Improved estimators for mean estimation in presence of missing information

Pandey, Awadhesh K.; Singh, G. N.; Sayed-Ahmed, Neveen; Abu-Zinadah, Hanaa

doi:10.1016/j.aej.2021.04.053

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…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]. Mean imputation and Median imputation are two common missing value imputation methods when data are MCAR.…”

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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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 can be completed with specific substitutes and data can be analyzed using standard methods. Information about unit of characteristic of interest observed and auxiliary variable help improve the accuracy of demographic parameter estimates (Pandey et al, 2021). Hansen and Hurwitz (1946) was the first researcher who considered the problem of non-response.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers also worked on imputation methods to deal with non-response and missing values among them include; Lee et al (1994), Singh and Horn (2000), Singh and Deo (2003), Toutenburg et al (2008), Singh (2009), Wang and Wang (2006), Kadilar and Cingi (2008), Diana and Perri (2010), Al-Omari et al (2013), Singh et al (2014), Gira (2015), Singh et al (2016), Bhushan and Pandey (2016), Prasad (2016), Audu et al (2020a,b,c), Audu et al (2021a,b,c,d), Audu and Singh (2021), Yusuf et al (2022). However, the estimators of the scheme proposed by Audu et al (2020a) and Pandey et al (2021) are functions of the unknown parameters of the study variable which makes the schemes and estimators impracticable in real life application unless if the unknown parameters are estimated using large sample which may required additional resources in the conduct of survey. ( )…”

Section: Introductionmentioning

confidence: 99%

“…where Pandey et al, (2021) proposed improved estimators for mean estimation in presence of missing information.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study is to modify the imputation scheme proposed by Pandey et al (2021) and test for the efficiency of the proposed estimator and some existing related estimators considered in the study theoretically using real life data.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency of modified generalized imputation scheme for estimating population mean with known auxiliary information

Adejumobi¹,

Audu²,

Yunusa³

et al. 2022

Bayero J. Pure App. Sci.

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Different authors for estimating population mean have proposed several Imputation schemes. Recently, some authors have suggested generalized imputation schemes that their estimators are functions of unknown parameters of the study variable. These unknown parameters need to be estimated for the estimators to be applicable and this may require additional resources. This paper considered a class of imputation scheme that is independent of unknown parameter and the point estimator of the suggested scheme for estimating population mean was derived. The properties (bias and MSE) of an efficient estimators presented were derived up to first order approximation and also conditions for which the estimators of the proposed scheme is more efficient than other estimators of the existing schemes considered in the study were also examined. The result of the empirical study revealed that the suggested estimators are more efficient than the existing ones considered in the study.

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Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia

Jawa¹

2022

Alexandria Engineering Journal

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During the Coronavirus disease (COVID-19) period, the world witnessed a complete closure of all aspects of social life, and people must go for home quarantine to limit the spread of Corona virus disease. This study aims to use multiple logistic regression model (MLR) to identify the impact of home quarantine on psychological stability of individuals and its relationship to various factors. Maximum likelihood is used to estimate the parameters of MLR. Hosmer-Lemeshow method and Wald statistic are calculated to assess the significance of MLR. Cross sectional study was carried out and MLR was fitted to the collected data. This study applied to 846 residents of Makkah region during the COVID-19 period. High percentage of respondents felt psychological stability during the period of home quarantine. Logistic regression model showed that education level and psychological disorders during home quarantine were the significant risk factors for psychological stability during COVID-19. In addition, the positive impact of the home quarantine that people became more attention to their health, more interested to self-development and the families become close to each other. On the other hand, people on private employee or unemployed had psychological disorders more than others due to home quarantine and decrease their income.

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Improved estimators for mean estimation in presence of missing information

Cited by 9 publications

References 20 publications

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

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

Efficiency of modified generalized imputation scheme for estimating population mean with known auxiliary information

Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia

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