检索结果-内蒙古大学图书馆

Quantile regression in random effects meta-analysis model

STATISTICAL METHODS AND APPLICATIONS 2023年第2期32卷 469-492页

作者： Dai, Xiaowen Jin, Libin Shi, Lei Shanghai Lixin Univ Accounting & Finance Sch Stat & Math Shanghai 201209 Peoples R China Shanghai Lixin Univ Accounting & Finance Interdisciplinary Res Inst Data Sci Shanghai 201209 Peoples R China Yunnan Univ Finance & Econ Sch Stat & Math Kunming 650221 Yunnan Peoples R China

In meta-analysis model, due to the appearance of publication bias or outliers, as well as the small sample size, the normal assumption is usually unreliable. Therefore, the exploration of more robust estimation, such quantile regression (QR) method, is extremely important in meta-analysis area. This paper studies the QR estimation method in random-effects meta-analysis model based on the reformulation by asymmetric Laplace distribution (ALD). The maximum likelihood estimation using Monte Carlo Expectation Maximization algorithm and the Bayesian estimation using Markov chain Monte Carlo (mcmc) algorithm are proposed for computation of the QR estimates. The significance tests of regression coefficients are suggested using likelihood ratio statistics. For mcmc algorithm, a simple and efficient Gibbs sampling algorithm is employed based on a location-scale mixture representation of the ALD, and information criterions are considered for choosing the hyper-parameters. Monte Carlo simulations are conducted to study the finite sample performance of the proposed methodology and analysis of two real data sets are presented for illustrations. Our results show that QR estimation methods perform very well, especially in case of non-normal assumption in meta-regression models. The detailed algorithms and software code are available for easy use in applications.

关键词： Random-effects meta-analysis model Quantile regression Asymmetric Laplace distribution MCEM algorithm mcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

Graph-Guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information

引用

STATISTICS IN BIOSCIENCES 2024年 1-17页

作者： Li, Wenrui Zhang, Qiyiwen Qu, Kewen Long, Qi Univ Connecticut Dept Stat 215 Glenbrook Rd Storrs CT 06269 USA Univ Pittsburgh Dept Med 3550 Terrace St Pittsburgh PA 15261 USA Univ Penn Dept Biostat Epidemiol & Informat 423 Guardian Dr Philadelphia PA 19104 USA

There is a growing body of literature on factor analysis that can capture individual and shared structures in multi-modal data. However, few of these approaches incorporate biological knowledge such as functional genomics and functional metabolomics. Graph-guided statistical learning methods that can incorporate knowledge of underlying networks have been shown to improve predication and classification accuracy, and yield more interpretable results. Moreover, these methods typically use graphs extracted from existing databases or rely on subject matter expertise which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian factor model that can account for network noise and identify globally shared, partially shared and modality-specific latent factors in multi-modal data. Specifically, we use two sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed features in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian factor analysis model with shrinkage priors to encourage feature-wise and modal-wise sparsity, thereby allowing feature selection and identification of factors of each type. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of gene expression and metabolomics datasets for Alzheimer's disease.

关键词： Bayesian shrinkage Factor analysis Latent scale network model mcmc algorithm Noisy graph

来源：评论

学校读者我要写书评

暂无评论

Prediction under an adaptive progressive type-II censoring scheme for Burr Type-XII distribution

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第12期51卷 4029-4041页

作者： Ateya, Saieed F. Amein, M. M. Mohammed, Heba S. Taif Univ Fac Sci Dept Math & Stat At Taif Saudi Arabia Assiut Univ Fac Sci Dept Math Assiut Egypt Al Azhar Univ Fac Sci Dept Math Cairo Egypt New Valley Univ Fac Sci Dept Math El Kharga Egypt Princess Nourah Bint Abdularhman Univ Math Sci Fac Sci Riyadh Saudi Arabia

In this article, Burr type-XII distribution (BXIID) is employed to make one- and two-sample Bayesian prediction schemes for future order statistics and record values in case of adaptive progressive type-II censoring scheme (APTIICS). Real data set are presented as an application. Also, some numerical computations using simulated data from the underlying distribution in case of ATIIPCS are obtained.

