國家衛生研究院 NHRI:Item 3990099045/8501
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/8501


    Title: Composite likelihood estimation for models of spatial ordinal data and spatial proportional data with zero/one values
    Authors: Feng, XP;Zhu, J;Lin, PS;Steen Adams, MM
    Contributors: Division of Biostatistics and Bioinformatics
    Abstract: In this paper, we consider a spatial ordered probit model for analyzing spatial ordinal data with two or more ordered categories and, further, a spatial Tobit model for spatial proportional data with zero/one values. We develop a composite likelihood approach for parameter estimation and inference, which aims to balance statistical efficiency and computational efficiency for large datasets. The parameter estimates are obtained by maximizing a composite likelihood function via a quasi-Newton algorithm. The asymptotic properties of the maximum composite likelihood estimates are established under suitable regularity conditions. An estimate of the inverse of the Godambe information matrix is used for computing the standard errors, and the computation is further expedited by parallel computing. A simulation study is conducted to evaluate the performance of the proposed methods, followed by a real ecological data example. The connections between the spatial ordered probit model and the spatial Tobit model are explored using both simulated and real data.
    Date: 2014-12
    Relation: Environmetrics. 2014 Dec;25(8):571-583.
    Link to: http://dx.doi.org/10.1002/env.2306
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1180-4009&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000346908700003
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84925851454
    Appears in Collections:[Pei-Sheng Lin] Periodical Articles

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