- frequency data in finance. Manuscript, ETH Zurich, 81.

    Hashorva, E. and Ratovomirija, G. (2015). On sarmanov mixed erlang risks in insurance applications. ASTIN Bulletin: The Journal of the IAA, 45(1):175–205.

    Huang, C.-Y., Wang, M.-C., and Zhang, Y. (2006). Analysing panel count data with in- formative observation times. Biometrika, 93(4):763–775.

    Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press. Kotz, S., Balakrishnan, N., and Johnson, N. L. (2004). Continuous multivariate distribu-
    tions, Volume 1: Models and applications, volume 1. John Wiley & Sons.

    Nelsen, R. B. (2006). An introduction to copulas. Springer Science & Business Media.

    Ross, S. M., Kelly, J. J., Sullivan, R. J., Perry, W. J., Mercer, D., Davis, R. M., Washburn, T. D., Sager, E. V., Boyce, J. B., and Bristow, V. L. (1996). Stochastic processes, volume 2. Wiley New York.

    Sarmanov, O. V. (1966). Generalized normal correlation and two-dimensional fréchet classes. In Doklady Akademii Nauk, volume 168, pages 32–35. Russian Academy of Sciences.
    Shi, P. and Zhao, Z. (2020). Regression for copula-linked compound distributions with applications in modeling aggregate insurance claims. The Annals of Applied Statistics, 14(1):357–380.

    Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. inst. statist. univ. Paris, 8:229–231.
    Sun, J., Zhao, X., et al. (2013). Statistical analysis of panel count data. Springer.

    Ting Lee, M.-L. (1996). Properties and applications of the sarmanov family of bivariate distributions. Communications in Statistics-Theory and Methods, 25(6):1207–1222.

    Wellner, J. A. and Zhang, Y. (2000). Two estimators of the mean of a counting process with panel count data. The Annals of statistics, 28(3):779–814.

    Wellner, J. A. and Zhang, Y. (2007). Two likelihood-based semiparametric estimation methods for panel count data with covariates. The Annals of Statistics, 35(5):2106– 2142.
    Description: 碩士
    國立政治大學
    統計學系
    109354017
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109354017
    Data Type: thesis
    DOI: 10.6814/NCCU202200735
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    401701.pdf546KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback