Small Area Estimation - Sampling Spatial Units for Agricultural Surveys - page 298

As noted by Pfeffermann ( 2013 ), model-based predictors are generally
more accurate. They allow predictions for non-sampled areas, for which no
design-based model exists. Unless the sample sizes in all areas are sufficiently
large, model-based approaches appear preferable to design-based techniques.
See Pfeffermann ( 2013 ) for an interesting comparison of the ML-based and
Bayesian approaches.
Finally, spatially distributed data is common in agricultural surveys, so it
appears that the spatial approach to SAE must have a central role in future
research.

References

Anselin L (1988) Spatial econometrics, methods and models. Kluwer Academic, Boston
Arora V, Lahiri P (1997) On the superiority of the Bayes method over the BLUP in small area
estimation problems. Stat Sin 7:1053-1063
Battese GE, Harter RM, Fuller WA (1988) An error component model for prediction of county
crop areas using survey and satellite data. J Am Stat Assoc 83:28-36
Benedetti R, Filipponi D (2010) Estimation of land cover parameters when some covariates are
missing. In: Benedetti R, Bee M, Espa G, Piersimoni F (eds) Agricultural survey methods.
Wiley, Chichester, pp 213-230
Brackstone GJ (1987) Small area data: policy issues and technical challenges. In: Platek R, Rao
JNK, S¨rndal CE, Singh MP (eds) Small area statistics. Wiley, New York, pp 3-20
Chambers R, Tzavidis N (2006) M-quantile models for small area estimation. Biometrika
93:255-268
Cressie N (1991) Small area prediction of undercount using the general linear model. In: Pro-
ceedings of statistics symposium 90: measurement and improvement of data quality, Statistics
Canada, Ottawa, pp 93-105
Cressie N (1992) REML estimation in empirical Bayes smoothing of census undercount. Surv
Methodol 18:75-94
Datta GS, Fay RE, Ghosh M (1991) Hierarchical and empirical Bayes multivariate analysis in
small area estimation. In: Proceedings of Bureau of the Census 1991 annual research confer-
ence, US Bureau of the census, Washington DC, pp 63-79
Drew D, Singh MP, Choudhry GH (1982) Evaluation of small area estimation techniques for the
Canadian Labour Force Survey. Surv Methodol 8:17-47
Efron B, Morris C (1975) Data analysis using Stein

s estimator and its generalizations. J Am Stat

'

Assoc 70:311-313
Eklund B (1998) Small area estimation of coverage error for the 1997 Census of Agriculture. In:
Proceedings of the section on survey research methods, American Statistical Association,
Alexandria, VA.
Fay RE (1987) Application of multivariate regression to small domain estimation. In: Platek R,
Rao JNK, S¨rndal CE, Singh MP (eds) Small area statistics. Wiley, New York, pp 91-102
Fay RE, Herriot RA (1979) Estimates of income for small places: an application of James-Stein
procedures to census data. J Am Stat Assoc 74:269-277
Fuller WA, Harter RM (1987) The multivariate components of variance model for small area
estimation. In: Platek R, Rao JNK, S ¨ rndal CE, Singh MP (eds) Small area statistics. Wiley,
New York, pp 103-123
Ghosh M, Rao JNK (1994) Small area estimation: an appraisal. Stat Sci 9:55-93