On Nonparametric Kernel-Type Estimate of the Bernoulli Regression Function

2020 ◽  
pp. 19-36
Author(s):  
Petre K. Babilua ◽  
Elizbar A. Nadaraya
Keyword(s):  
Regression Function ◽  
Type Estimate ◽  
Nonparametric Kernel ◽  
Kernel Type

scholarly journals LOCAL LINEAR FITTING UNDER NEAR EPOCH DEPENDENCE: UNIFORM CONSISTENCY WITH CONVERGENCE RATES

2012 ◽  
Vol 28 (5) ◽  
pp. 935-958 ◽  
Author(s):  
Degui Li ◽  
Zudi Lu ◽  
Oliver Linton
Keyword(s):  
Convergence Rates ◽  
Regression Function ◽  
Linear Estimator ◽  
Uniform Consistency ◽  
Linear Fitting ◽  
Semiparametric Modeling ◽  
Local Linear Fitting ◽  
Local Linear ◽  
Local Linear Estimator ◽  
Nonparametric Kernel

Local linear fitting is a popular nonparametric method in statistical and econometric modeling. Lu and Linton (2007, Econometric Theory23, 37–70) established the pointwise asymptotic distribution for the local linear estimator of a nonparametric regression function under the condition of near epoch dependence. In this paper, we further investigate the uniform consistency of this estimator. The uniform strong and weak consistencies with convergence rates for the local linear fitting are established under mild conditions. Furthermore, general results regarding uniform convergence rates for nonparametric kernel-based estimators are provided. The results of this paper will be of wide potential interest in time series semiparametric modeling.

scholarly journals Enhancing Optical Correlation Decision Performance for Face Recognition by Using a Nonparametric Kernel Smoothing Classification

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5092
Author(s):  
Matthieu Saumard ◽  
Marwa Elbouz ◽  
Michaël Aron ◽  
Ayman Alfalou ◽  
Christian Brosseau
Keyword(s):  
Face Recognition ◽  
Image Recognition ◽  
Fourier Transforms ◽  
Kernel Smoothing ◽  
Kernel Estimation ◽  
Regression Function ◽  
Correlation Peak ◽  
Optical Correlation ◽  
The Individual ◽  
Nonparametric Kernel

Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize decision making according to the location, height, and shape of the correlation peak within the correlation plane. Additionally, correlation is very sensitive to image rotation and scale. To overcome these issues, in this study, we propose a method of nonparametric modelling of the correlation plane. Our method is based on a kernel estimation of the regression function used to classify the individual images in the correlation plane. The basic idea is to improve the decision by taking into consideration the energy shape and distribution in the correlation plane. The method relies on the calculation of the Hausdorff distance between the target correlation plane (of the image to recognize) and the correlation planes obtained from the database (the correlation planes computed from the database images). Our method is tested for a face recognition application using the Pointing Head Pose Image Database (PHPID) database. Overall, the results demonstrate good performances of this method compared to competitive methods in terms of good detection and very low false alarm rates.

scholarly journals Kernel-Type Estimators of Jump Points and Values of a Regression Function

1993 ◽  
Vol 21 (3) ◽  
pp. 1545-1566 ◽  
Author(s):  
J. S. Wu ◽  
C. K. Chu
Keyword(s):  
Regression Function ◽  
Kernel Type

UNIFICATION OF STANDS SELECTION FOR FINAL FELLING IN STRATEGIC AND TACTICAL PLANNING

2015 ◽  
Author(s):  
Ainārs GRĪNVALDS
Keyword(s):  
Strategic Planning ◽  
Net Present Value ◽  
Strategic Plan ◽  
Regression Function ◽  
Tactical Planning ◽  
Present Value ◽  
Black Alder ◽  
Value Maximization ◽  
Annual Changes ◽  
Selection For

The stand selection for cutting in tactical planning should be done according to the same principles like in strategic planning – to maximize net present value. The simple way of how to transfer the net present value maximization principle from strategic planning to tactical planning was created in Sweden. The method is based on annual changes in the net present value by postponing final felling. Forest inventory data and forestry modelling system was used for calculation of changes in net present value for pine, spruce, birch, aspen and black alder stands. And changes in net present value were described by regression function with factors from stand parameters. The regression function allows calculating annual changes in net present value for each stand. And stands with higher decrease in net present value have higher cutting priority. Stands selected for the final felling in strategic plan were compared with the stands selected in tactical plan with two methods, first, by using annual changes in the net present value, second, by traditional planning principles. Stands selected by annual changes in the net present value were similar to stands that were selected for cutting in strategic plan, but stands selected by traditional planning principles – not.

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