Nonparametric tilted density function estimation:A cross-validation criterion

2018 ◽  
Vol 197 ◽  
pp. 51-68 ◽  
Author(s):  
Hassan Doosti ◽  
Peter Hall ◽  
Jorge Mateu
Keyword(s):  
Density Function ◽  
Cross Validation ◽  
Validation Criterion

scholarly journals KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA

2018 ◽  
Vol 7 (3) ◽  
pp. 326-336
Author(s):  
Puput Ramadhani ◽  
Dwi Ispriyanti ◽  
Diah Safitri
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Density Function ◽  
Control Chart ◽  
Cross Validation ◽  
Kernel Density ◽  
Process Capability ◽  
Conformity Index ◽  
Denim Fabric ◽  
Kernel Density Function

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords:     kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

Best subset selection via cross-validation criterion

Top ◽  
2020 ◽  
Vol 28 (2) ◽  
pp. 475-488 ◽  
Author(s):  
Yuichi Takano ◽  
Ryuhei Miyashiro
Keyword(s):  
Cross Validation ◽  
Subset Selection ◽  
Validation Criterion

Gray-box identification with regularized FIR models

2018 ◽  
Vol 66 (9) ◽  
pp. 704-713 ◽  
Author(s):  
Tobias Münker ◽  
Timm J. Peter ◽  
Oliver Nelles
Keyword(s):  
Dynamic System ◽  
Prior Knowledge ◽  
Cross Validation ◽  
Finite Impulse Response ◽  
Black Box ◽  
Model Structure ◽  
Linear Dynamic ◽  
Novel Approach ◽  
Box Models ◽  
Validation Criterion

Abstract The problem of modeling a linear dynamic system is discussed and a novel approach to automatically combine black-box and white-box models is introduced. The solution proposed in this contribution is based on the usage of regularized finite-impulse-response (FIR) models. In contrast to classical gray-box modelling, which often only optimizes the parameters of a given model structure, our approach is able to handle the problem of undermodeling as well. Therefore, the amount of trust in the white-box or gray-box model is optimized based on a generalized cross-validation criterion. The feasibility of the approach is demonstrated with a pendulum example. It is furthermore investigated, which level of prior knowledge is best suited for the identification of the process.

Iterative Bias Correction of the Cross-Validation Criterion

2011 ◽  
Vol 39 (1) ◽  
pp. 116-130 ◽  
Author(s):  
HIROKAZU YANAGIHARA ◽  
HIRONORI FUJISAWA
Keyword(s):  
Bias Correction ◽  
Cross Validation ◽  
Validation Criterion ◽  
The Cross

Latest views of the sparse Radon transform

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 386-399 ◽  
Author(s):  
Daniel Trad ◽  
Tadeusz Ulrych ◽  
Mauricio Sacchi
Keyword(s):  
Radon Transform ◽  
Processing Time ◽  
Cross Validation ◽  
Gradient Algorithm ◽  
Missing Information ◽  
Subspace Methods ◽  
Amplitude Variation ◽  
Frequency Domains ◽  
Validation Criterion ◽  
Number Of Iterations

The Radon transform (RT) suffers from the typical problems of loss of resolution and aliasing that arise as a consequence of incomplete information, including limited aperture and discretization. Sparseness in the Radon domain is a valid and useful criterion for supplying this missing information, equivalent somehow to assuming smooth amplitude variation in the transition between known and unknown (missing) data. Applying this constraint while honoring the data can become a serious challenge for routine seismic processing because of the very limited processing time available, in general, per common midpoint. To develop methods that are robust, easy to use and flexible to adapt to different problems we have to pay attention to a variety of algorithms, operator design, and estimation of the hyperparameters that are responsible for the regularization of the solution. In this paper, we discuss fast implementations for several varieties of RT in the time and frequency domains. An iterative conjugate gradient algorithm with fast Fourier transform multiplication is used in all cases. To preserve the important property of iterative subspace methods of regularizing the solution by the number of iterations, the model weights are incorporated into the operators. This turns out to be of particular importance, and it can be understood in terms of the singular vectors of the weighted transform. The iterative algorithm is stopped according to a general cross validation criterion for subspaces. We apply this idea to several known implementations and compare results in order to better understand differences between, and merits of, these algorithms.

Median cross-validation criterion

1997 ◽  
Vol 42 (23) ◽  
pp. 1956-1959 ◽  
Author(s):  
Yang Ying
Keyword(s):  
Cross Validation ◽  
Validation Criterion
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