This paper deals with the conditional density estimator of a real response variable given a functional random variable (i.e., takes values in an infinite-dimensional space). Specifically, we focus on the functional index model, and this approach represents a good compromise between nonparametric and parametric models. Then we give under general conditions and when the variables are independent, the quadratic error and asymptotic normality of estimator by local linear method, based on the single-index structure. Finally, we complete these theoretical advances by some simulation studies showing both the practical result of the local linear method and the good behaviour for finite sample sizes of the estimator and of the Monte Carlo methods to create functional pseudo-confidence area.
{"title":"Asymptotic properties of a nonparametric conditional density estimator in the local linear estimation for functional data via a functional single-index model","authors":"Fadila Benaissa, Abdelmalek Gagui, Abdelhak Chouaf","doi":"10.1080/24754269.2021.1965945","DOIUrl":"https://doi.org/10.1080/24754269.2021.1965945","url":null,"abstract":"This paper deals with the conditional density estimator of a real response variable given a functional random variable (i.e., takes values in an infinite-dimensional space). Specifically, we focus on the functional index model, and this approach represents a good compromise between nonparametric and parametric models. Then we give under general conditions and when the variables are independent, the quadratic error and asymptotic normality of estimator by local linear method, based on the single-index structure. Finally, we complete these theoretical advances by some simulation studies showing both the practical result of the local linear method and the good behaviour for finite sample sizes of the estimator and of the Monte Carlo methods to create functional pseudo-confidence area.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"208 - 219"},"PeriodicalIF":0.5,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48999810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-08DOI: 10.1080/24754269.2021.1946373
Z. Sheng, Yukun Liu, J. Qin
ABSTRACT Due to cost-effectiveness and high efficiency, two-phase case-control sampling has been widely used in epidemiology studies. We develop a semi-parametric empirical likelihood approach to two-phase case-control data under the logistic regression model. We show that the maximum empirical likelihood estimator has an asymptotically normal distribution, and the empirical likelihood ratio follows an asymptotically central chi-square distribution. We find that the maximum empirical likelihood estimator is equal to Breslow and Holubkov (1997)'s maximum likelihood estimator. Even so, the limiting distribution of the likelihood ratio, likelihood-ratio-based interval, and test are all new. Furthermore, we construct new Kolmogorov–Smirnov type goodness-of-fit tests to test the validation of the underlying logistic regression model. Our simulation results and a real application show that the likelihood-ratio-based interval and test have certain merits over the Wald-type counterparts and that the proposed goodness-of-fit test is valid.
{"title":"Empirical likelihood inference and goodness-of-fit test for logistic regression model under two-phase case-control sampling","authors":"Z. Sheng, Yukun Liu, J. Qin","doi":"10.1080/24754269.2021.1946373","DOIUrl":"https://doi.org/10.1080/24754269.2021.1946373","url":null,"abstract":"ABSTRACT Due to cost-effectiveness and high efficiency, two-phase case-control sampling has been widely used in epidemiology studies. We develop a semi-parametric empirical likelihood approach to two-phase case-control data under the logistic regression model. We show that the maximum empirical likelihood estimator has an asymptotically normal distribution, and the empirical likelihood ratio follows an asymptotically central chi-square distribution. We find that the maximum empirical likelihood estimator is equal to Breslow and Holubkov (1997)'s maximum likelihood estimator. Even so, the limiting distribution of the likelihood ratio, likelihood-ratio-based interval, and test are all new. Furthermore, we construct new Kolmogorov–Smirnov type goodness-of-fit tests to test the validation of the underlying logistic regression model. Our simulation results and a real application show that the likelihood-ratio-based interval and test have certain merits over the Wald-type counterparts and that the proposed goodness-of-fit test is valid.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"265 - 276"},"PeriodicalIF":0.5,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1946373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44383814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.1080/24754269.2021.1946372
Zhongheng Cai, Dongchu Sun
In Bayesian quantile smoothing spline [Thompson, P., Cai, Y., Moyeed, R., Reeve, D., & Stander, J. (2010). Bayesian nonparametric quantile regression using splines. Computational Statistics and Data Analysis, 54, 1138–1150.], a fixed-scale parameter in the asymmetric Laplace likelihood tends to result in misleading fitted curves. To solve this problem, we propose a new Bayesian quantile smoothing spline (NBQSS), which considers a random scale parameter. To begin with, we justify its objective prior options by establishing one sufficient and one necessary condition of the posterior propriety under two classes of general priors including the invariant prior for the scale component. We then develop partially collapsed Gibbs sampling to facilitate the computation. Out of a practical concern, we extend the theoretical results to NBQSS with unobserved knots. Finally, simulation studies and two real data analyses reveal three main findings. Firstly, NBQSS usually outperforms other competing curve fitting methods. Secondly, NBQSS considering unobserved knots behaves better than the NBQSS without unobserved knots in terms of estimation accuracy and precision. Thirdly, NBQSS is robust to possible outliers and could provide accurate estimation.
