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Zeitschriftenartikel zum Thema „Multinomial mixture model“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Malyutov, M. B., and D. A. Stolyarenko. "On Multisample Multinomial Mixture Model." American Journal of Mathematical and Management Sciences 21, no. 1-2 (2001): 101–7. http://dx.doi.org/10.1080/01966324.2001.10737540.

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2

Bashir, Shaheena, and Edward M. Carter. "Penalized multinomial mixture logit model." Computational Statistics 25, no. 1 (2009): 121–41. http://dx.doi.org/10.1007/s00180-009-0165-9.

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3

Holland, Mark D., and Brian R. Gray. "Multinomial mixture model with heterogeneous classification probabilities." Environmental and Ecological Statistics 18, no. 2 (2010): 257–70. http://dx.doi.org/10.1007/s10651-009-0131-2.

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4

Abdul Rahman, Teh Faradilla, Norshita Mat Nayan, Nurhilyana Anuar, and Aminatul Solehah Idris. "Dirichlet Multinomial Modelling Approaches in Analyzing Anxiety Therapy Messages." Journal of Information and Knowledge Management 15, no. 1 (2025): 98–108. https://doi.org/10.24191/jikm.v15i1.4541.

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Despite the effectiveness of anxiety therapy through text messages, limited research was found to analyse the topics included in the therapy session. It is also unclear of which topic modelling approaches is the best in extracting anxiety therapy topics from text messages. Thus, this study aims to compare the performance of four topic modelling methods, namely Latent Feature Di-richlet Multinomial Mixture (LFDMM), Gibbs Sampling Dirichlet Multinomi-al Mixture, Generalized Polya-urn Dirichlet Multinomial Mixture and Pois-son-based Dirichlet Multinomial Mixture Model on 28 text messages of anxi-

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Portela, J. "Clustering Discrete Data Through the Multinomial Mixture Model." Communications in Statistics - Theory and Methods 37, no. 20 (2008): 3250–63. http://dx.doi.org/10.1080/03610920802162623.

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Mazarura, Jocelyn, Alta de Waal, and Pieter de Villiers. "A Gamma-Poisson Mixture Topic Model for Short Text." Mathematical Problems in Engineering 2020 (April 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/4728095.

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Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in the literature are adm

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Becker, Mark P., and Ilsoon Yang. "7. Latent Class Marginal Models for Cross-Classifications of Counts." Sociological Methodology 28, no. 1 (1998): 293–325. http://dx.doi.org/10.1111/0081-1750.00050.

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The standard latent class model is a finite mixture of indirectly observed multinomial distributions, each of which is assumed to exhibit statistical independence. Latent class analysis has been applied in a wide variety of research contexts, including studies of mobility, educational attainment, agreement, and diagnostic accuracy, and as measurement error models in social research. One of the attractive features of the latent class model in these settings is that the parameters defining the individual multinomials are readily interpretable marginal probabilities, conditional on the unobserved

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Cruz-Medina, I. R., T. P. Hettmansperger, and H. Thomas. "Semiparametric mixture models and repeated measures: the multinomial cut point model." Journal of the Royal Statistical Society: Series C (Applied Statistics) 53, no. 3 (2004): 463–74. http://dx.doi.org/10.1111/j.1467-9876.2004.05203.x.

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9

Désir, Antoine, Vineet Goyal, and Jiawei Zhang. "Technical Note—Capacitated Assortment Optimization: Hardness and Approximation." Operations Research 70, no. 2 (2022): 893–904. http://dx.doi.org/10.1287/opre.2021.2142.

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Assortment optimization is an important problem arising in various applications. In many practical settings, the assortment is subject to a capacity constraint. In “Capacitated Assortment Optimization: Hardness and Approximation,” Désir, Goyal, and Zhang study the capacitated assortment optimization problem. The authors first show that adding a general capacity constraint makes the problem NP-hard even for the simple multinomial logit model. They also show that under the mixture of multinomial logit model, even the unconstrained problem is hard to approximate within any reasonable factor when

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Li, Minqiang, and Liang Zhang. "Multinomial mixture model with feature selection for text clustering." Knowledge-Based Systems 21, no. 7 (2008): 704–8. http://dx.doi.org/10.1016/j.knosys.2008.03.025.

