Branch Meetings

Branch Meetings for 2018 Poster

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Statistical Society of Australia Canberra Branch Meetings
The Canberra branch of the SSA is pleased to present our meeting schedule for 2018. Please see  http://www.statsoc.org.au/wp-content/uploads/2018/02/canberra-branch-poster-2018.pdf
for a poster of the schedule containing our statistically and practically awesome lineup of speakers for the year!
Next meeting: Branch Meeting

Date: Tuesday 29 May 2018

Times:

5.15pm Refreshments, Allan Barton Forum, Level 2, Room 248, College of Business and Economics, ANU (Map).

6.00pm Presentation in Allan Barton Forum 

7.30pm After the talk, there will be a dinner at Mama’s Trattoria, Shop 4 Melbourne Building 45 West Row (Restaurant).


Please RSVP Francis Hui (SSA Canberra or reply to this email directly) by Monday 28 May if you wish
 to attend the dinner. 

Speaker: Stephen Haslett, Statistical Consulting Unit, Australian National University

Topic: Measuring nutrition, health and poverty in small areas — how low can you go? A comparison of alternative methods for poverty estimation in developing countries

Abstract:

Efficient targeting of aid relies on detailed information. The complication is that even large scale national sample surveys are usually not accurate enough to provide this detail. Small area estimation is a statistical technique developed over the last twenty years or so which can improve accuracy of surveys by using statistical modelling. A particular type of small area estimation technique, which is generically called poverty mapping and links a survey with a census at unit record level, is now often used for estimating and mapping poverty estimates at a fine level. This method has now been applied in more than 70 countries. 
 
Where there is no recent census, alternative methods exist. Small area estimation can also be extended, for example to estimating malnutrition including stunting, underweight and wasting in children under five years of age. Three unit-level SAE techniques: the method of Elbers, Lanjouw, and Lanjouw (2003) also known as ELL or World Bank method, the Empirical Best Prediction (EBP) method of Molina and Rao (2010), and the M-Quantile (MQ) method of Tzavidis et al. (2008), have been widely used to estimate the micro-level FGT poverty indicators: poverty incidence, gap and severity (Foster et al., 1984). The three methods have in common that they use both unit level survey data and (possibly model-based) unit level census data. However, they differ in their applicability because real data sets do not always follow their underlying assumptions. The performance of these three methods is compared in terms of small area poverty estimates and their standard errors. The effects of using a model-based unit record census data reconstructed from available cross-tabulations are discussed, as are the effects of small areaheterogeneity and cluster-heterogeneity in the over-arching superpopulation model. The three methods also have variants. A three-level nested-error regression model-based ELL method is applied for comparison with the standard two-level model-based ELL method which does not contain a random component at small area level, and with EBP and MQ. A comparison study uses a simulation based on 2003 data from Bangladesh. An important finding is that the number of small areas for which a method is able to produce sufficiently accurate estimates is more often driven by the type of data available than by the model per se. 
 
This talk covers context, what small area estimation is and the level at which it can work, discussion of alternative techniques when survey and census unit record data is available, and why small area estimation is useful.
 
Biography:

Stephen Haslett is Professor and Director of the Statistical Consulting Unit at the Australian national University. He is a Fellow of the Royal Statistical Society, a Chartered Statistician (UK), and formerly President of the New Zealand Statistical Association and Managing Editor of the Australian and New Zealand Journal of Statistics. 

Stephen has been a statistical consultant for over 40 years during which time he has published over 100 academic papers and written more than 120 research and consulting reports. His published papers are in a wide range of journals including Mathematical Statistics, Medicine, Health Sciences, Psychology, Human Development, Political Science, History, the Humanities, Social Sciences, Zoology, Botany, Marine Biology, Ecology, Economics, Veterinary Science, Horticulture, Agriculture, Labour Relations, Financial Auditing, Criminology, Geology, Epidemiology, Law, Business, Developmental Psychology, Sociology, Linguistics, and Education. 

Over the last ten years he has been principal statistician in a range of large scale statistical modelling projects for small area estimation of poverty and of undernutrition in children for the United Nations in Bangladesh, Bhutan, Cambodia, Indonesia, Nepal, Philippines, and Timor-Leste. He has also been involved extensively in research in Kiribati, Samoa, Solomon Islands, Tonga and Vanuatu, as well as in Finland, New Zealand, UK and USA.

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