Small Area Estimation Using Estimated Population Level Auxiliary Data

Chandra, Hukum; Sud, U. C.; Gharde, Yogita

doi:10.1080/03610918.2013.810255

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…Lombardía et al, 2017;, we did not account for this additional variability and treated X dir d as a non-random quantity in the subsequent steps of the statistical inference (the estimation of MSE and the construction of CPIs and SPIs). In the context of LMM-based prediction of population means, Chandra et al (2015) these departures. We carried out statistical tests and analysed diagnostic plots.…”

Section: A Coruña Lugomentioning

confidence: 99%

“…Remark Due to the lack of access to administrative registers, we replaced population means of auxiliary variables X dir d by their SSHG estimates X dir d which is a common approach in the SAE literature (see, e.g. Chandra et al, 2015;Lombardía et al, 2017;. As pointed out correctly by one of our referees, this step increases the total uncertainty of the predictor (in our case EBLUP…”

Section: A Coruña Lugomentioning

confidence: 99%

“…variability of b μ d with respect to the variability of b β and b u d (in fact, the correction of Chandra et al (2015) is hardly ever used in applied survey-based studies in spite of its relevance). A comprehensive study measuring the relative increase of the variability of b μ d by replacing X dir d by X dir d as well as the construction of the optimal MSE estimator in this context could be interesting topics of further research.…”

Section: A2 Proof Of Propositionmentioning

confidence: 99%

“…Remark Due to the lack of access to administrative registers, we replaced population means of auxiliary variables

{\overset{bold-italicX}{true}}_{d}^{d i r}

by their SSHG estimates

{\overset{\overset{bold-italicX}{true}}{true}}_{d}^{d i r}

which is a common approach in the SAE literature (see, e.g. Chandra et al , 2015; Lombardía et al , 2017; 2018). As pointed out correctly by one of our referees, this step increases the total uncertainty of the predictor (in our case EBLUP

{\overset{μ}{true}}_{d} = {\overset{\overset{bold-italicX}{true}}{true}}_{d}^{d i r} \overset{bold-italicβ}{true} + {\overset{bold-italicu}{true}}_{d}

, but this applies more generally to EBP and other predictors) of the small area parameter of interest.…”

Section: Application To the Household Income Data Of Galiciamentioning

confidence: 99%

See 3 more Smart Citations

Simultaneous inference for linear mixed model parameters with an application to small area estimation

Reluga

Lombardía

Sperlich

2022

Int Statistical Rev

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Over the past decades, linear mixed models have attracted considerable attention in various fields of applied statistics. They are popular whenever clustered, hierarchical or longitudinal data are investigated. Nonetheless, statistical tools for valid simultaneous inference for mixed parameters are rare. This is surprising because one often faces inferential problems beyond the pointwise examination of fixed or mixed parameters. For example, there is an interest in a comparative analysis of cluster-level parameters or subject-specific estimates in studies with repeated measurements. We discuss methods for simultaneous inference assuming a linear mixed model. Specifically, we develop simultaneous prediction intervals as well as multiple testing procedures for mixed parameters. They are useful for joint considerations or comparisons of cluster-level parameters. We employ a consistent bootstrap approximation of the distribution of max-type statistic to construct our tools. The numerical performance of the developed methodology is studied in simulation experiments and illustrated in a data example on household incomes in small areas.

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Section: A Coruña Lugomentioning

confidence: 99%

Section: A Coruña Lugomentioning

confidence: 99%

Section: A2 Proof Of Propositionmentioning

confidence: 99%

“…Remark Due to the lack of access to administrative registers, we replaced population means of auxiliary variables

{\overset{bold-italicX}{true}}_{d}^{d i r}

by their SSHG estimates

{\overset{\overset{bold-italicX}{true}}{true}}_{d}^{d i r}

{\overset{μ}{true}}_{d} = {\overset{\overset{bold-italicX}{true}}{true}}_{d}^{d i r} \overset{bold-italicβ}{true} + {\overset{bold-italicu}{true}}_{d}

, but this applies more generally to EBP and other predictors) of the small area parameter of interest.…”

Section: Application To the Household Income Data Of Galiciamentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous inference for linear mixed model parameters with an application to small area estimation

Reluga

Lombardía

Sperlich

2022

Int Statistical Rev

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show abstract

“…Most PSMs are suitable for certain fine scales, i.e., fine scale population estimation (Chandra et al, 2015;Dong et al, 2010;Leyk et al, 2013a;Stevens et al, 2015). However, an adaptive multi-scale solution of PSM is necessary for both macro-analyses and micro-analyses.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Scale Population Spatialization Model Constrained by Multiple Factors: A Case Study of Russia

Liu

2016

The Cartographic Journal

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Population spatialization is the foundation for the visualization and analysis of population integrated with other information, such as environmental resources, economy, and public health. The existing population spatialization models have solved many problems for population distribution, but most of these studies have focused on a specific, single-scale approach and ignored the scale transformation for population spatialization. However, multi-scale visualization and the analysis of spatial information need multi-scale information. Meanwhile, the population distribution map as one kind of thematic map is always overlaid with the digital vector map or remote sensing map and visualized in the Web Geographic Information Systems (Web GIS), so it should adapt to the map scale showed in browser, when the user zoom in and zoom out. Hence adaptive multi-scale is necessary for population spatialization. Therefore, in this study, an adaptive multi-scale population spatialization model (APSM) is proposed with comprehensive factors constrained. These factors are residential area, land cover, public transport, hydrology, terrain, and climate. All of them are closely associated with population distribution. The overall methodology of APSM and the process for APSM are expounded in this paper. Meanwhile, with a case study of Russia, the processes of APSM for Russia are stated, and a population spatialization tool is implemented for expanding the application of APSM. The experimental analysis showed that APSM satisfied the requirement of geospatial analysis well and obtained a reliable accuracy.

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Small area estimation combining data from two surveys

Islam

Chandra

2019

Communications in Statistics - Simulation and Computation

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Small Area Estimation Using Estimated Population Level Auxiliary Data

Cited by 4 publications

References 9 publications

Simultaneous inference for linear mixed model parameters with an application to small area estimation

Simultaneous inference for linear mixed model parameters with an application to small area estimation

Adaptive Multi-Scale Population Spatialization Model Constrained by Multiple Factors: A Case Study of Russia

Small area estimation combining data from two surveys

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