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学者姓名：杜江
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Abstract ：
统计学专业建设需要紧跟人工智能和数字技术发展步伐,该文通过对统计学专业实践教学体系的现状分析与存在问题剖析,提出人工智能与数字技术背景下统计学专业实践教学体系优化与设计的思路,进而给出人工智能与数字技术背景下实践教学体系的总体框架和相应的优化设计方案,进一步提升学生将统计理论与人工智能、数字技术的融会贯通的能力,满足我国人工智能产业的发展和数字中国建设的人才需求。
Keyword ：
人才培养 人才培养 实践教学体系 实践教学体系 数字技术 数字技术 人工智能 人工智能 统计学专业 统计学专业
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| GB/T 7714 | 杜江 , 戴君 , 曹瑞元 . 人工智能与数字技术背景下统计学专业实践教学体系优化及设计 [J]. | 高教学刊 , 2024 , 10 (09) : 115-118 . |
| MLA | 杜江 等. "人工智能与数字技术背景下统计学专业实践教学体系优化及设计" . | 高教学刊 10 . 09 (2024) : 115-118 . |
| APA | 杜江 , 戴君 , 曹瑞元 . 人工智能与数字技术背景下统计学专业实践教学体系优化及设计 . | 高教学刊 , 2024 , 10 (09) , 115-118 . |
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Abstract ：
Computer model calibration is a crucial step in building a reliable computer model. In the face of massive physical observations, a fast estimation of the calibration parameters is urgently needed. To alleviate the computational burden, we design a two-step algorithm to estimate the calibration parameters by employing the subsampling techniques. Compared with the current state-of-the-art calibration methods, the complexity of the proposed algorithm is greatly reduced without sacrificing too much accuracy. We prove the consistency and asymptotic normality of the proposed estimator. The form of the variance of the proposed estimation is also presented, which provides a natural way to quantify the uncertainty of the calibration parameters. The obtained results of two numerical simulations and two real-case studies demonstrate the advantages of the proposed method.
Keyword ：
massive data massive data Poisson sampling Poisson sampling optimal subsampling optimal subsampling weighted least squares calibration weighted least squares calibration
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| GB/T 7714 | Lv, Shurui , Yu, Jun , Wang, Yan et al. Fast Calibration for Computer Models with Massive Physical Observations [J]. | SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION , 2023 , 11 (3) : 1069-1104 . |
| MLA | Lv, Shurui et al. "Fast Calibration for Computer Models with Massive Physical Observations" . | SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION 11 . 3 (2023) : 1069-1104 . |
| APA | Lv, Shurui , Yu, Jun , Wang, Yan , Du, Jiang . Fast Calibration for Computer Models with Massive Physical Observations . | SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION , 2023 , 11 (3) , 1069-1104 . |
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Abstract ：
In this paper, it is aim to propose an instrumental variable rank estimation method for varying coefficient spatial autoregressive models. The newly proposed method provides a highly efficient and robust alternative to the existing quasi-maximum likelihood estimation or GMM estimation, and can be implemented using the existing R software package conveniently. Under mild conditions, the consistency and asymptotic normality of the resulting estimators are established. The finite sample properties of the proposed method are investigated through Monte Carlo simulation studies. Finally, the Boston house price data and crime data of Tokyo are analyzed to illustrate the usefulness of the proposed estimation method.
Keyword ：
Rank estimation Rank estimation Instrumental variable Instrumental variable Varying-coefficient Varying-coefficient Spatial autoregressive model Spatial autoregressive model
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| GB/T 7714 | Tang, Yangbing , Zhang, Zhongzhan , Du, Jiang . Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models [J]. | STATISTICAL PAPERS , 2023 , 65 (3) : 1805-1839 . |
| MLA | Tang, Yangbing et al. "Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models" . | STATISTICAL PAPERS 65 . 3 (2023) : 1805-1839 . |
| APA | Tang, Yangbing , Zhang, Zhongzhan , Du, Jiang . Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models . | STATISTICAL PAPERS , 2023 , 65 (3) , 1805-1839 . |
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Abstract ：
We propose a fully Bayesian estimation approach for partially linear varying coefficient spatial autoregressive models on the basis of B-spline approximations of nonparametric components. A computational efficient MCMC method that combines the Gibbs sampler with Metropolis-Hastings algorithm is implemented to simultaneously obtain the Bayesian estimates of unknown parameters, as well as their standard error estimates. Monte Carlo simulations are used to investigate the finite sample performance of the proposed method. Finally, a real data analysis of Boston housing data is used to illustrate the usefulness of the proposed methodology.
