pythontotal-least-square UpdatedJan 12, 2019 Python Solve many kinds of least-squares and matrix-recovery problems linear-regressionestimationleast-squaresimputationoutlier-detectionmissing-datamatrix-completion
python total-least-square Updated Jan 12, 2019 Jupyter Notebook KaramMawas / Topology_Optimization Star 1 Code Issues Pull requests Determination of a Regression Line using Total Least Squares computer-vision topology-optimization total-least-square Updated Sep 9, 2018 MATLAB Sri-Sai-Chara...
GRN total number of interactions prediction. (a–c) Models to estimate the total number of interactions in a GRN. (a) Edge regression model (EdR). (b) Density invariance model (DI) whereDgwas obtained from average density of most complete graphs. (c) Density proportionality model (DP), w...
In statistics, ordinary least squares (OLS) or linear least squares is a method for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared vertical distances between the observed responses in the dataset, and the responses predicted by the linear ...
We analyze the commonly used node-wise least squares regression LeastSquares and prove that it has the near-optimal sample complexity. We also study a couple of new algorithms for the problem: BatchAvgLeastSquares takes the average of several batches of least squares solutions at each node, so...
Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 2003, 86, 542–553. [Google Scholar] [CrossRef] Cho, M.A.; Skidmore, A.K. A new technique for extracting the ...
(a) Partial least squares PLS is a typical parametric regression method, which has been widely used in studies owing to the good performance [62,63]. It is applicable to the case where the amount of highly collinear data and variables significantly exceeds the number of available samples. The...
In column (1) of Table 9, consistent with the baseline findings, the regression coefficient of digital transformation is significantly positive, indicating that digital transformation drives the total factor productivity of SOEs. Conversely, in column (2), the same impact coefficient has no ...