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Honda, Katsuhiro, Shunnya Oshio, and Akira Notsu. "Fuzzy Co-Clustering Induced by Multinomial Mixture Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 717–26. http://dx.doi.org/10.20965/jaciii.2015.p0717.

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A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be

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Lijoi, Antonio, Igor Prünster, and Tommaso Rigon. "The Pitman–Yor multinomial process for mixture modelling." Biometrika 107, no. 4 (2020): 891–906. http://dx.doi.org/10.1093/biomet/asaa030.

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Summary Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably when used to model latent features, such as in clustering, mixtures and curve fitting. They are effective and well-developed tools, though their infinite dimensionality is unsuited to some applications. If one restricts to a finite-dimensional simplex, very little is known beyond the traditional Dirichlet multinomial process, which is mainly motivated by conjugacy. This paper introduces an alternative based on the Pitman–Yor process, which provides greater flexibility while preserving an

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Tu, Wangshu, and Sanjeena Subedi. "Penalized logistic normal multinomial factor analyzers for high dimensional compositional data." Journal of Statistical Research 56, no. 2 (2023): 185–216. http://dx.doi.org/10.3329/jsr.v56i2.67469.

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   Model-based clustering utilizes a finite mixture model to identify underlying patterns or clusters across samples. A finite mixture model is a convex combination of two or more distributions, where appropriate distributions are chosen depending on the type of the data. Recently, there has been a great interest in clustering human microbiome data. Microbiome data are compositional (yielding relative abundance) and are high-dimensional. Previously, a family of logistic normal multinomial factor analyzers (LNM-FA) for model-based clus- tering of high-dimensional microbiome da

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Afroz, Farzana, and Zillur Rahman Shabuz. "Comparison Between Two Multinomial Overdispersion Models Through Simulation." Dhaka University Journal of Science 68, no. 1 (2020): 45–48. http://dx.doi.org/10.3329/dujs.v68i1.54596.

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A key assumption when using the multinomial distribution is that the observations are independent. In many practical situations, the observations could be correlated or clustered and the probabilities within each cluster might vary, which may lead to overdispersion. In this paper we discuss two well-known approaches to model overdispersed multinomial data, the Dirichlet-multinomial model and the finite-mixture model. The difference between these two models has been illustrated via simulation study. The forest pollen data is considered as a practical example of overdisperse multinomial data. Th

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Rigouste, Loïs, Olivier Cappé, and François Yvon. "Inference and evaluation of the multinomial mixture model for text clustering." Information Processing & Management 43, no. 5 (2007): 1260–80. http://dx.doi.org/10.1016/j.ipm.2006.11.001.

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Yang, Ni, and Youpeng Zhang. "Railway Fault Text Clustering Method Using an Improved Dirichlet Multinomial Mixture Model." Mathematical Problems in Engineering 2022 (July 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/7882396.

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Railway signal equipment fault data (RSEFD) are one of the issues with in-depth traffic big data analysis throughout the life cycle of intelligent transportation. In the course of daily operation and maintenance, the railway electrical maintenance department records equipment malfunction information in a natural language. The data have the characteristics of strong professionalism, short text, unbalanced category, and low efficiency of manual analysis and processing. How to effectively mine the information contained in these fault texts to provide help for on-site operation and maintenance pla

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Frimane, Âzeddine, Mohammed Aggour, Badr Ouhammou, and Lahoucine Bahmad. "A Dirichlet-multinomial mixture model-based approach for daily solar radiation classification." Solar Energy 171 (September 2018): 31–39. http://dx.doi.org/10.1016/j.solener.2018.06.059.

