检索结果-内蒙古大学图书馆

Likelihood inference for COM-Poisson cure rate model with interval-censored data and Weibull lifetimes

STATISTICAL METHODS IN MEDICAL RESEARCH 2017年第5期26卷 2093-2113页

作者： Pal, Suvra Balakrishnan, N. Univ Texas Arlington Dept Math Arlington TX 76019 USA McMaster Univ Dept Math & Stat Hamilton ON Canada

In this paper, we consider a competing cause scenario and assume the number of competing causes to follow a Conway-Maxwell Poisson distribution which can capture both over and under dispersion that is usually encountered in discrete data. Assuming the population of interest having a component cure and the form of the data to be interval censored, as opposed to the usually considered right-censored data, the main contribution is in developing the steps of the expectation maximization algorithm for the determination of the maximum likelihood estimates of the model parameters of the flexible Conway-Maxwell Poisson cure rate model with Weibull lifetimes. An extensive Monte Carlo simulation study is carried out to demonstrate the performance of the proposed estimation method. Model discrimination within the Conway-Maxwell Poisson distribution is addressed using the likelihood ratio test and information-based criteria to select a suitable competing cause distribution that provides the best fit to the data. A simulation study is also carried out to demonstrate the loss in efficiency when selecting an improper competing cause distribution which justifies the use of a flexible family of distributions for the number of competing causes. Finally, the proposed methodology and the flexibility of the Conway-Maxwell Poisson distribution are illustrated with two known data sets from the literature: smoking cessation data and breast cosmesis data.

关键词： Long-term survival model maximum likelihood estimate expectation maximization algorithm profile likelihood likelihood ratio test Akaike information criterion Bayesian information criterion

来源：评论

学校读者我要写书评

暂无评论

Estimation of diffusion constants from single molecular measurement without explicit tracking

引用

BMC SYSTEMS BIOLOGY 2018年第Sup1期12卷 15-15页

作者： Teraguchi, Shunsuke Kumagai, Yutaro Tohoku Univ Tohoku Med Megabank Org Aoba Ku 2-1 Seiryo Machi Sendai Miyagi 9808573 Japan Osaka Univ Immunol Frontier Res Ctr Quantitat Immunol Res Unit 3-1 Yamada Oka Suita Osaka 5650871 Japan

Background: Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density. Methods: To circumvent this problem, we develop an algorithm to estimate diffusion constants without relying on SPT. The proposed algorithm is based on a probabilistic model of the distance to the nearest point in subsequent frames. This probabilistic model generalizes the model of single particle Brownian motion under an isolated environment into the one surrounded by indistinguishable multiple particles, with a mean field approximation. Results: We demonstrate that the proposed algorithm provides reasonable estimation of diffusion constants, even when other methods suffer due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements. Conclusions: The proposed algorithm based on the probabilistic model of indistinguishable Brownian particles provide accurate estimation of diffusion constants even in the regime where the traditional SPT methods underestimate them due to linking errors.

关键词： Brownian motion Diffusion constants expectation maximization algorithm Probabilistic model Single molecular measurement

来源：评论

学校读者我要写书评

暂无评论

Inference for a series system with dependent masked data under progressive interval censoring

引用

JOURNAL OF APPLIED STATISTICS 2017年第1期44卷 3-15页

作者： Cai, Jing Shi, Yimin Liu, Bin Northwestern Polytech Univ Dept Appl Math Xian Peoples R China Guizhou Minzu Univ Coll Sci Guiyang Peoples R China

This paper considers the statistical analysis of masked data in a series system with Burr-XII distributed components. Based on progressively Type-I interval censored sample, the maximum likelihood estimators for the parameters are obtained by using the expectation maximization algorithm, and the associated approximate confidence intervals are also derived. In addition, Gibbs sampling procedure using important sampling is applied for obtaining the Bayesian estimates of the parameters, and Monte Carlo method is employed to construct the credible intervals. Finally, a simulation study is proposed to illustrate the efficiency of the methods under different removal schemes and masking probabilities.

关键词： Dependent masked data masking probability progressively Type-I interval censoring expectation maximization algorithm Gibbs sampling Monte Carlo method

来源：评论

学校读者我要写书评

暂无评论

Statistical Analysis of Massive AIS Trajectories Using Gaussian Mixture Models 2

Statistical Analysis of Massive AIS Trajectories Using Gauss...

引用

2nd International Conference on Multimedia and Image Processing (ICMIP)

作者： Hu, Bin Liu, Ryan Wen Wang, Kai Li, Yan Liang, Maohan Li, Huanhuan Liu, Jingxian Wuhan Univ Technol Sch Nav Wuhan 430063 Hubei Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China

ISBN: (纸本)9781509059546

The Automatic Identification System (AIS) is an automatic tracking system which has been widely applied in the fields of intelligent transportation systems, e.g., collision avoidance, navigation, maritime supervision and management. Compare with other positioning systems, e.g., very high frequency (VHF) and radar, AIS can conquer the human errors and it is almost not affected by the external environment. To make better use of the AIS data, it is necessary to statistically analyze the massive AIS trajectories. The statistical results could make us better understand the potential properties of AIS trajectories. It is well known that most current practical applications are strongly dependent on the geometrical structures of AIS trajectories. In this paper, a Gaussian Mixture Model (GMM) is introduced to investigate the longitude and latitude differences of AIS trajectory data. The parameters of GMM are estimated using the expectation maximization (EM) algorithm. The experimental results have illustrated the superior performance of our proposed method.

