# A new concept for tuning design weights in survey sampling : by Sarjinder Singh, Stephen A. Sedory, Maria Del Mar Rueda, PDF

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 notion for Tuning layout Weights in Survey Sampling: Jackknifing in thought and Practice* introduces the hot inspiration of tuning layout weights in survey sampling by means of featuring 3 recommendations: calibration, jackknifing, and imputing the place wanted. This new technique permits survey statisticians to improve statistical software program for studying info in a extra accurately and pleasant means than with current ideas.

- Explains the right way 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**

**Example text**

90) j¼1 nyn À yj has its usual meaning. Determine c such that μ ^rðJackÞ can be used nÀ1 as a jackknife estimator of the rth central moment μr. Hint: Finucan, Galbraith, and Stone (1974). 10 Consider a farmer growing organic pumpkins and chemically treated pumpkins. A buyer took a random sample of n organic pumpkin and another random sample of m treated pumpkins, both using SRSWR schemes. Let yn and ym be the sample mean weights of the first and second samples, respectively. 91) be the pooled estimator of the pooled population mean weight, Y, of both types of pumpkins on the farm.

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. 11). 18) where λ0 and λ1 are the Lagrange multiplier constants. ( j2s ! 25) where 2 6 6 6 ^ βTunedðcsÞ ¼ 6 6 4 X j2s ! qj X j2s X j2s qj ! X qj xn ð jÞ yn ð jÞ À ! X j2s j2s ! qj ðxn ð jÞÞ 2 À ! 27) where n β^ols ¼ n X xi yi À n X i¼1 ! xi i¼1 n n X i¼1 x2i À n X n X !

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. R, was used to study the coverage by the newly tuned jackknife estimator based on a chi-square type distance function. max>YB)/nreps, 4)->cov6 cat (n, cov1, cov2, cov3, cov4, cov5, cov6, ‘\n’) } Tuning of jackknife estimator 37 In the preceding R code, the variables cov4, cov5, and cov6 give the actual coverage of the nominal 90%, 95%, and 99% confidence interval when using the traditional linear regression estimator, assuming SRSWR sampling.

### 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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