检索结果-内蒙古大学图书馆

3DACN: 3D Augmented convolutional network for time series data

INFORMATION SCIENCES 2020年 513卷 17-29页

作者： Pei, Songwen Shen, Tianma Wang, Xianrong Gu, Chunhua Ning, Zhong Ye, Xiaochun Xiong, Naixue Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Fudan Univ Sch Management Shanghai 200433 Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing 100190 Peoples R China Northeastern State Univ Dept Comp Sci Tahlequah OK 74464 USA

Time series data and non-time series data are increasing in the credit system of financial market, so that an effective and intelligent data mining model plays a critical role to analyze hybrid time series data. In addition, traditional mining models sometimes fail to converge because of imbalanced data problem. Therefore, we propose a 3D Augmented Convolutional Network (3DACN) to extract time series information and solve the serious imbalanced data problem. By using the augmented algorithm on time series data, hybrid time series data are enlarged to generate more examples on the minority classes. 3DACN ensures the latent variables with an expectation-maximization(EM) algorithm to improve F1 score (F1) and Area Under Curve (AUC). Experimental results show that in the benchmark of Bank database, it can gain F1 score by 81.1% and the AUC by 88.2% respectively;while in the benchmark of Credit Risk database, the 3DACN can reach high performance on F1 score by 88.1% and the AUC by 88.4%. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Time series data Gated recurrent units Convolutional neural network expectation-maximization algorithm Augmented algorithm

来源：评论

学校读者我要写书评

暂无评论

Lognormal and log-logistic distribution mixture analysis and application

引用

MODERN PHYSICS LETTERS B 2022年第5期36卷

作者： Kocovic, Jelena Mitic, Vojislav V. Koprivica, Marija Rajic, Vesna Lazovic, Goran Univ Belgrade Fac Econ Dept Stat & Math Belgrade Serbia SASA Inst Tech Sci Belgrade Serbia Univ Nis Fac Elect Engn Nish Serbia Univ Belgrade Fac Mech Engn Dept Math Belgrade Serbia

In this paper, we analyze a mixture of Lognormal and Log-Logistic distribution. We estimate the parameters of the introduced distribution by using the expectation-maximization (EM) algorithm. Various phenomena in the field of medicine and economy could be modeled by this mixture. In this paper, it is used to construct new mortality model for determining the unisex premium rates in life insurance. The application of the model is illustrated in the case of Serbian population and its advantages are presented in the context of life insurance premium calculation.

关键词： Mixture distribution expectation-maximization algorithm mortality model unisex premium rate

来源：评论

学校读者我要写书评

暂无评论

Fulfilling the information need after an earthquake: statistical modelling of citizen science seismic reports for predicting earthquake parameters in near realtime

引用

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2020年第3期183卷 857-882页

作者： Finazzi, Francesco Univ Bergamo Dalmine Italy

When an earthquake affects an inhabited area, a need for information immediately arises among the population. In general, this need is not immediately fulfilled by official channels which usually release expert-validated information with delays of many minutes. Seismology is among the research fields where citizen science projects succeeded in collecting useful scientific information. More recently, the ubiquity of smartphones is giving the opportunity to involve even more citizens. This paper focuses on seismic intensity reports collected through smartphone applications while an earthquake is occurring. The aim is to provide a framework for predicting and updating in near realtime earthquake parameters that are useful for assessing the effect of the earthquake. This is done by using a multivariate space-time model based on time-varying coefficients and a spatial latent variable. As a case-study, the model is applied to more than 200000 seismic reports globally collected over a period of around 4 years by the Earthquake Network citizen science project. It is shown how the time-varying coefficients are needed to adapt the model to an information content that changes with time, and how the spatial latent variable can capture the local seismicity and the heterogeneity in the people's response across the globe.

关键词： D-STEM software Dynamic kriging expectation-maximization algorithm Smartphone application Spatiotemporal modelling

来源：评论

学校读者我要写书评

暂无评论

Penalized estimation of semiparametric transformation models with interval-censored data and application to Alzheimer's disease

引用

STATISTICAL METHODS IN MEDICAL RESEARCH 2020年第8期29卷 2151-2166页

作者： Li, Shuwei Wu, Qiwei Sun, Jianguo Guangzhou Univ Sch Econ & Stat Guangzhou Guangdong Peoples R China Univ Missouri Dept Stat Columbia MO 65211 USA

Variable selection or feature extraction is fundamental to identify important risk factors from a large number of covariates and has applications in many fields. In particular, its applications in failure time data analysis have been recognized and many methods have been proposed for right-censored data. However, developing relevant methods for variable selection becomes more challenging when one confronts interval censoring that often occurs in practice. In this article, motivated by an Alzheimer's disease study, we develop a variable selection method for interval-censored data with a general class of semiparametric transformation models. Specifically, a novel penalized expectation-maximization algorithm is developed to maximize the complex penalized likelihood function, which is shown to perform well in the finite-sample situation through a simulation study. The proposed methodology is then applied to the interval-censored data arising from the Alzheimer's disease study mentioned above.

