A Combined Family of Ratio Estimators for Population Mean using an Auxiliary Variable in Simple Random Sampling

Jaroengeratikun, Uraiwan; Lawson, Nuanpan

doi:10.5614/j.math.fund.sci.2019.51.1.1

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…In fact, estimating the population mean is a fundamental statistical tool of data analysis in general research. Furthermore, auxiliary information can improve the precision of the estimator, so several authors including Bahl and Tuteja [8], Singh and Pal [9], Jaroengeratikun and Lawson [10] defined the estimator of population mean using auxiliary information under a simple random sample without replacement scheme (SRSWOR).…”

Section: 5mentioning

confidence: 99%

Estimating the Mean of PM2.5 with Missing Data in the Area Around Electricity Generating Authority of Thailand Using the Improved Compromised Imputation Method

Dachochaiporn

Chodjuntug

2023

Curr. Appl. Sci. Technol.

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Particulate matter with an aerodynamic diameter of less than 2.5 m or , is one of the air pollutants that has been found to be at unsafe levels for a number of years in Thailand, leading to public health concerns. In order to lessen the detrimental effects of air pollution, monitoring and analysis of concentration are crucial. Following the study of data from the Pollution Control Department Report in the area around the Electricity Generating Authority of Thailand in January 2019, it was found that there was data missing in the information. It is well-known that missing data can reduce the accuracy of data analysis. To solve the missing data problem, this paper proposes an improved method of compromised imputation and a corresponding resultant estimator to deal with estimating the mean of concentrations in the area. The bias and mean square error of the estimator obtained from the proposed method were derived. The conditions which favor the performance of our estimator over other estimators obtained from the mean, ratio, and compromised imputation methods were obtained using mean square error to apply in the area. The mean of concentrations in this case using the proposed estimator was equal to 47.13 g/m3, indicating that it did not exceed unsafe levels (<50 g/m3) under certain conditions. In order to support more accurate data analysis that will lead to effective management of air pollution problems in the future, this research proposes a new method that is more effective than the existing methods under missing data problem.

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Section: 5mentioning

confidence: 99%

Estimating the Mean of PM2.5 with Missing Data in the Area Around Electricity Generating Authority of Thailand Using the Improved Compromised Imputation Method

Dachochaiporn

Chodjuntug

2023

Curr. Appl. Sci. Technol.

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“…In literature various modified estimators of population mean of study variable using auxiliary variables have been given by various authors. For detailed study of the modified ratio type estimators, latest references can be made of Cingi (2004, 2009), Singh (2003), Subramani (2013), Tailor and Sharma ( 2009), Yadav and Adewara (2013), Yadav et al, (2016aYadav et al, ( , 2016bYadav et al, ( , 2016cYadav et al, ( , 2016d, Yadav et al, (2017aYadav et al, ( , 2017bYadav et al, ( , 2017c, Gupta (2017, 2018), Yadav et al, (2018aYadav et al, ( , 2018bYadav et al, ( , 2018c, Yadav et al, (2019) and Jaroengeratikun and Lawson (2019) Following table-1 represents some modified estimators, their constants, biases and mean squared errors.…”

Section: Where mentioning

confidence: 99%

“…Motivated by Jaroengeratikun and Lawson (2019) estimator of population mean, we propose the following ratio type estimator of the population mean using information on the on size of the sample as, Where c is any known constant. In our research paper we will take c = n (sample size).…”

Section: Proposed Estimatormentioning

confidence: 99%

Utilizing Sample Size Information for Improved Estimation of Population Mean in Agriculture Surveys

Misra¹

2021

Int.J.Curr.Microbiol.App.Sci

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On exploring the generalized mixture estimators under simple random sampling and application in health and finance sector

Iqbal,

Muslim Raza,

Butt

et al. 2024

AIP Advances

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There are numerous studies where data on population units’ auxiliary variables and attributes are simultaneously available. Therefore, due to cost-effectiveness and ease of recording, the study variable and several linearly related auxiliary variables are recorded. These auxiliary variables are commonly observed as quantitative and qualitative (attributes) variables and are jointly used to estimate the study variable’s population mean using a mixture estimator. In order to achieve this, a family of generalized mixture estimators was proposed under simple random sampling with the aim of improving performance under symmetrical and asymmetrical distributions. In addition, the estimator’s behavior for various sample sizes was examined with regard to its convergence to the normal distribution. The suggested generalized mixture estimator’s mean square error is deduced up to the first order of approximation. It is discovered that for the normal, uniform, Weibull, and gamma distributions, the suggested estimator estimates the population mean of the study variable with greater accuracy than the competing estimators. It is also revealed that when the proposed estimator converges to normality, the sample size is at least taken as 110, 1000, and 120. Furthermore, the implementation of three real-life datasets related to the finance and health sectors is also presented to support the proposed estimator’s significance.

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A Combined Family of Ratio Estimators for Population Mean using an Auxiliary Variable in Simple Random Sampling

Cited by 10 publications

References 7 publications

Estimating the Mean of PM2.5 with Missing Data in the Area Around Electricity Generating Authority of Thailand Using the Improved Compromised Imputation Method

Estimating the Mean of PM2.5 with Missing Data in the Area Around Electricity Generating Authority of Thailand Using the Improved Compromised Imputation Method

Utilizing Sample Size Information for Improved Estimation of Population Mean in Agriculture Surveys

On exploring the generalized mixture estimators under simple random sampling and application in health and finance sector

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