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SMALL AREA ESTIMATION, SPRING 2015

Lecturer

 Risto Lehtonen

Code

78405

Type and Credits

Intermediate level course
Exam (6 cu) or exam plus (optional) practical/theoretical homework (2 cu), total 8 cu

Advanced level course
Exam (6 cu) plus (compulsory) practical/theoretical homework (2 cu), total 8 cu

Scope

Lectures 15 hours, PC classes 15 hours

Description

The course covers topics in modern statistical methods for the estimation of parameters for population subgroups or domains and small areas (Small Area Estimation, SAE). Topics include sampling design for SAE, design-based model-assisted methods (generalized regression estimation, calibration techniques), model-based methods (synthetic, EBLUP and EBP estimators), variance and MSE estimation, SAS tools, R tools, and real-world applications (mainly in social and health sciences and official statistics). Case studies include applications in the estimation of poverty indicators (poverty rate, inequality indicators) for regional areas (small or large). The course is of applied type.

When completing the course, students are expected to be familiar with approaches, methods and computational tools in the estimation for regions and other population subgroups and becoming capable to apply the methods in typical real-world analysis situations. Basic knowledge in statistical modelling, sampling methods and statistical computation (R, SAS) would help successful participation.

Target group

The course is intended to fit for students majoring or graduating in statistics and for Master level and post-graduate (doctoral) students in quantitative studies in applied sciences incl. social and behavioral sciences and economics (e.g. REMS). As an applied type course, the course also would fit well for statisticians and researchers in research institutes and elsewhere.

Schedule

III period

Lecture sessions:  Tuesday at 16-19 Exactum CK111, Kumpula campus (note time change)

PC training sessions: Thursday at 16-19 Exactum C128, Kumpula campus

Exam: Tuesday 24.2.2015 at 16-18 Exactum CK111

  

  

Lectures

  
   

PC training

  

Tuesday13.1.

 

-

-

 

Thursday 22.1.

Tuesday 27.1.

 

Thursday 29.1.

Tuesday 3.2.

 

Thursday 5.2.

Tuesday 10.2.

 

Thursday 12.2.
R tools for SAE

Tuesday 17.2.

 

Thursday 19.2.

EXAM
Tuesday 24.2.
at 16:00-18:00

  

 

Textbooks and selected articles

 Rao J.N.K. (2003). Small Area Estimation. New York: John Wiley & Sons.

Lehtonen R. and Pahkinen E. (2004). Practical Methods for Design and Analysis of Complex Surveys. Second Edition. Chichester: Wiley. Chapter 6.
e-book: Dawsonera Helka

Lehtonen R. and Veijanen A. (2009). Design-based methods of estimation for domains and small areas. In: C. R. Rao and D. Pfeffermann (eds.), Handbook of Statistics 29B. Sample Surveys: Inference and Analysis. Amsterdam: Elsevier. pp. 219-249.
Download here

Lehtonen R. and Veijanen A. Model-assisted methods to small area estimation of poverty indicators. In: Pratesi M. (Ed.) (2015). Analysis of Poverty Data by Small Area Estimation. Chichester: Wiley. (Forthcoming, to be distributed to participants)

Lehtonen R. and Djerf K. (2008). Survey sampling reference guidelines. Luxembourg: Eurostat Methodologies and Working papers.
Download here

Web materials

VLISS-virtual laboratory in survey sampling http://vliss.helsinki.fi

Small Area Estimation resources

Lecture materials

Topic 1 Introduction to SAE

Topic 2 Basic concepts and approaches

Topic 3 Direct estimators for domains
Example
Supplement to Topic 3

Topic 4 - Part 1
Extract (Lehtonen-Veijanen 2009)

Topic 4 - Part 2
Supplement: Summary of examples

Topic 4 - Part 3
Supplement: Extended family of GREG estimators

Topic 5                            
Supplement: EBLUP
Summary

Additional reference: Saei and Chambers (2003)
Small area estimation under linear and generalized linear mixed models with time and area effects.
University of Southampton: S3RI Methodology Working Paper M03/15.

Case studies                   
Case Study 1
Case Study 2

PC training materials

PC class 1 (updated in PC class 22 Jan.)

PC class 2 (updated in PC class 29 Jan.)
SAS macro for simulation

PC class 3 (updated in PC class 5 Feb.)
Technical summary for HT and GREG

PC class 4 on Thursday 12 Feb.    
Guest speaker: Adj.Prof. Ari Veijanen
RDomest materials (zipped folder)

PC class 5 (updated 19 Feb.)

Data

Population dataset (SAS-data to be downloaded) 

Population dataset (pop.txt)

SAS tools
Small area estimation in SAS

SAS macro EBLUPGREG (Dr Ari Veijanen)
EBLUPGREG manual
Macro EBLUPGREG code
SAS Catalog

Resources to help you learn and use SAS
(UCLA Statistical Consulting Group )
http://www.ats.ucla.edu/stat/sas/

R tools
RDomest  (Dr Ari Veijanen)

Additional R tools
Package Survey (Thomas Lumley)
Package SAE (Isabel Molina)

Homework

Homework assignment (for intermediate and advanced levels)

Register for the course

Did you forget to register? Please contact tilasto-info[at]helsinki.fi.

 

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