Jackknife variance estimation under two-phase sampling

Article type: Research Article

Authors: Ramasubramanian, V.^{; *} | Rai, Anil | Singh, Randhir

Affiliations: Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi 110 012, India

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Two new Jackknife methods, as the counterparts of two existing Bootstrap methods of variance estimation under two-phase sampling, have been proposed. A simulation study has been conducted under both design-based and Conditional inference frameworks by generating two-phase samples from an infinite population for comparison of the proposed methods with five existing Jackknife and Bootstrap methods. The first method, the two-phase post-stratified Jackknife, reduces to an existing Jackknife variance estimation method considered under sampling from infinite population set up. The performance of the second method, the two-phase proportionate Jackknife, was better than two existing Jackknife methods while performing at par with another Jackknife method as well as with the two Bootstrap methods considered.

Keywords: Bootstrap, conditional inference, design-based inference, Jackknife, simulation

DOI: 10.3233/MAS-2007-2104

Journal: Model Assisted Statistics and Applications, vol. 2, no. 1, pp. 27-35, 2007