Best Predictive Small Area Estimation

Jiang, Jiming; Nguyen, Thuan; Rao, J. Sunil

doi:10.1198/jasa.2011.tm10221

Cited by 71 publications

(88 citation statements)

References 13 publications

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“…For example, in case that is completely unknown, it can be shown that essentially the same derivation of Jiang et al [25] goes through, and the resulting measure of lack-of-fit, ( ), is the minimizer of ( − ) Γ Γ( − ) − 2t (Γ Σ), assuming that Σ is known. It is also possible to extend the idea of predictive model selection to the generalized linear mixed models (GLMMs; e.g., Jiang [9]).…”

Section: Predictive Model Selectionmentioning

confidence: 96%

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The Fence Methods

Jiang

2014

Advances in Statistics

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Section: Predictive Model Selectionmentioning

confidence: 96%

“…Here, again, E denotes expectation under the true model. Jiang et al [25] showed that the MSPE has another expression, which is the key to our derivation:…”

Section: Predictive Model Selectionmentioning

confidence: 99%

The Fence Methods

Jiang

2014

Advances in Statistics

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“…For example, Copas and Eguchi (2005) discuss a similar issue that they term as incomplete-data bias , in which the maximum likelihood estimators can be (sometimes severely) biased when incomplete data are present, and an incorrect model is being fit, and yet still appears to give a good fit to the available data. Jiang et al (2011a) showed that if one derives the parameter estimators by evaluating the best predictor (BP) under the assumed model, say, M , using the distribution also under M , the resulting predictor is not robust in the sense that it may perform poorly when M is not the true model. Here, the failure of the BP is due to a similar double-dipping strategy, that is, (1) the measure of lack-of-fit (sum of squared prediction errors), is for the BP under M ; and (2) the distribution under which the measure of lack-of-fit is evaluated is also under on M .…”

Section: Outline Of Our Main Contributionsmentioning

confidence: 99%

The E-MS Algorithm: Model Selection With Incomplete Data

Jiang

Nguyen

Rao

2015

Journal of the American Statistical Association

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We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang et al. 2008). We prove numerical convergence of the E-MS algorithm as well as consistency in model selection of the limiting model of the E-MS convergence, for E-MS with GIC and E-MS with AF. We study the impact on model selection of different missing data mechanisms. Furthermore, we carry out extensive simulation studies on the finite-sample performance of the E-MS with comparisons to other procedures. The methodology is also illustrated on a real data analysis involving QTL mapping for an agricultural study on barley grains.

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“…Previous research (Jiang et al 2011 andBondell et al 2010) discussed two issues in Linear Mixed Model (LMM), which is the GLMM with identity link and Gaussian assumption. Jiang et al (2011) shows how to obtain the best prediction in the LMM (Linear Mixed Model), and Bondell et al(2010) uses the Lasso to select random effect in LMM for the estimation purpose.…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al (2011) shows how to obtain the best prediction in the LMM (Linear Mixed Model), and Bondell et al(2010) uses the Lasso to select random effect in LMM for the estimation purpose. However, there are no existing methods for selecting both random effects and fixed effects for the purpose of best prediction via the Lasso (Tibshirani 2011) in LMM.…”

Section: Introductionmentioning

confidence: 99%

Best Predictive Generalized Linear Mixed Model with Predictive Lasso for High-Speed Network Data Analysis

Hu¹,

Choi²,

Sim³

et al. 2015

IJSP

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Optimizing network usage is important to maximize the network performance. When the network usage grows rapidly, it is important to build an accurate predictive model. We present a new predictive algorithm which can analyze the network performance in various network conditions and traffic patterns. Our approach is based on the best predictive generalized linear mixed model (GLMM). The parameters of the best predictive GLMM are estimated by minimizing the mean squared prediction error (MSPE). To expedite the parameter learning with the big data collected through the network, our algorithm introduced regularization, LASSO, and an innovative bootstrap. The merits of our new approach validated through data and simulation are that (1) the highest prediction accuracy even under a model misspecification; and (2) the least computation time compared to the Estimationoriented GLMM with Lasso and Stepwise Selection GLMM. A major computational advantage of our method is that, unlike some of the current approaches, our method does not require the EM (Expectation-Maximization algorithm) procedure.

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Best Predictive Small Area Estimation

Cited by 71 publications

References 13 publications

The Fence Methods

The Fence Methods

The E-MS Algorithm: Model Selection With Incomplete Data

Best Predictive Generalized Linear Mixed Model with Predictive Lasso for High-Speed Network Data Analysis

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