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

ISBN-10: 0081005946

ISBN-13: 9780081005941

ISBN-10: 0081005954

ISBN-13: 9780081005958

ISBN-10: 3900051070

ISBN-13: 9783900051075

*A New proposal for Tuning layout Weights in Survey Sampling: Jackknifing in idea and Practice* introduces the hot notion of tuning layout weights in survey sampling through providing 3 recommendations: calibration, jackknifing, and imputing the place wanted. This new technique permits survey statisticians to increase statistical software program for studying info in a extra accurately and pleasant means than with current options.

- Explains find out how 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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**Extra resources for A new concept for tuning design weights in survey sampling : jackknifing in theory and practice**

**Sample text**

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.

1. 1 shows that for the population considered, when the sample size is small and the estimator v^ðylr Þ is used, the coverage by the usual linear regression estimator is less than expected. On the other hand, if the estimator v^TunedðcsÞ is used, we note that coverage is much closer to the nominal coverage. 06% coverage. Thus, intervals desired from the newly tuned jackknife estimator of the population mean of the weight of the pumpkins shows quite good coverage if the sample size is small, which suggests good reliability of the newly tuned methodology in real practice.

Construct 90%, 95%, and 99% confidence interval estimates in each situation by estimating the variance using the method discussed in the chapter. Alternatively, construct your own confidence interval estimates that you can claim to be better based on some scientific criterion. 2 Let the population variance σ 2x ¼ N À1 i2Ω variable be known. 94) n 2 N 2 σ^x and S2x ¼ σ . Report any changes observed in the resulting nÀ1 N À1 x estimator. 95) Tuning of jackknife estimator 51 where s2x ð jÞ ¼ À Á2 ðn À 1Þ2 s2x À n xj À xn ðn À 1 Þ ð n À 2Þ or equivalently ðn À 1Þs2x À s2x ð jÞ ¼ À Á2 xj À xn À ðn À 1Þ ðxn ð jÞ À xn Þ2 ð n À 2Þ and again report any changes observed in the resulting estimator.

### A new concept for tuning design weights in survey sampling : jackknifing in theory and practice by Sarjinder Singh, Stephen A. Sedory, Maria Del Mar Rueda, Antonio Arcos, Raghunath Arnab

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