Keyword ：
Spatial autoregressive models Spatial autoregressive models B-spline B-spline Bayesian estimate Bayesian estimate Gibbs sampler Gibbs sampler Partially linear varying coefficient models Partially linear varying coefficient models
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| GB/T 7714 | Tian, Ruiqin , Xu, Dengke , Du, Jiang et al. Bayesian estimation for partially linear varying coefficient spatial autoregressive models [J]. | STATISTICS AND ITS INTERFACE , 2022 , 15 (1) : 105-113 . |
| MLA | Tian, Ruiqin et al. "Bayesian estimation for partially linear varying coefficient spatial autoregressive models" . | STATISTICS AND ITS INTERFACE 15 . 1 (2022) : 105-113 . |
| APA | Tian, Ruiqin , Xu, Dengke , Du, Jiang , Zhang, Junfei . Bayesian estimation for partially linear varying coefficient spatial autoregressive models . | STATISTICS AND ITS INTERFACE , 2022 , 15 (1) , 105-113 . |
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Abstract ：
A Susceptible Infective Recovered (SIR) model is usually unable to mimic the actual epidemiological system exactly. The reasons for this inaccuracy include observation errors and model discrepancies due to assumptions and simplifications made by the SIR model. Hence, this work proposes calibration and prediction methods for the SIR model with a one-time reported number of infected cases. Given that the observation errors of the reported data are assumed to be heteroscedastic, we propose two predictors to predict the actual epidemiological system by modeling the model discrepancy through a Gaussian Process model. One is the calibrated SIR model, and the other one is the discrepancy-corrected predictor, which integrates the calibrated SIR model with the Gaussian Process predictor to solve the model discrepancy. A wild bootstrap method quantifies the two predictors' uncertainty, while two numerical studies assess the performance of the proposed method. The numerical results show that, the proposed predictors outperform the existing ones and the prediction accuracy of the discrepancy-corrected predictor is improved by at least 49.95%.
Keyword ：
calibration calibration Wild bootstrap Wild bootstrap uncertainty quantification uncertainty quantification Heteroscedastic noise Heteroscedastic noise Gaussian Process model Gaussian Process model Inexact SIR model Inexact SIR model
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| GB/T 7714 | Wang, Yan , Lu, Guichen , Du, Jiang . Calibration and prediction for the inexact SIR model [J]. | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (3) : 2800-2818 . |
| MLA | Wang, Yan et al. "Calibration and prediction for the inexact SIR model" . | MATHEMATICAL BIOSCIENCES AND ENGINEERING 19 . 3 (2022) : 2800-2818 . |
| APA | Wang, Yan , Lu, Guichen , Du, Jiang . Calibration and prediction for the inexact SIR model . | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (3) , 2800-2818 . |
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Abstract ：
The functional linear quantile regression model is widely used to characterize the relationship between a scalar response and a functional covariate. Most existing research results are based on a correct assumption that the response is related to the functional predictor through a linear model for given quantile levels. This paper focuses on investigating the adequacy check of the functional linear quantile regression model. We propose a nonparametric U-process test statistic based on the functional principal component analysis. It is proved that the test statistic follows a normal distribution asymptotically under the null hypothesis and diverges to infinity for any misspecified models. Therefore, the test is consistent against any fixed alternative. Moreover, it is shown that the test has asymptotic power one for the, local alternative hypothetical models converging to the null hypothesis at the rates n(-1/2). The finite sample properties of the test statistic are illustrated through extensive simulation studies. A real data set of 24 hourly measurements of ozone levels in Sacramento, California is analyzed by the proposed test. (C) 2020 Elsevier B.V. All rights reserved.
Keyword ：
Quantile regression Quantile regression Hypothesis test Hypothesis test Kernel smoothing Kernel smoothing Functional linear models Functional linear models
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| GB/T 7714 | Shi, Gongming , Du, Jiang , Sun, Zhihua et al. Checking the adequacy of functional linear quantile regression model [J]. | JOURNAL OF STATISTICAL PLANNING AND INFERENCE , 2021 , 210 : 64-75 . |
| MLA | Shi, Gongming et al. "Checking the adequacy of functional linear quantile regression model" . | JOURNAL OF STATISTICAL PLANNING AND INFERENCE 210 (2021) : 64-75 . |
| APA | Shi, Gongming , Du, Jiang , Sun, Zhihua , Zhang, Zhongzhan . Checking the adequacy of functional linear quantile regression model . | JOURNAL OF STATISTICAL PLANNING AND INFERENCE , 2021 , 210 , 64-75 . |
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Abstract ：
Logistic mixed-effects models are widely used to study the relationship between the binary response and covariates for longitudinal data analysis, where the random effects are typically assumed to have a fully parametric distribution. As this assumption is likely limited or unreasonable in a multitude of practical researches, a semiparametric Bayesian approach for relaxing it is developed in this paper. In the context of binomial distribution logistic mixed-effects models, a general Bayesian framework is presented in which a semiparametric hierarchical modelling with an approximate truncated Dirichlet process prior distribution is specified for the random effects. The stick-breaking prior and the blocked Gibbs sampler using Polya-Gamma mixture are employed to efficiently sample in the posterior analysis. Besides, a procedure calculating DIC for Bayesian model comparison is addressed. The methodology is demonstrated through simulation studies and a real example.
