Article
Engineering, Electrical & Electronic
Qin Shu, Jinyan He, Chang Wang
Summary: This article proposes a method for estimating utility harmonic impedance based on semiparametric estimation, which does not rely on conventional assumptions. The method utilizes the stochastic characteristic of utility harmonic current, obtains impedance through kernel density estimation, and validates its performance superiority over existing methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Statistics & Probability
Eckhard Liebscher, Ostap Okhrin
Summary: This paper explores the semi-parametric estimation of the multivariate elliptical distribution when the dimensionality increases with the sample size. We prove the almost sure convergence of the estimator and derive its convergence rates, which depend on the sample size, dimensionality, and kernel bandwidth. Additionally, we demonstrate the almost sure convergence and convergence rates of the sample covariance matrix under the Frobenius norm. Extensive simulation studies provide support for the theoretical findings.
JOURNAL OF MULTIVARIATE ANALYSIS
(2023)
Article
Computer Science, Information Systems
David Atienza, Concha Bielza, Pedro Larranaga
Summary: Semiparametric Bayesian networks combine parametric and nonparametric conditional probability distributions to incorporate the advantages of both components. By considering different types of conditional probability distributions and modifying learning algorithms, the proposed approach achieves comparable performance to state-of-the-art methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Lei Wang, Puying Zhao, Jun Shao
Summary: This article introduces three different semi-parametric estimation methods to estimate distribution functions and quantiles of a response variable. An instrumental covariate is used to address the identifiability problem, and dimension reduction technique is employed to improve efficiency. The proposed estimators have been shown to be consistent and asymptotically normal.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Mathematics
Xia Zheng, Yaohua Rong, Ling Liu, Weihu Cheng
Summary: This paper proposes a novel penalized kernel machine method for a semiparametric logistic model, which can guarantee accurate estimation, flexibly describe the complex relationship between responses and predictors, and remove redundant variables. Numerical experiments show that this method yields higher prediction accuracies compared to competing approaches.
Article
Statistics & Probability
Emmanuel De Dieu Nkou
Summary: This paper focuses on the application of the kernel method in estimating sliced average variance estimation (SAVE), particularly the properties of the kernel version. The asymptotic property of this method is obtained under weaker assumptions.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Geochemistry & Geophysics
Mingke Zhang, Jinghuai Gao, Zhiguo Wang, Yang Yang, Naihao Liu, Qiansheng Wei
Summary: Seismic attenuation, described by quality factor Q, is crucial for seismic resolution enhancement and reservoir characterization. This study proposes an error modeling-based discriminative approach in machine learning to address the unreliability of conventional Q estimation methods. By constructing a classifier and introducing error models, the robustness and accuracy of seismic data processing can be improved.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Economics
Luis Portugal, Athanasios A. Pantelous, Richard Verrall
Summary: In actuarial practice, it is crucial to select suitable claims reserving methods for each line of business, and it may not always be suitable for all triangles within the business portfolio. The developed Generalized Link Ratios framework allows for the calculation of prediction errors and simultaneous estimation of loss development factors, reserves, and prediction errors without the use of recursive formulas. The model selection criterion based on the lowest prediction error also estimates a key parameter related to heteroscedasticity.
INSURANCE MATHEMATICS & ECONOMICS
(2021)
Article
Statistics & Probability
Matthieu Marbac, Mohammed Sedki, Christophe Biernacki, Vincent Vandewalle
Summary: This study focuses on parameter estimation in regression models with missing group variables, proposing a simultaneous estimation approach for clustering and regression. Numerical experiments and real data analysis illustrate the effectiveness of the new method.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Article
Multidisciplinary Sciences
Yijun Wang, Weiwei Wang
Summary: This paper discusses a flexible model introducing time-varying coefficients in panel count data, using quantile regression for statistical inference, providing asymptotic results on estimator convergence, evaluating the finite-sample performance through simulation studies. Finally, applications of bladder cancer data and US flight delay data are analyzed using the proposed method.
Article
Mathematics
Wenjuan Li, Wenying Wang, Jingsi Chen, Weidong Rao
Summary: Sufficient dimension reduction (SDR) is a useful tool for nonparametric regression with high-dimensional predictors, but many existing SDR methods rely on certain assumptions about the distribution of predictors. In this study, inspired by Wang et al., we propose a novel and effective method that combines the aggregate method and the kernel inverse regression estimation. Our proposed approach accurately estimates the dimension reduction directions and substantially improves the exhaustivity of the estimates with complex models. It is not dependent on the arrangement of slices and reduces the influence of extreme values of the response. The method performs well in both numerical examples and a real data application.
Article
Statistics & Probability
Zhong Guan
Summary: This study proposes maximum likelihood estimation for parameters and underlying densities in a semiparametric density ratio model, where the nonparametric baseline density is approximated by the Bernstein polynomial model. The EM algorithm is used to obtain the maximum approximate Bernstein likelihood estimates. The proposed method is illustrated with two real medical research data and simulation results demonstrate its better performance compared to existing methods. Some asymptotic results are also presented and proved.
JOURNAL OF NONPARAMETRIC STATISTICS
(2023)
Article
Geochemistry & Geophysics
Jie Han, Songlin Zhang, Zhen Ye
Summary: In this article, a nonblind deconvolution method is proposed to correct inaccurate blur kernels by using bias correction based on the classic errors-in-variables (EIVs) model. The bias caused by errors of inaccurate blur kernels in the blurry image formation model is analyzed in detail. The latent sparsity property of jointed latent image and errors of inaccurate blur kernels is statistically counted and imposed as a new regularization term in the objective function of the new nonblind method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Biology
Chenguang Wang, Ao Yuan, Leslie Cope, Jing Qin
Summary: This paper introduces a semiparametric regression model based on the assumption that the random error follows a skewed distribution in an isotonic regression model. An expectation-maximization algorithm is developed for obtaining maximum likelihood estimates of the model parameters, and the asymptotic properties of the estimators are examined. The proposed model is applied to evaluate the DNA-RNA-protein relationship and identify key genes in tumor progression through simulation studies.
Article
Computer Science, Theory & Methods
Danli Xu, Yong Wang
Summary: This article studies the density estimation of multivariate toroidal data based on semiparametric mixtures. By fixing the maximum of the component density, the likelihood function of the mixture is bounded and a satisfactory density estimate is provided.
STATISTICS AND COMPUTING
(2023)
