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

• Authors: Prachi Garg , Namita Srivastava , Manoj Kumar Srivastava

Abstracts

EN

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

EN

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

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2025
• Volume: 26
• Issue: 2
• Pages: 177-198

Dates

published

2025

Contributors

author

Prachi Garg

• Department of Statistics, St. John's College Agra, Dr. B.R. Ambedkar University, Agra (U.P.), India

• https://orcid.org/0009000188094464

author

Namita Srivastava

• Department of Statistics, St. John's College Agra, Dr. B.R. Ambedkar University, Agra (U.P.), India

• https://orcid.org/0000000186959148

author

Manoj Kumar Srivastava

• Shaheed Mahendra Karma Vishwavidyalaya, Bastar, Chhattisgarh, India

• https://orcid.org/0000000282561439

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Identifiers

DOI

10.1080/03610920903009400

Biblioteka Nauki

61791777