检索结果-内蒙古大学图书馆

Bayesian Time Series Analysis of Structural Changes in Level and Trend

COmmUNICATIONS IN STATISTICS-ThEORY AND mEThODS 2013年第21期42卷 3949-3964页

作者： Wong, heung Ip, Wai Cheung Liu, Jin Shan Long, Jian Yan Hong Kong Polytech Univ Dept Appl Math Hong Kong Hong Kong Peoples R China South China Agr Univ Dept Appl Math Coll Sci Guangzhou 510642 Guangdong Peoples R China

In this article we consider the problem of detecting changes in level and trend in time series model in which the number of change-points is unknown. The approach of Bayesian stochastic search model selection is introduced to detect the configuration of changes in a time series. The number and positions of change-points are determined by a sequence of change-dependent parameters. The sequence is estimated by its posterior distribution via the maximum a posteriori (mAP) estimation. markov chain monte Carlo (mCmC) method is used to estimate posterior distributions of parameters. Some actual data examples including a time series of traffic accidents and two hydrological time series are analyzed.

关键词： Bayesian stochastic search selection Bayesian time series analysis mCmC m-h algorithm Structural changes 62E15 62h99

来源：评论

学校读者我要写书评

暂无评论

mAXImUm LIKELIhOOD ESTImATION FOR SImPLEX DISTRIBUTION NONLINEAR mIXED mODELS VIA AN Em algorithm

引用

INTERNATIONAL JOURNAL OF BIOmAThEmATICS 2009年第1期2卷 9-17页

作者： Wei, hongjie Zhang, Wenzhuan Chinese Acad Trop Agr Sci Rubber Res Inst Danzhou 571737 Hainan Peoples R China Guizhou Coll Finance & Econ Sch Math & Stat Guiyang 550004 Guizhou Peoples R China

Longitudinal continuous proportional data is common in many fields such as biomedical research, psychological research and so on. As shown in [16], such data can be fitted with simplex models. Based on the original models of [16] which assumed a fixed effect for every subject, this paper extends the models by adding random effects and proposes simplex distribution nonlinear mixed models which are one kind of nonlinear reproductive dispersion mixed models. By treating the random effects in the models as hypothetical missing data and applying metropolis-hastings (m-h) algorithm, this paper develops an Em algorithm with markov chain monte-Carlo method for maximum likelihood estimation in the models. The method is illustrated with the same data from an ophthalmology study on the use of intraocular gas in retinal surgeries in [16] for ease of comparison.

关键词： Simplex distribution nonlinear mixed models maximum likelihood estimates mCEm algorithm m-h algorithm Newton-Raphson iteration longitudinal proportional data

来源：评论

学校读者我要写书评

暂无评论

Dirichlet Lasso: A Bayesian approach to variable selection

引用

STATISTICAL mODELLING 2015年第3期15卷 215-232页

作者： Das, Kiranmoy Sobel, marc Indian Stat Inst Bayesian & Interdisciplinary Res Unit Kolkata 700108 India Temple Univ Dept Stat Philadelphia PA 19122 USA

Selection of the most important predictor variables in regression analysis is one of the key problems statistical research has been concerned with for long time. In this article, we propose the methodology, Dirichlet Lasso (abbreviated as DLASSO) to address this issue in a Bayesian framework. In many modern regression settings, large set of predictor variables are grouped and the coefficients belonging to any one of these groups are either all redundant or all important in predicting the response;we say in those cases that the predictors exhibit a group structure. We show that DLASSO is particularly useful where the group structure is not fully known. We exploit the clustering property of Dirichlet Process priors to infer the possibly missing group information. The Dirichlet Process has the advantage of simultaneously clustering the variable coefficients and selecting the best set of predictor variables. We compare the predictive performance of DLASSO to Group Lasso and ordinary Lasso with real data and simulation studies. Our results demonstrate that the predictive performance of DLASSO is almost as good as that of Group Lasso when group label information is given;and superior to the ordinary Lasso for missing group information. For high dimensional data (e.g., genetic data) with missing group information, DLASSO will be a powerful approach of variable selection since it provides a superior predictive performance and higher statistical accuracy.

关键词： Bayesian Lasso Dirichlet process prior Group lasso Gibbs sampling m-h algorithm

来源：评论

学校读者我要写书评

暂无评论

mAXImUm LIKELIhOOD ESTImATION FOR SImPLEX DISTRIBUTION NONLINEAR mIXED mODELS VIA ThE STOChASTIC APPROXImATION algorithm

引用

ROCKY mOUNTAIN JOURNAL OF mAThEmATICS 2008年第5期38卷 1863-1875页

作者： Zhang, Wenzhuan Wei, hongjie Guizhou Coll Finance & Econ Sch Math & Stat Guiyang 550004 Guizhou Peoples R China Chinese Acad Trop Agr Sci Rubber Res Inst Danzhou 571737 Hainan Peoples R China

