EVENTS NOVEMBER – DECEMBER 2017 within the “Jean Monnet Chair Small Area Methods for Monitoring of Poverty and Living conditions in EU (SAMPL-EU)”

–   On 4th and 5th of December 2017 from 15.00 to 16.30 (Magna Room),  Prof. Natalie Shlomo (University of Manchester) , will give the  distance lectures “Handling Missing Data, Statistical Data Editing and Imputation”;

 –   On 30^th November and 1^st  December 2017 from 12.15 to 13.45 (Room F2),  Prof. Ulrich Rendtel  (Freie University) , will give the Seminar “Issues of the analysis of socio-economic panel surveys”:

Lecturer

Ulrich Rendtel Freie Universität Berlin, Economic Department, Institute for Statistics and Econometrics

Prof. Rendtel teaches Applied Statistics at the Economic Department of the Freie Universität Berlin. He worked for 9 years in the German Socio-Economic Panel (SOEP) project, a household panel survey started in 1984. He collaborated with Statistics Finland in an evaluation project of the Finnish subsample of the European Community Household Panel (ECHP) and EU-SILC. His recent research is on nonresponse and calibration in panel surveys.

Type

Advanced studies. The orientation is applied.

The course is intended to fit to studies in quantitative methods for post-graduate (doctoral) students in social and behavioral sciences and economics. The course also can be taken as advanced studies in statistics for students in statistics and mathematics.

Lectures and PC training

Lectures are given at the Polo Piagge, Via Matteotti, 11 Pisa (Room F2): Thursday 30.11.17  at 12.15-13.45 Friday     30.11.17 at 12.15-13.45

Course contents

The course will be given in two parts:

Part 1: – General issues of the analysis of data from panel surveys

– Treatment of nonresponse:

Panel attrition treated by Pattern Mixture models

Initial nonresponse and its stability in panel surveys:

• A useful contraction theorem from Markov Chain Theory
• The speed of the Fade-away effect
• Extensions to longitudinal profiles

Part 2:   – Empirical results from register based panel surveys:

• PASS (Panel on Labor Market and Social Security): Transitions between social benefit states
• ECHP and EU-SILC: Transitions between income quintiles

–       True change or Fade-away of an initial nonresponse bias?

–       The Fade-away effect in regression analysis

Measurement Issues:

–       Measurement of Change: Latent Markov Models for changes between poverty states

–       Measurement of duration: The identification of heaping effect for duration of unemployment

–On 27th November 2017 from 12.15 to 13,45 (Room L2), Prof. Parthasarathi Lahiri,  University of Maryland, College Park will give the Seminar “Big Data, Big Promise, Big Challenge: Can Small Area Estimation Play a Role in the Big Data Centric World?”:

–          Abstract:  The demand for various socio-economic, transportation, and health statistics for small geographical areas is steadily increasing at a time when survey agencies are desperately looking for ways to reduce costs to meet fixed budgetary requirements.  In the current survey environment, the application of standard sample survey methods for small areas, which require a large sample, is generally not feasible when considering the costs.    One of the key factors that lead to the success of small area estimation (SAE) methodology is the availability of strong auxiliary variables.  The accessibility of big data from different sources is now bringing new opportunities for statisticians to develop innovative SAE methods.  In this talk, I will provide an outline of how SAE methods can be adapted to incorporate big data in improving local area statistics.  Then I will discuss my recent collaboration with my UMD colleagues — Professor Cinzia Cirillo of Department of Civil and Environmental Engineering, and Professor Joseph JaJa of Department of Electrical and Computer Engineering, and the University of Maryland Institute for Advanced Computer Studies (UMIACS).  Finally, as an example from our different collaborative research projects, I will explain how SAE can help solve a seemingly different problem of predicting in real-time traffic by exploiting rich vehicle probe big data.

–          Short Bio

Dr. Partha Lahiri is a Professor of Survey Methodology and Mathematics at the University of Maryland, College Park and an Adjunct Research Professor at the Institute of Social Research, University of Michigan, Ann Arbor.  Before coming to Maryland, Dr. Lahiri was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His research interests include big data, Bayesian statistics, record linkage, and small-area estimation.  Dr. Lahiri has served on a number of advisory committees, including the U.S. Census Advisory committee and U.S. National Academy panel.  Over the years Dr. Lahiri advised various local and international organizations such as the United Nations Development Program, the World Bank, and the Gallup Organization.  He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.

We invite you to visit the website http://sampleu.ec.unipi.it for a description of courses (and the project) and write an email to sampleu@ec.unipi.it for more information about it and if you want to attend it.

For the complete timetable please check:
https://calendar.google.com/calendar/embed?src=t9085fnf0ff4lppr0uolp3ohg8@group.calendar.google.com&ctz=Europe/Rome