DeepAI AI Chat
Log In Sign Up

Robust Fusion Methods for Structured Big Data

by   Catherine Aaron, et al.

We address one of the important problems in Big Data, namely how to combine estimators from different subsamples by robust fusion procedures, when we are unable to deal with the whole sample. We propose a general framework based on the classic idea of `divide and conquer'. In particular we address in some detail the case of a multivariate location and scatter matrix, the covariance operator for functional data, and clustering problems.


page 1

page 2

page 3

page 4


Several Typical Paradigms of Industrial Big Data Application

Industrial big data is an important part of big data family, which has i...

Sampling techniques for big data analysis in finite population inference

In analyzing big data for finite population inference, it is critical to...

Classification of Big Data with Application to Imaging Genetics

Big data applications, such as medical imaging and genetics, typically g...

Writing summary for the state-of-the-art methods for big data clustering in distributed environment

Big Data processing systems handle huge unstructured and structured data...

Learning over inherently distributed data

The recent decades have seen a surge of interests in distributed computi...

Random Forests for Big Data

Big Data is one of the major challenges of statistical science and has n...

Applying the Delta method in metric analytics: A practical guide with novel ideas

During the last decade, the information technology industry has adopted ...