Longitudinal continuous proportional data. is common in many fields such as biomedical research, psychological research and so on, e.g., the percent decrease in glomerular filtration rate at different follow-up times from the baseline. As shown in Song and Tan [16] such data can be fitted with simplex models. however, the original models of [16] for such longitudinal continuous proportional data assumed a fixed effect for every subject. This paper extends the models of Song and Tan [16] by adding random effects, and proposes simplex distribution nonlinear mixed models which are one kind of nonlinear reproductive dispersion mixed model. By treating random effects in the models as hypothetical missing data and applying the metropolis-hastings (m-h) algorithm, this paper develops the stochastic approximation (SA) algorithm with markov chain monte-Carlo (mCmC) method for maximum likelihood estimation in the models. Finally, for ease of comparison, the method is illustrated with the same data from an ophthalmology study on the use of intraocular gas in retinal surgeries in [16].

关键词： Simplex distribution nonlinear mixed models maximum likelihood estimates stochastic approximation algorithm m-h algorithm longitudinal continuous proportional data

来源：评论

学校读者我要写书评

暂无评论

An Improved metropolis-hastings algorithm Based on Particle Filter

An Improved Metropolis-Hastings Algorithm Based on Particle ...

引用

IITA International Conference on Control, Automation and Systems Engineering

作者： Yang, Yanfang Zhang, Yanjie Zhou, Yingjun Zhang, Wenhua Mudanjiang Med Univ Dept Med Image Mudanjiang Peoples R China

ISBN: (纸本)9780769537283

Particle filter is a kind of monte Carlo simulation method under the framework of Bayesian theory, and it can deal with state estimation problem of nonlinear models with non-Gaussian noise. But the use of resampling scheme to decreases the degeneracy phenomenon also introduces the sample impoverishment. m-h moves step is presented to increase the diversity of the samples and improve the performance of filtering. Aim at the acceptance ratio in standard m-h algorithm and RWm are too low, this paper proposes an improved m-h based particle filter. By improving the candidate proposal distribution, the algorithm reduces the estimate errors and increases the rate of accepted candidates. The simulation shows that the method achieves better performance of filtering compared to general particle filters and several other m-h based particle filters.

关键词： particle filter sample degeneracy m-h algorithm

来源：评论

学校读者我要写书评

暂无评论

Bayesian array signal processing in additive generalized Gaussian noise

Bayesian array signal processing in additive generalized Gau...

引用

11th IEEE Workshop on Statistical Signal Processing (SSP 2001)

作者： Kannan, B Natl Univ Singapore Ctr Wireless Commun Singapore 11764 Singapore

ISBN: (纸本)0780370112

In this paper, we present a Bayesian approach for DOA and frequency estimation of narrow band signals in additive generalized Gaussian noise. Using Bayesian techniques, the posterior probability densities for DOA (Direction Of Arrival) and frequency parameters are derived from the signal and noise models. These posterior probabilities are then used in the metropolis-hastings (m-h) algorithm to derive the samples for the DOA and frequency parameters. The performances of our algorithms are studied by plotting the mSEs (mean Square Errors) of the parameters for various SNRs. The mSEs of the parameters are compared with the CRLBs (Cramer Rao Lower Bound) for the generalized Gaussian models.

关键词： non-Gaussian signal processing sensor array processing Bayesian estimation m-h algorithm

来源：评论

学校读者我要写书评

暂无评论

Vibration-based Structural Damage Identification under Interval Uncertainty

Vibration-based Structural Damage Identification under Inter...

引用

9th ASmE Annual Dynamic Systems and Control Conference

作者： Yoo, David Tang, Jiong Univ Connecticut Coll Engn Dept Mech Engn Storrs CT 06269 USA

ISBN: (纸本)9780791850695

Identifying damages in mechanical structures in advance is essential part of preventing catastrophic losses. Among several non-destructive methods, the vibration-based method, which utilizes global characteristics of the structures, has several advantages such as not requiring prior information on possible damage location and physical access to it. In the meantime, the mechanical structures are inevitably subject to uncertainties, whose distribution is often unknown in practical situations due to such as limited amount of available data. Uncertainties are treated as interval uncertainty in such cases. In this regard, this study presents vibration-based damage identification under interval uncertainty. To obtain reliable result, this research does not assume any random distribution, e.g., uniform distribution, inside interval. Since detected damage is not assumed to be monotonic function with respect to interval uncertainty either, traditional fuzzy interval arithmetic is not applicable. Instead, we first carry out exhaustive search to see the effect of the interval uncertainty on the identified damage;i.e., discretizing interval uncertainty into sub-intervals and executing damage identification under all possible combinations to see the effect of the interval uncertainty on the identified damage. We then develop the unique algorithm based on m-h algorithm to facility computational efficiency.

关键词： Interval Uncertainty m-h algorithm Reliability Vibration-based Damage Identification

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：