{"title":"Bayesian analysis for quantile smoothing spline","authors":"Zhongheng Cai, Dongchu Sun","doi":"10.1080/24754269.2021.1946372","DOIUrl":"https://doi.org/10.1080/24754269.2021.1946372","url":null,"abstract":"In Bayesian quantile smoothing spline [Thompson, P., Cai, Y., Moyeed, R., Reeve, D., & Stander, J. (2010). Bayesian nonparametric quantile regression using splines. Computational Statistics and Data Analysis, 54, 1138–1150.], a fixed-scale parameter in the asymmetric Laplace likelihood tends to result in misleading fitted curves. To solve this problem, we propose a new Bayesian quantile smoothing spline (NBQSS), which considers a random scale parameter. To begin with, we justify its objective prior options by establishing one sufficient and one necessary condition of the posterior propriety under two classes of general priors including the invariant prior for the scale component. We then develop partially collapsed Gibbs sampling to facilitate the computation. Out of a practical concern, we extend the theoretical results to NBQSS with unobserved knots. Finally, simulation studies and two real data analyses reveal three main findings. Firstly, NBQSS usually outperforms other competing curve fitting methods. Secondly, NBQSS considering unobserved knots behaves better than the NBQSS without unobserved knots in terms of estimation accuracy and precision. Thirdly, NBQSS is robust to possible outliers and could provide accurate estimation.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"346 - 364"},"PeriodicalIF":0.5,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1946372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44589463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1080/24754269.2021.1963398
Jun Shao, Yincai Tang
{"title":"Editorial Foreword","authors":"Jun Shao, Yincai Tang","doi":"10.1080/24754269.2021.1963398","DOIUrl":"https://doi.org/10.1080/24754269.2021.1963398","url":null,"abstract":"","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"171 - 171"},"PeriodicalIF":0.5,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43410982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-22DOI: 10.1080/24754269.2021.1907519
Juan Wang, Xinmin Li, Huasu Liang
To test variance homogeneity, various likelihood-ratio based tests such as the Bartlett's test have been proposed. The null distributions of these tests were generally derived asymptotically or approximately. We re-examine the restrictive maximum likelihood ratio (RELR) statistic, and suggest a Monte Carlo algorithm to compute its exact null distribution, and so its p-value. It is much easier to implement than most existing methods. Simulation studies indicate that the proposed procedure is also superior to its competitors in terms of type I error and powers. We analyse an environmental dataset for an illustration.
{"title":"A new exact p-value approach for testing variance homogeneity","authors":"Juan Wang, Xinmin Li, Huasu Liang","doi":"10.1080/24754269.2021.1907519","DOIUrl":"https://doi.org/10.1080/24754269.2021.1907519","url":null,"abstract":"To test variance homogeneity, various likelihood-ratio based tests such as the Bartlett's test have been proposed. The null distributions of these tests were generally derived asymptotically or approximately. We re-examine the restrictive maximum likelihood ratio (RELR) statistic, and suggest a Monte Carlo algorithm to compute its exact null distribution, and so its p-value. It is much easier to implement than most existing methods. Simulation studies indicate that the proposed procedure is also superior to its competitors in terms of type I error and powers. We analyse an environmental dataset for an illustration.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"81 - 86"},"PeriodicalIF":0.5,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1907519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49003841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-22DOI: 10.1080/24754269.2021.1913977
Yu Han, Chunming Zhang
This paper develops the empirical likelihood ( ) inference procedure for parameters in autoregressive models with the error variances scaled by an unknown nonparametric time-varying function. Compared with existing methods based on non-parametric and semi-parametric estimation, the proposed test statistic avoids estimating the variance function, while maintaining the asymptotic chi-square distribution under the null. Simulation studies demonstrate that the proposed procedure (a) is more stable, i.e., depending less on the change points in the error variances, and (b) gets closer to the desired confidence level, than the traditional test statistic.
