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Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...
Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...
We exploit differences across U.S. states' exposure to trade to study the effects of changes in the exchange rate on economic activity. Across states, trade-weighted exchange rate depreciations are associated with increased state exports, reduced state une ...
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...
Research Summary Research on necessity entrepreneurship has generated important insights, yet it views necessity entrepreneurs in developed countries as one encompassing group of unemployed individuals-ignoring that the level of need is not uniform but ins ...
Statistical methods for inference on spatial extremes of large datasets are yet to be developed. Motivated by standard dimension reduction techniques used in spatial statistics, we propose an approach based on empirical basis functions to explore and model ...
Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a better understanding of large random matrices. These advances have enabled interesting applications in the domain of communication. Although this theory can ...
Here we discuss "hidden variables", which are typically introduced during an experiment as a consequence of the application of two independent variables together to create a stimulus. With increased sophistication in modern chemical biology tools and relat ...
