Three‐step imputation of missing values in condition monitoring datasets

Liu, Huan; Wang, Youyuan; Chen, Weigen

doi:10.1049/iet-gtd.2019.1446

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…For different miss rates and dimensions, we compare various imputation methods to simulate experimental results [17]. Observing Tables 5, 6 and 7, we can draw the following conclusions.…”

Section: Scenariomentioning

confidence: 80%

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

Tian,

Majahar Ali,

Pei Shan

et al. 2023

Mal. J. Fund. Appl. Sci.

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The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.

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“…For different miss rates and dimensions, we compare various imputation methods to simulate experimental results [17]. Observing Tables 5, 6 and 7, we can draw the following conclusions.…”

Section: Scenariomentioning

confidence: 80%

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

Tian,

Majahar Ali,

Pei Shan

et al. 2023

Mal. J. Fund. Appl. Sci.

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“…To elucidate if a non-linear relationship existed between occupational PA and hypertension prevalence, we used a restricted cubic spline model with three knots set at the 5th, 50th, and 95th percentiles of occupational PA [ 33 , 34 ] without using multiple imputation [ 35 ]. The model was adjusted for the variables included in Model 3, except for household income, which contained missing values.…”

Section: Methodsmentioning

confidence: 99%

Different associations of occupational and leisure-time physical activity with the prevalence of hypertension among middle-aged community dwellers in rural Khánh Hòa, Vietnam

et al. 2023

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Background In contrast to high-income countries where physical activity (PA), particularly leisure-time PA, has been shown to be protective against hypertension, few studies have been conducted in low- and middle-income countries. We examined the cross-sectional association between PA and hypertension prevalence among rural residents in Vietnam. Methods We used data collected in the baseline survey of a prospective cohort study, among 3000 people aged 40–60 years old residing in rural Khánh Hòa, Vietnam. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the use of antihypertensive medication. We assessed occupational PA and leisure-time PA using the Global Physical Activity Questionnaire. A robust Poisson regression model was used to investigate the associations, with adjustment for covariates. Results The prevalence of hypertension was 39.6%. After adjusting for socio-demographic and lifestyle-related variables, leisure-time PA was positively associated with hypertension prevalence (prevalence ratio [PR]: 1.03 per 10 MET-hour/week, 95% confidence interval [CI] 1.01–1.06). Occupational PA was inversely associated with hypertension prevalence (PR: 0.98 per 50 MET-hour/week, 95% CI = 0.96–0.996). After adjusting for BMI and other health-related variables, the association related to occupational PA became statistically non-significant, while the association related to leisure-time PA remained statistically significant. Conclusion In contrast to previous studies in high-income countries, we found that leisure-time PA was positively associated with hypertension prevalence and occupational PA was associated with a lower hypertension prevalence. This suggests that the association between PA and hypertension might differ depending on the context.

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“…After studying them, we found that there are some algorithms that are less sensitive to the interconnectedness of variables in an employee dataset. Imputing missing values in isolation [21] can break the relationships between variables and lead to inconsistent or implausible imputed data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Probabilistic Approach for Missing Data Imputation

Arefin,

Masum

2024

Complexity

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In the context of data analysis, missing data imputation is a vital issue due to the typically large scale and complexity of the datasets. It often results in a higher incidence of missing data. So, addressing missing data through the imputation technique is essential to ensure the integrity and completeness of the data. It will ultimately improve the accuracy and validity of the data analysis. The prime objective of this study is to propose an imputation model. This paper presents a method for imputing missing employee data through a combination of features and probability calculations. The study utilized employee datasets that were collected from the Kaggle along with primary data collected from RMG factories located in Chittagong. The suggested algorithm demonstrated a notable level of accuracy on the datasets, and the average accuracy for each identified technique was also quite satisfactory. This study contributes to the existing body of research on missing data imputation in big data analysis and offers practical implications for handling missing data in different datasets. Usage of this technique will enhance the accuracy of data analysis and decision-making in organizations.

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Three‐step imputation of missing values in condition monitoring datasets

Cited by 13 publications

References 29 publications

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China

Different associations of occupational and leisure-time physical activity with the prevalence of hypertension among middle-aged community dwellers in rural Khánh Hòa, Vietnam

A Probabilistic Approach for Missing Data Imputation

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