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 concept and Practice* introduces the hot notion 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 increase statistical software program for reading information in a extra accurately and pleasant manner than with current options.

- 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

**Read Online or Download A new concept for tuning design weights in survey sampling : jackknifing in theory and practice PDF**

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

**Sample text**

Let y1, y2, …, yn be the delivery times (in minutes) required by a random sample of n workers using those vehicles. 98) ið6¼jÞ2s denote the jackknife estimator of the population harmonic mean H where the jth unit is dropped from the sample. 100) j¼1 À Á Determine, if possible, the values of weights cj such that v^ H^Jack can be considered as an estimator of the variance of the harmonic mean estimator. Support your findings by a simulation study. 13 (a) Suppose that a pumpkin farmer is interested in selling his pumpkins by weight.

2. 2. 22). Note that the modified greg estimator is far from the traditional greg estimator. Therefore the estimator yTunedðcsÞ is recommended so long as one is concerned about estimating the weight of a pumpkin using small samples. For large samples, the modified greg may perform just as well because better coverage is expected for the regression type estimator yTunedðcsÞ . Here “better coverage” means coverage close to the nominal or anticipated coverage. 5 Numerical illustration In the following example, we explain the computational steps involved in the construction of a confidence interval estimate with the tuned estimator.

Let y1, y2, …, yn be the number of insects observed by the farmer on his n random visits to his farm. 94) ið6¼jÞ¼1 denote the jackknife estimator of the population geometric mean G after the jth unit is dropped from the sample. 96) j¼1 À Á Determine, if possible, the values of weights cj such that v^ G^Jack can be considered as an estimator of the variance of the geometric mean estimator. Support your views with a simulation study. 12 Harmonic mean A pumpkin farmer has a pumpkin pie factory and several vehicles that deliver his product to several destinations within the United States.