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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. 

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. 

Abstract Centromeres in most multicellular eukaryotes are composed of long arrays of repetitive DNA sequences. Interestingly, several transposable elements, including the well-known long terminal repeat centromeric retrotransposon of maize (CRM), were found to be enriched in functional centromeres marked by the centromeric histone H3 (CENH3). Here, we report a centromeric long interspersed nuclear element (LINE), Celine, in Populus species. Celine has colonized preferentially in the CENH3-associated chromatin of every poplar chromosome, with 84% of the Celine elements localized in the CENH3-binding domains. In contrast, only 51% of the CRM elements were bound to CENH3 domains in Populus trichocarpa. These results suggest different centromere targeting mechanisms employed by Celine and CRM elements. Nevertheless, the high target specificity seems to be detrimental to further amplification of the Celine elements, leading to a shorter life span and patchy distribution among plant species compared with the CRM elements. Using a phylogenetically guided approach, we were able to identify Celine-like LINE elements in tea plant (Camellia sinensis) and green ash tree (Fraxinus pennsylvanica). The centromeric localization of these Celine-like LINEs was confirmed in both species. We demonstrate that the centromere targeting property of Celine-like LINEs is of primitive origin and has been conserved among distantly related plant species. 

We propose a new classified mixed model prediction (CMMP) procedure, called pseudo-Bayesian CMMP,that uses network information in matching the group index between the training data and new data, whosecharacteristics of interest one wishes to predict. The current CMMP procedures do not incorporate suchinformation; as a result, the methods are not consistent in terms of matching the group index. Although, asthe number of training data groups increases, the current CMMP method can predict the mixed effects ofinterest consistently, its accuracy is not guaranteed when the number of groups is moderate, as is the case inmany potential applications. The proposed pseudo-Bayesian CMMP procedure assumes a flexible workingprobability model for the group index of the new observation to match the index of a training data group,which may be viewed as a pseudo prior. We show that, given any working model satisfying mild conditions,the pseudo-Bayesian CMMP procedure is consistent and asymptotically optimal both in terms of matchingthe group index and in terms of predicting the mixed effect of interest associated with the new observations.The theoretical results are fully supported by results of empirical studies, including Monte-Carlo simulationsand real-data validation. 

Abstract We develop a method of classified mixed model prediction based on generalized linear mixed models that incorporate pseudo‐prior information to improve prediction accuracy. We establish consistency of the proposed method both in terms of prediction of the true mixed effect of interest and in terms of correctly identifying the potential class corresponding to the new observations if such a class matching one of the training data classes exists. Empirical results, including simulation studies and real‐data validation, fully support the theoretical findings.