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Empirical Likelihood Test for Regression Coefficients in High Dimensional Partially Linear Models

Journal of Systems Science & Complexity 2021年第3期34卷 1135-1155页

作者： LIU Yan REN Mingyang ZHANG Sanguo School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

This paper considers tests for regression coefficients in high dimensional partially linear *** authors first use the B-spline method to estimate the unknown smooth function so that it could be linearly ***,the authors propose an empirical likelihood method to test regression *** authors derive the asymptotic chi-squared distribution with two degrees of freedom of the proposed test statistics under the null *** addition,the method is extended to test with nuisance *** show that the proposed method have a good performance in control of type-I error rate and *** proposed method is also employed to analyze a data of Skin Cutaneous Melanoma(SKCM).

关键词： Empirical likelihood test high dimensional analysis partially linear models regression coefficients

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Macroprudential policy in retail lending: Concept and results - a case study of the Bank of Russia 11

Macroprudential policy in retail lending: Concept and result...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Eremina, Ekaterina Shi, Yong School of Economics and Management University of Chinese Academy of Sciences No.19A Yuquan Road Beijing100049 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences No.19A Yuquan Road Beijing100049 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences No.19A Yuquan Road Beijing100049 China

Macroprudential regulation of the banking system is a significant instrument for ensuring the financial stability of the state. Unsecured consumer lending is growing at a steadily high rate (in August-October 2022, the average monthly debt growth was 0.9%), and it remains a priority segment for banks because of its high margins. The share of unsecured consumer loans provided to borrowers with debt burden indicator (DBI) of more than 80% amounted to more than 32% in the third quarter of 2022 (28% in the second quarter of 2022). The share of consumer loans for a term of more than 5 years remains at a high level (15% in July-September 2022). The share of MFO loans issued to borrowers with a DBI of more than 80% was 41% in the second quarter of 2022 (38% in the first quarter of 2022). A further increase in the citizens' debt burden in the context of the economy restructuring may create additional macroeconomic risks. Problems of citizens with servicing loans and borrowings can lead to a reduction in demand in the economy, and losses of banks as a result of writing off "bad" loans can lead to a decrease in their ability to lend to the economy. To limit the risks associated with an increase in the citizens' debt burden, since the first quarter of 2023, the Bank of Russia has established direct quantitative restrictions on the issuance of certain types of credits (loans) and the application of increased buffers to risk coefficients if creditors exceed established limits. This article discusses the main approaches to implementation of the macroprudential policy of the Central Bank of the Russian Federation, its goals and instruments, and peculiarities of application in the current conditions. © 2024 The Authors.

关键词： Decentralized finance

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Graph classification based fault detection in nuclear power plants with graph formulation 10

Graph classification based fault detection in nuclear power ...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Qu, Yi Xue, Xiaodong Shi, Yong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska OmahaNE68182 United States

In the routine production and operation of nuclear power plants (NPPs), safety management is extremely important, and consequently, a fault detection system yielding high accuracy could provide effective decision support and prevent the accidents to the maximum extent. Most of existing studies are based on the analysis of time-series data, while researches oriented to the implementation of graph data mining are barely seen. In this paper, we propose an improved fault detection method to address this task from the perspective of graph representation learning. By calculating the similarity between indicators, the multivariate temporal sequences of the running characteristics of NPPs have been formulated as a group of graphs with corresponding labels, on which the graph convolutional network (GCN) has been introduced to detect fault samples in the way of graph classification. Compared to conventional supervised learning classifiers, our proposed methodology performs more superior identification of major faults, and the sensitivity regarding significant parameter or settings in the model design has been analyzed to investigate their optimal arrangements. The findings in this article not only present the promising prospects of introducing graph learning techniques into safety management, also indicate that various kinds of digital signals could be reformulated into graph dataset based on which the original task could be executed more effectively in the way of graph representation learning. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Nuclear power plants

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A hybrid model for stock price prediction based on multi-view heterogeneous data

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Financial Innovation 2024年第1期10卷 2411-2460页

作者： Wen Long Jing Gao Kehan Bai Zhichen Lu School of Economics and Management University of Chinese Academy of SciencesBeijing100190People’s Republic of China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing100190People’s Republic of China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100190People’s Republic of China Department of Mathematics Beijing Jiaotong UniversityBeijing100044People’s Republic of China

Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information ***,this study uses market data and news to investigate their joint impact on stock price ***,combining these two types of information is difficult because of their completely different *** study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information *** with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant *** the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s ***,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

关键词： Market data Financial news Support vector machine Multi-view learning Heterogeneous data

