The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data

Stead, Alexander D.; Wheat, Phill

doi:10.1016/j.ejor.2019.06.042

Cited by 16 publications

(10 citation statements)

References 53 publications

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“…5 0.184). Supervision and control influence "just-in-time" and ensure high quality final outcomes (Stead and Wheat, 2020). Communication among the project participants was vital to prevent opportunism and prevent disputes (Deep et al, 2019;Shrestha and Batista, 2020).…”

Section: Project Successmentioning

confidence: 99%

Evaluating the impact of constraints on project success: empirical study of highway projects

Deep

Bhoola

Vidhani

et al. 2022

BEPAM

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PurposeHighway projects in India have a history of cost overruns and delay in project delivery, thus incurring huge losses to public funds. The study aimed to measure the extent of influence of various constraints on the overall success of highway projects (e.g. National highway project, state highway projects and major district roads). Based on the literature, various factors were grouped under three categories (a) Project Constraints, (b) Project Governance Constraints and (c) Stakeholder Induced Constraints.Design/methodology/approachA quantitative approach was adopted. A questionnaire-based survey was conducted with 435 respondents from the field of highway projects with a minimum experience of 1 year as Project Manager. The data was analyzed by partial least squares structural equation modeling technique.FindingsIt was identified that the impact of project constraints (ES = 0.313) on project success is more than that of project governance constraints (ES = 0.231) and stakeholder induced constraints. Additionally, it was found that project governance constraints have a strong impact on project constraints (ES = 0.535) and stakeholder induced constraints (ES = 0.403).Originality/valueThe study presented in this paper is first of its kind to measure and articulate the impact of project governance constraint, project constraint and stakeholder induced constraints on project success.

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Section: Project Successmentioning

confidence: 99%

Evaluating the impact of constraints on project success: empirical study of highway projects

Deep

Bhoola

Vidhani

et al. 2022

BEPAM

View full text Add to dashboard Cite

show abstract

“…The estimated values can then be calculated from these results (Van Ginkel et al, 2014). The error of a single imputation is large, and the estimated standard error Tower crane accident prediction cannot be obtained (Stead et al, 2020). MI can reduce the error of a data imputation (Harel et al, 2007).…”

Section: Prediction and Data Imputationmentioning

confidence: 99%

Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

Jiang

Zhao

Feng

et al. 2021

ECAM

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PurposeThis research is aimed at predicting tower crane accident phases with incomplete data.Design/methodology/approachThe tower crane accidents are collected for prediction model training. Random forest (RF) is used to conduct prediction. When there are missing values in the new inputs, they should be filled in advance. Nevertheless, it is difficult to collect complete data on construction site. Thus, the authors use multiple imputation (MI) method to improve RF. Finally the prediction model is applied to a case study.FindingsThe results show that multiple imputation RF (MIRF) can effectively predict tower crane accident when the data are incomplete. This research provides the importance rank of tower crane safety factors. The critical factors should be focused on site, because the missing data affect the prediction results seriously. Also the value of critical factors influences the safety of tower crane.Practical implicationThis research promotes the application of machine learning methods for accident prediction in actual projects. According to the onsite data, the authors can predict the accident phase of tower crane. The results can be used for tower crane accident prevention.Originality/valuePrevious studies have seldom predicted tower crane accidents, especially the phase of accident. This research uses tower crane data collected on site to predict the phase of the tower crane accident. The incomplete data collection is considered in this research according to the actual situation.

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“…When data are MAR, the fact that the data are missing is systematically related to the observed but not the unobserved data [10,12]. When data are NMAR, the fact that the data are missing is systematically related to the unobserved data, that is, the missingness is related to events or factors that are not measured by the researcher [10,13]. Following these three categories, there are some efficient strategies to coordinate data with missing values, appropriately known as "imputation methods" [14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression

et al. 2022

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Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data. Moreover, most imputation methods prefer low-missing-rate datasets to relatively high-missing-rate datasets. This paper proposes a novel missing data imputation method, called FTLRI, for time series air quality data based on the traditional logistic regression and a presented “first Five & last Three” model, which can explain relationships between disparate attributes and extract data that are extremely relevant, both in terms of time and attributes, to the missing data, respectively. To investigate the performance of FTLRI, it is benchmarked with five classical baselines and a new dynamic imputation method using a neural network with average hourly concentration data of pollutants from three disparate stations in Lanzhou in 2019 under different missing rates. The results show that FTLRI has a significant advantage over the compared imputation approaches, both in the particular short-term and long-term time series air quality data. Furthermore, FTLRI has good performance on datasets with a relatively high missing rate, since it only selects the data extremely related to the missing values instead of relying on all the other data like other methods.

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The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data

Abstract: This is a repository copy of The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data.

Cited by 16 publications

References 53 publications

Evaluating the impact of constraints on project success: empirical study of highway projects

Evaluating the impact of constraints on project success: empirical study of highway projects

Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression

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