smooth surface

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smooth sur·face

(smūdh sŭrfăs)
Proximal, buccal, and lingual areas of teeth.
Medical Dictionary for the Dental Professions © Farlex 2012
References in periodicals archive ?
This value, which we refer to as the concurvity [R.sup.2] statistic for p in Equation 5, indicates that 57% of the variation in p can be explained by a smooth function of location and thus tells us that there is a moderate degree of concurvity in the data.
Step 2 generates a set of completely random air pollution variables, [p.sub.i], and a smooth function h.
The smooth function h is not a loess surface of the sort used to fit f in Equation 5, because this would imply perfect concurvity and an unidentifiable model.
Positive spatial autocorrelation in a variable [alpha] implies that some of the variation in [alpha] can be explained by modeling [alpha] as a smooth function of location.
2000) that the smooth function be selected so that the residual time series is consistent with a white noise process, It seems clear now that estimates of the air pollution effect are sensitive to the method of modeling time and weather, although this sensitivity can vary by location and season depending on how these variables are correlated.
We have demonstrated that dynamic population study and time-series designs provide the same relative rate estimates of mortality associated with exposure to air pollution under the following conditions: a) the environmental covariates vary in time and not between individuals; b) on any given day, the probability of death is small; c) each subject of the at-risk population has the same probability of death after adjusting for known risk factors; d) all members of at-risk population share a common effect of environmental covariates on mortality; and e) the population-average baseline hazard function and association between risk factors and death can be approximated adequately by smooth functions of time.
These models had a smooth function of P[M.sub.2.5] from traffic and linear functions of P[M.sub.2.5] from the other sources in each city.
In the first stage, we fit log-linear models including smooth functions of P[M.sub.2.5] in each city, controlling for season, weather, and day of the week.
GAMs are distinguished by allowing us to use smooth functions [S.sub.i] instead of linear terms to control for covariates, such as temperature, that may affect daily deaths in a nonlinear way.
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