The experimental random error and desired valuse of non observed points in dynamic indexes were estimated by establishing the linear regression equations about variety regulations of dynamic indexes.The methods for d...The experimental random error and desired valuse of non observed points in dynamic indexes were estimated by establishing the linear regression equations about variety regulations of dynamic indexes.The methods for difference significant test among different treatments using dynamic point as indexes were presented without setting the replication on each dynamic point observed.展开更多
Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol ...Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).展开更多
We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functi...We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.展开更多
Spatio-temporal data analysis is an emerging research area due to the development and application ofnovel computational techniques allowing for the analysis of large spatiotemporal databases.We consider a general clas...Spatio-temporal data analysis is an emerging research area due to the development and application ofnovel computational techniques allowing for the analysis of large spatiotemporal databases.We consider a general class of spatio-temporal linear models,where the number of structural breaks can tend to infinity.A procedure for simultaneously detecting all the change points is developed rigorously via the construction of adaptive group lasso penalty.Consistency of the multiple change point estimation is established under mild technical conditions even when the true number of change points sn diverges with the series length n.The adaptive group lasso can be substituted by the group lasso and other non-convex group selection penalty functions such as group SCAD or group MCP.The simulation studies demonstrate that our procedure is stable and accurate.Two empirical examples from property market,including the housing transaction price in Baton Rouge and the commodity apartment price in Hong Kong,are analyzed to fully illustrate the proposed methodology.展开更多
文摘The experimental random error and desired valuse of non observed points in dynamic indexes were estimated by establishing the linear regression equations about variety regulations of dynamic indexes.The methods for difference significant test among different treatments using dynamic point as indexes were presented without setting the replication on each dynamic point observed.
基金supported by the National Nature Science Foundation of China(U21A20166)the Science and Technology Development Foundation of Jilin Province(20230508095RC)+2 种基金the Major Science and Technology Projects of Jilin Province and Changchun City(20220301033GX)the Development and Reform Commission Foundation of Jilin Province(2023C034-3)the Interdisciplinary Integration and Innovation Project of JLU(JLUXKJC2020202).
文摘Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).
基金supported by National Natural Science Foundation of China (Grant No. 11271080)
文摘We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.
基金National Natural Science Foundation of China(General Program,No.11571337,71873128,Key Program,No.71631006)Natural Sciences and Engineering Research Council of Canada(Grant No.RGPIN-2017-05720)。
文摘Spatio-temporal data analysis is an emerging research area due to the development and application ofnovel computational techniques allowing for the analysis of large spatiotemporal databases.We consider a general class of spatio-temporal linear models,where the number of structural breaks can tend to infinity.A procedure for simultaneously detecting all the change points is developed rigorously via the construction of adaptive group lasso penalty.Consistency of the multiple change point estimation is established under mild technical conditions even when the true number of change points sn diverges with the series length n.The adaptive group lasso can be substituted by the group lasso and other non-convex group selection penalty functions such as group SCAD or group MCP.The simulation studies demonstrate that our procedure is stable and accurate.Two empirical examples from property market,including the housing transaction price in Baton Rouge and the commodity apartment price in Hong Kong,are analyzed to fully illustrate the proposed methodology.
