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Award ID contains: 2210569

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  1. ; (, Canadian Journal of Statistics)
    Abstract We consider estimation of the mean squared prediction error (MSPE) for observed best prediction (OBP) in small area estimation with count data. The OBP method has been previously developed in this context by Chen et al. (Journal of Survey Statistics and Methodology, 3, 136–161, 2015). However, estimation of the MSPE remains a challenging problem due to potential model misspecification that is considered in this setting. The latter authors proposed a bootstrap method for estimating the MSPE, whose theoretical justification is not clear. We propose to use a Prasad–Rao‐type linearization method to estimate the MSPE. Unlike the traditional linearization approaches, our method is computationally oriented and easier to implement in the same regard. Theoretical properties and empirical performance of the proposed method are studied. A real‐data application is considered. 
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    Free, publicly-accessible full text available December 1, 2025
  2. (, The Annals of Statistics)
    Free, publicly-accessible full text available June 1, 2026
  3. ; (, Statistica Sinica)
    Free, publicly-accessible full text available January 1, 2026
  4. ; (, Statistics in Medicine)
    In nowadays biomedical research, there has been a growing demand for making accurate prediction at subject levels. In many of these situations, data are collected as longitudinal curves and display distinct individual characteristics. Thus, prediction mechanisms accommodated with functional mixed effects models (FMEM) are useful. In this paper, we developed a classified functional mixed model prediction (CFMMP) method, which adapts classified mixed model prediction (CMMP) to the framework of FMEM. Performance of CFMMP against functional regression prediction based on simulation studies and the consistency property of CFMMP estimators are explored. Real‐world applications of CFMMP are illustrated using real world examples including data from the hormone research menstrual cycles and the diffusion tensor imaging. 
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    Full Text Available 
  5. ; ; ; (, Statistica Sinica)
    Full Text Available 
  6. ; ; (, Journal of the American Statistical Association)
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  7. ; (, Computational Statistics)
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