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Consistent model check of errors-in-variables varying-coefficient model with auxiliary variable

JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2019年 198卷 13-28页

作者： Liu, Zhifan Liu, Chunling Sun, Zhihua Univ Chinese Acad Sci Beijing 100049 Peoples R China Hong Kong Polytech Univ Kowloon Hong Kong Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China

In this paper, we consider the adequacy check of the varying-coefficient model when covariates are measured with error and some auxiliary variable is available. With the help of auxiliary variable, we calibrate the measurement error and obtain an estimator of the unobservable true variable. The empirical-process-based test is built by applying the calibrated estimator of the model error. The asymptotic properties of the proposed test are rigorously investigated under the null hypothesis, local and global alternatives. It is shown that the proposed test is consistent and has good properties of power. We illustrate that the naive method cannot control Type I error and loses effect completely. But the proposed calibrated method performs well in terms of the empirical sizes close to the test level and high empirical powers. Simulation studies and two real data analyses are conducted to demonstrate the performance of the proposed approach. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Varying-coefficient model Measurement error Auxiliary variable Model check Empirical process

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The Feature of the B&R Exchange Rate: Comparison with Main Currency Based on EMD Algorithm and Grey Relational Degrees 6th

The Feature of the B&R Exchange Rate: Comparison with Main C...

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6th International Conference on data Science, ICDS 2019

作者： Yixi, Cui Zixin, Liu Ziran, Li Kun, Guo University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management School of Economics and Management UCAS Beijing China Minzu University of China Beijing China CFFEX Institute for Financial Derivatives Beijing China

ISBN: (纸本)9789811528095

According to BELT AND ROAD PORTAL, China has signed 173 cooperative documents with 125 countries and 29 international organizations along the Belt and Road (B&R) until April 2019, and the exchange rate volatility of currencies in the B&R are usually higher. In this paper, we firstly constructed the B&R exchange rate index on the basis of both the trade volumes and the foreign investment situation. After that, we compared it with the RMB exchange rate index. First, the EMD algorithm was used to decompose each index respectively into 8 IMFs and residual signal. Afterwards, based on grey comprehensive relational degrees, we reconstructed the market fluctuation term and noise term of each exchange rate index, and also get the trend term which is the residual signal. Then, we used comparative analysis to discuss and explain the features and relationship of exchange rate risk of RMB between the B&R and against main currencies, and found that in the long term, there was more devaluation risk of the currencies and regions along the B in the middle term, there was a lead-lag relationship between the two indexes, and the B&R exchange rate index is going to decline;in the short term, the exchange risk in the countries and regions along the B&R is greater than the worldwide RMB exchange rate risk. Finally, we put forward several relevant suggestions to help going-out enterprises to avoid and manage exchange rate risks effectively. © Springer Nature Singapore Pte Ltd 2020.

关键词： Roads and streets

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Comparative research on multi-source heterogeneous data fusion technologies 11

Comparative research on multi-source heterogeneous data fusi...

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

作者： Long, Wen Gao, Jing Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 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

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect. © 2024 The Authors.

关键词： data accuracy

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Stochastic joint homecare service and capacity planning with nested decomposition approaches

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期295卷 203-222页

作者： Zheng, Chenyang Wang, Shuming Li, Ningxin Wu, Yuanhao Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Columbia Univ Fu Fdn Sch Engn & Appl Sci New York NY 10027 USA Beijing Normal Univ Sch Management Zhuhai Peoples R China

In this paper, we study a joint service authorization and capacity planning problem for the home health care (HHC) system with demand uncertainty. We formulate the problem as a two-stage stochastic programming with recourse which maximizes the expected total revenue. The service authorization and capacity planning are the mixed binary-and-continuous first stage decisions, and the service hour (resource) allocation is modeled as the second-stage decision which adapts to the demand realizations. The resulting sample average approximation problem (of the stochastic program) could be a large scaled mixed integer linear program. To solve the model in a more scalable fashion, we propose a supergradient-based nested decomposition algorithm that exploits the nice decomposable structure of the problem to cope with the binary and continuous variables separately. The proposed nested decomposition scheme consists of two-layer of decomposition, where the outer decomposition solves for the authorization decision (binaries) with the supergradient cuts and each supergradient can be computed iteratively in the inner decomposition scheme. The proposed nested decomposition algorithm is feasibility-cut-free and is guaranteed to reach the exact optimality in finite steps. Furthermore, we extend our stochastic HHC planning model to a more general framework of Conditional Value-at-Risk (CV@R), and by model transformation with variable change we show that the CV@R model can actually be reformulated in a structure that the proposed nested decomposition scheme can be applied almost directly. Finally, a comprehensive computational study is performed which demonstrates the effectiveness of our model and the performance of the algorithm. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Decomposition algorithm Linear programming Joint service authorization and capacity planning

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f-Fractional Bit Minwise Hashing for Large-Scale Learning

f-Fractional Bit Minwise Hashing for Large-Scale Learning

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Tang, Jingjing Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China

