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User credibility evaluation for reputation measurement of online service

INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTemS 2024年第2期20卷 176-194页

作者： Xiong, Yahan Fu, Xiaodong Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Yunnan Key Lab Comp Technol Applicat Kunming Yunnan Peoples R China

PurposeUsers often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report ***/methodology/approachIn this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users' credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user *** analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating ***/valueThe proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

关键词： User credibility Peer prediction Online service reputation em algorithm

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Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment

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STATISTICS IN MEDICINE 2025年第3-4期44卷 e10332页

作者： Wu, Ruiqian Zhang, Ying Bakoyannis, Giorgos Univ Nebraska Med Ctr Dept Biostat Omaha NE 68198 USA Indiana Univ Dept Biostat & Hlth Data Sci Indianapolis IN USA

The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task. The Gamma-Frailty conditional Markov model has been shown to be an efficient computation model for studying semi-competing risks data. Building on recent advances in studying semi-competing risks data, this work proposes a non-parametric pseudo-likelihood method equipped with an em-like algorithm to study semi-competing risks data with event misascertainment under the restricted Gamma-Frailty conditional Markov model. A thorough simulation study is conducted to demonstrate the inference validity of the proposed method and its numerical stability. The proposed method is applied to a large HIV cohort study, EA-IeDEA, that has a severe death under-reporting issue to assess the degree of adverse impact of the interruption of ART care on HIV mortality.

关键词： em algorithm illness-death model missing cause of failure non-parametric pseudo-likelihood estimation semi-competing risks

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Model-Based Tensor Low-Rank Clustering

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2024年第1期33卷 208-218页

作者： Li, Junge Mai, Qing Florida State Univ Dept Stat Tallahassee FL 32306 USA

Tensors have become prevalent in business applications and scientific studies. It is of great interest to analyze and understand the heterogeneity in tensor-variate observations. We propose a novel tensor low-rank mixture model (TLMM) to conduct efficient estimation and clustering on tensors. The model combines the Tucker low-rank structure in mean contrasts and the separable covariance structure to achieve parsimonious and interpretable modeling. To implement efficient computation under this model, we develop a low-rank enhanced expectation-maximization (LEem) algorithm. The pseudo E-step and the pseudo M-step are carefully designed to incorporate variable selection and efficient parameter estimation. Numerical results in extensive experiments demonstrate the encouraging performance of the proposed method compared to popular vector and tensor methods. for this article are available online.

关键词： Dimension reduction em algorithm Mixture models Sparsity Tucker decomposition

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Finite Mixtures of Latent Trait Analyzers With Concomitant Variables for Bipartite Networks: An Analysis of COVID-19 Data

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MULTIVARIATE BEHAVIORAL RESEARCH 2024年第4期59卷 801-817页

作者： Failli, Dalila Marino, Maria Francesca Martella, Francesca Univ Firenze Dipartimento Stat Informat Applicazioni Florence Italy Sapienza Univ Roma Dipartimento di Sci Stat Rome Italy

Networks consist of interconnected units, known as nodes, and allow to formally describe interactions within a system. Specifically, bipartite networks depict relationships between two distinct sets of nodes, designated as sending and receiving nodes. An integral aspect of bipartite network analysis often involves identifying clusters of nodes with similar behaviors. The computational complexity of models for large bipartite networks poses a challenge. To mitigate this challenge, we employ a Mixture of Latent Trait Analyzers (MLTA) for node clustering. Our approach extends the MLTA to include covariates and introduces a double em algorithm for estimation. Applying our method to COVID-19 data, with sending nodes representing patients and receiving nodes representing preventive measures, enables dimensionality reduction and the identification of meaningful groups. We present simulation results demonstrating the accuracy of the proposed method.

关键词： Model-based clustering network data nodal attributes em algorithm variational inference COVID-19 pandemic

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A General Inferential Framework for Singly-Truncated Bivariate Normal Models with Applications in Economics

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COMPUTATIONAL ECONOMICS 2024年第5期64卷 2747-2781页

作者： Liu, Yin Tian, Guo-Liang Zhang, Chi Qin, Hong Zhongnan Univ Econ & Law Sch Math & Stat Wuhan Hubei Peoples R China Southern Univ Sci & Technol Dept Stat & Data Sci Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Econ Shenzhen Guangdong Peoples R China

To analyze the singly-truncated bivariate economic data, we establish a class of singly-truncated bivariate normal distributions via stochastically representing the original bivariate normal random vector as a mixture of the singly-truncated part and its complementary components. Aided with the stochastic representaion, we creatively construct two novel unified and simple algorithms-the expectation-maximization algorithm as well as the minorization-maximization algorithm-to calculate the maximum likelihood estimates of the means and covariance matrix for the model of interest. In addition, we also develop a DA algorithm for posterior sampling in Bayesian analysis. Both simulation results and two real data applications in economics, collaborated by comparisons with existing methods, demonstrate the effectiveness and stability of proposed methodologies.

