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We consider the nonparametric regression problem, where we take fixed design points xi∈[0,1].
The same property is established for the regression quantile estimator at different fixed design points.
This paper studies nonparametric estimation of regression quantiles under the fixed design model.
The present study suggested a priority order for modifying fixed design parameters to increase compactness.
In the case of the equally spaced fixed design nonparametric regression, the local polynomial M-smoother (LPM) is proposed for estimating the regression function.
Designs were compared in terms of EE outcomes given fixed design criteria, with results indicating reductions between 23.7% and 49.1% when utilising post-tensioned construction methods.
For instance, fixed design clinical studies for estimating the key parameter of a dose response function might not treat trial patients optimally.
In this case, since the functional architecture of the product is already fixed, design conflicts arise due to contradictory geometrical requirements.
For nonparametric regression model with fixed design, it is well known that obtaining a correct bandwidth is difficult when errors are correlated.
A sensitivity analysis of Pareto optimization was conducted for two types of parameters: 1) coolant flow rate, and 2) fixed design parameters.
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In a random-effects model, the former studies would add to the heterogeneity variance whereas the latter studies would inform fixed design-by-treatment interaction parameters.
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