The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management

Li, Mei; Lin, Ying; Huang, Shuai; Crossland, Craig

doi:10.1002/smj.2450

Cited by 5 publications

(4 citation statements)

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“…For example, Foster et al (2018) perform cluster analysis to identify patterns that impact emergency room physicians' performance. Li et al (2016) use inverse covariance estimation technique to detect operational patterns associated with high performing manufacturing firms. Predictive analytics also analyzes past data, with the intention to predict future outcomes.…”

Section: Descriptive Predictive and Prescriptive Data Analyticsmentioning

confidence: 99%

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Efficient Detection of Environmental Violators: A Big Data Approach

Chang¹,

Ying-hui

Mei

et al. 2021

Production and Operations Management

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The detection of environmental violators is critical to the long‐term adoption of sustainability in supply chain management. However, there exist manufacturing facilities that report false environmental monitoring data, thereby seriously hampering governments’ efforts to identify true offenders and to properly intervene. We integrate waste gas data from the world’s largest Continuous Emission Monitoring System (CEMS) with a publicly available Violation and Punishment Dataset (VPD) to build prediction models for the identification of environmental violators. We utilize and create innovative machine learning approaches to overcome analytical challenges associated with empirical data. First, we use a feature engineering approach to generate features from the raw, and possibly fraudulent, reporting data. This overcomes the challenges associated with low fidelity, irregularity, and the presence of extreme values in the raw dataset. Second, while building prediction models, we develop new approaches to positive and unlabeled learning to overcome the challenges posed by sparsity and mislabeled data. Our prediction model achieves satisfactory results in a related field test. Our study develops new techniques for big data analytics, which greatly improve the efficiency and effectiveness in detection of environmental violators and enhance operational outcomes of environmental protection agencies. This research is a joint effort between academia and practitioners, as evidenced by the participation of the Ministry of Ecology and Environment of People’s Republic of China. The Ministry kindly granted us direct data access, as well as opportunities to interview Subject Matter Experts at the Ministry, which led to research insights incorporated in this manuscript. Our research findings have global implications, as CEMS devices are universally adopted to monitor waste gas emissions.

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Section: Descriptive Predictive and Prescriptive Data Analyticsmentioning

confidence: 99%

“…Li et al. (2016) use inverse covariance estimation technique to detect operational patterns associated with high performing manufacturing firms. Predictive analytics also analyzes past data, with the intention to predict future outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Detection of Environmental Violators: A Big Data Approach

Chang¹,

Ying-hui

Mei

et al. 2021

Production and Operations Management

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Section: Descriptive Predictive and Prescriptive Data Analyticsmentioning

confidence: 99%

Efficient Detection of Environmental Violators: A Big Data Approach

et al. 2019

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“…In the era of big data, management accounting needs to perform the function of providing decision-making information for business management [3]. One of the most important professional diferences between management accounting and strategic management, human resource management, marketing management, and so on is "measurement," otherwise, management accounting will be mixed with other management.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Hypothesis Generation Strategy of Management Accounting Data Mining

Zhang

2023

Mathematical Problems in Engineering

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Management accounting plays a greater role in providing business decisions for enterprises. As the core technology of big data processing, data mining technology can process a large amount of complex, structured, and unstructured information very efficiently. In this paper, the nonlinear system is taken as the research object, and the obtained system data is directly used. In the framework of divide and conquer strategy, multi-model method is combined with data clustering and a local modeling algorithm to study the nonlinearity of management accounting data mining. Through online adaptive estimation of parameters, aiming at the zero dynamic problem of the non-minimum phase system, through the decoupling matrix analysis of the nonlinear system, the state of the original strategy is dynamically extended to make the estimated parameters asymptotically approach the true value. The estimated parameters are applied to the nonlinear hypothesis strategy to achieve the goal of linearization control. It is assumed that the generation strategy is applied to the mining of different related types of rules without manually setting parameters, which greatly improves the efficiency of rule discovery. Through case analysis, this paper demonstrates the feasibility of applying nonlinear hypothesis strategy of data mining technology to management accounting and puts forward the relevant application process framework. The validity and feasibility of nonlinear hypothesis strategy data mining technology in management accounting are verified.

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The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management

Cited by 5 publications

References 59 publications

Efficient Detection of Environmental Violators: A Big Data Approach

Efficient Detection of Environmental Violators: A Big Data Approach

Efficient Detection of Environmental Violators: A Big Data Approach

Nonlinear Hypothesis Generation Strategy of Management Accounting Data Mining

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