Some Improved Estimators of Population Mean using Two-Phase Sampling Scheme in the Presence of Non-Response

In the present paper, we have proposed some improved estimators of the population mean utilizing the information on two auxiliary variables adopting the idea of two-phase sampling under non-response. In order to propose the estimators, we have assumed that the study variable and first auxiliary variable suffer from non-response while the second (additional) auxiliary variable is free from non-response. We have derived the expressions for biases and mean square errors of the proposed estimators and compared them with that of usual estimator and some well known existing estimators of the population mean. The theoretical results have also been illustrated with some empirical data.

Some Improved Estimators of Population Mean Under Systematic Sampling

Auxiliary information is very important in constructing estimators for the population parameters for increasing the efficiency different sampling schemes. In this paper, we consider the problem of estimation of population mean using information on auxiliary variables in systematic sampling. We derive the expressions for the bias and mean squared error (MSE) of the suggested estimators up to the 1st degree of approximation. Proposed estimators are compared with usual mean estimator in systematic sampling scheme theoretically as well as empirically.

Improved Estimators using Exponential Function for the Population Mean in Simple and Stratified Random Samplings

In this article, we investigated estimators with the exponential function for the estimation of the population mean in the simple and stratified random samplings. Family of estimators based on the exponential function is proposed for both sampling methods. The proposed estimators are compared with estimators in literature. Moreover, we provide an application on different data sets to demonstrate the efficiency of the proposed estimators. As a result, the proposed estimators are more efficient than other estimators in literature under the obtained conditions in theory.

Enhanced Estimators of Population Variance with the Use of Supplementary Information in Survey Sampling

In the present study, we propose the proficient class of estimators of the finite population mean, while incorporating the nonconventional location and nonconventional measures of dispersion with coefficient of variation of the auxiliary variable. Properties associated with the suggested class of improved estimators are derived, and an efficiency comparison with the usual unbiased ratio estimator and other existing estimators under consideration in the present study is established. An empirical study has also been provided to validate the theoretical results. Finally, it is established that the proposed class of estimators of the finite population variance proves to be more efficient than the existing estimators mentioned in this study.

Mitigating contamination in LSS surveys: a comparison of methods

ABSTRACT Future large-scale structure surveys will measure the locations and shapes of billions of galaxies. The precision of such catalogues will require meticulous treatment of systematic contamination of the observed fields. We compare several existing methods for removing such systematics from galaxy clustering measurements. We show how all the methods, including the popular pseudo-Cℓ Mode Projection and Template Subtraction methods, can be interpreted under a common regression framework and use this to suggest improved estimators. We show how methods designed to mitigate systematics in the power spectrum can be used to produce clean maps, which are necessary for cosmological analyses beyond the power spectrum, and we extend current methods to treat the next-order multiplicative contamination in observed maps and power spectra, which reduced power spectrum errors from $\Delta \chi ^2_{\rm C_\ell }\simeq 10$ to ≃ 1 in simulated analyses. Two new mitigation methods are proposed, which incorporate desirable features of current state-of-the-art methods while being simpler to implement. Investigating the performance of all the methods on a common set of simulated measurements from Year 5 of the Dark Energy Survey, we test their robustness to various analysis cases. Our proposed methods produce improved maps and power spectra when compared to current methods, while requiring almost no user tuning. We end with recommendations for systematics mitigation in future surveys, and note that the methods presented are generally applicable beyond the galaxy distribution to any field with spatial systematics.