Tail probabilities of the maxima of multilinear forms and their applications

By Satoshi Kuriki, Akimichi Takemura. 2001

71 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a0be89215de4681a14bffc2852b0e783709f1ef4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Jonathan E. Taylor
2. Keith J. Worsley
3. Akimichi Takemura
4. Satoshi Kuriki
5. Robert J. Adler
6. Thorsten Dickhaus

In Computational Mathematics

1. Keith J. Worsley
2. Jonathan E. Taylor
3. Akimichi Takemura
4. Satoshi Kuriki
5. Satoshi Aoki

In Control and Systems Engineering

1. Keith J. Worsley
2. Jonathan E. Taylor
3. Akimichi Takemura
4. Satoshi Kuriki
5. Robert Tibshirani

Citations in the corpus

• Holonomic gradient method for the distribution function of the largest root of a Wishart matrix (2012, unknown): “As mentioned in the previous section, this tail probability can be approximated by the tube method ([11], [12]).”
• Random fields of multivariate test statistics, with applications to shape analysis (2008, background): “In fact, there is a strong link between this paper and Kuriki and Takemura [11].”
• ON THE EQUIVALENCE OF THE TUBE AND EULER CHARACTERISTIC METHODS FOR THE DISTRIBUTION OF THE MAXIMUM OF GAUSSIAN FIELDS OVER PIECEWISE SMOOTH DOMAINS (2002, methodology): “obtain the following result from Corollary 2.1 by integrating out � z� (see Sun (1993) and Kuriki and Takemura (2001) ):”