关键词： Alzheimer's disease expectation-maximization algorithm Penalized likelihood Transformation models Variable selection

来源：评论

学校读者我要写书评

暂无评论

Analysing the course of public trust via hidden Markov models: a focus on the Polish society

引用

STATISTICAL METHODS AND APPLICATIONS 2020年第2期29卷 399-425页

作者： Pennoni, Fulvia Genge, Ewa Univ Milano Bicocca Dept Stat & Quantit Methods Via Arcimboldi 8 I-20126 Milan Italy Univ Econ Dept Econ & Financial Anal Katowice Poland

We investigate public trust among the society by a statistical model suitable for panel data. At this aim, using trust's levels measured from individual items recorded through a long-term survey we dispose of key variables with appropriate meaning. We account for the repeated and missing item responses by a hidden Markov model using longitudinal sampling weights. Since trust may be conceived as a psychological unobservable process of each person that fluctuates over time we consider observed time-varying and time-fixed individual covariates. We estimate the model parameters by a weighted log-likelihood through the expectation-maximization algorithm by using data collected in an East-Central European country like Poland. The latter is a country where the level of support to the national and international institutions is one of the lowest among the European member states. We apply a suitable algorithm based on the posterior probabilities to predict the best allocation to each latent typology. The proposed model is validated by generating out-of-sample responses and we find reasonable predictive values. We disentangle four hidden groups of Poles: discouraged, with no opinion, with selective trust and with fully public trust. We reveal an increasing number of people that are going to trust only some selected institutions over time.

关键词： expectation-maximization algorithm Missing responses Panel data Sampling weights Trust-building policy discussion

来源：评论

学校读者我要写书评

暂无评论

Robust Parameter Estimation for a Class of Nonlinear System With EM algorithm

引用

IEEE ACCESS 2020年 8卷 30797-30804页

作者： Zhang, Tingting Liu, Xin Liu, Xiaofeng Hohai Univ Coll Internet Things Engn Changzhou 213022 Jiangsu Peoples R China

This paper is concerned with the robust parameter estimation for linear parameter varying (LPV) finite impulse response (FIR) model. The practical process data are typically polluted by outliers and conventional parameter estimation methods may fail to derive an unbiased estimate. In order to deal with outliers, the Laplace noise model is adopted and the robust system model for the described parameter estimation problem is established. The robust parameter estimation for LPV FIR model is formulated and solved in the EM algorithm scheme and the equations to estimate all the unknown parameters are derived. The efficacy of the proposed method is verified through a numerical simulation and a chemical unit.

关键词： expectation-maximization algorithm Laplace distribution LPV FIR model outliers robust parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Application of unsupervised learning and process simulation for energy optimization of a WWTP under various weather conditions

引用

WATER SCIENCE AND TECHNOLOGY 2020年第8期81卷 1541-1551页

作者： Borzooei, Sina Miranda, Gisele H. B. Abolfathi, Soroush Scibilia, Gerardo Meucci, Lorenza Zanetti, Maria Chiara Politecn Torino Dept Environm Land & Infrastruct Engn DIATI I-10129 Turin Italy KTH Royal Inst Technol Sch Elect Engn & Comp Sci Lindstedtsvagen 3 S-10044 Stockholm Sweden Sci Life Lab Tomtebodavagen 23A S-17165 Solna Sweden Univ Warwick Sch Engn Warwick Water Res Grp Coventry CV4 7AL W Midlands England SMAT Soc Metropolitana Acque Torino Res Ctr Corso Unita Italia 235-3 I-10127 Turin Italy

This paper outlines a hybrid modeling approach to facilitate weather-based operation and energy optimization for the largest Italian wastewater treatment plant (WWTP). Two clustering methods,K-means algorithm and Gaussian mixture model (GMM) based on the expectation-maximization (EM) algorithm, were applied to an extensive dataset of historical and meteorological records. This study addresses the problem of determining the intrinsic structure of clustered data when no information other than the observed values is available. Two quantitative indexes, namely the Bayesian information criterion (BIC) and the Silhouette coefficient using Euclidean distance, as well as two general criteria, were implemented to assess the clustering quality. Furthermore, seven weather-based influent scenarios were introduced to the process simulation model, and sets of aeration strategies are proposed. The results indicate that incorporating weather-based aeration strategies in the operation of the WWTP improves plant energy efficiency.

