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Your search keyword '"Chen, Ray-Bing"' showing total 142 results
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142 results on '"Chen, Ray-Bing"'

3. The Variational Bayesian Inference for Network Autoregression Models

Author

Lai, Wei-Ting, Chen, Ray-Bing, Chen, Ying, and Koch, Thorsten

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework. The VB approach allows for the automatic identification of the dynamic structure of such a model and obtains a direct approximation of the posterior density. Compared to Markov Chain Monte Carlo (MCMC) based sampling approaches, the VB approach achieves enhanced computational efficiency without sacrificing estimation accuracy. In the simulation study conducted here, the proposed VB approach detects various types of proper active structures for dynamic network models. Compared to the alternative approach, the proposed method achieves similar or better accuracy, and its computational time is halved. In a real data analysis scenario of day-ahead natural gas flow prediction in the German gas transmission network with 51 nodes between October 2013 and September 2015, the VB approach delivers promising forecasting accuracy along with clearly detected structures in terms of dynamic dependence., Comment: 40 pages, 38 figures

Published
2021

4. Finding Optimal Points for Expensive Functions Using Adaptive RBF-Based Surrogate Model Via Uncertainty Quantification

Author

Chen, Ray-Bing, Wang, Yuan, and Wu, C. F. Jeff

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Global optimization of expensive functions has important applications in physical and computer experiments. It is a challenging problem to develop efficient optimization scheme, because each function evaluation can be costly and the derivative information of the function is often not available. We propose a novel global optimization framework using adaptive Radial Basis Functions (RBF) based surrogate model via uncertainty quantification. The framework consists of two iteration steps. It first employs an RBF-based Bayesian surrogate model to approximate the true function, where the parameters of the RBFs can be adaptively estimated and updated each time a new point is explored. Then it utilizes a model-guided selection criterion to identify a new point from a candidate set for function evaluation. The selection criterion adopted here is a sample version of the expected improvement (EI) criterion. We conduct simulation studies with standard test functions, which show that the proposed method has some advantages, especially when the true surface is not very smooth. In addition, we also propose modified approaches to improve the search performance for identifying global optimal points and to deal with the higher dimension scenarios., Comment: 35 pages, 5 figures, 5 tables

Published
2020

7. Greedy Active Learning Algorithm for Logistic Regression Models

Author

Hsu, Hsiang-Ling, Chang, Yuan-Chin Ivan, and Chen, Ray-Bing

Subjects
Statistics - Machine Learning
Abstract

We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model, comparing with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) to confirm the performance of our method.

Published
2018

10. Multiple-Instance Logistic Regression with LASSO Penalty

Author

Chen, Ray-Bing, Cheng, Kuang-Hung, Chang, Sheng-Mao, Jeng, Shuen-Lin, Chen, Ping-Yang, Yang, Chun-Hao, and Hsia, Chi-Chun

Subjects
Statistics - Machine Learning, Statistics - Applications
Abstract

In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model. In order to compute the maximum likelihood estimation of the unknown coefficient, an expectation-maximization algorithm is proposed, and the proposed modeling approach can be extended to identify the important covariates by adding the coefficient penalty term into the likelihood function. In addition to essential technical details, we demonstrate the usefulness of the proposed method by simulations and real examples.

Published
2016

11. Standardized maximim D-optimal designs for enzyme kineticinhibition models

Author

Chen, Ping-Yang, Chen, Ray-Bing, Tung, Heng-Chin, and Wong, Weng Kee

Subjects
Approximate design, Locally D-optimal design, Nonlinear model, Particle swarm optimization
Abstract

Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters.An alternative is the maximin approach that allows the user to specify a range of values for each parameter ofinterest. However, the maximin approach is difficult because we first have to determine the locally optimal designfor each set of nominal values before maximin types of optimal designs can be found via a nested optimizationprocess. We show that particle swarm optimization (PSO) techniques can solve such complex optimizationproblems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae forstandardized maximin D-optimal designs for nonlinear model with 3 or 4 parameters on the compact andnonnegative design space. Additionally, we show locally and standardized maximin D-optimal designs for inhibitionmodels are not necessarily supported at a minimum number of points. To facilitate use of such designs, wecreate a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs.

Published
2017

12. Category tree Gaussian process for computer experiments with many-category qualitative factors and application to cooling system design

Author

Lin, Wei-Ann, Sung, Chih-Li, and Chen, Ray-Bing

Abstract

AbstractIn computer experiments, Gaussian process (GP) models are commonly used for emulation. However, when both qualitative and quantitative factors are in the experiments, emulation using GP models becomes challenging. In particular, when the qualitative factors contain many categories in the experiments, existing methods in the literature become cumbersome due to the curse of dimensionality. Motivated by the computer experiments for the design of a cooling system, a new tree-based GP is proposed that emulates computer models with many-category qualitative factors, which we call category tree GP. The proposed method incorporates a tree structure to split the categories of the qualitative factors, and GP or mixed-input GP models are employed for modeling the simulation outputs in the leaf nodes. The splitting rule takes into account the cross-correlations between the categories of the qualitative factors, which have been shown by a recent theoretical study to be a crucial element for improving the prediction accuracy. In addition, a pruning procedure based on the cross-validation error is proposed to ensure the prediction accuracy. The application to the design of a cooling system indicates that the proposed method not only enjoys marked computational advantages and produces accurate predictions, but also provides valuable insights into the cooling system by discovering the tree structure.