关键词： automatic identification system Gaussian mixture model expectation maximization algorithm statistical analysis

来源：评论

学校读者我要写书评

暂无评论

Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows

引用

IFAC-PapersOnLine 2017年第1期50卷 10778-10783页

作者： Yao L. Ge Z. Yuan X. Wang P. State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China School of Information Science and Engineering Central South University Changsha 410083 China School of Engineering Huzhou University Huzhou 313000 China

An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Bayes’ rule for quality estimation. However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. © 2017

关键词： Adaptive soft sensor expectation maximization algorithm Model integration Moving window Quality prediction Supervised latent factor analysis Weighted model

来源：评论

学校读者我要写书评

暂无评论

Robust track-to-track association algorithm based on t-distribution mixture model 20

Robust track-to-track association algorithm based on t-distr...

引用

20th International Conference on Information Fusion (Fusion)

作者： Li, Baozhu Liu, Ningbo Wang, Guoqing Qi, Lin Dong, Yunlong Naval Aeronaut & Astronaut Univ Inst Informat Fus Yantai Peoples R China

ISBN: (纸本)9780996452700

To address multi-sensor robust track-to-track association in the presence of sensor biases and missed detections, where sensors biases is time-varying and non-uniform, the target of different sensors is non-identical, the robust track-to-track association algorithm based on t-distribution mixture model is proposed. The robust track-to-track association problem is turned into the non-rigid point matching problem. Firstly, the orthogonal normalization reduce the general affine case of track point set;second, the heavy-tailed t-distribution mixture model is established with better robustness to tracks of non-common, solved by expectation maximization (EM) algorithm. The conditional expectation function is added a regular item of point sets that the points have a feature of Coherent Point Drift (CPD). Adaptability experiments are established to demonstrate the effectiveness of the proposed approaches compared with competing

关键词： Track association Sensor biases t-distribution mixture model expectation maximization algorithm Coherent Point Drift

来源：评论

学校读者我要写书评

暂无评论

A New Simple Model for Underwater Wireless Optical Channels in the Presence of Air Bubbles

A New Simple Model for Underwater Wireless Optical Channels ...

引用

IEEE Global Telecommunications Conference (GLOBECOM)

作者： Zedini, Emna Oubei, Hassan M. Kammoun, Abla Hamdi, Mounir Ooi, Boon S. Alouini, Mohamed-Slim HBKU Coll Sci & Engn Doha Qatar KAUST CEMSE Div Thuwal Saudi Arabia

ISBN: (纸本)9781509050192

A novel statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical channels in the presence of air bubbles for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture ExponentialGamma distribution. We use the expectation maximization (EM) algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all the channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, the application of the Exponential-Gamma model leads to closed-form and analytically tractable expressions for key system performance metrics such as the outage probability and the average bit-error rate.

关键词： Underwater wireless optical communication (UWOC) channel modeling distribution fitting maximum likelihood estimation expectation maximization algorithm mixture models

来源：评论

学校读者我要写书评

暂无评论

An expectation maximization approach to Joint Modeling of Multidimensional Ratings derived from Multiple Annotators 17

An Expectation Maximization approach to Joint Modeling of Mu...

引用

17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Ramakrishna, Anil Gupta, Rahul Grossman, Ruth B. Narayanan, Shrikanth S. Univ Southern Calif Signal Anal & Interpretat Lab Los Angeles CA 90007 USA Emerson Coll Dept Commun Sci & Disorders Boston MA 02116 USA

ISBN: (纸本)9781510833135

Ratings from multiple human annotators are often pooled in applications where the ground truth is hidden. Examples include annotating perceived emotions and assessing quality metrics for speech and image. These ratings are not restricted to a single dimension and can be multidimensional. In this paper, we propose an expectation-maximization based algorithm to model such ratings. Our model assumes that there exists a latent multidimensional ground truth that can be determined from the observation features and that the ratings provided by the annotators are noisy versions of the ground truth. We test our model on a study conducted on children with autism to predict a four dimensional rating of expressivity, naturalness, pronunciation goodness and engagement. Our goal in this application is to reliably predict the individual annotator ratings which can be used to address issues of cognitive load on the annotators as well as the rating cost. We initially train a baseline directly predicting annotator ratings from the features and compare it to our model under three different settings assuming: (i) each entry in the multidimensional rating is independent of others, (ii) a joint distribution among rating dimensions exists, (iii) a partial set of ratings to predict the remaining entries is available.

关键词： Multiple Annotator Modeling expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows

Adaptive Soft Sensor Modeling Based on Weighted Supervised L...

引用

20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Yao, Le Ge, Zhiqiang Yuan, Xiaofeng Wang, Peiliang Zhejiang Univ Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Huzhou Univ Sch Engn Huzhou 313000 Peoples R China

An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Hayes' rule for quality estimation However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Supervised latent factor analysis Weighted model expectation maximization algorithm Adaptive soft sensor Moving window Model integration Quality prediction

来源：评论

学校读者我要写书评

暂无评论

Robust soft sensor development using multi-rate measurements

Robust soft sensor development using multi-rate measurements

引用

20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Wu, Ouyang Kodamana, Hariprasad Jan, Nabil Magbool Tan, Ruomu Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2R3 Canada

Two different types of measurements are often available for the key quality variables in process industries- (a) an accurate "slow-rate" laboratory measurements, and (b) a less accurate "fast-rate" online analyser measurements. Also, the analyser measurements are prone to fail due to hardware issues. Therefore, the main objective of this work is to present a novel approach for developing an accurate, fast-rate, inferential model of quality variables which is robust to outliers. For this purpose, we present a maximum likelihood based approach to integrate the multi-rate output data in the model building task, using expectation maximization algorithm. The efficacy of the proposed approach is demonstrated using a simulation example. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： soft sensor static model multi-rate expectation maximization algorithm t-distribution flat-topped t-distribution

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：