1. 1 ESSnet on Small Area Estimation Meeting no. 4 ESSnet on Small Area Estimation Meeting no. 4 Neuchatel, 7-8 July 2011 WP4: Software Tools

2. 2 2 Deliverables  4.1 review of available SAE routines and software, focusing on open source software (final report: October 2011)  4.2: review on the software in use in the ESS (final report: October 2011)  4.3: recommendations for standardisation and certification of ESS tools for SAE (final report: October 2011)  4.4: collection of existing routines and/or development of new routines (final version by October 2011)

3. 3 3 Task 4.1: Review of available SAE routines and software, focusing on open source software  R (CBS); SAS (GUS); MLwiN, SPSS, Stata, BUGS/WinBUGS (Istat): Contents: General description, Small area estimation, References CBS: OK GUS: Small area estimation, References Istat: References Deadline: 15 August 2011

4. 4 4 Task 4.2: Review on the software in use in the ESS Each member: brief section summarizing the questionnaires (only CBS sent it) Deadline: 15 August 2011

5. 5 5 Task 4.3: Recommendations for standardisation and certification of ESS tools for SAE  Artificial population: –From R. Valiant, A. H. Dorfman, R. M. Royall (2001) “Finite Population Sampling and Inference”, section 5.7.1, page 153 –Input dataset: Hospital: sample of short-stay hospitals with fewer than 1000 beds (Appendix B.2, page 424) –Variables: number of beds (x), number of discharged patients (y) –Artificial population : –Number of records: 1,000,000 –Number of areas: 1,000 –Number of dependent variables: 6

6. 6 6 Task 4.3: Recommendations for standardisation and certification of ESS tools for SAE Scatter plots of y1, y2, y3,y4,y5, y6 vs x. The line is the regression line of y given x

7. 7 7 Task 4.3: Recommendations for standardisation and certification of ESS tools for SAE  Artificial population:  Between area variance equals to 1/10 and 1/1000 of the within area.  Other covariates other than x. It could be an option to use transformation of x, both continuous and categorigal. Another option might be to add a higher regional level by grouping areas into classes. Some dependence of the target variables on these covariates may then be introduced. The regional variable could then be used as the level of estimation (so that we can try SAE with fewer areas) or as a categorical covariate (so that we can try a model with larger number of fixed effects ).  Another option might be to add a higher regional level by grouping areas into classes. Some dependence of the target variables on these covariates may then be introduced. The regional variable could then be used as the level of estimation (so that we can try SAE with fewer areas) or as a categorical covariate (so that we can try a model with larger number of fixed effects).  how many samples to draw from the populations? They might be simple random samples, e.g. one of size 20.000 and one of size 100.000? Or 200,000? How many areas?

8. 8 8 Task 4.4: Collection of existing routines and/or development of new routines  Computation of the estimates and MSE (Istat):  Linear mixed model:  Unit level: Synthetic, EBLUP (uncorrelated random effects), EBLUP (correlated random effects)  Area level: Synthetic, EBLUP (uncorrelated random effects)  Logistic mixed model  Model choice (CBS):  Conditional AIC (unit and area level linear mixed model)  Cross validation (unit and area level linear mixed model)  Model diagnostic (Istat): ???  Bias diagnostic  Goodness of fit diagnostic  Coverage diagnostic  Calibration diagnostic Deadline: beta version 15 August, final version 30 September 2011

9. 9 9 Task 4.4: Collection of existing routines and/or development of new routines  R-style documentation Deadline: 31 August 2011  Manual with small example Deadline: 30 September 2011  R-package?