BIRS Workshop Lecture Videos
Statistical Aggregation in Massive Data Environment Lin, Nan
Due to their size and complexity, massive data sets bring many computational challenges for statistical analysis, such as overcoming the memory limitation and improving computational efficiency of traditional statistical methods. In this talk, I will discuss the statistical aggregation strategy to conquer such challenges posed by massive data sets. Statistical aggregation partitions the entire data set into smaller subsets, compresses each subset into certain low-dimensional summary statistics and aggregates the summary statistics to approximate the desired computation based on the entire data. Results from statistical aggregation are required to be asymptotically equivalent. Statistical aggregation is particularly useful to support sophisticated statistical analyses for online analytical processing in data cubes. We will detail its application to two large families of statistical methods, estimating equation estimation and U-statistics.
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