Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures...Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures the smooth global trend and the second one models local details.TheCThas an advantage in the local variability compared to general T-process.Furthermore,a composite T-process regression(CTP)model is developed,based on the composite T-process.It inherits many nice properties as GPR,while it is more robust against outliers than GPR.Numerical studies including simulation and real data application show that CTP performs well in prediction.展开更多
The crane&shuttle-based storage and retrieval system(C&SBS/RS)is the first automated warehouse technology that supports pallet picking,case picking and item picking.In the C&SBS/RS,aisle-captive cranes per...The crane&shuttle-based storage and retrieval system(C&SBS/RS)is the first automated warehouse technology that supports pallet picking,case picking and item picking.In the C&SBS/RS,aisle-captive cranes perform pallet picking,while tier-captive shuttles handle cases and items picking.To balance picking tasks,we propose an order dividing algorithm to fulfil required specific picking sequences.The optimisation objective is to minimise the order line picking time.Therefore,we modelled and analyzed the C&SBS/RS and considered single and dual command cycles for each resource(i.e.cranes,rail-guided vehicles,shuttles and lifters)separately according to their respective operation processes.Finally,numerical experiments were conducted to analyze impact factors and a real case to verify the power of the proposed order dividing algorithm.展开更多
In generalized linear models with fixed design, under the assumption λ↑_n→∞ and other regularity conditions, the asymptotic normality of maximum quasi-likelihood estimator ^↑βn, which is the root of the quasi-li...In generalized linear models with fixed design, under the assumption λ↑_n→∞ and other regularity conditions, the asymptotic normality of maximum quasi-likelihood estimator ^↑βn, which is the root of the quasi-likelihood equation with natural link function ∑i=1^n Xi（yi -μ（Xi′β）） = 0, is obtained, where λ↑_n denotes the minimum eigenvalue of ∑i=1^nXiXi′, Xi are bounded p × q regressors, and yi are q × 1 responses.展开更多
Various forms of penalized estimators with good statistical and computational properties have been proposed for variable selection respecting the grouping structure in the variables. The attractive properties of these...Various forms of penalized estimators with good statistical and computational properties have been proposed for variable selection respecting the grouping structure in the variables. The attractive properties of these shrinkage and selection estimators, however, depend critically on the choice of the tuning parameter.One method for choosing the tuning parameter is via information criteria, such as the Bayesian information criterion(BIC). In this paper, we consider the problem of consistent tuning parameter selection in high dimensional generalized linear regression with grouping structures. We extend the results of the extended regularized information criterion(ERIC) to group selection methods involving concave penalties and then investigate the selection consistency with diverging variables in each group. Moreover, we show that the ERIC-type selector enables consistent identi?cation of the true model and that the resulting estimator possesses the oracle property even when the number of group is much larger than the sample size. Simulations show that the ERIC-type selector can signi?cantly outperform the BIC and cross-validation selectors when choosing true grouped variables,and an empirical example is given to illustrate its use.展开更多
This note discusses the method of randomly weighted bootstrap to derive the approximation of M-estimates in linear models, and shows that the approximation is asymptotically valid under some mild conditions.
基金supported by National Natural Science Foundation of China(Grant No.11971457)Anhui Provincial Natural Science Foundation(Grant No.1908085MA06).
文摘Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures the smooth global trend and the second one models local details.TheCThas an advantage in the local variability compared to general T-process.Furthermore,a composite T-process regression(CTP)model is developed,based on the composite T-process.It inherits many nice properties as GPR,while it is more robust against outliers than GPR.Numerical studies including simulation and real data application show that CTP performs well in prediction.
基金China Scholarship Council:[Grant Number 202006220116].
文摘The crane&shuttle-based storage and retrieval system(C&SBS/RS)is the first automated warehouse technology that supports pallet picking,case picking and item picking.In the C&SBS/RS,aisle-captive cranes perform pallet picking,while tier-captive shuttles handle cases and items picking.To balance picking tasks,we propose an order dividing algorithm to fulfil required specific picking sequences.The optimisation objective is to minimise the order line picking time.Therefore,we modelled and analyzed the C&SBS/RS and considered single and dual command cycles for each resource(i.e.cranes,rail-guided vehicles,shuttles and lifters)separately according to their respective operation processes.Finally,numerical experiments were conducted to analyze impact factors and a real case to verify the power of the proposed order dividing algorithm.
基金the National Natural Science Foundation of China under Grant Nos.10171094,10571001,and 30572285the Foundation of Nanjing Normal University under Grant No.2005101XGQ2B84+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No.07KJD110093the Foundation of Anhui University under Grant No.02203105
文摘In generalized linear models with fixed design, under the assumption λ↑_n→∞ and other regularity conditions, the asymptotic normality of maximum quasi-likelihood estimator ^↑βn, which is the root of the quasi-likelihood equation with natural link function ∑i=1^n Xi（yi -μ（Xi′β）） = 0, is obtained, where λ↑_n denotes the minimum eigenvalue of ∑i=1^nXiXi′, Xi are bounded p × q regressors, and yi are q × 1 responses.
基金supported by National Natural Science Foundation of China (Grant Nos. 11571337 and 71631006)the Fundamental Research Funds for the Central Universities (Grant No. WK2040160028)
文摘Various forms of penalized estimators with good statistical and computational properties have been proposed for variable selection respecting the grouping structure in the variables. The attractive properties of these shrinkage and selection estimators, however, depend critically on the choice of the tuning parameter.One method for choosing the tuning parameter is via information criteria, such as the Bayesian information criterion(BIC). In this paper, we consider the problem of consistent tuning parameter selection in high dimensional generalized linear regression with grouping structures. We extend the results of the extended regularized information criterion(ERIC) to group selection methods involving concave penalties and then investigate the selection consistency with diverging variables in each group. Moreover, we show that the ERIC-type selector enables consistent identi?cation of the true model and that the resulting estimator possesses the oracle property even when the number of group is much larger than the sample size. Simulations show that the ERIC-type selector can signi?cantly outperform the BIC and cross-validation selectors when choosing true grouped variables,and an empirical example is given to illustrate its use.
文摘This note discusses the method of randomly weighted bootstrap to derive the approximation of M-estimates in linear models, and shows that the approximation is asymptotically valid under some mild conditions.
