Results 51 to 60 of about 411,514 (294)

Nonparametric Inference in Mixture Cure Models

Proceedings, 2018
A completely nonparametric method for the estimation of mixture cure models is proposed. Nonparametric estimators for the cure probability (incidence) and for the survival function of the uncured population (latency) are introduced.
Ana López-Cheda, Ricardo Cao, Mª Amalia Jácome, Ingrid Van Keilegom   +3 more
doaj   +1 more source

Regularized nonparametric Volterra kernel estimation [PDF]

Automatica, 2017
In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional ...
Georgios Birpoutsoukis, Anna Marconato, John Lataire, Johan Schoukens   +3 more
openaire   +5 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Estimation of Weighted Extropy with Focus on Its Use in Reliability Modeling

Entropy
In the literature, estimation of weighted extropy is infrequently considered. In this paper, some non-parametric estimators of weighted extropy are given.
Muhammed Rasheed Irshad, Krishnakumar Archana, Radhakumari Maya, Maria Longobardi   +3 more
doaj   +1 more source

Pseudo likelihood and dimension reduction for data with nonignorable nonresponse

Statistical Theory and Related Fields, 2018
Tang et al. (2003. Analysis of multivariate missing data with nonignorable nonresponse. Biometrika, 90(4), 747–764) and Zhao & Shao (2015. Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data.
Ji Chen, Bingying Xie, Jun Shao
doaj   +1 more source

Rating Crop Insurance Policies with Efficient Nonparametric Estimators That Admit Mixed Data Types

Journal of Agricultural and Resource Economics, 2006
The identification of improved methods for characterizing crop yield densities has experienced a recent surge in activity due in part to the central role played by crop insurance in the Agricultural Risk Protection Act of 2000 (estimates of yield ...
Jeffrey S. Racine, Alan P. Ker
doaj   +1 more source

Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution

Information, 2023
Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan, Christian Micheloni, Niki Martinel   +2 more
doaj   +1 more source

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior

, 2013
Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object.
A. O’Hagan, B. Silverman, D. Cremers, D. Cremers, D. Cremers, D. Cremers, D. Freedman, D. Freedman, D. Hunter, G. Slabaugh, J. Chang, J. Malcolm, J. Malcolm, J. Schindelin, J. Zhu-Jacquot, Joshua C. Chang, L. Grady, N. El Zehiry, N. Vu, O. Veksler, P. Das, P. Kohli, P. Viola, S. Belongie, S. Dambreville, S. Lee, S. Tabbone, S.C. Pei, T. Heimann, T. Jiang, Tom Chou, V. Lempitsky, V. Lempitsky, V. Vineet, Y. Boykov, Y. Boykov, Y. Boykov   +36 more
core   +1 more source

Global Polynomial Kernel Hazard Estimation [PDF]

SSRN Electronic Journal, 2000
This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it symptotically reduces bias with unchanged variance.
Nielsen, Jens Perch, Tanggaard, Carsten
openaire   +3 more sources

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

Advanced Engineering Materials, EarlyView.
Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar, Dhanadeep Dutta, G. Bharat Reddy   +2 more
wiley   +1 more source