JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 342
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Quality and Productivity Section
Abstract #317106
Title: Statistical Analysis of Remote Sensing Data Sets Using Basis-Function Representations
Author(s): Matthias Katzfuss*
Companies: Texas A&M University
Keywords: Massive data ; Full-scale approximation ; Parallel computation ; Remote-sensing ; Gaussian process ; Kriging
Abstract:

The spatial statistical analysis of remote-sensing datasets poses several challenges. The datasets are large or even massive, which leads to computational infeasibility. Often, it is advantageous to combine ("fuse") measurements on the same or related spatial processes from several instruments, but these instruments typically exhibit different spatial footprints and measurement-error characteristics. In addition, complementary, massive datasets might be stored in different locations and are costly to move to one location, which means that the analysis must be moved to the data, instead of the other way around. I will discuss how all of these problems can be tackled using statistical models that can be written as linear combinations of spatial basis functions at multiple resolutions. These basis functions can represent arbitrary processes, allow change-of-support, and enable scalable, parallel, and distributed computations.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program





For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home