Ratio regression type estimators of the population mean for missing data in sample surveys

Prachi  Garg; Namita Srivastava; Manoj Kumar Srivastava

doi:https://doi.org/10.59139/stattrans-2025-021

Ratio regression type estimators of the population mean for missing data in sample surveys

Prachi Garg Department of Statistics, St. John’s College Agra, Dr. B.R. Ambedkar University, Agra (U.P.), India ORCID:https://orcid.org/0009-0001-8809-4464 , Namita Srivastava Department of Statistics, St. John’s College Agra, Dr. B.R. Ambedkar University, Agra (U.P.), India ORCID:https://orcid.org/0000-0001-8695-9148 , Manoj Kumar Srivastava Shaheed Mahendra Karma Vishwavidyalaya, Bastar, Chhattisgarh, India ORCID:https://orcid.org/0000-0002-8256-1439 Statistics in Transition new series, vol. 26, 2025, 2, pages: 177-198 Published online: 13 June 2025 https://doi.org/10.59139/stattrans-2025-021 Citation: Garg P., Srivastava N., Srivastava M. K., 2025. Ratio regression type estimators of the population mean for missing data in sample surveys. Statistics in Transition new series, 26(2), pp. 177-198; https://doi.org/10.59139/stattrans-2025-021

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ABSTRACT

In this article, new ratio regression type estimators with imputation have been proposed as means to overcome the problem of missing data relating to a studied variable in a sample survey. It has been shown that the suggested estimators are more efficient than the mean method of imputation, the ratio method of imputation, the regression method of imputation, and the estimators given by Singh and Horn (2000), Singh and Deo (2003), Singh (2009), Diana and Perri (2010) and Gira (2015). The biases and their mean square errors of the suggested estimators are derived. A comparative study is conducted using real and simulated data. The results are found to be encouraging showing improvement of all the methods discussed in this article.

KEYWORDS

imputation methods, Bias, Mean square error (MSE), Efficiency, Ratio- Regression type estimators.

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