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Sason, Itay, Damian Wojtowicz, Welles Robinson, Mark D. M. Leiserson, Teresa M. Przytycka, and Roded Sharan. "A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer." iScience 23, no. 3 (2020): 100900. http://dx.doi.org/10.1016/j.isci.2020.100900.

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Berchtold, André. "Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models." Symmetry 12, no. 3 (2020): 351. http://dx.doi.org/10.3390/sym12030351.

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The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation resul

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Tian, Zhaoyang, Kun Liang, and Pengfei Li. "Maximum multinomial likelihood estimation in compound mixture model with application to malaria study." Journal of Nonparametric Statistics 33, no. 1 (2021): 21–38. http://dx.doi.org/10.1080/10485252.2021.1898609.

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Bilancia, Massimo, and Rade Dačević. "A Dirichlet-Multinomial mixture model of Statistical Science: Mapping the shift of a paradigm." Journal of Informetrics 19, no. 1 (2025): 101633. https://doi.org/10.1016/j.joi.2024.101633.

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Silverthorn, Bryan, and Risto Miikkulainen. "Latent Class Models for Algorithm Portfolio Methods." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 167–72. http://dx.doi.org/10.1609/aaai.v24i1.7546.

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Different solvers for computationally difficult problems such as satisfiability (SAT) perform best on different instances. Algorithm portfolios exploit this phenomenon by predicting solvers' performance on specific problem instances, then shifting computational resources to the solvers that appear best suited. This paper develops a new approach to the problem of making such performance predictions: natural generative models of solver behavior. Two are proposed, both following from an assumption that problem instances cluster into latent classes: a mixture of multinomial distributions, and a mi

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Afroz, Farzana. "Proposing a New Estimator of Overdispersion for Multinomial Data." Dhaka University Journal of Science 72, no. 1 (2024): 56–62. http://dx.doi.org/10.3329/dujs.v72i1.71247.

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The classical approach of estimating overdispersion parameter, Φ, by Pearson's goodness of fit statistic is not appropriate when the data are sparse. We have considered several estimators of Φ, derived from the Pearson's statistic and the deviance statistic for multinomial data. The proposed estimator of Φ depending on the deviance statistic is shown to perform the best for increasing level of sparsity and overdispersion, regarding the root mean squared error. As a practical example dead recovery data collected on Herring gulls from Kent Island, Canada are considered. A parametric extra variat

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Li, Ximing, Jiaojiao Zhang, and Jihong Ouyang. "Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7884–91. http://dx.doi.org/10.1609/aaai.v33i01.33017884.

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Conventional topic models suffer from a severe sparsity problem when facing extremely short texts such as social media posts. The family of Dirichlet multinomial mixture (DMM) can handle the sparsity problem, however, they are still very sensitive to ordinary and noisy words, resulting in inaccurate topic representations at the document level. In this paper, we alleviate this problem by preserving local neighborhood structure of short texts, enabling to spread topical signals among neighboring documents, so as to correct the inaccurate topic representations. This is achieved by using variation

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Alhadabi, Amal, and Jian Li. "Trajectories of Academic Achievement in High Schools: Growth Mixture Model." Journal of Educational Issues 6, no. 1 (2020): 140. http://dx.doi.org/10.5296/jei.v6i1.16775.

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The current study investigated patterns of growth in academic achievement trajectories among American high school students (N = 12,314) that were obtained from a nationally representative, public-use dataset (the High School Longitudinal Study of 2009) in relation to key demographic information (i.e., gender, grade level, socioeconomic status [SES] in ninth grade, and ethnicity) and a distal outcome (i.e., applying for college). Unconditional growth mixture model showed that the three-class model was most appropriate in capturing the latent heterogeneity (i.e., low-achieving/increasing, modera

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DeCarlo, Lawrence T. "A Signal Detection Model for Multiple-Choice Exams." Applied Psychological Measurement 45, no. 6 (2021): 423–40. http://dx.doi.org/10.1177/01466216211014599.

