检索结果-内蒙古大学图书馆

Control chart for monitoring the Weibull shape parameter under two competing risks

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2019年第7期48卷 2125-2137页

作者： Haghighi, Firoozeh Castagliola, Philippe Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran IRCCyN UMR CNRS Nantes France Univ Nantes UMR 6004 LS2N Nantes France

In this paper, we propose a control chart to monitor the Weibull shape parameter where the observations are censored due to competing risks. We assume that the failure occurs due to two competing risks that are independent and follow Weibull distribution with different shape and scale parameters. The control charts are proposed to monitor one or both of the shape parameters of competing risk distributions and established based on the conditional expected values. The proposed control chart for both shape parameters is used in certain situations and allows to monitor both shape parameters in only one chart. The control limits depend on the sample size, number of failures due to each risk and the desired stable average run length (ARL). We also consider the estimation problem of the target parameters when the Phase I sample is incomplete. We assumed that some of the products that fail during the life testing have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the expectation-maximization (em) algorithm is proposed to estimate the parameters. For both cases, with and without masking, the behaviour of ARLs of charts is studied through the numerical methods. The influence of masking on the performance of proposed charts is also studied through a simulation study. An example illustrates the applicability of the proposed charts.

关键词： Censoring Competing risks Complete-data likelihood Conditional expected values Control chart em algorithm Masked data Missing Weibull distribution

来源：评论

学校读者我要写书评

暂无评论

Compounded inverse Weibull distributions: Properties, inference and applications

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2019年第7期48卷 2012-2033页

作者： Chakrabarty, Jimut Bahan Chowdhury, Shovan Indian Inst Management Quantitat Methods & Operat Management Area Kozhikode India

In this paper two probability distributions are analyzed which are formed by compounding inverse Weibull with zero-truncated Poisson and geometric distributions. The distributions can be used to model lifetime of series system where the lifetimes follow inverse Weibull distribution and the subgroup size being random follows either geometric or zero-truncated Poisson distribution. Some of the important statistical and reliability properties of each of the distributions are derived. The distributions are found to exhibit both monotone and non-monotone failure rates. The parameters of the distributions are estimated using the expectation-maximization algorithm and the method of minimum distance estimation. The potentials of the distributions are explored through three real life data sets and are compared with similar compounded distributions, viz. Weibull-geometric, Weibull-Poisson, exponential-geometric and exponential-Poisson distributions.

关键词： em algorithm Geometric distribution Inverse Weibull distribution Maximum likelihood estimation Method of minimum distance Zero-truncated Poisson distribution

来源：评论

学校读者我要写书评

暂无评论

Parameter Estimation of Mt/M/1/K Queueing Systems With Utilization Data

引用

IEEE ACCESS 2019年 7卷 42664-42671页

作者： Li, Chen Okamura, Hiroyuki Dohi, Tadashi Hiroshima Univ Grad Sch Engn Dept Informat Engn Social Comp Lab Higashihiroshima Japan Hiroshima Univ Grad Sch Engn Dept Informat Engn Dependable Syst Lab Higashihiroshima Japan

Utilization data are defined as the time series data consisting of time fractions of busy periods in fixed time intervals and are practically used to represent server conditions, such as CPU utilization. In general, it is more challenging to estimate the model parameters from the utilization data since we do not know the exact job arrival time and the service time from the utilization data. In this paper, we consider an approach to estimate the model parameters from the utilization data by assuming a few model assumptions. In particular, we suppose an M-t/M/1/K queueing system whose job arrival follows a Non-homogeneous Poisson Process (NHPP) and propose a parameter estimation method for the NHPP approximately from the utilization data based on the maximum likelihood estimation (MLE) via the expectation maximization (em) algorithm. In numerical experiments, we generate the simulated utilization data of an M-t/M/1/K queueing system and investigate the effectiveness of our method. Also, we use the real CPU utilization data to exhibit the performance evaluation.

关键词： Queueing systems time intervals utilization data em algorithm

来源：评论

学校读者我要写书评

暂无评论

Multilevel logistic cluster-weighted model for outcome evaluation in health care*

引用

STATISTICAL ANALYSIS AND DATA MINING 2019年第5期12卷 434-443页

作者： Berta, Paolo Vinciotti, Veronica Univ Bicocca Milan Dept Stat & Quantitat Methods Milan Italy Brunel Univ London Dept Math London England

