Finite Population Sampling With Multivariate Auxiliary Information

Wright, Roger L.

doi:10.2307/2288199

Cited by 27 publications

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“…We note in passing that one may try to use the super-population model (l.l) to obtain an efficient stratification of II (cf. Wright, 1983; see also Siimdal et al, 1992). However, at this point, we shall consider the situation that the stratification of IT in L subpopulations (strata) is already known a priori, as appears to be frequently the case in sample surveys.…”

Section: Possible Extensionsmentioning

confidence: 92%

“…Instead of e RE' consider eRE,c = Nyn +8RE (1-::) as our estimator based on ()RE· The estimator eRE,c for the population total 8w also appears in Wright (1983) as the "combined regression through the origin" estimator. We note in passing that Wright (1983); see also Sarndal et al (1992) views eRA and eRE as "predictors" of the random variable 8w, because the randomness induced by the s-model is taken into account. In contrast, we condition on the finite population at hand, but use the auxiliary information to motivate the estimators '(}RA and fJ RE,c• for the population total.…”

Section: Preliminariesmentioning

confidence: 99%

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Wild Bootstrapping in Finite Populations with Auxiliary Information

Helmers

Wegkamp

1998

Scandinavian J Statistics

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ABSTRACT. Consider a finite population u, which can be viewed as a realization of a superpopulation model. A simple ratio model (linear regression, without intercept) with heteroscedastic errors is supposed to have generated u. A random sample is drawn without replacement from u. In this set-up a two-stage wild bootstrap resampling scheme as well as several other useful forms of bootstrapping in finite populations will be considered. Some asymptotic results for various bootstrap approximations for normalized and Studentized versions of the well-known ratio and regression estimator are given. Bootstrap based confidence intervals for the population total and for the regression parameter of the underlying ratio model are also discussed.

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Section: Possible Extensionsmentioning

confidence: 92%

Section: Preliminariesmentioning

confidence: 99%

Wild Bootstrapping in Finite Populations with Auxiliary Information

Helmers

Wegkamp

1998

Scandinavian J Statistics

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“…Application of a regression estimator in simple random sampling utilizes auxiliary information during the estimation phase only. Model-based stratified sampling (Wright, 1983) is a hybrid design that utilizes auxiliary information during both the design and estimation phases. The design is described also by Särndal et al (1992), who present an elegant and very general framework for sampling designs that includes all the standard designs as well as some rather complex ones.…”

Section: Model-based Stratified Samplingmentioning

confidence: 99%

Stemflow estimation in a redwood forest using model‐based stratified random sampling

Lewis

2003

Environmetrics

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SUMMARYModel-based stratified sampling is illustrated by a case study of stemflow volume in a redwood forest. The approach is actually a model-assisted sampling design in which auxiliary information (tree diameter) is utilized in the design of stratum boundaries to optimize the efficiency of a regression or ratio estimator. The auxiliary information is utilized in both the design and estimation phases. Stemflow and its variance were modelled as powers of diameter and a generalized non-linear least squares model was used to estimate the exponents and to impute values for missing storm events prior to application of the ratio estimator. The advantage of the ratio estimator over standard stratified sampling formulas is greatest for species in which stemflow is strongly dependent on diameter. With measurements on 24 trees in a 1-hectare stand, annual stemflow was estimated with coefficients of variation of 9 per cent and 10 per cent in 2 years of study. Striking stemflow differences were found between species. Published in

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“…Särndal (2007). Some of the substantial references for GREG are Cassel, Särndal and Wretman (1976), Särndal (1980), Isaki and Fuller (1982), Wright (1983), Deville and Särndal (1992), Deville, Särndal and Sautory (1993), Kalton and Flores-Cervantes (2003), Ardilly and Tillé (2006) and Tikkiwal, Rai and Ghiya (2012) studies.…”

Section: Introductionmentioning

confidence: 99%

Sequential Data Weighting Procedures for Combined Ratio Estimators in Complex Sample Surveys

Alkaya

Ayhan

Esin

2017

Statistics in Transition New Series

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In sample surveys weighting is applied to data to increase the quality of estimates. Data weighting can be used for several purposes. Sample design weights can be used to adjust the differences in selection probabilities for non-self weighting sample designs. Sample design weights, adjusted for nonresponse and noncoverage through the sequential data weighting process. The unequal selection probability designs represented the complex sampling designs. Among many reasons of weighting, the most important reasons are weighting for unequal probability of selection, compensation for nonresponse, and post-stratification. Many highly efficient estimation methods in survey sampling require strong information about auxiliary variables, x. The most common estimation methods using auxiliary information in estimation stage are regression and ratio estimator. This paper proposes a sequential data weighting procedure for the estimators of combined ratio mean in complex sample surveys and general variance estimation for the population ratio mean. To illustrate the utility of the proposed estimator, Turkish Demographic and Health Survey 2003 real life data is used. It is shown that the use of auxiliary information on weights can considerably improve the efficiency of the estimates.

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Finite Population Sampling With Multivariate Auxiliary Information

Cited by 27 publications

References 0 publications

Wild Bootstrapping in Finite Populations with Auxiliary Information

Wild Bootstrapping in Finite Populations with Auxiliary Information

Stemflow estimation in a redwood forest using model‐based stratified random sampling

Sequential Data Weighting Procedures for Combined Ratio Estimators in Complex Sample Surveys

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