Missing Data Analysis in Regression

Marcelino, Carolina G.; Leite, Gabriel Matos Cardoso; Celes, P.; Pedreira, Carlos Eduardo

doi:10.1080/08839514.2022.2032925

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Missing data are observations that are not stored for a variable in the desired observation (Kang, 2013). Most researchers assume that missing observations do not intrinsically interfere with statistical analysis of data sets (Marcelino et al, 2022), but it becomes a more critical problem when missing observations involve a multi-item instrument, due to the lack of information even in one of the data sets, the item which leads to the inability to calculate the total score of the instrument (Tsiampalis & Panagiotakos, 2020). Statistical analysis tends to be biased when more than 10% of data are missing (Bennet, 2001).…”

Section: A Introductionmentioning

confidence: 99%

Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion

Balqis

Astutik

Solimun

2022

JTAM

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This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases.

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Section: A Introductionmentioning

confidence: 99%

Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion

Balqis

Astutik

Solimun

2022

JTAM

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“…Azad et al (2022) have collected daily reservoir water level data from Red Hills reservoir, Tamil Nadu to predict the water quality using SARIMA, Data of 2190 samples collected from the River Canal in the Yangtze River Delta of Jiangnan, China as used to determine the water quality by analyzing the DO parameters using RF, ABM and GBU models(Huan et al, 2022). Based on MAE, RMSE and R 2 attention-based GRU model was proved best as it outperformed the other models Marcelino et al (2022). have used the KNN technique to understand whether the missing data values may affect the regression results.…”

mentioning

confidence: 99%

Study of the effectiveness of Wavelet Genetic Programming model for Water Quality Analysis in the Uttar Pradesh region

Saxena

Gaonkar

Singh

2023

Preprint

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Water constitutes an essential part of the Earth as it helps in making the environment greener, supporting people to survive and in transporting various nutrients and minerals. But all these factors are drastically impacted due to rising water pollution and its poor sanitation leading to poor water quality. In India, according to the latest survey, due to the excessive use of chemicals by the industries, fertilizers and pesticides by the farmers, various water bodies are getting contaminated, causing a lack of good quality water suitable for consumption. About 2 lakh people die every year due to various diseases caused by consumption of contaminated water. Not only the surface water, groundwater and river water are also getting contaminated. Hence, there is a need to analyze the quality of water in the existing water sources. In this work, a workflow is proposed for analyzing the water quality in selected regions of Uttar Pradesh state. The proposed work uses a hybrid Wavelet Genetic Programming model for analysis and visualization of 13 rivers of Uttar Pradesh region. Prediction of Dissolved Oxygen (DO) level is used as the determinant for water quality assessment. Continuous Wavelet Transform is utilized to decompose the DO dataset and the decomposed DO values are treated as input variables for the model using Morlet wavelet function. The results have proved that the proposed model is suitable for the accurate prediction of DO values.

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“…Data of 2190 samples collected from the River Canal in the Yangtze River Delta of Jiangnan, China as used to determine the water quality by analyzing the DO parameters using RF, ABM and GBU models(Huan et al, 2022). Based on MAE, RMSE and R 2 attention-based GRU model was proved best as it outperformed the other models Marcelino et al (2022). have used the KNN technique to understand whether the missing data values may affect the regression results.…”

mentioning

confidence: 99%

Study of the effectiveness of wavelet genetic programming model for water quality analysis in the Uttar Pradesh region

Saxena

Gaonkar

Singh

2023

Environ Monit Assess

View full text Add to dashboard Cite

Water constitutes an essential part of the Earth as it helps in making the environment greener, supporting people to survive and in transporting various nutrients and minerals. But all these factors are drastically impacted due to rising water pollution and its poor sanitation leading to poor water quality. In India, according to the latest survey, due to the excessive use of chemicals by the industries, fertilizers and pesticides by the farmers, various water bodies are getting contaminated, causing a lack of good quality water suitable for consumption. About 2 lakh people die every year due to various diseases caused by consumption of contaminated water. Not only the surface water, groundwater and river water are also getting contaminated. Hence, there is a need to analyze the quality of water in the existing water sources. In this work, a work ow is proposed for analyzing the water quality in selected regions of Uttar Pradesh state. The proposed work uses a hybrid Wavelet Genetic Programming model for analysis and visualization of 13 rivers of Uttar Pradesh region.Prediction of Dissolved Oxygen (DO) level is used as the determinant for water quality assessment. Continuous Wavelet Transform is utilized to decompose the DO dataset and the decomposed DO values are treated as input variables for the model using Morlet wavelet function. The results have proved that the proposed model is suitable for the accurate prediction of DO values.

show abstract

Missing Data Analysis in Regression

Cited by 11 publications

References 29 publications

Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion

Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion

Study of the effectiveness of Wavelet Genetic Programming model for Water Quality Analysis in the Uttar Pradesh region

Study of the effectiveness of wavelet genetic programming model for water quality analysis in the Uttar Pradesh region

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