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A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, wi

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Norets, Andriy, and Debdeep Pati. "ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES." Econometric Theory 33, no. 4 (2016): 980–1012. http://dx.doi.org/10.1017/s0266466616000220.

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We consider a nonparametric Bayesian model for conditional densities. The model is a finite mixture of normal distributions with covariate dependent multinomial logit mixing probabilities. A prior for the number of mixture components is specified on positive integers. The marginal distribution of covariates is not modeled. We study asymptotic frequentist behavior of the posterior in this model. Specifically, we show that when the true conditional density has a certain smoothness level, then the posterior contraction rate around the truth is equal up to a log factor to the frequentist minimax r

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Contreras-Reyes, Javier, and Daniel Cortés. "Bounds on Rényi and Shannon Entropies for Finite Mixtures of Multivariate Skew-Normal Distributions: Application to Swordfish (Xiphias gladius Linnaeus)." Entropy 18, no. 11 (2016): 382. http://dx.doi.org/10.3390/e18110382.

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Mixture models are in high demand for machine-learning analysis due to their computational tractability, and because they serve as a good approximation for continuous densities. Predominantly, entropy applications have been developed in the context of a mixture of normal densities. In this paper, we consider a novel class of skew-normal mixture models, whose components capture skewness due to their flexibility. We find upper and lower bounds for Shannon and Rényi entropies for this model. Using such a pair of bounds, a confidence interval for the approximate entropy value can be calculated. In

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Quaez, Uday Jabbar. "An Information-Theoretic Approach for Multivariate Skew Laplace Normal Distributions." Mustansiriyah Journal of Pure and Applied Sciences 2, no. 3 (2024): 1–17. http://dx.doi.org/10.47831/mjpas.2024.2.3.1-17.

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Due to its flexibility, the skew distributions (univariate and multivariate) have received widespread attention over the last two decades because they're become widely used in the modelling and analysis of skewed data sets. The main goal of this paper is to introduce asymptotic expressions for entropy of multivariate skew Laplace normal distribution to deal with the issue by providing a flexible model for modeling skewness and heavy tiredness simultaneously. Thus, we extend this study to the class of mixture model of these distributions. In addition, upper and lower bounds of Rényi entropy of

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Quaez, Uday Jabbar. "An Information-Theoretic Approach for Multivariate Skew Laplace Normal Distributions." Mustansiriyah Journal of Pure and Applied Sciences 2, no. 3 (2024): 1–17. http://dx.doi.org/10.47831/mjpas.v2i3.239.

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Due to its flexibility, the skew distributions (univariate and multivariate) have received widespread attention over the last two decades because they're become widely used in the modelling and analysis of skewed data sets. The main goal of this paper is to introduce asymptotic expressions for entropy of multivariate skew Laplace normal distribution to deal with the issue by providing a flexible model for modeling skewness and heavy tiredness simultaneously. Thus, we extend this study to the class of mixture model of these distributions. In addition, upper and lower bounds of Rényi entropy of

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Agarwal, Neha, Geeta Sikka, and Lalit Kumar Awasthi. "Evaluation of web service clustering using Dirichlet Multinomial Mixture model based approach for Dimensionality Reduction in service representation." Information Processing & Management 57, no. 4 (2020): 102238. http://dx.doi.org/10.1016/j.ipm.2020.102238.

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Glasgow, Garrett. "Mixed Logit Models for Multiparty Elections." Political Analysis 9, no. 2 (2001): 116–36. http://dx.doi.org/10.1093/oxfordjournals.pan.a004867.

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Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choi

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Wang, Yan, Tonghui Xu, and Jiabin Shen. "Incorporating machine learning into factor mixture modeling: Identification of covariate interactions to explain population heterogeneity." Methodology 19, no. 3 (2023): 303–22. http://dx.doi.org/10.5964/meth.9487.