{"title":"Empirical likelihood inference in autoregressive models with time-varying variances","authors":"Yu Han, Chunming Zhang","doi":"10.1080/24754269.2021.1913977","DOIUrl":"https://doi.org/10.1080/24754269.2021.1913977","url":null,"abstract":"This paper develops the empirical likelihood ( ) inference procedure for parameters in autoregressive models with the error variances scaled by an unknown nonparametric time-varying function. Compared with existing methods based on non-parametric and semi-parametric estimation, the proposed test statistic avoids estimating the variance function, while maintaining the asymptotic chi-square distribution under the null. Simulation studies demonstrate that the proposed procedure (a) is more stable, i.e., depending less on the change points in the error variances, and (b) gets closer to the desired confidence level, than the traditional test statistic.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"129 - 138"},"PeriodicalIF":0.5,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1913977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42929358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-03DOI: 10.1080/24754269.2021.1904094
Jijia Wang, Jing Cao, Song Zhang, C. Ahn
In stepped wedge cluster randomised trials (SW-CRTs), clusters of subjects are randomly assigned to sequences, where they receive a specific order of treatments. Compared to conventional cluster randomised studies, one unique feature of SW-CRTs is that all clusters start from control and gradually transition to intervention according to the randomly assigned sequences. This feature mitigates the ethical concern of withholding an effective treatment and reduces the logistic burden of implementing the intervention at multiple clusters simultaneously. This feature, however, presents challenges that need to be addressed in experimental design and data analysis, i.e., missing data due to prolonged follow-up and complicated correlation structures that involve between-subject and longitudinal correlations. In this study, based on the generalised estimating equation (GEE) approach, we present a closed-form sample size formula for SW-CRTs with a binary outcome, which offers great flexibility to account for unbalanced randomisation, missing data, and arbitrary correlation structures. We also present a correction approach to address the issue of under-estimated variance by GEE estimator when the sample size is small. Simulation studies and application to a real clinical trial are presented.
{"title":"Sample size and power analysis for stepped wedge cluster randomised trials with binary outcomes","authors":"Jijia Wang, Jing Cao, Song Zhang, C. Ahn","doi":"10.1080/24754269.2021.1904094","DOIUrl":"https://doi.org/10.1080/24754269.2021.1904094","url":null,"abstract":"In stepped wedge cluster randomised trials (SW-CRTs), clusters of subjects are randomly assigned to sequences, where they receive a specific order of treatments. Compared to conventional cluster randomised studies, one unique feature of SW-CRTs is that all clusters start from control and gradually transition to intervention according to the randomly assigned sequences. This feature mitigates the ethical concern of withholding an effective treatment and reduces the logistic burden of implementing the intervention at multiple clusters simultaneously. This feature, however, presents challenges that need to be addressed in experimental design and data analysis, i.e., missing data due to prolonged follow-up and complicated correlation structures that involve between-subject and longitudinal correlations. In this study, based on the generalised estimating equation (GEE) approach, we present a closed-form sample size formula for SW-CRTs with a binary outcome, which offers great flexibility to account for unbalanced randomisation, missing data, and arbitrary correlation structures. We also present a correction approach to address the issue of under-estimated variance by GEE estimator when the sample size is small. Simulation studies and application to a real clinical trial are presented.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"162 - 169"},"PeriodicalIF":0.5,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1904094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42073527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-03DOI: 10.1080/24754269.2021.1904095
R. Lekivetz, Joseph Morgan
Testing complex software systems is an extraordinarily difficult task. Test engineers are faced with the challenging prospect of ensuring that a software system satisfies its requirements while working within a strict budget. Choosing a test suite for such an endeavour can be framed as a design of experiments problem. Combinatorial testing is a software testing methodology that may be viewed as a design of experiments approach to addressing the software testing challenge. We extend this methodology by introducing the concept of blocking factors for a test suite. We provide an example, using an open source software library, to illustrate our extension. Advantages of considering blocks are discussed, both in the design as well as after test execution, when fault localisation may be necessary.
