Research Article Impact of Correlated Measurement Errors on Some Efficient Classes of Estimators Anoop Kumar , 1 Shashi Bhushan , 2 Shivam Shukla , 3 Walid Emam , 4 Yusra Tashkandy , 4 and Rajesh Gupta 5 1 Department of Statistics, Amity School of Applied Sciences, Amity University Uttar Pradesh, Lucknow 226028, India 2 Department of Statistics, University of Lucknow, Lucknow 226007, India 3 Department of Mathematics & Statistics, Dr. Shakuntala Misra National Rehabilitation University, Lucknow 226017, India 4 Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia 5 Global Data Insight & Analytics (GDIA), Ford Motor Company, 1 American Rd, Dearborn, MI 48126, USA Correspondence should be addressed to Shashi Bhushan; bhushan_s@lkouniv.ac.in Received 7 July 2023; Revised 12 August 2023; Accepted 23 September 2023; Published 18 October 2023 Academic Editor: Ammar Alsinai Copyright©2023AnoopKumaretal.TisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It is well-known that the appearance of measurement errors spoils the traditional results in survey sampling. Te concept of correlated measurement errors (CMEs) is true in various practical situations, but this has been seldom considered by researchers in survey sampling. In this article, the infuence of the CME under simple random sampling (SRS) has been considered over some prominent classes of estimators for the population mean. Te frst-order approximated formulae of the mean square error of the introduced estimators are reported, and a comparative analysis has also been conducted with traditional estimators. Te the- oretical fndings are extended by a broad spectrum computational study using real and artifcial data. 1. Introduction In survey research, the primary objective of any surveyor is to enhance the efciency of the estimation procedures with the help of information on the auxiliary/supplementary variables that are usually associated with the research var- iable. In this context, the literature contains the ratio, re- gression, product, exponential methods, and their modifed forms for efciently estimating the parameters of interest. Tese estimation methods are further extended using two- or multiauxiliary information under diferent sampling schemes. Tese estimation methods either consist of a sup- plementary variable or only on research variable, pre- supposing that all data are independent from ME, but this presupposition practically never happen. Te data are tainted with or have hidden ME due to diferent types of reasons (readers can refer to Murthy [1] and Cochran [2]). Te discrepancy between the true and observed values is known as ME. Many attempts have been made to examine the impact of ME on various parameters of the population such as the mean, variance, total, and distribution function. Te efect of ME has been observed over the efciency of the estimation methods by many authors. Shalabh [3] examined the ME’s impact on the classical ratio estimators. Infuenced by Shalabh [3], Manisha and Singh [4] studied the ME’s impact using a new class of estimators for the population mean. Subsequently, Singh and Karpe [5–7] examined the efect of ME over the parameters of the population using diferent sampling strategies. Te variance computation in the existence of ME was provided by Diana and Giordan [8]. Hussain et al. [9] suggested the estimation of a fnite pop- ulation distribution function with the dual use of supple- mentary information under nonresponse. Tariq et al. [10] proposed a supplementary information-based variance es- timator to tackle the problem of ME. Tariq et al. [11] sug- gested a generalized variance estimator utilizing supplementary information in the presence and absence of ME. Zahid et al. [12] developed a generalized class of Hindawi Journal of Mathematics Volume 2023, Article ID 8140831, 27 pages https://doi.org/10.1155/2023/8140831