Results 141 to 150 of about 1,351,383 (295)
Mixed interpolating–smoothing splines and the ν-spline
, 2006 In their monograph, Bezhaev and Vasilenko have characterized the “mixed interpolating–smoothing spline” in the abstract setting of a Hilbert space. In this paper, we derive a similar characterization under slightly more general conditions.
Kersey, Scott N.
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Pest Management Science, EarlyView.The interaction between grass species and climatic season shapes the population dynamics of the cattle tick, Rhipicephalus microplus. This information highlights the potential of forage species to influence off‐host tick dynamics. Additionally, minimum temperature and minimum relative humidity were the most influential microclimatic predictors of ...
Valesca Henrique Lima +8 more
wiley +1 more source
Sidelobe Level Reduction in the ACF of NLFM Signals Using the Smoothing Spline Method
IEEE AccessThe high level of sidelobes in the autocorrelation function of the nonlinear frequency modulation signal is a challenge. One of the conventional methods to reduce the sidelobe levels is to use the principle of stationary phase.
Roohollah Ghavamirad +2 more
doaj +1 more source
, 1996 Estimation of growth curves or item response curves often involves monotone data smoothing. Methods that have been studied in the literature tend to be either less flexible or more difficult to compute when constraints such as monotonicity are ...
Peide Shi, Xuming He
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Polar‐low track prediction using machine‐learning methods
Quarterly Journal of the Royal Meteorological Society, EarlyView.Machine‐learning models are developed to produce reliable and efficient forecasts of polar‐low (PL) trajectories 12 hours ahead. A temporal model (RLSTM) benefiting from the rolling‐forecast strategy, improves overall prediction accuracy and is suitable for quick experimentation, while a spatiotemporal model (PL‐UNet), incorporating both historical and
Ziying Yang +4 more
wiley +1 more source
HISAPS: High-order smoothing spline with automatic parameter selection and shape constraints
SoftwareXObtaining a good functional fit with noisy data is difficult. This is especially true when the derivative of the fitted function is needed, which is often the case in engineering applications. One solution is to use smoothing splines.
Peter H. Broberg +8 more
doaj +1 more source
Smoothing Spline Models With Correlated Random Errors
, 1996 Spline smoothing is a popular method of estimating the functions in a nonparametric regression model. Its performance greatly depends on the choice of smoothing parameters.
Yuedong Wang
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Bivariate postprocessing of wind vectors
Quarterly Journal of the Royal Meteorological Society, EarlyView.We introduce three novel bivariate postprocessing approaches and analyze their performance for joint postprocessing of bivariate wind‐vector components in Germany. Bivariate vine‐copula‐based models, a bivariate gradient‐boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared with
Ferdinand Buchner +3 more
wiley +1 more source
Forecast‐Error Diagnostics in Neural Weather Models
Quarterly Journal of the Royal Meteorological Society, EarlyView.Deep learning weather prediction models enable efficient forecast‐error diagnostics through auto‐differentiation and low computational cost. We apply grid‐point relaxation and gradient‐based error sensitivity to identify key forecast‐error sources. Results show that medium‐range forecasts in the midlatitudes benefit most from relaxing the stratosphere ...
Uroš Perkan +2 more
wiley +1 more source
A new relational method for smoothing and projecting age-specific fertility rates: TOPALS [PDF]
Age-specific fertility rates can be smoothed using parametric models or splines. Alternatively a relational model can be used which relates the age profile to be fitted or projected to a standard age schedule.
Joop De Beer
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