ISBN: (纸本)9781467396189

Recently, document similarity detection technology captures a host of researchers' attention. In this paper, we propose to integrate linear SVM with f-fractional bit minwise hashing to make a wide range of choices for accuracy and storage space requirements. According to the derived properties of f-fractional bit minwise hashing, we obtained the optimal combination of fractional bit with the minimum estimator of variances and ultimately applied it to the process of integration. The innovation of this algorithm is the continuous selectivity of bit instead of the discrete integer value, which not only improves the theoretical system of b-bit minwise hashing SVM algorithm, but also satisfies the various needs of accuracy and storage space in the practical system. Due to the nonlinear of the resemblance matrix considered as kernel matrix, it can not be used in linear SVM training directly. However, in the theoretical analysis, we provide the proof of positive definiteness for resemblance matrix generated by f-fractional bit minwise hashing scheme, which is a logical and feasible basis for the integration. Meanwhile, experimental results on publicly available large-scale datasets validate the effectiveness of this algorithm.

关键词： minwise hashing f-fractional bit similarity estimation large-scale linear SVM

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Review of graph construction and graph learning in stock price prediction 9th

Review of graph construction and graph learning in stock pri...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Wang, Yunong Qu, Yi Chen, Zhensong 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 Engineering Capital University of Economics and Business Beijing100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks. © 2022 The Authors. Published by Elsevier B.V.

关键词： Financial markets

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Volatility and risk contagion of international stock market in the context of COVID-19 10

Volatility and risk contagion of international stock market ...

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

作者： Long, Wen Zeng, Yuhan Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 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

In order to reveal the impact of the outbreak of COVID-19 epidemic on risk contagion in the international stock market, this paper establishes an ARMA-GARCH-Copula model based on the R-Vine structure to empirically analyze the stock index data of 19 important countries(regions) around the world, and uses complex network to display the risk contagion path. This research shows that the risk contagion correlation of the global stock market has significantly improved due to the impact of COVID-19;The correlation of risk transmission among European countries is significantly higher than that of other regions, reflecting a closer integration connection between EU countries;Finally, unlike previous literature, this article finds that stock market of Hong Kong in China has become an important node in the international stock market, playing an important role in the risk network before and after the epidemic. © 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： COVID-19

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The asset allocation utility of digital cryptocurrency 9th

The asset allocation utility of digital cryptocurrency

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Long, Wen Wang, Maohua Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 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

In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance. © 2022 The Authors. Published by Elsevier B.V.

关键词： Electronic money

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Research on Liquidity Measurement and Influencing Factors of China Carbon Emission Allocation Trading Market 10

Research on Liquidity Measurement and Influencing Factors of...

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

作者： Shi, Yong Peng, Shuyang Yu, Anqi Guo, Kun School of Economics and Management University of Chinese Academy of 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

Considering multi-dimensional concepts of liquidity, we introduces two classical liquidity measures to quantify the liquidity of China carbon emission allowance(CCEA) trading markets, and analyze their trading activities. Then monthly unbalanced panel was constructed based on daily trading data to further study on the influencing factors of CCEA market. We find that: 1) No matter what method we take, the Hubei pilot is the most liquid in the context of overall running periods, and the national carbon market and Guangdong pilot are also well performed compared to the other markets. 2)The foundation of national generalized market could bring about noticeable changes on the liquidity of carbon market, and Guangdong market which is one of the 8 carbon market pilots become more developed and liquid, while no significant variation in the rest of 7 pilots. 3) The emission control enterprises of each pilot have no inborn incentive to trade proactively, therefore transactions are concentrated on the performance cycle. 4)Regression results show that the price at the end of each month, trading volumes aggregated in each month, trading frequency of each month are all positively correlated with the liquidity, and return volatility of each month, carbon market fragmentation are negatively related to the liquidity. Finally we put forward some suggestion and policy recommendation rested on the above qualitative and quantitative analysis. © 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： Emission control

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Identifying compromised hosts under APT using DNS request sequences

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2021年 152卷 67-78页

作者： Li, Ming Li, Qiang Xuan, Guangzhe Guo, Dong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Ctr Big Data & Network Management Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

Advanced persistent threats (APTs) have become a major cyber threat to large organizations. To steal confidential data from specific organizations, attackers adopt highly targeted intrusion schemes. Prior to stealing critical data, APT activities hide themselves in legitimate activities and consistently elevate their privileges, making them very difficult to detect. The detection of malicious domains during domain name service (DNS) analysis accounts for the majority of existing detection methods. However, a limited number of available samples and rapidly changing sets of malicious domain names reduce the efficacy of such approaches. By investigating numerous APT reports, we determined that the activities of DNS requests in APT attacks exhibit clear temporal patterns that are ignored by most existing schemes. Therefore, we can analyze the DNS sequences requested by each host and their time-related features to identify compromised hosts. This paper summarizes the patterns of host DNS requests and proposes several assumptions. We take advantage of machine learning to identify compromised hosts by quantifying these assumptions in the form of feature vectors. We deployed the proposed approach into large-scale network environments and experimental evaluations demonstrated that our method is able to detect hosts compromised by APTs efficiently with a precision of 97.3% and detection rate of 96.2%. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Advanced persistent threat (APT) Temporal patterns Unsupervised learning Compromised hosts Domain name service (DNS) request sequence

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