In health care, multilevel models are typically used to evaluate hospitals' performance and to rank hospitals accordingly. While multilevel models capture the hierarchical structure in the data, such as the grouping of patients into hospitals, these models do not account for additional latent structures. In this paper, we develop a novel multilevel logistic cluster-weighted model which can predict a binary outcome, such as mortality within 30 days of discharge, while accounting both for known and latent structures of the data. We develop an Expectation-Maximization algorithm for parameter estimation and a parametric bootstrap approach for assessing the variability of the estimators. Using a rich data set of the Lombardy (Italy) health care system and focussing on the two wards of cardiosurgery and medicine, we show how the proposed model detects, in both cases, two well-defined clusters within the patient to hospital hierarchical structure of the data. A comparison with standard multilevel and cluster-weighted approaches reveals a better fit of the proposed model and a greater insight into the structure of the data. We show how this can have implications in the resulting league tables and thus how the proposed model can be a useful tool for policy-makers and healthcare managers to conduct hospital evaluations.

关键词： cluster-weighted models em algorithm health care hospital evaluation model-based clustering multilevel models

来源：评论

学校读者我要写书评

暂无评论

Preserving Location Privacy in Spatial Crowdsourcing Under Quality Control

引用

IEEE ACCESS 2019年 7卷 155851-155859页

作者： Chu, Xiang Liu, Jun Gong, Daqing Wang, Rui Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Beijing Foreign Studies Univ Int Business Sch Beijing 100089 Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Dalian Univ Technol Fac Econ & Management Dalian 116000 Peoples R China

emerging spatial crowdsourcing (SC) provides an approach for collecting and analyzing spatiotemporal information from intelligent transportation systems. However, the exposure of massive location privacy to potential adversaries for the purpose of quality control makes workers more vulnerable. To protect workers location privacy, an obfuscation scheme is proposed to incorporate uncertainties into the SC quality control problem through obfuscating the standard location data in terms of both space and time. Two measures, location entropy and results accuracy, are used to evaluate the performance of location privacy protection. We theoretically and experimentally confirm the security and accuracy of the obfuscation approach. The results of experiments show that: a) hiding workers location from the requester reduces the quality of SC;and b) obfuscation arithmetic with appropriate obfuscation coefficients protects workers location privacy with little effect on SC quality. Under the protection of this obfuscation scheme, the new system provides better security and similar quality compared to the existing SC system.

关键词： Task analysis Privacy Quality control Crowdsourcing Uncertainty Entropy Data privacy Spatial crowdsourcing obfuscation location privacy of workers quality control em algorithm

来源：评论

学校读者我要写书评

暂无评论

Immune intelligent online modeling for the stretching process of fiber

引用

INFORMATION SCIENCES 2019年 485卷 219-229页

作者： Chen, Lei Ding, Yongsheng Hao, Kuangrong Donghua Univ Engn Res Ctr Digitized Text & Fash Technol Minist Educ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

An intelligent online modeling approach using the immune mechanisms for the stretching process of fiber is proposed in this paper. Linear parameter varying model is utilized as the process model, and the parameters are estimated under the framework of the expectation maximization algorithm. The proposed approach is composed of offline modeling and online modeling. For the offline modeling, the parameters of the local models as well as the weighting functions are estimated simultaneously, and this is the process of producing the first antibodies. Since the process is dynamic and continuous, when new operating point is different from the historical data, the online modeling approach is applied to estimate the parameters of new local models and weighting functions, and this is the process of producing the forthcoming antibodies. The proposed intelligent online modeling approach has the capability of learning and evolution, and can update the parameters adaptively. Apply the proposed approach to the stretching process and compare with other modeling methods, and then the Friedman and the Nemenyi post-hoc tests for assessing the statistical significance of differences in performance are analyzed. The feasibility and efficiency are demonstrated. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Online modeling Immune mechanisms Intelligent modeling approach Stretching process em algorithm

来源：评论

学校读者我要写书评

暂无评论

An improved clustering algorithm based on finite Gaussian mixture model

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第17期78卷 24285-24299页

作者： He, Zhilin Ho, Chun-Hsing Yuncheng Univ Math & Informat Technol Sch 1155 Fudan West St Yuncheng Shanxi Peoples R China No Arizona Univ Dept Civil Engn Construct Management & Environm E POB 15600 Flagstaff AZ 86011 USA

The Finite Gaussian Mixture Model (FGMM) is the most commonly used model for describing mixed density distribution in cluster analysis. An important feature of the FGMM is that it can infinitely approximate any continuous distribution, as long as the model contains enough number of components. In the clustering analysis based on the FGMM, the em algorithm is usually used to estimate the parameters of the model. The advantage is that the computation is stable and the convergence speed is fast. However, the em algorithm relies heavily on the estimation of incomplete data. It does not use any information to reduce the uncertainty of missing data. To solve this problem, an em algorithm based on entropy penalized maximum likelihood estimation is proposed. The novel algorithm constructs the conditional entropy model between incomplete data and missing data, and reduces the uncertainty of missing data through incomplete data. Theoretical analysis and experimental results show that the novel algorithm can effectively adapt to the FGMM, improve the clustering results and improve the efficiency of the algorithm.

