Some imputation methods for missing data in sample surveys

Abstract

The present work suggests some imputation methods to deal with the problems of non-response in sample surveys. The imputation methods presented in this work lead to the precise estimation strategies of population mean. Empirical studies are carried out with the help of data borrowed from natural populations to show the superiorities of the suggested imputation methods over usual mean, ratio and regression methods of imputation in terms of the mean square error criterions. Suitable recommendations have been put forward for the survey practitioners.

An Exponential‐Cum‐Sine‐Type Hybrid Imputation Technique for Missing Data

“…e ratio estimator tries to make an improvement over the mean estimator by incorporating auxiliary information into a correlated variable. Various other estimators that make innovative use of auxiliary information have been proposed, for instance, the estimator proposed in [30], regression-type estimators proposed in [10], and exponential type estimators in [31], among others. e structures of some of these estimators have been given in Table 2, while the expressions for their respective variances (V) or mean square errors (MSEs) have been given in Table 3.…”

Modified Compromized Type Method of Imputation for Estimating Population Mean

“…These missing values create difficulty in analysis, processing and handling of data. The problem of non-response has been considered by many authors including Singh and Horn (2000), Singh and Deo (2003), Wang and Wang (2006), Kadilar and Cingi (2008), Toutenburg et al (2008), Singh (2009), Diana and Perri (2010), Al-Omari et al (2013), Singh et al (2014), Gira (2015), Singh et al (2016), Singh, et al (2010), Bhushan and Pandey (2016) and Prasad (2017), Audu et al (2020a, b, c), Audu et al (2021). Singh and Deo (2003) and Prasad (2017) estimators converged to sample mean as the values of unknown parameters in their estimators converged to zero while Singh and Horn (2000), Singh et al (2014) estimators converged to sample mean as the values of unknown parameters converged to one.…”

Regression-type Imputation Class of Estimators using Auxiliary Attributes

“…However, when the study variables are characterized by non-response, the aforementioned estimators are not applicable. Authors like Singh and Horn [10], Singh and Deo [11], Wang and Wang [12], Toutenburg et al [13], Kadilar and Cingi [14], Singh [15], Diana and Perri [16], Al-Omari et al [17], Singh et al [18], Gira (2015), Singh et al [19], Bhushan and Pandey [20], Prasad [21], Audu et al [22][23][24], have studied different schemes and estimators in the presence of non-response. Situations arise when the auxiliary characters are qualitative in nature e.g.…”