Published
2024
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30. Numerical Methods for Finding A-optimal Designs Analytically

Author

Chen, Ping-Yang, Chen, Ray-Bing, Chen, Yu-Shi, and Wong, Weng Kee

Abstract

The traditional way in statistics to find optimal designs for regression models is an analytical approach. Technical conditions that may be restrictive in practice are sometimes imposed to obtain the analytical results. Even then, the mathematical technique is invariably not amendable to find an optimal design under a different criterion or for the same criterion with a slightly changed model, suggesting that developing flexible and effective algorithms to search for the optimum is very useful. In particular, numerical results from an algorithm can be helpful to find analytical descriptions of optimal designs. As an example, particle swarm optimization has been shown to be quite effective for finding optimal designs for hard design problems and this paper demonstrates how its output can be used to find new analytic A-optimal approximate designs for the Gamma and inverse Gaussian models, each with the inverse link function. The methodology is quite general and may be applied to find analytical A-optimal designs for other models, like the Poisson model with the log link function, or other types of optimal designs.

Published
2023
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33. Risk patterns associated with transient hearing impairment and permanent hearing loss in infants born very preterm: A retrospective study.

Author

Yu, Wen‐Hao, Lin, Yung‐Chieh, Chu, Chi‐Hsiang, Chen, Ray‐Bing, Wu, Jiunn‐Liang, and Huang, Chao‐Ching

Subjects
HEARING disorders, AUDIOMETRY, INFANTS, INTRAVENTRICULAR hemorrhage, PROPENSITY score matching, PRINCIPAL components analysis, CONDUCTIVE hearing loss
Abstract

Aim: To determine the risk patterns associated with transient hearing impairment (THI) and permanent hearing loss (PHL) of infants born very preterm who failed hearing screenings. Method: We enrolled 646 infants (347 males, 299 females) born at no more than 30 weeks' gestation between 2006 and 2020 who received auditory brainstem response screening at term‐equivalent age. Audiological examinations of infants who failed the screening revealed THI, when hearing normalized, or PHL, defined as a persistent unilateral or bilateral hearing threshold above 20 dB. Principal component analysis (PCA) was used to characterize risk patterns. Results: Among the 646 infants, 584 (90.4%) had normal hearing, 42 (6.5%) had THI, and 20 (3.1%) had PHL. Compared with the group with normal hearing, the THI and PHL groups had significantly higher rates of neurodevelopmental impairment at 24 months corrected age. PCA of risk patterns showed the THI group and especially the PHL group had more severe haemodynamic and respiratory instability. Moreover, severe intraventricular haemorrhage (IVH) was also a risk for PHL. Propensity score matching revealed an association of haemodynamic and respiratory instability with PHL. Interpretation: In infants born preterm, the severity and duration of haemodynamic and respiratory instability are risk patterns for both THI and PHL; severe IVH is an additional risk for PHL. What this paper adds: Neurodevelopmental delay was more common in infants born preterm who failed hearing screening.Principal component analysis revealed the risk patterns associated with hearing impairment.Haemodynamic–respiratory instability was associated with transient and permanent hearing impairment outcomes.Severe haemodynamic–respiratory instability and intraventricular haemorrhage was associated with permanent hearing loss. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach.

Author

Lee, Kuo‐Jung, Chen, Ray‐Bing, Kwak, Min‐Sun, Lee, Keunbaik, Lee, Kuo-Jung, Chen, Ray-Bing, and Kwak, Min-Sun

Subjects
*GIBBS sampling, *FATTY liver, *BODY mass index, *DATA analysis, *COMPUTER simulation, *RESEARCH, *MULTIVARIATE analysis, *RESEARCH methodology, *MEDICAL cooperation, *EVALUATION research, *COMPARATIVE studies, *ALGORITHMS, *PROBABILITY theory
Abstract

In this article, we present a Bayesian framework for multivariate longitudinal data analysis with a focus on selection of important elements in the generalized autoregressive matrix. An efficient Gibbs sampling algorithm was developed for the proposed model and its implementation in a comprehensive R package called MLModelSelection is available on the comprehensive R archive network. The performance of the proposed approach was studied via a comprehensive simulation study. The effectiveness of the methodology was illustrated using a nonalcoholic fatty liver disease dataset to study correlations in multiple responses over time to explain the joint variability of lung functions and body mass index. Supplementary materials for this article, including a standardized description of the materials needed to reproduce the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Published
2021
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48. A review of Bayesian group selection approaches for linear regression models.

Author

Lai, Wei‐Ting and Chen, Ray‐Bing

Subjects
*REGRESSION analysis, *GROUP extensions (Mathematics), *STATISTICAL models, *DATA analysis, *GRAPHICAL modeling (Statistics)
Abstract

Grouping selection arises naturally in many statistical modeling problems. Several group selection methods have been proposed in the last two decades. In this paper, we review the Bayesian group selection approaches for linear regression models. We start from the Bayesian indicator approach and then move to the Bayesian group LASSO methods. In addition, we also consider the Bayesian methods for the sparse group selection that can be treated as an extension of the group selection. Finally, we mention some extensions of Bayesian group selection for the generalized linear models and the multiple response models. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Dimension ReductionStatistical and Graphical Methods of Data Analysis > Bayesian Methods and TheoryStatistical Models > Model Selection [ABSTRACT FROM AUTHOR]

Published
2021
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