least squares

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least squares

(lēst skwārz),
A principle of estimation invented by Gauss in which the estimates of a set of parameters in a statistical model are the quantities that minimize the sum of squared differences between the observed values of the dependent variable and the values predicted by the model.
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References in periodicals archive ?
There is a problem with least-squares fitting based on minimisation of the sum-of-squares residuals.
We employ Monte Carlo simulations to validate the efficacy of the proposed procedure in comparison with the ordinary least-squares method and Eadie-Hofstee, Hanes-Woolf and Lineweaver-Burk plots.
This level of accuracy is satisfactory for predicting the ICOP for use with the recursive least-squares algorithm in this paper.
SHIN, Pseudo-spectral least-squares method for the second-order elliptic boundary value problem, SIAM J.
Recently, Wang and Tang studied the identification algorithms for a class of linear-in-parameters single-input singleoutput (SISO) systems with colored noises using the recursive least-squares method [35].
Chen, "Iterative least-squares solutions of coupled Sylvester matrix equations," Systems & Control Letters, vol.
Liu, "Hierarchical least-squares based iterative identification for multivariable systems with moving average noises," Mathematical and Computer Modelling, vol.
The basic strategy of DPLSQ-SS is the same as that of DPLSQ: least-squares fitting is used for parameter estimation and dynamic programming is used for minimizing the sum of least-squares errors when adding and deleting edges.
First of all, the least-squares method is recommended for degenerate and unstable problems.
Previous analyses of wait time to treatment within the Network for the Improvement of Addiction Treatment (NIATx) employed a conventional least-squares regression analysis and estimated rates of change in the mean of the monthly averaged outcome variables (McCarty et al.
The loss of least-squares fit property affects the visual appearance some what adversely (Figure 1).

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