Results 161 to 170 of about 3,647,752 (207)

Parallel Bootstrap and Optimal Subsample Lengths in Smooth Function Models

Communications in Statistics - Simulation and Computation, 2016
Parallel bootstrap is an extremely useful statistical method with good performance. In the present study, we introduce a working correlation matrix on the method, which is called parallel bootstrap matrix. We consider some properties of it and the optimal size of the subsample in smooth function models.
Guangbao Guo, Lu Lin
openaire   +1 more source

Block Motion Model for Optical Flow with Smoothness Prior Function

2006 9th International Conference on Control, Automation, Robotics and Vision, 2006
An explicit constraint is introduced into the Lucas-Kanade gradient based block motion model. This constraint helps the estimation process to consider surrounding motion vectors during its calculation. Consequently a better motion field can be produced than that by the original block motion model.
Stephanus Surijadarma Tandjung, Seah Hock Soon, Kemao Qian   +2 more
openaire   +1 more source

Additive models and many smooth univariate functions

2011
Additive models build a nonparametric extension of linear models and as such, they exhibit a substantial degree of flexibility. While the most important effects may still be detected by a linear model, substantial improvements are potentially possible by using the more flexible additive model class.
Peter Bühlmann, Sara van de Geer
openaire   +1 more source

Automatic Smoothing of Regression Functions in Generalized Linear Models

Journal of the American Statistical Association, 1986
Abstract We consider the penalized likelihood method for estimating nonparametric regression functions in generalized linear models (Nelder and Wedderburn 1972) and present a generalized cross-validation procedure for empirically assessing an appropriate amount of smoothing in these estimates.
Finbarr O'sullivan, Brian S. Yandell, William J. Raynor   +2 more
openaire   +1 more source

Deep networks with ReLU activation functions can be smooth statistical models

ESANN 2022 proceedings, 2022
Most Deep neural networks use ReLU activation functions. Since these functions are not differentiable in 0, we may believe that such models may have irregular behavior. In this paper, we will show that the issue is more in the data than in the model, and if the data are “smooth”, the model will be differentiable in a suitable sense.
openaire   +2 more sources

A nonlinear mixed-effects model for simultaneous smoothing and registration of functional data

Pattern Recognition Letters, 2014
We consider misaligned functional data, where data registration is necessary for proper statistical analysis. This paper proposes to treat misalignment as a nonlinear random effect, which makes simultaneous likelihood inference for horizontal and vertical effects possible.
Lars Lau Rakêt, Stefan Sommer, Bo Markussen   +2 more
openaire   +2 more sources

Effects of loss function and data sparsity on smooth manifold extraction with deep model

Expert Systems With Applications, 2022
Hongchun Qu, Jian Zheng, Xiaoming Tang   +2 more
exaly  

Smooth Mathematical Representation of the DER_A Aggregated Model

IEEE Access, 2023
Jesus D Vasquez-Plaza, Timothy M Hansen, Reinaldo Tonkoski   +2 more
exaly  

A Method for the Computer Calculation of Edgeworth Expansions for Smooth Function Models

, 1993
Paul Kabaila
semanticscholar   +1 more source

Adaptive Fuzzy Tracking Control of Nonlinear Time-Delay Systems With Dead-Zone Output Mechanism Based on a Novel Smooth Model

IEEE transactions on fuzzy systems, 2015
Zhi Liu, G. Lai, Yun Zhang, C. L. Chen
semanticscholar   +1 more source