关键词： An adaptive progressive type-II censoring scheme one and two sample prediction schemes mcmc algorithm Burr-XII distribution

来源：评论

学校读者我要写书评

暂无评论

Variational Bayesian inference for network autoregression models

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2022年 169卷 107406-107406页

作者： Lai, Wei-Ting Chen, Ray-Bing Chen, Ying Koch, Thorsten Natl Cheng Kung Univ Dept Stat Tainan Taiwan Natl Cheng Kung Univ Inst Data Sci Tainan Taiwan Natl Univ Singapore Dept Math Singapore Singapore Natl Univ Singapore Risk Management Inst Singapore Singapore Natl Univ Singapore Inst Data Sci Singapore Singapore Tech Univ Berlin Chair Software & Algorithms Discrete Optimizat Berlin Germany Zuse Inst Berlin Dept Appl Algorithm Intelligence Methods Berlin Germany

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 the Markov chain Monte Carlo (mcmc)-based sampling approaches, the VB approach achieves enhanced computational efficiency without sacrificing estimation accuracy. 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. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Dynamic network EM algorithm mcmc algorithm Vector autoregression

来源：评论

学校读者我要写书评

暂无评论

mBART: Multidimensional Monotone BART

引用

BAYESIAN ANALYSIS 2022年第2期17卷 515-544页

作者： Chipman, Hugh A. George, Edward I. McCulloch, Robert E. Shively, Thomas S. Acad Univ Dept Math & Stat Wolfville NS Canada Univ Penn Wharton Sch Dept Stat Philadelphia PA 19104 USA Arizona State Univ Sch Math & Stat Sci Tempe AZ USA Univ Texas Austin Dept Informat Risk & Operat Management Austin TX 78712 USA

For the discovery of regression relationships between Y and a large set of p potential predictors x(1), ... , x(p), the flexible nonparametric nature of BART (Bayesian Additive Regression Trees) allows for a much richer set of possibilities than restrictive parametric approaches. However, subject matter considerations sometimes warrant a minimal assumption of monotonicity in at least some of the predictors. For such contexts, we introduce mBART, a constrained version of BART that can flexibly incorporate monotonicity in any predesignated subset of predictors using a multivariate basis of monotone trees, while avoiding the further confines of a full parametric form. For such monotone relationships, mBART provides (i) function estimates that are smoother and more interpretable, (ii) better out-of-sample predictive performance, and (iii) less post-data uncertainty. While many key aspects of the unconstrained BART model carry over directly to mBART, the introduction of monotonicity constraints necessitates a fundamental rethinking of how the model is implemented. In particular, the original BART Markov Chain Monte Carlo algorithm relied on a conditional conjugacy that is no longer available in a monotonically constrained space. Various simulated and real examples demonstrate the wide ranging potential of mBART.

关键词： Bayesian nonparametrics ensemble model isotonic regression mcmc algorithm multidimensional nonparametric regression shape constrained inference

来源：评论

学校读者我要写书评

暂无评论

Bayesian variable selection in quantile regression with random effects: an application to Municipal Human Development Index

引用

JOURNAL OF APPLIED STATISTICS 2022年第13期49卷 3436-3450页

作者： Nascimento, Marcus G. L. Goncalves, Kelly C. M. Univ Fed Rio de Janeiro Dept Metodos Estatist Rio De Janeiro Brazil

According to the Atlas of Human Development in Brazil, the income dimension of Municipal Human Development Index (MHDI-I) is an indicator that shows the population's ability in a municipality to ensure a minimum standard of living to provide their basic needs, such as water, food and shelter. In public policy, one of the research objectives is to identify social and economic variables that are associated with this index. Due to the income inequality, evaluate these associations in quantiles, instead of the mean, could be more interest. Thus, in this paper, we develop a Bayesian variable selection in quantile regression models with hierarchical random effects. In particular, we assume a likelihood function based on the Generalized Asymmetric Laplace distribution, and a spike-and-slab prior is used to perform variable selection. The Generalized Asymmetric Laplace distribution is a more general alternative than the Asymmetric Laplace one, which is a common approach used in quantile regression under the Bayesian paradigm. The performance of the proposed method is evaluated via a comprehensive simulation study, and it is applied to the MHDI-I from municipalities located in the state of Rio de Janeiro.

关键词： Location-scale mixture representation hierarchical models mcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

A Mixed-Membership Model for Social Network Clustering

引用

Journal of Data Science 2023年第3期21卷 508-522页

作者： Ouyang, Guang Dey, Dipak K. Zhang, Panpan Department of Statistics University of Connecticut Storrs 06269 CT United States Department of Biostatistics Vanderbilt University Medical Center Nashville 37203 TN United States

We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network *** estimate the membership parameters using an mcmc algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies based on synthetic networks for further assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly. © 2023 The Author(s).