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Analyzing Multiple Phenotypes Based on Principal Component Analysis

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Acta Mathematicae Applicatae Sinica 2022年第4期38卷 843-860页

作者： De-liang BU San-guo ZHANG Na LI School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China LSC NCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phenotype analysis in genome-wide association *** component analysis(PCA),as a popular dimension reduction method,has been broadly used in the analysis of multiple *** PCA transforms the original phenotypes into principal components(PCs),it is natural to think that by analyzing these PCs,we can combine information across *** PCA-based methods can be divided into two categories,either selecting one particular PC manually or combining information from all *** this paper,we propose an adaptive principle component test(APCT)which selects and combines the PCs adaptively by using Cauchy combination *** proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special *** simulation shows that our method is robust and can generate powerful result in various *** real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.

关键词： multiple phenotypes principal component analysis cauchy combination method

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Review and research frontier analysis of low carbon economy - Based on bibliometric methods 10

Review and research frontier analysis of low carbon economy ...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Yao, Tongsheng Shi, Yong Chen, Ming School of Economics and Management University of Chinese Academy Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Management and Economics Beijing Institute of Technology Beijing100081 China

research in the carbon field has experienced rapid growth in recent years and has attracted widespread academic attention. This paper conducts a comprehensive analysis of the carbon field using bibliometric methods. By collecting a large number of academic publications related to carbon and utilizing bibliometric indicators, We performed statistical and descriptive analyses on the development trends, research areas, research institutions, and authors in the field of carbon economy literature. Additionally, we employed co-citation analysis and keyword co-occurrence analysis to identify the current research focuses in the carbon field and explore the temporal changes in research hotspots. Ultimately, we analyzed the future development trends of the carbon field, providing valuable insights for scholars to conduct further research in this field. These research findings are of great significance in promoting the development and application of the carbon economy field. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Carbon

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Convergence of Stochastic Gradient Descent in Deep Neural Network

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Acta Mathematicae Applicatae Sinica 2021年第1期37卷 126-136页

作者： Bai-cun ZHOU Cong-ying HAN Tian-de GUO School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

Stochastic gradient descent(SGD) is one of the most common optimization algorithms used in pattern recognition and machine *** algorithm and its variants are the preferred algorithm while optimizing parameters of deep neural network for their advantages of low storage space requirement and fast computation *** studies on convergence of these algorithms were based on some traditional assumptions in optimization ***,the deep neural network has its unique *** assumptions are inappropriate in the actual optimization process of this kind of *** this paper,we modify the assumptions to make them more consistent with the actual optimization process of deep neural *** on new assumptions,we studied the convergence and convergence rate of SGD and its two common variant *** addition,we carried out numerical experiments with LeNet-5,a common network framework,on the data set MNIST to verify the rationality of our assumptions.

关键词： stochastic gradient descent deep neural network convergence

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A Brief Survey of Distribution Robust Graph Neural Networks 11

A Brief Survey of Distribution Robust Graph Neural Networks

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Zheng, Lei Quan, Pei Shi, Yong Niu, Lingfeng The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China The College of Economics and Management Beijing University of Technology Beijing100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing1001090 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction. © 2024 The Authors.

关键词： Distribution generalization Graph neural networks Shift robust

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Impact of the China Railway Express on Employment Structure - Evidence from Urban Economic data 11

Impact of the China Railway Express on Employment Structure ...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Yan, Bochao Jiang, Hailiang Wu, Junfeng Tian, Xin China Agricultural University Beijing100083 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As the main international trade channel of China's "westward development", the opening of China Railway Express has significantly improved the trade facilitation of countries along the route. This paper investigates the impact of the opening of China Railway Express sites on the employment choices of individuals by using the difference in difference method based on the economic panel data of China's prefecture-level cities and the data on the opening of China Railway Express during the period of 2007~2019, which are matched by the correlation. The results of the study show that the opening of the China Railway Express increases the probability of individuals within the radiation area to enter the private sector for employment. The empirical results provide a new explanatory mechanism for the opening of the China Railway Express on economic growth, which has certain theoretical and policy value. © 2024 The Authors.

关键词： Railroads

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Real Estate with AI: An agent based on LangChain 11

Real Estate with AI: An agent based on LangChain

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Haurum, Kasper Raupach Ma, Ruiqi Long, Wen Business School Aalborg University Aalborg9220 Denmark Sino-Danish College University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the "unprofessional foundation " and " expensive consulting fee" problems encountered by ordinary investors in their investment decisions. © 2024 The Authors.

关键词： Chatbots

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