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Factor mixture modeling (FMM) has been widely adopted in health and behavioral sciences to examine unobserved population heterogeneity. Covariates are often included in FMM as predictors of the latent class membership via multinomial logistic regression to help understand the formation and characterization of population heterogeneity. However, interaction effects among covariates have received considerably less attention, which might be attributable to the fact that interaction effects cannot be identified in a straightforward fashion. This study demonstrated the utility of structural equation

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Rodrigo, Enrique G., Juan C. Alfaro, Juan A. Aledo, and José A. Gámez. "Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem." Entropy 23, no. 4 (2021): 420. http://dx.doi.org/10.3390/e23040420.

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The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permuta

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Manisera, Marica, Manlio Migliorati, Matteo Ventura, and Paola Zuccolotto. "A Mixture Model for the Analysis of Categorical Variables Measured on Five-point Semantic Differential Scales." Austrian Journal of Statistics 53, no. 3 (2024): 70–86. http://dx.doi.org/10.17713/ajs.v53i3.1744.

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Ordered response scales are often used in questionnaires to measure individuals' attitudes or perceptions. Among different response scale formats, we focus on multi-point semantic differential scales, requiring the respondent to position himself/herself on a rating between two bipolar adjectives. The obtained rating data require appropriate statistical models. We resort to the CUM model (Combination of a discrete Uniform and a - linearly transformed - Multinomial random variable), recently proposed in the framework of the CUB (Combination of discrete Uniform and shifted Binomial random variabl

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Ubukata, Seiki, Katsuya Koike, Akira Notsu, and Katsuhiro Honda. "MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 747–58. http://dx.doi.org/10.20965/jaciii.2018.p0747.

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In the field of cluster analysis, fuzzy theory including the concept of fuzzy sets has been actively utilized to realize flexible and robust clustering methods. FuzzyC-means (FCM), which is the most representative fuzzy clustering method, has been extended to achieve more robust clustering. For example, noise FCM (NFCM) performs noise rejection by introducing a noise cluster that absorbs noise objects and possibilisticC-means (PCM) performs the independent extraction of possibilistic clusters by introducing cluster-wise noise clusters. Similarly, in the field of co-clustering, fuzzy co-cluster

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Tereshko, Lauren, Xiaohui Zhao, Jake Gagnon, et al. "A novel method for quantitation of AAV genome integrity using duplex digital PCR." PLOS ONE 18, no. 12 (2023): e0293277. http://dx.doi.org/10.1371/journal.pone.0293277.

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Recombinant adeno-associated virus (rAAV) vectors have become a reliable strategy for delivering gene therapies. As rAAV capsid content is known to be heterogeneous, methods for rAAV characterization are critical for assessing the efficacy and safety of drug products. Multiplex digital PCR (dPCR) has emerged as a popular molecular approach for characterizing capsid content due to its high level of throughput, accuracy, and replicability. Despite growing popularity, tools to accurately analyze multiplexed data are scarce. Here, we introduce a novel statistical model to estimate genome integrity

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Bucklin, Randolph E., Sunil Gupta, and S. Siddarth. "Determining Segmentation in Sales Response across Consumer Purchase Behaviors." Journal of Marketing Research 35, no. 2 (1998): 189–97. http://dx.doi.org/10.1177/002224379803500205.

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The authors develop a joint estimation approach to segment households on the basis of their response to price and promotion in brand choice, purchase incidence, and purchase quantity decisions. The authors model brand choice (what to buy) by multinomial logit, incidence (whether to buy) by nested logit, and quantity (how much to buy) by poisson regression. Response segments are determined probabilistically using a latent mixture model. The approach simultaneously calibrates sales response on two dimensions: across segments and the three purchase behaviors. The procedure permits market-level sa

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Gemma, Marco, Fulvia Pennoni, Roberta Tritto, and Massimo Agostoni. "Risk of adverse events in gastrointestinal endoscopy: Zero-inflated Poisson regression mixture model for count data and multinomial logit model for the type of event." PLOS ONE 16, no. 6 (2021): e0253515. http://dx.doi.org/10.1371/journal.pone.0253515.