关键词： cluster analysis clustering validation energy optimization expectation-maximization algorithm Gaussian mixture models K-means algorithm

来源：评论

学校读者我要写书评

暂无评论

Pseudo-stochastic EM for sub-Gaussian α-stable mixture models

引用

DIGITAL SIGNAL PROCESSING 2020年第0期99卷 102671-000页

作者： Zarei, Shaho Mohammadpour, Adel Amirkabir Univ Technol Fac Math & Comp Sci Dept Stat Tehran Polytech Tehran Iran Univ Kurdistan Fac Sci Dept Stat Sanandaj Iran McGill Univ Dept Math & Stat Montreal PQ Canada

Due to the non-existence of a closed-form expression for sub-Gaussian alpha-stable densities, the M-step of the expectation-maximization (EM) algorithm for the Sub-Gaussian alpha-Stable Mixture Models (SGaSMMs) is intractable, and the EM algorithm for SGaSMM is still an open problem. SGaSMM can model non-homogeneous Gaussian data, accommodate outliers, and high leverage data points, which are concepts of primary importance in robust mixture models. These models are robust and useful tools in modeling heterogeneous data with outlier observations, such as clustering financial or impulsive data. In this paper, a new EM algorithm based on a combination of EM (the first part) and stochastic EM (the second part) algorithms, is used to obtain the maximum likelihood estimators of the parameters of SGaSMM in the M-step. In the first part, the model parameters, except as, are estimated from an analytical form via EM. In the second part, based on a stochastic EM, the maximum likelihood estimator of alpha, in each component, is calculated from pseudo-simulated data obtained by suitable rejection sampling. The efficiency of the proposed algorithm is illustrated by using both real and simulated data. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Mixture models Sub-Gaussian alpha-stable distribution expectation-maximization algorithm Rejection sampling

来源：评论

学校读者我要写书评

暂无评论

Space-time inhomogeneous background intensity estimators for semi-parametric space-time self-exciting point process models

引用

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2020年第4期72卷 945-967页

作者： Li, Chenlong Song, Zhanjie Wang, Wenjun Tianjin Univ Sch Math Tianjin 300072 Peoples R China Wilfrid Laurier Univ Dept Math Waterloo ON N2L 3C5 Canada Tianjin Univ Visual Pattern Anal Res Lab Tianjin 300072 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China

Histogram maximum likelihood estimators of semi-parametric space-time self-exciting point process models via expectation-maximization algorithm can be biased when the background process is inhomogeneous. We explore an alternative estimation method based on the variable bandwidth kernel density estimation (KDE) and EM algorithm. The proposed estimation method involves expanding the semi-parametric models by incorporating an inhomogeneous background process in space and time and applying the variable bandwidth KDE to estimate the background intensity function. Using an example, we show how the variable bandwidth KDE can be estimated this way. Two simulation examples based on residual analysis are designed to evaluate and validate the ability of our methods to recover the background intensity function and parametric triggering intensity function.

关键词： Space-time point process models Kernel density estimation expectation-maximization algorithm Maximum likelihood

来源：评论

学校读者我要写书评

暂无评论

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

引用

BRIEFINGS IN BIOINFORMATICS 2022年第1期23卷 bbab466-bbab466页

作者： Yang, Yi Shi, Xingjie Liu, Wei Zhou, Qiuzhong Lau, Mai Chan Lim, Jeffrey Chun Tatt Sun, Lei Ng, Cedric Chuan Young Yeong, Joe Liu, Jin Duke NUS Med Sch Hlth Serv & Syst Res Program Singapore 16985 Singapore East China Normal Univ Acad Stat & Interdisciplinary Sci Shanghai Peoples R China Duke NUS Med Sch Vasc & Metab Disorder Program Singapore Singapore ASTAR IMCB Singapore Singapore Duke NUS Med Sch Cardiovasc & Metab Disorders Program Singapore Singapore Natl Canc Ctr Singapore Canc Discovery Hub Singapore Singapore

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19

关键词： spatial transcriptomics cell phenotype empirical Bayes hidden Markov random field expectation-maximization 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：