Discussion of "Small Area Estimation: its Evolution in Five Decades", by Malay Ghosh

• Autorzy: David Newhouse
• Języki publikacji: EN

Słowa kluczowe

EN

Small area estimates; Review

PL

Statystyka małych obszarów; Recenzja

• Czasopismo: Statistics in Transition
• Rocznik: 2020
• Tom: 21
• Numer: nr 4 Special Issue
• Strony: 45--50

Twórcy

autor

David Newhouse

• The World Bank, Washington DC, USA

Bibliografia

• 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.

Identyfikatory

DOI

10.21307/stattrans-2020-027