A functional marked point process model for lupus data

被引:5
|
作者
Fok, Carlotta Ching Ting [1 ,2 ]
Ramsay, James O. [3 ]
Abrahamowicz, Michal [4 ]
Fortin, Paul [5 ]
机构
[1] Univ Alaska Fairbanks, Dept Psychol, Fairbanks, AK 99775 USA
[2] Univ Alaska Fairbanks, Ctr Alaska Native Hlth Res, Inst Arctic Biol, Fairbanks, AK 99775 USA
[3] McGill Univ, Ottawa, ON K2B 6W9, Canada
[4] McGill Univ, Res Inst, Ctr Hlth, Montreal, PQ H3H 2R9, Canada
[5] Toronto Western Res Inst, Arthrit Soc, Toronto, ON, Canada
关键词
Frequency function; functional data analysis; inhomogeneous Poisson process; lupus flares; marked point process; mark variable; point process; SLEDAI score; MSC 2010: Primary 60G55; secondary; 62P10;
D O I
10.1002/cjs.11136
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Modelling for marked point processes is an important problem, but has received remarkably little attention in the statistical literature. The authors developed a marked point process model that incorporates the use of functional data analysis in a joint estimation of the frequency function of the point process and the intensity of the mark, with application to data from 22 lupus patients consisting of times of flares in symptom severity combined with a quantitative assessment of the severity. The data indicate that a rapid decrease in drug dose is significantly associated with a decrease in flare frequency. Experiments with simulated data designed to model the actual data further support this conclusion. The Canadian Journal of Statistics 40: 517529; 2012 (c) 2012 Statistical Society of Canada
引用
收藏
页码:517 / 529
页数:13
相关论文
共 50 条
  • [21] Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model
    Hatsuda, Hiroshi
    BIOINFORMATICS, 2012, 28 (18) : I633 - I639
  • [22] A marked point process perspective in fitting spatial point process models
    Guan, Yongtao
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2008, 138 (07) : 2143 - 2153
  • [23] Object identification by marked point process
    Dong, Gang
    Acton, Scott T.
    2005 39th Asilomar Conference on Signals, Systems and Computers, Vols 1 and 2, 2005, : 294 - 297
  • [24] CHAOTIC BEHAVIOR OF A MARKED POINT PROCESS
    HIBEY, JL
    PHYSICS LETTERS A, 1990, 151 (08) : 385 - 388
  • [25] Marked point process in image analysis
    Descombes, X
    Zerubia, J
    IEEE SIGNAL PROCESSING MAGAZINE, 2002, 19 (05) : 77 - 84
  • [26] Definition of distance for nonlinear time series analysis of marked point process data
    Iwayama, Koji
    Hirata, Yoshito
    Aihara, Kazuyuki
    PHYSICS LETTERS A, 2017, 381 (04) : 257 - 262
  • [27] A Marked Point Process for Automated Tree Detection from Mobile Laser Scanning Point Cloud Data
    Yu, Yongtao
    Li, Jonathan
    Guan, Haiyan
    Wang, Cheng
    Cheng, Ming
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMPUTER VISION IN REMOTE SENSING, 2012, : 140 - 145
  • [28] Target extraction from LiDAR point cloud data using irregular geometry marked point process
    Zhao Q.-H.
    Zhang H.-Y.
    Li Y.
    Zhao, Quan-Hua (zqhlby@163.com), 2018, Chinese Academy of Sciences (26): : 1201 - 1210
  • [29] Decomposition of a chemical spectrum using a marked point process and a constant dimension model
    Mazet, V.
    Brie, D.
    Idier, J.
    BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING, 2006, 872 : 288 - +
  • [30] Conditional -minimax prediction with a precautionary loss function in a marked point process model
    Lazar, Daniel
    STATISTICS, 2016, 50 (06) : 1411 - 1420