2nd International Conference on Applied Statistics 2019
Emerging Challenges in a Data-Centric World
Emerging Challenges in a Data-Centric World
Organized by: Institute of Statistical Research and Training (ISRT)
University of Dhaka, Dhaka 1000
Bangladesh
Since early times, the data has been the cornerstone of decision making thanks to statistics that has enabled us to unlock the power of data. In modern times, the role of the data has become even more prominent in almost all spheres of life as the world shifts from being data poor to data rich. But the current data deluge has brought new challenges for the applied statistician. Nowadays, data come in all shapes and sizes: tall data, wide data, tall and wide data. Handling the volume, variety and velocity of data requires new statistical methodologies and data management skills. Moreover, much of the data at our disposal are ‘found’ data that are often collected using a non-ideal sampling paradigm. As statistics becomes increasingly interdisciplinary, new application areas have emerged that have necessitated the development of new statistical methodologies. Statisticians in Bangladesh and elsewhere must lead the way forward in the new data-centric world to secure a better future for all mankind. Keeping this in mind, ISRT welcomes you to attend the Second International Conference on Applied Statistics (ICAS) 2019 with the theme Emerging Challenges in a Data-Centric World. The aim of this conference is to highlight the challenges and discuss the solutions to emerging statistical problems in the era of the data revolution. Researchers, academicians, and representatives from business, industry, government and non-governments organizations are invited to participate in the event.
The main objectives of this conference are
Scope of the Conference
The conference invites papers in all areas of statistics including the following: actuarial science; Bayesian methods; big data; bioinformatics; biostatistics; categorical data analysis; clinical trials; data mining; data science; demography; design of experiments; distribution theory; econometrics; epidemiology; health surveys; impact evaluation; industrial statistics; longitudinal data analysis; model selection techniques; machine learning; model-centric approaches, data-centric approaches, multivariate methods; neural networks; nonparametric methods; official statistics; operations research; pharmaceutical statistics; predictive analytics; probability theory; quality control; random effects models; reliability theory; resampling methods; sampling techniques; semi-parametric methods; spatial and spatio-temporal modeling; statistical genetics; statistical measures of poverty; statistical modeling; sustainable development goals; agricultural statistics; stochastic models; survival analysis; time series analysis.