... The output variable is the student score on the national higher education exit exam (Saber Pro), understood as a proxy of the academic level when finishing a university program. Our objective is, using the same input ...
... National Health Service National Institute for Clinical Excellence Non-standard preference reversal Ordinary least squares Positive and Negative Affect Schedule Probability equivalent / [r] ...
... pooled ordinaryleastsquares (OLS) regressions of Tobin’s Q on the principal component of the Blau indices for the proportions of board directors with law, business, MBA, ...
... estimator into stochastic system, showed that if use the standard least square method with a large enough FF for tracking, the error keeps bounded, and has in according to them “an interesting expression”. In ...
... Predictor weights of the four Partial Least Squares Regression PLSR analyses explaining the relationship between species richness, a component of species composition, maximum regional de[r] ...
... This paper introduces the use of Moving LeastSquares (MLS) approximations for the development of high-order finite volume discretizations on unstructured grids. The field variables and their succesive ...
... In the evaluation, the basic model generates an output (straight line) and the local model uses the knowledge of local errors taken from the polynomial to refine the output. For the s[r] ...
... moving leastsquares or residual kernel particle method methods to obtain the shape functions needed in MM, since such methods are good enough in the inner of the integration domains, but not so accurate in ...
... In practice, the construction of very high order schemes of this kind has been severely limited by the absence of robust approximation techniques, capable of providing accurate estimates of the succesive derivatives of ...
... Abstract: The aim of this paper is to develop and compare different models of a two-wheeled inverted pendulum robot Lego Mindstorms. Two models are developed, a complete nonlinear one, and a linear one based on ...
... Weighted LeastSquares estimator previously proposed in [1] assuming that the resultant error from the dead reckoning method has a M-Erlang distribution (exponentially distributed multipath interarrival ...
... The kernel partial leastsquares (KPLS) method was originally focused on soft-sensor calibration for pre- dicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique ...
... a least-squares fit of the image that may also allow a space-varying kernel (Alard & Lupton 1998; Alard ...by least-squares solutions of equations related to minimizing the value of ...the ...
... econometric models, whose primary interest has been to evaluate explanatory power of the traditional approach to tax evasion. They generally estimate some measure of evaded income as a function of fiscal parameters, ...
... To improve the performances in such nonstationary environment, we propose a new recursive total least square regressive AR algorithm with variable forgetting factor (VFF-RTLS-AR). This method can estimate the ...
... El modelado de ecuaciones estructurales por mínimos cuadrados par- ciales presenta diferencias fundamentales con su contraparte basada en co- varianzas (BC) siendo la principal la forma [r] ...