Introduction

Reliable information about a target population in form of a representative data-set is indispensable for any statistical analysis. The accuracy of the estimates or the measures computed by a statistician depends directly on the information contained in the data-set he or she uses. However, often such data-sets may not have adequate information to produce estimates of required accuracy. It may not have enough observations, which would reduce the accuracy of the estimates. More frequently, it may not have enough variables to build a meaningful model for the response of interest. Another common problem is that the available data is not representative of the target population because of informative sampling and what is even more problematic, because of high rates of informative non-response. Even though a statistician would prefer to design and collect appropriate data for the study of interest, data collection is often prohibitively expensive. As a result, one needs to devise procedures either for merging different data-sets or for borrowing information from similar observations within the same data.

Easy collection and storage of big data-sets, obtained from web-based applications, social networks or medical records provide interesting opportunities. Such data sources provide what a statistician needs, that is, millions of observations on thousands of variables. These data-sets, however, are not collected in any designed way. In other words, they are observational and may not represent the population targeted by the analyst. Use of big data sources with or without integration with carefully designed survey data would often be beneficial in computing official statistics, which are required for making policy decisions. Integration of various data sources is a popular topic of research in several branches of current statistics.

This conference on the "Current Trends in Survey Statistics" would showcase the recent progress in the broad field of analysis of survey data, by putting a special emphasis on emerging areas dedicated to solve problems posed by the advances in data collection and computational techniques.  It would also investigate the future directions of growth in these areas of interest. A partial list of subtopics to be discussed are:

Small Area Estimation
Data confidentiality
Record Linkage and Entity Resolution
Synthetic Data and Statistical Disclosure Limitation
Big Data and Survey Sampling
Big data in Official Statistics
Multiple Imputation Techniques
Computational Social Science and Digital Humanities
Longitudinal Survey
Poverty Mapping
Microsimulation Models
Social Networks
Survey in the Developing World

This conference is a part of a broader programme on "Statistical Data Integration", to be held in the Institute for Mathematical Science, National University of Singapore, from 5th to 16th August 2019. The broader programme would also include a "Workshop on Statistical Data Integration" to be held from 5th to 8th August 2019.

The conference is a satellite to the 62nd ISI World Statistics Congress, to be held in Kuala Lumpur from 18th to 23rd August 2019.

The broader programme is partially supported Institute for Mathematical Science, National University of Singapore and is endorsed by the International Association for Survey Statisticians.