检索结果-内蒙古大学图书馆

Simulation-Based Performance Evaluation of Missing Data Handling in Network Analysis

MULTIVARIATE BEHAVIORAL RESEARCH 2024年第3期59卷 461-481页

作者： Nehler, Kai Jannik Schultze, Martin Goethe Univ Frankfurt Dept Psychol Frankfurt Germany Goethe Univ Frankfurt Dept Psychol Theodor W Adorno Pl 6 D-60323 Frankfurt Germany

Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step em algorithm with separated steps for missing handling and regularization, a combined direct em algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples (n >= 500) or small networks (p = 10). Comparing the missing data approaches, the direct em appears to be more sensitive and superior in nearly all chosen conditions. The two-step em yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct em is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step em could improve its performance.

关键词： Network analysis missing values simulation study graphical lasso regularization em algorithms

来源：评论

学校读者我要写书评

暂无评论

Modelling and analysing the daily temperature of several cities using mixture Gaussian distributions

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHemATICS 2023年第4期17卷 320-341页

作者： Al-Hemyari, Zuhair A. Al Abbasi, Jamal N. Univ Nizwa CAS Dept Math & Phys Sci POB 33 Nizwa 616 Oman AL Nahrain Univ Dept Stat Baghdad Iraq

The rise in the surface temperature of the Earth since the 19th century is a clear indication of the negative and significant impact humans have had upon it. One of the results of this is that many researchers have studied meteorological problems. Some of the important applications for modelling the daily maximum and minimum temperatures reveal the importance of modelling the data for these phenomena using mixed distributions. In order to model the data of three cities over 40 years of daily maximum/minimum temperatures, heavy statistical computations have been carried out, and several single and mixture Gaussian distributions have developed the modelling of data of three cities for the period of 1980-2019 of daily maximum/minimum temperatures. In addition, the computations of expectation maximisation algorithm estimators, the tests of goodness, and the frequency distributions have been developed to carry out accurate analyses. The data is analysed, several indicators are seized, and the conclusions, recommendations, limitations, and future research directions are discussed.

关键词： Gaussian distribution mixture distributions MLE em algorithms goodness of fit Kolmogorov-Smirnov test Anderson-Darling test daily temperatures

来源：评论

学校读者我要写书评

暂无评论

Zero-inflated modeling part I: Traditional zero-inflated count regression models, their applications, and computational tools

引用

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2022年第1期14卷 e1541-e1541页

作者： Young, Derek S. Roemmele, Eric S. Yeh, Peng Univ Kentucky Dr Bing Zhang Dept Stat 323 Multidisciplinary Sci Bldg Lexington KY 40536 USA Merck Res Labs Upper Gwynedd PA USA

Count regression models maintain a steadfast presence in modern applied statistics as highlighted by their usage in diverse areas like biometry, ecology, and insurance. However, a common practical problem with observed count data is the presence of excess zeros relative to the assumed count distribution. The seminal work of Lambert (1992) was one of the first articles to thoroughly treat the problem of zero-inflated count data in the presence of covariates. Since then, a vast literature has emerged regarding zero-inflated count regression models. In this first of two review articles, we survey some of the classic and contemporary literature on parametric zero-inflated count regression models, with emphasis on the utility of different univariate discrete distributions. We highlight some of the primary computational tools available for estimating and assessing the adequacy of these models. We concurrently emphasize the diverse data problems to which these models have been applied. This article is categorized under: Statistical Models > Generalized Linear Models Software for Computational Statistics > Software/Statistical Software algorithms and Computational Methods > Maximum Likelihood Methods

关键词： data dispersion em algorithms generalized linear models marginalized models mixture models statistical software

来源：评论

学校读者我要写书评

暂无评论

DOA Estimation for Low-Cost Array Antenna System Based on GROUSE and em algorithms

DOA Estimation for Low-Cost Array Antenna System Based on GR...

引用

IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

作者： Yamada, Keita Ichige, Koichi Yokohama Natl Univ Dept Elect & Comp Engn Hodogaya Ku Yokohama Kanagawa 2408501 Japan

ISBN: (纸本)9781479952304

This paper proposes a high-resolution DOA (Direction Of Arrival) estimation method using GROUSE and em algorithms for low-cost array antenna system. The low-cost array antenna system, which uses less receiver modules than array elements, often leads worse DOA estimation accuracy when comparing with the full-cost array antenna system. The proposed method first employs the modified GROUSE algorithm to pre-estimate DOAs, and then the em algorithm is applied to further improve the DOA estimation accuracy. Performance of the proposed approach is evaluated through computer simulation.

