Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh


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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability


ISSN: 1234-7655
eISSN: 2450-0291





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VOLUME 21 , ISSUE 4 (August 2020) > List of articles

Special Issue

Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh

David Newhouse

Citation Information : Statistics in Transition New Series. Volume 21, Issue 4, Pages 45-50, DOI: https://doi.org/10.21307/stattrans-2020-027

License : (CC BY-NC-ND 4.0)

Received Date : 31-January-2020 / Accepted: 30-June-2020 / Published Online: 15-September-2020



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BLUMENSTOCK, JOSHUA, GABRIEL CADAMURO, ROBERT ON, (2015). Predicting poverty and wealth from mobile phone metadata. Science 350.6264: pp. 1073−1076.

CORRAL, PAUL, ISABEL MOLINA, MINH CONG NGUYEN, (2020). Pull your sae up by the bootstraps, mimeo.

ELBERS, CHRIS, JEAN O. LANJOUW, PETER LANJOUW, (2003). Micro–level estimation of poverty and inequality, Econometrica, 71.1: pp. 355−364.

ELBERS, CHRIS, PETER LANJOUW, PHILLIPPE GEORGE LEITE, (2008). Brazil within Brazil: Testing the poverty map methodology in Minas Gerais, The World Bank.

ENGSTROM, RYAN, JONATHAN HERSH, DAVID NEWHOUSE, (2017). Poverty from space: Using high-resolution satellite imagery for estimating economic wellbeing. The World Bank.

ENGSTROM, RYAN, DAVID NEWHOUSE, VIDHYA SOUNDARARAJAN, (2019a). Estimating Small Area Population Density Using Survey Data and Satellite Imagery: An Application to Sri Lanka, The World Bank.

GONZÁLEZ-MANTEIGA, W., LOMBARDÍA, M. J., MOLINA, I., MORALES, D., SANTAMARÍA, L., (2008). Bootstrap mean squared error of a small-area EBLUP. Journal of Statistical Computation and Simulation, 78(5), pp. 443−462.

HAY, SIMON I., et al., (2009). A world malaria map: Plasmodium falciparum endemicity in 2007, PLoS medicine 6.3.

JEAN, NEAL, et al., (2016). Combining satellite imagery and machine learning to predict poverty, Science 353.6301, pp. 790−794.

JIN, Z., AZZARI, G., BURKE, M., ASTON, S., LOBELL, D. B., (2017). Mapping smallholder yield heterogeneity at multiple scales in eastern Africa, Remote Sensing, 9.9.

LANGE, S., UTZ JOHANN PAPE, PETER PÜTZ, (2018). Small area estimation of poverty under structural change, The World Bank.

LOBELL, D. B., AZZARI, G., BURKE, M., GOURLAY, S., JIN, Z., KILIC, T., MURRAY, S., (2019). Eyes in the sky, boots on the ground: assessing satellite- and ground-based approaches to crop yield measurement and analysis. American Journal of Agricultural Economics.

MARHUENDA, Y., et al., (2017). Poverty mapping in small areas under a twofold nested error regression model. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180.4, pp. 1111−1136.

MOLINA, I., J. N. K. RAO, (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics, 38.3, pp. 369−385.

MOLINA, I., MARHUENDA, Y., (2015). sae: An R package for small area estimation. The R Journal, 7(1), pp. 81−98.

NGUYEN, MINH, C., et al., (2017). Small Area Estimation: An extended ELL approach. mimeo.

POKHRIYAL, N., DAMIEN CHRISTOPHE J., (2017). Combining disparate data sources for improved poverty prediction and mapping. Proceedings of the National Academy of Sciences, 114.46, E9783−E9792.

STEELE, JESSICA, E., et al., (2017). Mapping poverty using mobile phone and satellite data. Journal of The Royal Society Interface, 14.127, 20160690.

TORABI, M., RAO, J. N. K., (2014). On small area estimation under a sub-area level model. Journal of Multivariate Analysis, 127, pp. 36−55.

VAN DER WEIDE, ROY, (2014). GLS estimation and empirical Bayes prediction for linear mixed models with Heteroskedasticity and sampling weights: a background study for the POVMAP project, The World Bank.

WARDROP, N. A., et al., (2018). Spatially disaggregated population estimates in the absence of national population and housing census data, Proceedings of the National Academy of Sciences, 115.14, pp. 3529−3537.

WATMOUGH, GARY, R., et al., (2019). Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences, 116.4, pp. 1213−1218.

ZHAO, QINGHUA., (2006). User manual for POVMAP, World Bank. http://siteresources. worldbank. org/INTPGI/Resources/342674- 1092157888460/Zhao_ ManualPovMap. pdf.