Results 31 to 40 of about 164,145 (281)
Efficient rare event simulation for failure problems in random media [PDF]
, 2014 In this paper we study rare events associated to solutions of elliptic partial differential equations with spatially varying random coefficients. The random coefficients follow the lognormal distribution, which is determined by a Gaussian process.
Liu, Jingchen, Lu, Jianfeng, Zhou, Xiang
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A New Tobit Ridge-Type Estimator of the Censored Regression Model With Multicollinearity Problem
Frontiers in Applied Mathematics and Statistics, 2022 In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem.
Issam Dawoud +3 more
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A New Liu Type of Estimator for the Restricted SUR Estimator
Journal of Modern Applied Statistical Methods, 2020 A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may be used when estimating the parameters vector in the presence of multicollinearity if the it is suspected to belong to a linear subspace. The dispersion matrices and the mean squared error (MSE) are derived.
Kristofer Månsson +2 more
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Modified One-Parameter Liu Estimator for the Linear Regression Model
Modelling and Simulation in Engineering, 2020 Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model.
Adewale F. Lukman +3 more
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IEEE Access, 2021 A situation in which an image is combined with multiple images to form interferometric pairs is often observed in small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) deformation inversion, and this situation leads to a near linear
Min Zhai +6 more
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Mathematics, 2023 Following the idea presented with regard to the elastic-net and Liu-LASSO estimators, we proposed a new penalized estimator based on the Kibria–Lukman estimator with L1-norms to perform both regularization and variable selection.
Adewale Folaranmi Lukman +5 more
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A New Perspective on Robust $M$-Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing [PDF]
, 2017 Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber in 1973 [{\it Ann.
Bose, Koushiki +3 more
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Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis [PDF]
, 2015 Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is characterized by a graph, where nodes represent neural elements such as neurons and brain regions, and links represent statistical dependences that ...
Li, Lexin, Xia, Yin
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Research in StatisticsThis article proposes some new estimators, namely Stein’s estimators for ridge regression and Kibria and Lukman estimator and compares their performance with some existing estimators, namely maximum likelihood estimator (MLE), ridge regression estimator,
Md Ariful Hoque, B. M. Golam Kibria
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Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors [PDF]
Journal of Sciences, Islamic Republic of Iran, 2019 In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression ...
Hoda Mohammadi, Abdolrahman Rasekh
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