Exact(1)
A class of semiparametrically structured models is proposed in which the predictor decomposes into components that may be given by a parametric term, an additive form of unspecified smooth functions of covariates, varying-coefficient terms or terms which vary smoothly (or not) across the repetitions in a repeated measurement design.
Similar(59)
Superharmonic components of the ordern, supported by the parametric terms of the excitation and the non-linear coupling terms, are registered in the response for circular frequency ω≃ω1/n.
These models contained terms for seed thickness and PSQI score as well as a parametric interaction term for seed thickness × PSQI score.
The "tail" of the p.d.f. is estimated by a parametric tail model and its "body" by a non-parametric method in terms of a finite linear combination of trigonometric functions.
The variable x1 was estimated using parametric methods, while x2 was included as a non-parametric term with cublic spline smoothing.
In this study, by a parametric representation of interval numbers, two parametric representations for interval-valued functions are presented.
Data were fitted by a parametric logistic equation.
Confidence limits (ellipsoids) for adaptive optima were estimated by a parametric bootstrap (resampled 5000 times).
Confidence intervals were obtained by a parametric bootstrap approach based on 1000 replicates performed in ARLEQUIN.
Effect estimates for parametric terms of a semi-parametric additive hazards regression model with day of criminal conviction as a recurrent event outcome and opoid maintenance treatment (OMT) a time-dependent explanatory variable.
With the GAM, three variables were estimated with a smoothing parameter of 1 (NBR, CTI, bulk density); therefore we fit the model with these variables as parametric terms, as recommended by Wood (2006).
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