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Background and aims We analyze the possible predictive variables for Adverse Events (AEs) during sedation for gastrointestinal (GI) endoscopy. Methods We consider 23,788 GI endoscopies under sedation on adults between 2012 and 2019. A Zero-Inflated Poisson Regression Mixture (ZIPRM) model for count data with concomitant variables is applied, accounting for unobserved heterogeneity and evaluating the risks of multi-drug sedation. A multinomial logit model is also estimated to evaluate cardiovascular, respiratory, hemorrhagic, other AEs and stopping the procedure risk factors. Results In 7.55% o

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Goeken, Nils, Peter Kurz, and Winfried Steiner. "Hierarchical Bayes Conjoint Choice Models - Model Framework, Bayesian Inference, Model Selection, and Interpretation of Estimation Results." Marketing ZFP 43, no. 3 (2021): 49–66. http://dx.doi.org/10.15358/0344-1369-2021-3-49.

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Choice-based conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets). Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model due to the existence of closed form solutions for co

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Akande, Olanrewaju, Andrés Barrientos, and Jerome P. Reiter. "Simultaneous Edit and Imputation For Household Data with Structural Zeros." Journal of Survey Statistics and Methodology 7, no. 4 (2018): 498–519. http://dx.doi.org/10.1093/jssam/smy022.

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Abstract Multivariate categorical data nested within households often include reported values that fail edit constraints—for example, a participating household reports a child’s age as older than his biological parent’s age—and have missing values. Generally, agencies prefer datasets to be free from erroneous or missing values before analyzing them or disseminating them to secondary data users. We present a model-based engine for editing and imputation of household data based on a Bayesian hierarchical model that includes (i) a nested data Dirichlet process mixture of products of multinomial d

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Hu, Qiang, Jiaji Shen, Kun Wang, Junwei Du, and Yuyue Du. "A Web service clustering method based on topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model and service collaboration graph." Information Sciences 586 (March 2022): 239–60. http://dx.doi.org/10.1016/j.ins.2021.11.087.

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Benoît, Hugues P. "An empirical model of seasonal depth-dependent fish assemblage structure to predict the species composition of mixed catches." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 2 (2013): 220–32. http://dx.doi.org/10.1139/cjfas-2012-0166.

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Reliable catch statistics are essential for assessing fishing impacts on individual stocks. However, fisheries that capture a mixture of stocks or species for which catch statistics are not disaggregated pose a challenge. Nonetheless, catch composition can be inferred given information on fishing date and location and a prevalent role of season and habitat in structuring fish assemblage composition. Here, a harmonic regression model for multinomial data, intended to predict the species composition of catches based on season and depth, is developed using bottom-trawl survey data. Model developm

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Si, Yajuan, Jerome P. Reiter, and D. Sunshine Hillygus. "Semi-parametric Selection Models for Potentially Non-ignorable Attrition in Panel Studies with Refreshment Samples." Political Analysis 23, no. 1 (2015): 92–112. http://dx.doi.org/10.1093/pan/mpu009.

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Panel studies typically suffer from attrition. Ignoring the attrition can result in biased inferences if the missing data are systematically related to outcomes of interest. Unfortunately, panel data alone cannot inform the extent of bias due to attrition. Many panel studies also include refreshment samples, which are data collected from a random sample of new individuals during the later waves of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by non-ignorable attrition while reducing reliance on strong assumptions about the attrition proces

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Cahill, Paul, and Brendan Bunting. "Latent variable mixture modelling of treated drug misuse in Ireland." Advances in Methodology and Statistics 1, no. 1 (2004): 213–23. http://dx.doi.org/10.51936/yafa1077.

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This study provides analyses and profiles of illegal drug usage in the Republic of Ireland. Two questions are addressed: a) can individuals be grouped into homogeneous classes based upon their type of drug consumption, and b) how do these classes differ in terms of other key background variables? The data reported in this study is from the National Drug Treatment Reporting System database in the Republic of Ireland. All analyses were carried out in collaboration with the Drug Misuse Research Division (the Irish REITOX / EMCDDA focal point). This database contains information on all 6994 indivi

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Vidotto, Davide, Jeroen K. Vermunt, and Katrijn Van Deun. "Bayesian Latent Class Models for the Multiple Imputation of Categorical Data." Methodology 14, no. 2 (2018): 56–68. http://dx.doi.org/10.1027/1614-2241/a000146.