关键词： Economic index em algorithm MM algorithm Singly-truncated bivariate normal distribution Stochastic representation Truncated data

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Detection of anomalous behaviour using mixtures of von Mises distributions and an extension of the CUSUM algorithm

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2025年第7期95卷 1556-1571页

作者： Lacomba, Diego Simo, Amelia Belmonte, Oscar Univ Jaume 1 Dept Math Avinguda Riu Sec S-N Castellon de La Plana Spain Univ Jaume 1 IMAC Avinguda Riu Sec S-N Castellon de La Plana Spain Univ Jaume 1 Dept Comp Languages & Syst Castellon de La Plana Spain

The detection of anomalies in the daily behaviour of a monitored person is of crucial interest to discover degenerative diseases, medication changes or any important problem in the health of the monitored person. In this work we focus on the detection of anomalies in the transit between the different rooms of his/her house. For this purpose, we propose to model the transit times using a mixture of von Mises distributions and estimate the parameters using the em algorithm. An extension of the CUSUM algorithm is proposed to detect changes. This extension is based on reformulating this algorithm as a hypothesis test on the model parameters, using the likelihood ratio. In order to verify the validity of the method, extensive experimentation has been performed.

关键词： Anomalies detection von Mises distribution em algorithm CUSUM algorithm

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Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data

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STATISTICS IN MEDICINE 2024年第20期43卷 3899-3920页

作者： Zhao, Kaiqiong Oualkacha, Karim Zeng, Yixiao Shen, Cathy Klein, Kathleen Lakhal-Chaieb, Lajmi Labbe, Aurelie Pastinen, Tomi Hudson, Marie Colmegna, Ines Bernatsky, Sasha Greenwood, Celia M. T. York Univ Dept Math & Stat Toronto ON Canada Univ Quebec Montreal Dept Math Montreal PQ Canada Jewish Gen Hosp Lady Davis Inst Med Res Montreal PQ Canada Univ Laval Dept Math & Stat Quebec City PQ Canada HEC Montreal Dept Sci Decis Montreal PQ Canada Childrens Mercy Genom Med Ctr Independence MO USA McGill Univ Dept Med Montreal PQ Canada McGill Univ Res Inst Hlth Ctr Montreal PQ Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada McGill Univ Dept Human Genet Montreal PQ Canada McGill Univ Gerald Bronfman Dept Oncol Montreal PQ Canada

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (em) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the em iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

关键词： additive dispersion binomial DNA methylation em algorithm measurement error multiplicative dispersion

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Row mixture-based clustering with covariates for ordinal responses

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COMPUTATIONAL STATISTICS 2024年第5期39卷 2511-2555页

作者： Preedalikit, Kemmawadee Fernandez, Daniel Liu, Ivy McMillan, Louise Nai Ruscone, Marta Costilla, Roy Univ Phayao Sch Sci Phayao 56000 Thailand Univ Politecn Catalunya BarcelonaTech UPC Dept Stat & Operat Res DEIO Barcelona 08028 Spain Inst Math UPC BarcelonaTech IMTech Barcelona 08028 Spain Victoria Univ Wellington Sch Math & Stat Wellington 6140 New Zealand Univ Genoa DIEC Dept Econ I-16126 Genoa Italy AgResearch Ltd Ruakura Res Ctr Hamilton 3214 New Zealand

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the individuals' similar patterns of responses and the effects of the covariates on the individuals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set.

关键词： Cluster analysis em algorithm Mixture models Ordinal responses Proportional odds model

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A multivariate modified skew-normal distribution

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STATISTICAL PAPERS 2024年第2期65卷 511-555页

作者： Mondal, Sagnik Arellano-Valle, Reinaldo B. Genton, Marc G. King Abdullah Univ Sci & Technol Stat Program Thuwal 239556900 Saudi Arabia Pontificia Univ Catolica Chile Dept Stat Santiago Chile

We introduce a multivariate version of the modified skew-normal distribution, which contains the multivariate normal distribution as a special case. Unlike the Azzalini multivariate skew-normal distribution, this new distribution has a nonsingular Fisher information matrix when the skewness parameters are all zero, and its profile log-likelihood of the skewness parameters is always a non-monotonic function. We study some basic properties of the proposed family of distributions and present an expectation-maximization (em) algorithm for parameter estimation that we validate through simulation studies. Finally, we apply the proposed model to the univariate frontier data and to a trivariate wind speed data, and compare its performance with the Azzalini skew-normal model.

关键词： em algorithm Fisher information matrix Modified skew-normal Skew-elliptical Skew-generalized normal Skew-normal

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A new approach to modeling the cure rate in the presence of interval censored data

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COMPUTATIONAL STATISTICS 2024年第5期39卷 2743-2769页

作者： Pal, Suvra Peng, Yingwei Aselisewine, Wisdom Univ Texas Arlington Dept Math Arlington TX 76019 USA Queens Univ Dept Publ Hlth Sci Kingston ON K7L 3N6 Canada

We consider interval censored data with a cured subgroup that arises from longitudinal followup studies with a heterogeneous population where a certain proportion of subjects is not susceptible to the event of interest. We propose a two component mixture cure model, where the first component describing the probability of cure is modeled by a support vector machine-based approach and the second component describing the survival distribution of the uncured group is modeled by a proportional hazard structure. Our proposed model provides flexibility in capturing complex effects of covariates on the probability of cure unlike the traditional models that rely on modeling the cure probability using a generalized linear model with a known link function. For the estimation of model parameters, we develop an expectation maximization-based estimation algorithm. We conduct simulation studies and show that our proposed model performs better in capturing complex effects of covariates on the cure probability when compared to the traditional logit link-based two component mixture cure model. This results in more accurate (smaller bias) and precise (smaller mean square error) estimates of the cure probabilities, which in-turn improves the predictive accuracy of the latent cured status. We further show that our model's ability to capture complex covariate effects also improves the estimation results corresponding to the survival distribution of the uncured. Finally, we apply the proposed model and estimation procedure to an interval censored data on smoking cessation.

关键词： Support vector machine Platt scaling Sequential minimal optimization Mixture cure rate model em algorithm

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