'M-estimators of scatter with eigenvalue shrinkage' by Prof. Esa Ollila

preview_player
Показать описание
Presentation of paper "M-estimators of scatter with eigenvalue shrinkage" by Prof. Esa Ollila delivered at the conference IEEE ICASSP 2020.

Paper: Esa Ollila, Daniel P. Palomar, and Frédéric Pascal, “M-estimators of scatter with eigenvalue shrinkage,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Catalonia, Spain, May 4-8, 2020.

Authors:

Follow me:
  1. Daniel Palomar
  2. shrinkage estimator
  3. covariance matrix
  4. heavy tail
Рекомендации по теме
'M-estimators of scatter 0:16:47

'M-estimators of scatter with eigenvalue shrinkage' by Prof. Esa Ollila

DATAIA Seminar on 1:07:47

DATAIA Seminar on signal processing | Esa Ollila «Linear pooling of sample covariance matrices»

Lecture 8: Inference 1:05:11

Lecture 8: Inference with M-Estimators

M-estimator | Wikipedia 0:11:54

M-estimator | Wikipedia audio article

The POP estimator 0:13:23

The POP estimator for the degree of freedom parameter of multivariate t-distribution

Matrix Times a 0:05:10

Matrix Times a Vector, Repeated, Equals First Eigenvector in R/RStudio using Mtcars

Filtering time-dependent covariance 0:33:30

Filtering time-dependent covariance matrices with time-independent eigenvalues - Christian Bongiorno

Why Eigenvectors? The 0:46:59

Why Eigenvectors? The Mathematics Behind Principal Component Analysis

Estimation of covariance 0:18:33

Estimation of covariance matrices

Anomaly detection for 0:48:34

Anomaly detection for multivariate high-dimensional data by Mia Hubert

Melanie Weber (6/16/23): 0:58:03

Melanie Weber (6/16/23): Exploiting geometric structure in matrix-valued optimization

Sub-Gaussian Mean Estimation 0:44:55

Sub-Gaussian Mean Estimation in Polynomial Time

Econ 480 - 1:22:59

Econ 480 - Lecture 16: Cluster Covariance Estimation

Financial Engineering Playground: 1:06:11

Financial Engineering Playground: Signal Processing, Robust Estimation, Kalman, Optimization

How to pre-process 0:44:18

How to pre-process your spectra for research (SNV, MSC, Derivatives, etc.)

Eigenvalues and Eigenvectors 0:24:21

Eigenvalues and Eigenvectors | Properties and Important Result | Matrices

Robust List Decoding 0:49:01

Robust List Decoding of Spherical Gaussians

Least Squares vs 0:04:49

Least Squares vs Maximum Likelihood

Ankur Moitra, Massachusetts 0:40:56

Ankur Moitra, Massachusetts Institute of Technology

PCA 11: Eigenvector 0:11:55

PCA 11: Eigenvector = direction of maximum variance

28: Principal Component 0:15:28

28: Principal Component Analysis

Cosplay by b.tech 0:00:15

Cosplay by b.tech final year at IIT Kharagpur

Introduction to Mplus 0:31:32

Introduction to Mplus

STAT636 - Lecture 0:46:43

STAT636 - Lecture 8