Keyword ：
Polya-Gamma mixture Polya-Gamma mixture Gibbs sampler Gibbs sampler Dirichlet process Dirichlet process Longitudinal binomial data Longitudinal binomial data model comparison model comparison
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| GB/T 7714 | Zhao, Yuanying , Xu, Dengke , Duan, Xingde et al. A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data [J]. | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , 2021 , 92 (7) : 1438-1456 . |
| MLA | Zhao, Yuanying et al. "A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data" . | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 92 . 7 (2021) : 1438-1456 . |
| APA | Zhao, Yuanying , Xu, Dengke , Duan, Xingde , Du, Jiang . A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data . | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , 2021 , 92 (7) , 1438-1456 . |
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Abstract ：
To mitigate the adverse structural responses, an improved version of the traditional tuned vibration absorber has been proposed based on the shape memory alloy spring, referred as the shape memory alloy-spring tuned vibration absorber. The finite element numerical models of the multi-degree-of-freedom structure (e.g., transmission tower) and shape memory alloy-spring tuned vibration absorber are developed by using the commercial software ANSYS, and the nonlinear behavior of the shape memory alloy spring is validated based on a previous experimental study. The damping mechanism of the shape memory alloy-spring tuned vibration absorber attached to a multi-degree-of-freedom structure under seismic excitations is investigated, and the nonlinear hysteretic behavior of the shape memory alloy spring is also discussed. The results show that the proposed damper has a two-stage damping mechanism, and its control performance is remarkable. Because the coupled system response is sensitive to the amplitude level, the optimal configuration of the shape memory alloy-spring tuned vibration absorber can be obtained by parametric analysis. Particularly, because of the nonlinear target energy transfer and transient resonance capture mechanism, the shape memory alloy-spring tuned vibration absorber exhibits stable control ability under different seismic waves, indicating a good stability in vibration control of a multi-degree-of-freedom system.
Keyword ：
transmission tower transmission tower damping mechanism damping mechanism Shape memory alloy spring Shape memory alloy spring hysteretic behavior hysteretic behavior seismic excitation seismic excitation tuned vibration absorber tuned vibration absorber
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| GB/T 7714 | Lu, Zheng , Rong, Kunjie , Tian, Li et al. Studies on the damping mechanism of shape memory alloy-spring tuned vibration absorber attached to a multi-degree-of-freedom structure [J]. | JOURNAL OF VIBRATION AND CONTROL , 2021 , 28 (19-20) : 2666-2677 . |
| MLA | Lu, Zheng et al. "Studies on the damping mechanism of shape memory alloy-spring tuned vibration absorber attached to a multi-degree-of-freedom structure" . | JOURNAL OF VIBRATION AND CONTROL 28 . 19-20 (2021) : 2666-2677 . |
| APA | Lu, Zheng , Rong, Kunjie , Tian, Li , Qiu, Canxing , Du, Jiang . Studies on the damping mechanism of shape memory alloy-spring tuned vibration absorber attached to a multi-degree-of-freedom structure . | JOURNAL OF VIBRATION AND CONTROL , 2021 , 28 (19-20) , 2666-2677 . |
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Abstract ：
受全球疫情蔓延的影响,世界经济面临着高度的不确定性。作为世界第一大经济体的美国因受疫情的冲击,经济增长受到重大影响。文章选取SPY 500的日收盘价作为研究数据,分别从序列水平和波动性两个角度,进行短期预测和波动性分析。结果显示:ARIMA模型对SPY 500日收盘价的短期预测值与实际值相对误差小,GARCH模型较好地拟合了股票价格,并估计出风险区间,希望能为短期投资者和股票决策者提供一定参考。
Keyword ：
条件异方差时间序列 条件异方差时间序列 整合移动平均自回归模型 整合移动平均自回归模型 广义自回归条件异方差模型 广义自回归条件异方差模型
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| GB/T 7714 | 许佳琪 , 杜江 , 郑强国 . 关于时间序列预测模型的实证研究--基于美国标准普尔500金融指数 [J]. | 科学大众 , 2020 , (11) : 274-275 . |
| MLA | 许佳琪 et al. "关于时间序列预测模型的实证研究--基于美国标准普尔500金融指数" . | 科学大众 11 (2020) : 274-275 . |
| APA | 许佳琪 , 杜江 , 郑强国 . 关于时间序列预测模型的实证研究--基于美国标准普尔500金融指数 . | 科学大众 , 2020 , (11) , 274-275 . |
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Abstract ：
This paper presents the nonparametric quantile regression estimation for the regression function operator when the functional data with the responses missing at random are considered. Then, the large sample properties of the proposed estimator are established under some mild conditions. Finally, a simulation study is conducted to investigate the finite sample properties of the proposed method.
Keyword ：
Inverse probability weighting estimator Inverse probability weighting estimator Quantile regression Quantile regression Functional data analysis Functional data analysis Asymptotic normality Asymptotic normality Missing at random Missing at random
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| GB/T 7714 | Xu, Dengke , Du, Jiang . Nonparametric quantile regression estimation for functional data with responses missing at random [J]. | METRIKA , 2020 , 83 (8) : 977-990 . |
| MLA | Xu, Dengke et al. "Nonparametric quantile regression estimation for functional data with responses missing at random" . | METRIKA 83 . 8 (2020) : 977-990 . |
| APA | Xu, Dengke , Du, Jiang . Nonparametric quantile regression estimation for functional data with responses missing at random . | METRIKA , 2020 , 83 (8) , 977-990 . |
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