By Sarjinder Singh, Stephen A. Sedory, Maria Del Mar Rueda, Antonio Arcos, Raghunath Arnab

*A New idea for Tuning layout Weights in Survey Sampling: Jackknifing in thought and Practice* introduces the hot suggestion of tuning layout weights in survey sampling via proposing 3 techniques: calibration, jackknifing, and imputing the place wanted. This new technique permits survey statisticians to improve statistical software program for interpreting info in a extra accurately and pleasant method than with present thoughts.

- Explains easy methods to calibrate layout weights in survey sampling
- Discusses how Jackknifing is required in layout weights in survey sampling
- Describes how layout weights are imputed in survey sampling

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**Additional resources for A new concept for tuning design weights in survey sampling : jackknifing in theory and practice**

**Sample text**

R, was used to study the coverage based on the newly tuned dell function. 3 Numerical illustration In the following example, we explain the computational steps involved in the construction of a confidence interval estimate with the dell estimator. 1 2397 Construct the 95% confidence interval estimate of the average weight by assuming the population mean circumference X ¼ 105:40in: is known. Solution. 2289600 where o2 À Ã Á2 n TunedðdellÞ n ð jÞ v ð j Þ ¼ n ð n À 1 Þ3 w yð jÞ À yTunedðdellÞ The tuned estimate of the average weight is yTunedðdellÞ ¼ 3497:648 and SE yTunedðdellÞ ¼ 909:5542.

Therefore, it may be said that the proposed newly tuned estimation methodology is as efficient as the linear regression estimator for the choice of qj ¼ 1 and hence is always more efficient than the sample mean estimator. The major motivation and benefit of the proposed newly tuned estimation methodology is that it is computer friendly for estimating the variance of the resultant estimator through the doubly jackknifed method. 1 Problem of undercoverage Let us recall that the main problem in survey sampling is estimation of the variance of an estimator of a population parameter.

Let (yi, xi), i ¼ 1,2, …, n be the values of the study variable and auxiliary variable of the ith unit in the sample s drawn using a simple random sampling (SRS) scheme. 4) i2s be the sample means for the study variable and the auxiliary variable, respectively. A New Concept for Tuning Design Weights in Survey Sampling. 00002-4 Copyright © 2016 Elsevier Ltd. All rights reserved. 8) is due to Owen (2001). 12) is the sample mean of the auxiliary variable obtained by removing the jth unit from the sample s.