{"title":"Combinatorial testing: using blocking to assign test cases for validating complex software systems","authors":"R. Lekivetz, Joseph Morgan","doi":"10.1080/24754269.2021.1904095","DOIUrl":"https://doi.org/10.1080/24754269.2021.1904095","url":null,"abstract":"Testing complex software systems is an extraordinarily difficult task. Test engineers are faced with the challenging prospect of ensuring that a software system satisfies its requirements while working within a strict budget. Choosing a test suite for such an endeavour can be framed as a design of experiments problem. Combinatorial testing is a software testing methodology that may be viewed as a design of experiments approach to addressing the software testing challenge. We extend this methodology by introducing the concept of blocking factors for a test suite. We provide an example, using an open source software library, to illustrate our extension. Advantages of considering blocks are discussed, both in the design as well as after test execution, when fault localisation may be necessary.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"114 - 121"},"PeriodicalIF":0.5,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1904095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46664354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT The multi-armed bandit (MAB) problem studies the sequential decision making in the presence of uncertainty and partial feedback on rewards. Its name comes from imagining a gambler at a row of slot machines who needs to decide the best strategy on the number of times as well as the orders to play each machine. It is a classic reinforcement learning problem which is fundamental to many online learning problems. In many practical applications of the MAB, the reward distributions may change at unknown time steps and the outliers (extreme rewards) often exist. Current sequential design strategies may struggle in such cases, as they tend to infer additional change points to fit the outliers. In this paper, we propose a robust change-detection upper confidence bound (RCD-UCB) algorithm which can distinguish the real change points from the outliers in piecewise-stationary MAB settings. We show that the proposed RCD-UCB algorithm can achieve a nearly optimal regret bound on the order of , where T is the number of time steps, K is the number of arms and S is the number of stationary segments. We demonstrate its superior performance compared to some state-of-the-art algorithms in both simulation experiments and real data analysis. (See https://github.com/woaishufenke/MAB_STRF.git for the codes used in this paper.)
{"title":"Robust sequential design for piecewise-stationary multi-armed bandit problem in the presence of outliers","authors":"Yaping Wang, Zhicheng Peng, Riquan Zhang, Qian Xiao","doi":"10.1080/24754269.2021.1902687","DOIUrl":"https://doi.org/10.1080/24754269.2021.1902687","url":null,"abstract":"ABSTRACT The multi-armed bandit (MAB) problem studies the sequential decision making in the presence of uncertainty and partial feedback on rewards. Its name comes from imagining a gambler at a row of slot machines who needs to decide the best strategy on the number of times as well as the orders to play each machine. It is a classic reinforcement learning problem which is fundamental to many online learning problems. In many practical applications of the MAB, the reward distributions may change at unknown time steps and the outliers (extreme rewards) often exist. Current sequential design strategies may struggle in such cases, as they tend to infer additional change points to fit the outliers. In this paper, we propose a robust change-detection upper confidence bound (RCD-UCB) algorithm which can distinguish the real change points from the outliers in piecewise-stationary MAB settings. We show that the proposed RCD-UCB algorithm can achieve a nearly optimal regret bound on the order of , where T is the number of time steps, K is the number of arms and S is the number of stationary segments. We demonstrate its superior performance compared to some state-of-the-art algorithms in both simulation experiments and real data analysis. (See https://github.com/woaishufenke/MAB_STRF.git for the codes used in this paper.)","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"122 - 133"},"PeriodicalIF":0.5,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1902687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46146259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-30DOI: 10.1080/24754269.2021.1905376
Yingyan Zeng, Xinwei Deng, Xiaoyu Chen, R. Jin
A good visualisation method can greatly enhance human-machine collaboration in target contexts. To aid the optimal selection of visualisations for users, visualisation recommender systems have been developed to provide the right visualisation method to the right person given specific contexts. A visualisation recommender system often relies on a user study to collect data and conduct analysis to provide personalised recommendations. However, a user study without employing an effective experimental design is typically expensive in terms of time and cost. In this work, we propose a prediction-oriented optimal design to determine the user-task allocation in the user study for the recommendation of visualisation methods. The proposed optimal design will not only encourage the learning of the similarity embedded in the recommendation responses (i.e., users' preference), but also improve the modelling accuracy of the similarities captured by the covariates of contexts (i.e., task attributes). A simulation study and a real-data case study are used to evaluate the proposed optimal design.
{"title":"A prediction-oriented optimal design for visualisation recommender systems","authors":"Yingyan Zeng, Xinwei Deng, Xiaoyu Chen, R. Jin","doi":"10.1080/24754269.2021.1905376","DOIUrl":"https://doi.org/10.1080/24754269.2021.1905376","url":null,"abstract":"A good visualisation method can greatly enhance human-machine collaboration in target contexts. To aid the optimal selection of visualisations for users, visualisation recommender systems have been developed to provide the right visualisation method to the right person given specific contexts. A visualisation recommender system often relies on a user study to collect data and conduct analysis to provide personalised recommendations. However, a user study without employing an effective experimental design is typically expensive in terms of time and cost. In this work, we propose a prediction-oriented optimal design to determine the user-task allocation in the user study for the recommendation of visualisation methods. The proposed optimal design will not only encourage the learning of the similarity embedded in the recommendation responses (i.e., users' preference), but also improve the modelling accuracy of the similarities captured by the covariates of contexts (i.e., task attributes). A simulation study and a real-data case study are used to evaluate the proposed optimal design.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"134 - 148"},"PeriodicalIF":0.5,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1905376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47541518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：