关键词： cosine similarity mcmc algorithm mixed membership social network clustering stochastic blockmodels

来源：评论

学校读者我要写书评

暂无评论

Bayesian transformation models with partly interval-censored data

引用

STATISTICS IN MEDICINE 2022年第7期41卷 1263-1279页

作者： Wang, Chunjie Jiang, Jingjing Song, Xinyuan Changchun Univ Technol Sch Math & Stat Changchun Peoples R China Chinese Univ Hong Kong Dept Stat Hong Kong Peoples R China

In many scientific fields, partly interval-censored data, which consist of exactly observed and interval-censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval-censored data are relatively limited and have mainly focused on additive or proportional hazards models. The general linear transformation model provides a highly flexible modeling framework that includes several familiar survival models as special cases. Despite such nice features, the inference procedure for this class of models has not been developed for partly interval-censored data. We propose a fully Bayesian approach coped with efficient Markov chain Monte Carlo methods to fill this gap. A four-stage data augmentation procedure is introduced to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive. The empirical performance of the proposed method is evaluated through two simulation studies, and the model is then applied to a dental health study.

关键词： Bayesian method data augmentation mcmc algorithm partly interval-censored data transformation model

来源：评论

学校读者我要写书评

暂无评论

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

引用

JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2022年第1期27卷 63-87页

作者： Logothetis, D. Malefaki, S. Trevezas, S. Cournede, P. -H. Natl & Kapodistrian Univ Athens Dept Math Athens 15784 Greece Univ Patras Dept Mech Engn & Aeronaut Rion 26500 Greece CentraleSupele MAS Lab MICS Chatenay Malabry France

Plant growth modeling has attracted a lot of attention due to its potential applications. Many scientific disciplines are involved, and a lot of research effort and intensive computer methods were needed to understand better the complex mechanisms underlying plant evolution. Among the numerous challenges, one can cite mathematical modeling, parameterization, estimation and prediction. One of the most promising models that have been proposed in the literature is the GreenLab functional-structural plant growth model. In this study, we focus only on one of its versions, named GreenLab-1, particularly adapted to a certain class of plants with known organogenesis, such as sugar beet, maize, rapeseed and other crop plants. The parameters of the model are related to plant functioning, and the vector of observations consists of organ masses measured only once at a given observation time. Previous efforts for parameter estimation in GreenLab-1 include Kalman-type filters, stochastic variants of EM and/or ECM algorithms, and hybrid sequential importance sampling algorithms with Bayesian estimation only for the functional parameters of the model. In this paper, the first purely Bayesian approach for parameter estimation of the GreenLab-1 model is proposed. This approach has much more flexibility in handling complex structures, thus providing a useful tool for analyzing such types of models. In order to sample from the posterior distribution an mcmc algorithm is used and its implementation issues are also discussed. The performance of this method is illustrated on a simulated and a real dataset from the sugar beet plant, and a comparison is made with the MLE approach.

关键词： Plant growth model Crop plants Bayesian approach mcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

Parametric Regression Approach for Gompertz Survival Times with Competing Risks

引用

Communications on Applied Mathematics and Computation 2022年第4期4卷 1175-1190页

作者： H.Rehman N.Chandra Department of Statistics Ramanujan School of Mathematical SciencesPondicherry UniversityPuducherry 605014India

Regression models play a vital role in the study of data regarding survival of *** Cox proportional hazards model for regression analysis has been frequently used in sur-vival *** survival studies,it is also possible that survival time may occur with multiple occurrences of event or competing *** situation of competing risks arises when there are more than one mutually exclusive causes of death(or failure)for the person(or subject).In this paper,we developed a parametric regression model using Gompertz distribution via the Cox proportional hazards model with competing *** discussed point and interval estimation of unknown parameters and cumulative cause-specific hazard function with maximum-likelihood method and Bayesian method of *** Bayes estimates are obtained based on non-informative priors and symmetric as well as asym-metric loss *** observe the finite sample behaviour of the proposed model under both estimation procedures,we carried out a Monte Carlo simulation *** demon-strate our methodology,we also included real data analysis.

关键词： Competing risks Regression model Cause-specific hazard Gompertz distribution Parametric model Bayesian estimation mcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：