关键词： antenna arrays direction-of-arrival estimation expectation-maximisation algorithm DOA estimation method em algorithms array antenna system direction of arrival modified GROUSE algorithm Accuracy Antenna arrays Arrays Direction-of-arrival estimation Estimation Receivers Signal to noise ratio Direction of arrival estimation expectation-maximisation algorithm Antenna arrays signal to noise ratios Receivers Grouse Angle of arrival ANTENNA SYSTem

来源：评论

学校读者我要写书评

暂无评论

A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network

引用

International Journal of Information Technology (Singapore) 2022年第4期14卷 2021-2033页

作者： Kalnoor, Gauri Gowrishankar, S. BMS College of Engineering Bangalore India

Significant growth of smart home devices is adopted, which provides security, convenience, and energy efficiency for users, in recent years. As an instance, consider a secured smart camera that detects movements of unauthorized objects, whereas the fire accidents can be detected by smoke sensors. Though, a surface for new cyber threats is some of the most recent examples that are open up in this field. Furthermore, recent examples also show that the distributed denial of service (DDoS) attacks are performed by misusing the smart devices which are hacked violating the privacy rules. In this proposed work, the application of machine learning in an environment of smart homes is explored so that the anomalous activities that occur can be identified. The sensor data at the network level is trained using a model based on the Markov chain known as hidden Markov model (HMM) and is created using smart devices and multiple sensors from a testbed. The model generated using HMM achieves 97% accuracy while detecting potential anomalies where attacks are indicated. In this approach, we construct the model and differentiate the analysed results with existing techniques. The starting step for securing IoT networks is intrusion detection and the next step is the intruders prediction which provides an active defence against the incoming attacks. The model employs an algorithm that does not depend on specific domain knowledge. The work has achieved an improvement in prediction accuracy of 5% for an alert category over the current variable length methods of Markov chain intrusion prediction, as they provided information more for a possible defence. The DDoS attack is considered as a coordinated attack mainly carried out based on a large scale depending on whether the target system’s resources or services are available. Thus, the novel approach also describes a novel machine learning-based technique using an algorithm known as variational dynamic Bayesian algorithm which helps to obtain

关键词： DDoS Dynamic Bayesian networks em algorithms Gibbs sampling Hidden Markov models Network attack State space models Variational Bayesian Variational methods

来源：评论

学校读者我要写书评

暂无评论

Robust estimation using multivariate t innovations for vector autoregressive models via ECM algorithm

引用

JOURNAL OF APPLIED STATISTICS 2021年第4期48卷 693-711页

作者： Nduka, Uchenna C. Ugah, Tobias E. Izunobi, Chinyeaka H. Univ Nigeria Dept Stat Nsukka Nigeria Fed Univ Technol Owerri Dept Stat Owerri Nigeria

This paper considers the vector autoregressive model of order p, VAR(p), with multivariate t error distributions, the latter being more prevalent in real life than the usual multivariate normal distribution. It is believed that the maximum-likelihood equations for the multivariate t distribution have convergence problem, hence we develop estimation procedures for VAR(p) model using the normal mean-variance mixture representation of multivariate t distribution. The procedure relies on the computational ease available in Expectation Maximization-based algorithms. The estimators obtained are explicit functions of sample observations and therefore are easy to compute. Extensive simulation experiments show that the estimators have negligible bias and are considerably more efficient than an existing method that uses the least-squares error approach. It is shown that the proposed estimators are robust to plausible deviations from an assumed distribution and hence are more advantageous when compared with the other estimator. One real-life example is given for illustration purposes.

关键词： em algorithms maximum likelihood estimation multivariate t distribution robust estimation vector autoregressive model

来源：评论

学校读者我要写书评

暂无评论

Functional mixtures-of-experts

引用

STATISTICS AND COMPUTING 2024年第3期34卷 98-98页

作者： Chamroukhi, Faicel Pham, Nhat Thien Hoang, Van Ha Mclachlan, Geoffrey J. IRT SystemX 2 Blvd Thomas Gobert F-91120 Palaiseau France Normandie Univ UNICAEN CNRS LMNO F-14000 Caen France Vietnam Natl Univ Univ Sci Ho Chi Minh City Vietnam Univ Queensland Sch Math & Phys Brisbane Australia

We consider the statistical analysis of heterogeneous data for prediction, in situations where the observations include functions, typically time series. We extend the modeling with mixtures-of-experts (ME), as a framework of choice in modeling heterogeneity in data for prediction with vectorial observations, to this functional data analysis context. We first present a new family of ME models, named functional ME (FME), in which the predictors are potentially noisy observations, from entire functions. Furthermore, the data generating process of the predictor and the real response, is governed by a hidden discrete variable representing an unknown partition. Second, by imposing sparsity on derivatives of the underlying functional parameters via Lasso-like regularizations, we provide sparse and interpretable functional representations of the FME models called iFME. We develop dedicated expectation-maximization algorithms for Lasso-like regularized maximum-likelihood parameter estimation strategies to fit the models. The proposed models and algorithms are studied in simulated scenarios and in applications to two real data sets, and the obtained results demonstrate their performance in accurately capturing complex nonlinear relationships and in clustering the heterogeneous regression data.

