Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a n...Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.展开更多
This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least sq...This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant coefficient.The semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.展开更多
We study the pricing game between competing retailers under various random coefficient attraction choice models.We characterize existence conditions and structure properties of the equilibrium.Moreover,we explore how ...We study the pricing game between competing retailers under various random coefficient attraction choice models.We characterize existence conditions and structure properties of the equilibrium.Moreover,we explore how the randomness and cost parameters affect the equilibrium prices and profits under multinomial logit(MNL),multiplicative competitive interaction(MCI)and linear attraction choice models.Specifically,with bounded randomness,for the MCI and linear attraction models,the randomness always reduces the retailer’s profit.However,for the MNL model,the effect of randomness depends on the product’s value gap.For high-end products(i.e.,whose value gap is higher than a threshold),the randomness reduces the equilibrium profit,and vice versa.The results suggest high-end retailers in MNL markets exert more effort in disclosing their exact product performance to consumers.We also reveal the effects of randomness on retailers’pricing decisions.These results help retailers in making product performance disclosure and pricing decisions.展开更多
In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = X^τα（U） ＋ ε when X are subject to missing at random. Based on the inverse probability-weighted idea, a clas...In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = X^τα（U） ＋ ε when X are subject to missing at random. Based on the inverse probability-weighted idea, a class of empirical log-likelihood ratios, as well as two maximum empirical likelihood estimators, are developed for α（u）. The resulting statistics are shown to have standard chi-squared or normal distributions asymptotically.Simulation studies are also constructed to illustrate the finite sample properties of the proposed statistics.展开更多
基金supported by National Natural Science Foundation of China (61703410,61873175,62073336,61873273,61773386,61922089)。
文摘Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.
基金supported by the National Natural Science Foundation of China(Grant No.52338009)the National Science Fund for Distinguished Young Scholars(Grant No.52025085)+4 种基金the Graduate Research Innovation Project of Hunan Province(Grant No.CX20220952)Xiaohui Liu’s research is supported by the NSF of China(Grant No.11971208)the National Social Science Foundation of China(Grant No.21&ZD152)the Outstanding Youth Fund Project of the Science and Technology Department of Jiangxi Province(Grant No.20224ACB211003)the NSF of China(Grant No.92358303).
文摘This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant coefficient.The semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.
基金partially supported by the National Natural Science Foundation of China(No.72001198 and Nos.71991464/71991460)the Fundamental Research Funds for the Central Universities(No.WK2040000027)+3 种基金the National Key R&D Program of China(Nos.2020AAA0103804/2020AAA0103800)USTC(University of Science and Technology of China)Research Funds of the Double First-Class Initiative(No.YD2040002004)Collaborative Research Fund(No.C1143-20G)General Research Fund(No.115080/17).
文摘We study the pricing game between competing retailers under various random coefficient attraction choice models.We characterize existence conditions and structure properties of the equilibrium.Moreover,we explore how the randomness and cost parameters affect the equilibrium prices and profits under multinomial logit(MNL),multiplicative competitive interaction(MCI)and linear attraction choice models.Specifically,with bounded randomness,for the MCI and linear attraction models,the randomness always reduces the retailer’s profit.However,for the MNL model,the effect of randomness depends on the product’s value gap.For high-end products(i.e.,whose value gap is higher than a threshold),the randomness reduces the equilibrium profit,and vice versa.The results suggest high-end retailers in MNL markets exert more effort in disclosing their exact product performance to consumers.We also reveal the effects of randomness on retailers’pricing decisions.These results help retailers in making product performance disclosure and pricing decisions.
基金supported in part by NSF of China(No.11461029)NSF of Jiangxi Province(No.20142BAB211014)YSFP of Jiangxi provincial education department(No.GJJ14350)
文摘In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = X^τα（U） ＋ ε when X are subject to missing at random. Based on the inverse probability-weighted idea, a class of empirical log-likelihood ratios, as well as two maximum empirical likelihood estimators, are developed for α（u）. The resulting statistics are shown to have standard chi-squared or normal distributions asymptotically.Simulation studies are also constructed to illustrate the finite sample properties of the proposed statistics.
