G AUSS F IT —A SYSTEM FOR LEAST SQUARES AND ROBUST ESTIMATION W. H. JEFFERYS, M. J. FITZPATRICK, and B. E. MCARTHUR Department of Astronomy, The University of Texas at Austin, Austin TX 78712, U.S.A.

Source URL: quasar.as.utexas.edu

Online Self-Calibration for Robotic Systems J´erˆome Maye, Hannes Sommer, Gabriel Agamennoni, Roland Siegwart, and Paul Furgale Autonomous Systems Lab, ETH Zurich, Switzerland email: {jerome.maye, hannes.sommer, gabrie

Source URL: europa2.informatik.uni-freiburg.de

• Statistics
• Estimation theory
• Algebra
• Regression analysis
• Mathematics
• Parametric statistics
• Least squares
• Linear filters
• Fisher information
• Kalman filter
• Matrix
• Calibration

Partial Least-Squares Functional Mode Analysis: Application to the Membrane Proteins AQP1, Aqy1, and CLC-ec1

Source URL: cmb.bio.uni-goettingen.de

Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms Michael W. Mahoney ∗

Source URL: www.stat.berkeley.edu

• Numerical linear algebra
• Matrix
• Principal component analysis
• Orthogonal matrix
• Gaussian elimination
• Singular value decomposition
• Low-rank approximation
• MoorePenrose pseudoinverse
• QR decomposition
• QR algorithm
• Matrix completion
• Linear least squares

Supporting Material Partial least squares functional mode analysis Tatyana Krivobokova1 Institute for Mathematical Stochastics and Courant Research Center PEG, Georg-August-University G¨ottingen, Germany Rodolfo Briones

Source URL: cmb.bio.uni-goettingen.de

Are Stocks Really Less Volatile in the Long Run? ˇ ´ LUBO Sˇ PASTOR

Source URL: www.q-group.org

• Statistics
• Data analysis
• Analysis
• Mathematical finance
• Least squares
• Variance
• Standard deviation
• Volatility
• Coefficient of determination
• Autoregressive model
• Covariance and contravariance
• Expected value

University of Texas, MD Anderson Cancer Center UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series Year 

Source URL: www.johndcook.com

• Statistics
• Mathematical analysis
• Probability
• Probability distributions
• Beta distribution
• Normal distribution
• Linear least squares

Least Squares Fi-ng and  Equa2on Solving with MPFIT  Craig Markwardt  University of Maryland and  NASA’s Goddard Spaceflight Center  h>p://purl.com/net/mpfit 

Source URL: cow.physics.wisc.edu

Which of the following provides the most reasonable approximation to the least squares regression line? (a) y=50+10x (b) Y=50+x (c) Y=10+50x (d) Y=1+50x

Source URL: www-stat.wharton.upenn.edu

MECH 576 Geometry in Mechanics October 21, 2009 The Inertia Matrix in Least-Squares Linear Fitting ABSTRACT Least squares fitting of point sets to lines, planes, curves and surfaces is carried out using eigenvalues and e

Source URL: www.cim.mcgill.ca