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Abstract. Latent class analysis has been recently proposed for the multiple imputation (MI) of missing categorical data, using either a standard frequentist approach or a nonparametric Bayesian model called Dirichlet process mixture of multinomial distributions (DPMM). The main advantage of using a latent class model for multiple imputation is that it is very flexible in the sense that it can capture complex relationships in the data given that the number of latent classes is large enough. However, the two existing approaches also have certain disadvantages. The frequentist approach is computa

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Geigle, Chase, Himel Dev, Hari Sundaram, and ChengXiang Zhai. "A Generative Model for Discovering Action-Based Roles and Community Role Compositions on Community Question Answering Platforms." Proceedings of the International AAAI Conference on Web and Social Media 13 (July 6, 2019): 181–92. http://dx.doi.org/10.1609/icwsm.v13i01.3220.

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This paper proposes a generative model for discovering user roles and community role compositions in Community Question Answering (CQA) platforms. While past research shows that participants play different roles in online communities, automatically discovering these roles and providing a summary of user behavior that is readily interpretable remains an important challenge. Furthermore, there has been relatively little insight into the distribution of these roles between communities. Does a community’s composition over user roles vary as a function of topic? How does it relate to the health of

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Lötsch, Jörn, and Alfred Ultsch. "Pitfalls of Using Multinomial Regression Analysis to Identify Class-Structure-Relevant Variables in Biomedical Data Sets: Why a Mixture of Experts (MOE) Approach Is Better." BioMedInformatics 3, no. 4 (2023): 869–84. http://dx.doi.org/10.3390/biomedinformatics3040054.

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Recent advances in mathematical modeling and artificial intelligence have challenged the use of traditional regression analysis in biomedical research. This study examined artificial data sets and biomedical data sets from cancer research using binomial and multinomial logistic regression. The results were compared with those obtained with machine learning models such as random forest, support vector machine, Bayesian classifiers, k-nearest neighbors, and repeated incremental clipping (RIPPER). The alternative models often outperformed regression in accurately classifying new cases. Logistic r

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Kim, Young Mi. "Classifying Longitudinal Changes and Examining Predictors in Abuse by Parents During Adolescence Using Growth Mixture Modeling." Korean Journal of Child Studies 45, no. 4 (2024): 363–73. http://dx.doi.org/10.5723/kjcs.2024.45.4.363.

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Objectives: This study aimed to explore distinct longitudinal trajectories of parental abuse during adolescence and to identify predictors that classify these distinctive latent class trajectories over time.Methods: Data were sourced from waves 2 to 6 of the Korean Children and Youth Panel Survey 2010, covering a five-year period from 8th grade to 12th grade. A total of 2,313 adolescents were analyzed. Growth mixture modeling was used to identify different trajectories of parental abuse, followed by multinomial logistic regression to examine predictors that distinguish these trajectories.Resul

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Pushpalatha M N and Mrunalini M. "Predicting the Severity of Open Source Bug Reports Using Unsupervised and Supervised Techniques." International Journal of Open Source Software and Processes 10, no. 1 (2019): 1–15. http://dx.doi.org/10.4018/ijossp.2019010101.

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The severity of the bug report helps for the bug triagers to prioritize the handling of bug reports for giving more importance to high critical bugs than less critical bugs, since the inexperienced developers and new users can make mistakes while assigning the severity. The manual labeling of severity is labor-intensive and time-consuming. In this article, both unsupervised and supervised learning algorithms are used to automate the prediction of bug report severity. Because the data was unlabeled, the Gaussian Mixture Model is used to group similar kinds of bug reports. The result is labeled

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