关键词： Gaussian mixture model em algorithm Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

Finite mixture biclustering of discrete type multivariate data

引用

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2019年第1期13卷 117-143页

作者： Fernandez, Daniel Arnold, Richard Pledger, Shirley Liu, Ivy Costilla, Roy CIBERSAM Inst Recerca St Joan de Deu Parc Sanitari St Joan de DeuDr Antoni Pujades 42 Barcelona 08830 Spain Victoria Univ Wellington Sch Math & Stat Wellington New Zealand Univ Queensland Inst Mol Biosci Brisbane Qld Australia

Many of the methods which deal with clustering in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. In general, it is not possible to use statistical inferences or select the appropriateness of a model via information criteria with these techniques because there is no underlying probability model. This article summarizes some recent model-based methodologies for matrices of binary, count, and ordinal data, which are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. The model parameter can be constructed from a linear predictor of parameters and covariates through link functions. This likelihood-based one-mode and two-mode fuzzy clustering provides maximum likelihood estimation of parameters and the options of using likelihood information criteria for model comparison. Additionally, a Bayesian approach is presented in which the parameters and the number of clusters are estimated simultaneously from their joint posterior distribution. Visualization tools focused on ordinal data, the fuzziness of the clustering structures, and analogies of various standard plots used in the multivariate analysis are presented. Finally, a set of future extensions is enumerated.

关键词： Classification em algorithm Fuzzy clustering Mixture models Ordinal data RJMCMC Visualisation tools

来源：评论

学校读者我要写书评

暂无评论

A generative model for exploring structure regularities in attributed networks

引用

INFORMATION SCIENCES 2019年 505卷 252-264页

作者： Chang, Zhenhai Jia, Caiyan Yin, Xianjun Zheng, Yimei Tianshui Normal Univ Sch Math & Stat Tianshui Gansu Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Cent Univ Finance & Econ Sch Stat & Math Beijing Peoples R China

Many real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. In this paper, by characterizing the potential relationship between communities of links and node attributes, a principled statistical model named PSB_PG that generates link topology and node attributes is proposed. This model for generating links is based on the stochastic blockmodels following a Poisson distribution. Therefore, it is capable of detecting a wide range of network structures including community structures, bipartite structures, and other mixture structures. The model for generating node attributes assumes that node attributes are high-dimensional, sparse, and also follow a Poisson distribution. This makes the model be uniform, and the model parameters can be directly estimated by the expectation-maximization (em) algorithm. Experimental results on artificial networks and real networks containing various structures have shown that the proposed model PSB_PG is not only competitive with the state-of-the-art models, but also provides a good semantic interpretation for each community via the learned relationship between the community and its related attributes. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Attributed network Community detection General structure Stochastic blockmodel em algorithm

来源：评论

学校读者我要写书评

暂无评论

Spectrum adapted the expectation-maximization algorithm for high-throughput peak shift analysis

引用

SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2019年第1期20卷 733-745页

作者： Matsumura, Tarojiro Nagamura, Naoka Akaho, Shotaro Nagata, Kenji Andoa, Yasunobu Natl Inst Adv Ind Sci & Technol Res Ctr Computat Design Adv Funct Mat CD FMat Tsukuba Ibaraki 3058568 Japan NIMS Res Ctr Adv Measurement & Characterizat Tsukuba Ibaraki Japan Japan Sci & Technol Agcy PRESTO Saitama Japan Natl Inst Adv Ind Sci & Technol AIRC Tokyo Japan

We introduce a spectrum-adapted expectation-maximization (em) algorithm for high-throughput analysis of a large number of spectral datasets by considering the weight of the intensity corresponding to the measurement energy steps. Proposed method was applied to synthetic data in order to evaluate the performance of the analysis accuracy and calculation time. Moreover, the proposed method was performed to the spectral data collected from graphene and MoS2 field-effect transistors devices. The calculation completed in less than 13.4 s per set and successfully detected systematic peak shifts of the C 1s in graphene and S 2p in MoS2 peaks. This result suggests that the proposed method can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed;and (2) stable and automatic calculation can be easily performed. [GRAPHICS] .

关键词： em algorithm peak separation spectral data XPS analysis machine learning

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：