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- W2508306657 abstract "Linear mixed models (LMM) are popular in a host of business and engineering applications. In this paper, we consider estimation of the regression parameter vector of the LMM when some of the predictors are suspected to be insignificant for prediction purpose. In many practical situations, the investigators may have some information about the important predictors in a given model. Such information, known as uncertain prior information (UPI), could originate from subjective judgement of the investigator based on acquaintance with the experimental or observational data. Further, it is possible to obtain such information based on a variable selection technique, which we refer to as auxiliary information (AE). In any event, whether the information is subjective or data-driven, the resulting submodels are subject to model selection bias. Consequentially, the estimators based on a selected submodel will be biased if the submodel is misspecified. On the other hand, the estimates based on a full model (including all the predictors) may have large variation and/or subject to interpretability issues. To deal with these issues, in the context of two competing models (full model and submodel), we suggest linear shrinkage and shrinkage pretest estimation strategies which combine full model and submodel estimators in an effective way as a trade-off between bias and variance. We examine the performance of the suggested estimators relative to the full model estimator by theoretically using the mean square criterion. We also conduct a Monte Carlo simulation study to assess the performance of listed estimation strategies numerically. Our proposed shrinkage and pretest estimators perform better than the benchmark estimator in a meaningful way. The proposed method is applied to the analysis of a real data set." @default.
- W2508306657 created "2016-09-16" @default.
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- W2508306657 date "2016-08-24" @default.
- W2508306657 modified "2023-09-25" @default.
- W2508306657 title "Submodel Selection and Post-Estimation of the Linear Mixed Models" @default.
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- W2508306657 doi "https://doi.org/10.1007/978-981-10-1837-4_53" @default.
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