Design-based and model-based inference in survey sampling: appreciating the difference

Gregoire, T. G.

doi:10.1139/x98-166

Cited by 257 publications

(96 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, Rao (2003, p. 2) includes model-dependent estimators, which often are referred to as model-based estimators in the forestry literature (e.g., Gregoire 1998), among the group of indirect domain estimators, whereas Goerndt et al (2011) denote small area model-based domain estimation as composite estimation. Composite estimators are assigned to the group of indirect estimators in the classification used in this study (Rao 2003, Ch.…”

Section: Introductionmentioning

confidence: 99%

Small area estimation of forest attributes in the Norwegian National Forest Inventory

2012

View full text Add to dashboard Cite

The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small sub-populations such as municipalities or protected areas. Frequently, too few sampled observations are available for such small areas to allow estimates with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates. The study aimed at estimating the mean above-ground forest biomass for small areas with high precision and accuracy, using SAE techniques. For this purpose, the simple random sampling (SRS) estimator, the generalized regression (GREG) estimator, and the unit-level empirical best linear unbiased prediction (EBLUP) estimator were compared. Mean canopy height obtained from a photogrammetric canopy height model (CHM) was the auxiliary variable available for every population element. The small areas were 14 municipalities within a 2,184 km 2 study area for which an estimate of the mean forest biomass was sought. The municipalities were between 31 and 527 km 2 and contained 1-35 NNFI sample plots located within forest. The mean canopy height obtained from the CHM was found to have a strong linear correlation with forest biomass. Both the SRS estimator and the GREG estimator result in unstable estimates if they are based on too few observations. Although this is not the case for the EBLUP estimator, the estimators were only compared for municipalities with more than five sample plots. The SRS resulted in the highest standard errors in all municipalities. Whereas the GREG and EBLUP standard errors were similar for small areas with many sample plots, the EBLUP standard error was usually smaller than the GREG standard error. The difference between the EBLUP and GREG standard error increased with a decreasing number of sample plots within the small area. The EBLUP estimates of mean forest biomass within the municipalities ranged between 95.01 and 153.76 Mg ha -1 , with standard errors between 8.20 and 12.84 Mg ha -1 .

show abstract

Section: Introductionmentioning

confidence: 99%

Small area estimation of forest attributes in the Norwegian National Forest Inventory

2012

View full text Add to dashboard Cite

show abstract

“…This paper requires a basic understanding of the concepts design-based and model-based inference (e.g., Cassel et al 1977, Särndal 1978, Gregoire 1998, McRoberts 2010.…”

Section: Design-based Inferencementioning

confidence: 99%

“…Note the distinction in nomenclature between estimating a fixed but unknown value (a population parameter) and predicting a random variable (e.g., Särndal 1978, Gregoire 1998. Note also that some authors (Chambers and Clark 2012) present the model-based predictor as a sum of two terms: the sum of the values of the sampled elements and the sum of the predictions for the non-sampled elements.…”

Section: Model-based Inferencementioning

confidence: 99%

“…Applications of models in large-area forest surveys often use the model-assisted estimation framework (Särndal et al 1992) where a model is used to support the estimation following probability sampling within the context of design-based inference (Gregoire 1998). Importantly, an inadequately specified model will not make the estimators biased in this case, but only affect the variance of the estimators.…”

Section: Introductionmentioning

confidence: 99%

“…Some applications of models in large-area forest surveys involve model-based inference (Gregoire 1998), which to a larger extent than model-assisted estimation relies on model assumptions. In this case an inadequately specified model might make the estimators both biased and imprecise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

et al. 2016

View full text Add to dashboard Cite

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

show abstract

Forestry, Statistics in

Grégoire

Lappi

2004

Encyclopedia of Statistical Sciences

View full text Add to dashboard Cite

Statistical methods are used in surveys of forest resources, and in the development of predictive models of individual‐tree size and growth, as well as the growth and yield of aggregations of trees in forest stands. Statistically designed experiments are conducted in order to understand better the responses to (i) management interventions, such as fertilization, genetic improvement, site preparation, harvesting; and to (ii) natural events like insect infestations, ice storms, hurricanes, temperature change.

show abstract

Design-based and model-based inference in survey sampling: appreciating the difference

Cited by 257 publications

References 0 publications

Small area estimation of forest attributes in the Norwegian National Forest Inventory

Small area estimation of forest attributes in the Norwegian National Forest Inventory

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

Forestry, Statistics in

Contact Info

Product

Resources

About