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Subgroup Analysis for Longitudinal Data via Semiparametric Additive mixed effects Model

Journal of Systems Science & Complexity 2023年第5期36卷 2155-2185页

作者： BO Xiaolin ZHANG Weiping Department of Statistics and Finance University of Science and Technology of ChinaHefei 230026China

This paper proposed a general framework based on semiparametric additive mixed effects model to identify subgroups on each covariate and estimate the corresponding regression functions simultaneously for longitudinal data,thus it could reveal which covariate contributes to the existence of subgroups among population.A backfitting combined with k-means algorithm was developed to detect subgroup structure on each covariate and estimate each semiparametric additive component across subgroups.A Bayesian information criterion is employed to estimate the actual number of groups.The efficacy and accuracy of the proposed procedure in identifying the subgroups and estimating the regression functions are illustrated through numerical studies.In addition,the authors demonstrate the usefulness of the proposed method with applications to PBC data and Industrial Portfolio's Return data and provide meaningful partitions of the populations.

关键词： Additive model backfitting mixed effects subgroup identification

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A Robust Segmented mixed Effect Regression Model for Baseline Electricity Consumption Forecasting

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 71-80页

作者： Xiaoyang Zhou Yuanqi Gao Weixin Yao Nanpeng Yu Department of Statistics University of CaliforniaRiversideCAUSA Department of Electrical and Computer Engineering University of CaliforniaRiversideUSA

Renewable energy production has been surging around the world in recent years.To mitigate the increasing uncertainty and intermittency of the renewable generation,proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation.One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately.In this paper,we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California,USA,by combining the ideas of random effect regression model,segmented regression model,and the least trimmed squares estimate.Since the log-likelihood of the considered model is not differentiable at breakpoints,we propose a new backfitting algorithm to estimate the unknown parameters.The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

关键词： Segmented regression model mixed effects trimmed maximum likelihood demand response electric load

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