Variance Estimation in the Presence of Nonresponse under Probability Proportional to Size Sampling

Lawson, Nuanpan

doi:10.5176/2251-1911_cmcgs17.32

Cited by 8 publications

(11 citation statements)

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“…be the population total of study variable y . Let i r denote the response indicator variable of i y where i r is equal to 1 if i y is observed; otherwise 0 i r = and let i p denote the response probability, Under the reverse framework with unequal probability sampling with replacement when the response probabilities are uniform and the sampling fraction is negligible, Lawson (2017) showed that the variance of ˆi…”

Section: Methodsmentioning

confidence: 99%

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A new variance estimator for unequal probability sampling without replacement in the presence of non-response

Lawson

Ponkaew

2019

j.appsci

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A new variance estimator for estimating the population total has been proposed under unequal probability sampling without replacement in the presence of nonresponse. The new variance estimator does not require the joint inclusion probability in the estimation under the reverse framework where the sampling fraction is negligible and equal response probabilities for all units. The efficiency of the new estimator is compared with the existing estimators via a simulation study.

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Section: Methodsmentioning

confidence: 99%

“…variance estimator requires ij π in the estimation. We can see that theLawson's (2017) estimator in equation (12) does not require ij π . In this section, we propose a new variance estimator following Lawson (2017) using Ponkaew & Lawson's (2019) estimator under PPSWOR when the sampling fraction is negligible.…”

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confidence: 99%

A new variance estimator for unequal probability sampling without replacement in the presence of non-response

Lawson

Ponkaew

2019

j.appsci

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“…The benefit of the Lawson estimator is that the response probability is not required in the estimation but is under the assumption that the probabilities of response are the same for all units and the sampling fraction can be omitted. Lawson's (2017)…”

Section: Basic Setupmentioning

confidence: 99%

“…Shao and Steel, 1999;Haziza and Rao, 2006;Haziza, 2010). Under this reverse method, the population total estimators and the GREG estimators along with their variance estimators were investigated within the MCAR and MAR nonresponse mechanisms and under different assumptions for the response probabilities and the sampling fractions (Lawson, 2017;Lawson and Ponkaew, 2019;Lawson and Siripanich, 2022;Ponkaew and Lawson, 2023).…”

Section: Introductionmentioning

confidence: 99%

Review of how the generalized regression estimators contribute to estimating the financial and economic data with missing observations under unequal probability sampling

Lawson

2024

AJEB

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PurposeKnowing financial and economic information beforehand benefits in planning and developing policies for every country especially for a developing country like Thailand and for other Asian countries. Unfortunately, missing data or non-response plays an essential role in many areas of studies including finance and economics. Eradication of missing data in a proper way before further analysis can gain remarkable outcomes and can be effective for planning policies. This review on the generalized regression estimators for population total can be applied to financial, economic and other data when missing data are present.Design/methodology/approachThe generalized regression estimators for estimating population total, including the variance estimators under unequal probability sampling without replacement with missing data are explored under the reverse framework. Applications to financial and economic data in Thailand are also reviewed.FindingsThe review of literatures related to the proposed estimator shows the best performance, giving smaller variances in all scenarios.Originality/valueThe generalized regression estimators can assist in estimating financial and economic data that contain missing values with different missing mechanisms and can be used in other applications which help gain more superior estimators.

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“…Lawson and Ponkaew [6] proposed a new GREG estimator for estimating total population using Lawson's estimator [7], which proposed a new population total estimator in the form of a nonlinear ratio estimator using unequal probability sampling without replacement. They also proposed the variance of the new GREG estimator under a reverse framework where the nonresponse mechanism was missing completely at random (MCAR).…”

Section: Introductionmentioning

confidence: 99%

New Generalized Regression Estimators Using a Ratio Method and Its Variance Estimation for Unequal Probability Sampling without Replacement in the Presence of Nonresponse

Ponkaew

Lawson

2022

Curr. Appl. Sci. Technol.

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One of the most important problems for planning in economics is that reliable data can be difficult to obtain either because it has not been recorded or because of nonresponse in surveys. This paper is aimed at proposing new generalized regression estimators using the ratio method of estimation for estimating population mean and population total and also variance estimators of the proposed generalized regression estimators in the presence of uniform nonresponse of a study variable. We show in theory that the proposed estimators are almost unbiased under unequal probability sampling without replacement when nonresponse occurs in the study. In the simulation studies, the performances of the proposed estimators were better when compared to the existing ones in terms of minimum relative bias and relative root mean square error. In an application to Thai maize in Thailand with 2019 data, we can see that the proposed estimators gave smaller variance estimates when compared to the existing estimators

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Variance Estimation in the Presence of Nonresponse under Probability Proportional to Size Sampling

Cited by 8 publications

References 0 publications

A new variance estimator for unequal probability sampling without replacement in the presence of non-response

A new variance estimator for unequal probability sampling without replacement in the presence of non-response

Review of how the generalized regression estimators contribute to estimating the financial and economic data with missing observations under unequal probability sampling

New Generalized Regression Estimators Using a Ratio Method and Its Variance Estimation for Unequal Probability Sampling without Replacement in the Presence of Nonresponse

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