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Shape Parameterization and Efficient Optimization Design Method for the Ray-like Underwater Gliders. [PDF]
Biomimetics (Basel)Zhang D, Zeng D, Zhou H, Bao C, Liu Q.
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Fitting high-dimensional mixture cure models using the hdcuremodelsR package. [PDF]
Comput Methods Programs BiomedArcher KJ, Fu H.
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Modeling Recurrent Events: A Tutorial Based on Relapse and Remitting Episodes during Medication-Assisted Treatment for Opioid Use Disorder. [PDF]
Med Decis MakingChrysanthopoulou SA +6 more
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Predictors of exclusive breastfeeding among infants under-six months in rural Ethiopia. [PDF]
PLoS OneHantal HS, Abite GM, Metkie KA.
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Identification in Parametric Models
Econometrica, 1971A theory of identification is developed for a general stochastic model whose probability law is determined by a finite number of parameters. It is shown under weak regularity conditions that local identifiability of the unknown parameter vector is equivalent to nonsingularity of the information matrix.
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Nonlinear parametric quantile models
Statistical Methods in Medical Research, 2020Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model.
Matteo Bottai, Giovanna Cilluffo
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Neuro-wavelet parametric modeling
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000This work describes Neuro-Wavelet Parametric Modeling, a neural-based technique to classify, model and forecast signals or problems which are functions of either time or space. The paper presents the base method and discusses on the selection of the optimal neuro-wavelet network. An industrial application is also presented.
COLLA, Valentina +2 more
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Flexible Parametric Measurement Error Models
Biometrics, 1999Summary.Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from ...
Carroll, Raymond J. +2 more
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Volume 1: 32nd Design Automation Conference, Parts A and B, 2006
Parametric modeling systems are fundamentally changing the design process practiced in the industry today. Practically all commercial CAD systems combine established solid modeling techniques with constraint solving and heuristic algorithms to create, edit and manipulate solid models, while enforcing the requirement that every such solid model must ...
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Parametric modeling systems are fundamentally changing the design process practiced in the industry today. Practically all commercial CAD systems combine established solid modeling techniques with constraint solving and heuristic algorithms to create, edit and manipulate solid models, while enforcing the requirement that every such solid model must ...
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