Design-based Methods of Estimation for Domains and Small Areas

Lehtonen, Risto; Veijanen, Ari

doi:10.1016/s0169-7161(09)00231-4

Cited by 52 publications

(34 citation statements)

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“…However, inference about a DBH distribution in a larger area, like a stratum, a population, or any area of interest represented by a sufficient probability sample is also an important area of targeted application (Chen 2004;Lehtonen and Veijanen 2009). Our results, and results from cited studies, suggest that as few as 12 plots with ten or more trees provide an estimate of a size distribution in an area of interest suitable for planning and analysis.…”

Section: Discussionmentioning

confidence: 67%

Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

Magnussen

Renaud

2016

Annals of Forest Science

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Abstract• Key message We demonstrate how multidimensional scaling can be used to combine forest inventory field data and airborne laser scanner data to obtain both predictions and model-assisted estimation of a tree stem diameter distribution.• Context The size distribution of forest trees is important both for management planning and analysis purposes. Yet field samples are rarely large enough to assuage a desired accuracy of a direct estimation in all areas of interest. Improvements in spatial coverage and accuracy are possible with a census-or a very large sample of one or more cost-effective auxiliary variables that can inform one about the tree size distribution.• Aims The objective of this study is to demonstrate how a relative frequency distribution of canopy heights from airborne laser scanner data can be used to improve direct estimates of a tree size distribution.• Methods Multidimensional scaling is used to link a relative frequency distribution of canopy heights to an observed plotlevel distribution of tree size.• Results A multivariate linear model can be used for both predictions and model-assisted estimation of a tree stem diameter distribution.• Conclusion Multidimensional scaling can provide a multivariate linear link between two relative frequency distributions and is therefore ideally suited for both stand-level predictions and design-based inference of tree size distributions.

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Section: Discussionmentioning

confidence: 67%

Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

Magnussen

Renaud

2016

Annals of Forest Science

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“…One can refer to Fuller (2009) for probability sampling theory in statistics. Under this estimation approach, the randomness is only induced by the sampling design used to select the sample with population measurements regarded as fixed (e.g., Lehtonen and Veijanen 2009;Pfeffermann 2002). These methods make use of survey weights and associated statistical inferences are based on the sample selection probabilities.…”

Section: Design-based Methodsmentioning

confidence: 99%

Small Area Estimation for Multivariate Repeated Measures Data

Ngaruye

2014

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This thesis considers Small Area Estimation with a main focus on estimation and prediction theory for repeated measures data. The demand for small area statistics is for both cross-sectional and repeated measures data. For instance, small area estimates for repeated measures data may be used by public policy makers for different purposes such as funds allocation, new educational or health programs and in some cases, they might be interested in a given group of population.It has been shown that the multivariate approach for model-based methods in small area estimation may achieve substantial improvement over the usual univariate approach. In this work, we consider repeated surveys including the same subjects at different time points. The population from which a sample has been drawn is partitioned into several subpopulations and within all subpopulations there is the same number of group units. For this setting a multivariate linear regression model is formulated. The aim of the proposed model is to borrow strength across small areas and over time with a particular interest of growth profiles over time. The model accounts for repeated surveys, group individuals and random effects variations.The estimation of model parameters is discussed with a restricted maximum likelihood based approach. The prediction of random effects and the prediction of small area means across time points, per group units and for all time points are derived. The theoretical results have also been supported by a simulation study and finally, suggestions for future research are presented.v Populärvetenskaplig sammanfattning I den här avhandling diskuteras small area estimation (SAE) med fokus pȧ skattningar av parametrarna samt prediktion för slumpvariabler givet en modell för upprepade mät-ningar. Efterfrȧgan pȧ statistisk inferens för undergrupper av en population (small area statistics) har ökat markant. Till exempel kan sȧdan inferens för upprepad mätningar användas av offentliga beslutsfattare vid tilldelning av ekonomiska resurser, planering av nya utbildnings-eller hälsoprogram och i vissa fall kan det vara av intresse att studera en specifik undergrupp av befolkningen.Det har visat sig att den multivariata framställningen för modellbaserade metoder vid SAE kan uppnȧ betydande bättre resultat jämfört med det vanliga endimensionella modellantagandet. I detta arbete betraktar vi upprepade undersökningar pȧ samma individer vid olika tidpunkter. Populationen frȧn vilket ett urval har tagits är uppdelat i flera delpopulationer och inom alla undergrupper finns det samma antal observationer. För dessa förutsättningar, är en multivariat linjär regressionsmodell formulerad. Syftet med den för-slagna modellen är att lȧna egenskaper mellan undergrupperna och över tid. I modellen ingȧr det upprepade undersökningar, grupper av individer samt slumpmässiga effekter.Parametrarna i den föreslagna modellen skattas med hjälp av restricted maximum likelihood metoden. Prediktion av slumpmässiga effekter samt prediktion av de förväntade värdena i u...

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“…For a complete account of model‐assisted estimators see Särndal et al . () and Lehtonen and Veijanen (), whereas for a model‐based review the reader is referred to Rao (), Datta () or Pfeffermann (), and Jiang and Lahiri (). Ghosh and Rao () took account of both approaches and, very recently, Pfeffermann () has reviewed the recent advances in design‐ and model‐based small area estimation in the last few years.…”

Section: Logistic Model‐based Estimatorsmentioning

confidence: 99%

Deriving Small Area Estimates from Information Technology Business Surveys

Militino

Ugarte

Goicoa

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Knowledge of the current state of the art in information and communication technology of businesses (ICTB) is an important issue for governments, markets and policy makers, because information technology improves access to information and plays an important role in firms' competitiveness. Statistical agencies use normalized surveys to provide harmonized statistics about the use of technology in enterprises. Classical design‐based estimators are appropriate for large domains, because direct estimates are consistent and easy to obtain by using sampling weights. However, to supply estimates for unplanned domains, where the sample size is random, model‐based estimators are usually required. In this paper, alternative logistic model‐based estimators are suggested to derive small area estimates from ICTB surveys. Final estimates are benchmarked to achieve coherence with direct estimates in larger domains, and standard errors are given by using bootstrap techniques. A Monte Carlo simulation study is conducted to compare the performance of the small area estimators proposed and to evaluate the behaviour of the mean‐squared error estimator. Results are illustrated with the 2010 ICTB survey of the Basque country (Spain).

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Design-based Methods of Estimation for Domains and Small Areas

Cited by 52 publications

References 0 publications

Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

Small Area Estimation for Multivariate Repeated Measures Data

Deriving Small Area Estimates from Information Technology Business Surveys

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