关键词： Mixtures-of-experts Functional data analysis em algorithms Maximum likelihood estimation Lasso regularization Clustering

来源：评论

学校读者我要写书评

暂无评论

Modeling the length-of-stay of patients with geriatric diseases or alcohol use disorder using phase-type distributions with covariates

引用

IISE TRANSACTIONS ON HEALTHCARE SYSTemS ENGINEERING 2021年第3期11卷 181-191页

作者： Gu, Wanlu Fan, Neng Liao, Haitao Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Univ Arkansas Dept Ind Engn Fayetteville AR USA

The hospital length-of-stay (LOS), as an important measure of the effectiveness of healthcare, represents the level of medical requirement and is highly related to the treatment costs. As the human life expectancy has being increased rapidly in the past few decades, there is a pressing need to improve health systems for geriatric patients. Similarly, the alcohol use disorder (AUD), as a chronic relapsing brain disease related to severe problem drinking, has caused negative impacts to society and put patients' health and safety at risk. In both cases, more efficient hospital management is in demand due to increasing requirements for long-term hospital treatment and the continuously rising medical cost. In order to improve the healthcare efficiency, an accurate modeling of the LOS data and the further analysis of potential influencing factors are necessary. In this paper, we utilize the Coxian Phase-Type (PH) distribution and apply Maximum Likelihood Estimation (MLE) to fit the patient flow information of both geriatric patients and AUD patients collected in a hospital. The influences of the covariates of age, gender, admission type, admit source, and financial class on LOS are assessed and compared through Expectation-Maximization (em) algorithms. The results show that the LOS data of both types of patients can be modeled well, and the differences with respect to covariates can be accurately identified by the proposed methods. Using the fitted Coxian PH distribution and the estimated coefficients of covariates will provide a guide for better decision-making in healthcare service and resource allocation.

关键词： Phase-type distribution healthcare quality length-of-stay covariate em algorithms

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal mixture process estimation to detect dynamical changes in population

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 126卷 102258-102258页

作者： Pruilh, Solange Jannot, Anne-Sophie Allassonniere, Stephanie Ecole Polytech Ctr Appl Math Route Saclay F-91128 Palaiseau France Sorbonne Univ Univ Paris INRIA INSERMUMR S1138 Paris France Hop Europeen Georges Pompidou AP HP Dept Stat Med Informat & Publ Hlth Paris France

Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatiotemporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (em) algorithm to better estimate the number of clusters and their parameters at the same time. This algorithm is compared to existing methods on several simulated datasets. We then combine the algorithm with a temporal statistical model, allowing for the detection of dynamical changes in population distributions, and call the result a spatio-temporal mixture process (STMP). We test STMPs on synthetic data, and consider several different behaviors of the distributions, to fit this process. Finally, we validate STMPs on a real data set of positive diagnosed patients to coronavirus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes.

关键词： Gaussian Mixture Model em algorithms Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Data-driven modeling of the temporal evolution of breakers' states in the French electrical transmission grid?

引用

NONLINEAR ANALYSIS-HYBRID SYSTemS 2022年第0期46卷

作者： Gonzalez, Mauricio Girard, Antoine EthiFinance EthiFinance Analyt 11 Ave Delcasse F-75008 Paris France Univ Paris Saclay CNRS CentraleSupelec L2S 3 rue Joliot Curie F-91190 Gif Sur Yvette France

In electrical transmission grids, it is common to observe the states of circuit breakers. While they are known at irregular times, system modeling and grid state estimation are of the highest importance to ensure secure operations. This paper proposes a richer method to estimate the grid state over its reference configurations based on the temporal evolution of its breakers' states. The first contribution consists in developing a general multi-observation continuous-time finite-state Hidden Markov Model with filter-based parameter estimation to infer the hidden state (e.g., the grid reference configuration) handling multiple observed processes with irregular ''jump"times (e.g., the breakers' states). As a second contribution, we build a numerical scheme with no discretization error adapted to all state jumps generated by the observed processes. Finally, we apply our model to simulated and real data to illustrate the approach's performance. The available data consists of historical records of breakers' states during the electrical transmission grid operated normally. For this real-data-driven application, we also present a clustering approach to identify the set of grid reference configurations. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Data-driven modeling Hidden Markov Models em algorithms Electrical transmission grid

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：