Download 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

A New thought for Tuning layout Weights in Survey Sampling: Jackknifing in conception and Practice introduces the recent thought of tuning layout weights in survey sampling by means of providing 3 thoughts: calibration, jackknifing, and imputing the place wanted. This new technique permits survey statisticians to improve statistical software program for interpreting information in a extra accurately and pleasant approach than with latest thoughts.

  • Explains the 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

Show description

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

Best probability & statistics books

Stochastic Storage Processes: Queues, Insurance Risk, Dams, and Data Communication

This can be a revised and multiplied model of the sooner variation. the recent fabric is on Markov-modulated garage techniques coming up from queueing and knowledge commu­ nication versions. The research of those types relies at the fluctuation thought of Markov-additive procedures and their discrete time analogues, Markov random walks.

Regression and factor analysis applied in econometrics

This e-book offers with the tools and useful makes use of of regression and issue research. An exposition is given of standard, generalized, - and three-stage estimates for regression research, the strategy of vital parts being utilized for issue research. whilst constructing an econometric version, the 2 methods of study supplement one another.

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

Example text

Qj ðxr ð jÞÞ j2sr 2 À ! qj yr ð jÞ X X ! 105) where r β^ols ¼ r X xi yi À i¼1 r X ! xi i¼1 r r X i¼1 x2i À r X r X ! 107) for j ¼ 1,2, …,r. Generate a population of reasonable size, and create an environment through a simulations process where nonresponse could happen. 108) Suggest and justify your choice of degree of freedom (df ). 111). 118) for j ¼ 1, 2,…, r. Generate such a population of reasonable size and create an environment through a simulations process where nonresponse could happen.

100) where yr ð jÞ ¼ r yr À yj 1 À wj  r ð jÞ ¼ and w rÀ1 rÀ1 are the usual jackknife estimators of the population mean and weight obtained by removing the jth unit from the responding sample sr for any set of weights wj with unit total. 102), where qj is some choice of weights. 104) where X ! X qj j2sr β^NRTuned ¼ j2sr X ! X qj xr ð jÞ yr ð j Þ À ! X qj j2sr j2sr ! qj ðxr ð jÞÞ j2sr 2 À ! qj yr ð jÞ X X ! 105) where r β^ols ¼ r X xi yi À i¼1 r X ! xi i¼1 r r X i¼1 x2i À r X r X ! 107) for j ¼ 1,2, …,r.

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.

Download PDF sample

Rated 4.57 of 5 – based on 19 votes