Journal articles: 'Multinomial mixture model'

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

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Bashir, Shaheena, and Edward M. Carter. "Penalized multinomial mixture logit model." Computational Statistics 25, no. 1 (August 14, 2009): 121–41. http://dx.doi.org/10.1007/s00180-009-0165-9.

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Holland, Mark D., and Brian R. Gray. "Multinomial mixture model with heterogeneous classification probabilities." Environmental and Ecological Statistics 18, no. 2 (January 28, 2010): 257–70. http://dx.doi.org/10.1007/s10651-009-0131-2.

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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 admixture models, making the assumption that a document is generated from a mixture of topics. In this study, we focus on short text. Many studies have shown that the simpler assumption of a mixture model fits short text better. With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic. One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model. The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model. The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus. The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora. Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.

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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 (August 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 latent variable(s), that are often of substantive interest. There are, however, settings where the local-independence axiom is not supported, and hence it is useful to consider some form of local dependence. In this paper we consider a family of models defined in terms of finite mixtures of multinomial models where the multinomials are parameterized in terms of a set of models for the univariate marginal distributions and for marginal associations. Local dependence is introduced through the models for marginal associations, and the standard latent class model obtains as a special case. Three examples are analyzed with the models to illustrate their utility in analyzing complex cross-classifications.

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Portela, J. "Clustering Discrete Data Through the Multinomial Mixture Model." Communications in Statistics - Theory and Methods 37, no. 20 (September 22, 2008): 3250–63. http://dx.doi.org/10.1080/03610920802162623.

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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 (August 2004): 463–74. http://dx.doi.org/10.1111/j.1467-9876.2004.05203.x.

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Li, Minqiang, and Liang Zhang. "Multinomial mixture model with feature selection for text clustering." Knowledge-Based Systems 21, no. 7 (October 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 (November 20, 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 properly tuned. The advantages of the dual fuzzy partition are demonstrated through several experimental results including document clustering applications.

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Lijoi, Antonio, Igor Prünster, and Tommaso Rigon. "The Pitman–Yor multinomial process for mixture modelling." Biometrika 107, no. 4 (June 5, 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 analytical tractability. Urn schemes and posterior characterizations are obtained in closed form, leading to exact sampling methods. In addition, the proposed approach can be used to accurately approximate the infinite-dimensional Pitman–Yor process, yielding improvements over existing truncation-based approaches. An application to convex mixture regression for quantitative risk assessment illustrates the theoretical results and compares our approach with existing methods.

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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 (January 30, 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. The overdispersion parameter,φ, has been estimated using two classical estimators. Dhaka Univ. J. Sci. 68(1): 45-48, 2020 (January)

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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 (September 2007): 1260–80. http://dx.doi.org/10.1016/j.ipm.2006.11.001.

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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 (March 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 (March 1, 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 results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.

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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 (January 2, 2021): 21–38. http://dx.doi.org/10.1080/10485252.2021.1898609.

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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 variational manifold regularization, constraining the close short texts should have similar variational topic representations. Upon this idea, we propose a novel Laplacian DMM (LapDMM) topic model. During the document graph construction, we further use the word mover’s distance with word embeddings to measure document similarities at the semantic level. To evaluate LapDMM, we compare it against the state-of-theart short text topic models on several traditional tasks. Experimental results demonstrate that our LapDMM achieves very significant performance gains over baseline models, e.g., achieving even about 0.2 higher scores on clustering and classification tasks in many cases.

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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 (October 26, 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 addition, an asymptotic expression for Rényi entropy by Stirling’s approximation is given, and upper and lower bounds are reported using multinomial coefficients and some properties and inequalities of L p metric spaces. Simulation studies are then applied to a swordfish (Xiphias gladius Linnaeus) length dataset.

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DeCarlo, Lawrence T. "A Signal Detection Model for Multiple-Choice Exams." Applied Psychological Measurement 45, no. 6 (May 25, 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, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.

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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 (May 10, 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, moderate-achieving/decreasing, and high-achieving/slightly increasing). Two covariates (i.e., gender and SES in ninth grade) were positively associated with the intercept growth factor (i.e., initial GPA) in two of the three achievement classes (i.e., high-achieving and moderate-achieving). In contrast, two other covariates (i.e., Hispanic and African American) were negatively associated with the intercept growth factor in all of the achievement classes. The multinomial logistic regression coefficients identified an increase in the likelihood of belonging to the following achievement classes: (1) Moderate-achieving, if the students were male or African American and of low SES, (2) Low-achieving, if the students were male and of low SES, and (3) High-achieving, if the students were female and of an ethnicity other than African American and high SES. The probability of not applying for college was higher among the low-achieving and the moderate-achieving classes compared with the high-achieving class (223 words).

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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 (July 2020): 102238. http://dx.doi.org/10.1016/j.ipm.2020.102238.

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Norets, Andriy, and Debdeep Pati. "ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES." Econometric Theory 33, no. 4 (July 13, 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 rate of estimation. An extension to the case when the covariate space is unbounded is also established. As our result holds without a priori knowledge of the smoothness level of the true density, the established posterior contraction rates are adaptive. Moreover, we show that the rate is not affected by inclusion of irrelevant covariates in the model. In Monte Carlo simulations, a version of the model compares favorably to a cross-validated kernel conditional density estimator.

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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 (March 31, 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 permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.

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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 (September 20, 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-clustering induced by multinomial mixture models (FCCMM) was proposed and extended to noise FCCMM (NFCCMM) in an analogous fashion to the NFCM. Ubukata et al. have proposed noise clustering-based possibilistic co-clustering induced by multinomial mixture models (NPCCMM) in an analogous fashion to the PCM. In this study, we develop an NPCCMM scheme considering variable cluster volumes and the fuzziness degree of item memberships to investigate the specific aspects of fuzzy nature rather than probabilistic nature in co-clustering tasks. We investigated the characteristics of the proposed NPCCMM by applying it to an artificial data set and conducted document clustering experiments using real-life data sets. As a result, we found that the proposed method can derive more flexible possibilistic partitions than the probabilistic model by adjusting the fuzziness degrees of object and item memberships. The document clustering experiments also indicated the effectiveness of tuning the fuzziness degree of object and item memberships, and the optimization of cluster volumes to improve classification performance.

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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 choice model than multinomial probit (MNP) in several respects, and can be applied to a wider variety of questions about voting behavior than MNP. An empirical example using data from the 1987 British General Election demonstrates the utility of MXL in the study of multicandidate and multiparty elections.

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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 (June 30, 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% of cases, one or more AEs occurred, most frequently cardiovascular (3.26%) or respiratory (2.77%). Our ZIPRM model identifies one population for non-zero counts. The AE-group reveals that age >75 years yields 46% more AEs than age <66 years; Body Mass Index (BMI) ≥27 27% more AEs than BMI <21; emergency 11% more AEs than routine. Any one-point increment in the American Society of Anesthesiologists (ASA) score and the Mallampati score determines respectively a 42% and a 16% increment in AEs; every hour prolonging endoscopy increases AEs by 41%. Regarding sedation with propofol alone (the sedative of choice), adding opioids to propofol increases AEs by 43% and adding benzodiazepines by 51%. Cardiovascular AEs are increased by age, ASA score, smoke, in-hospital, procedure duration, midazolam/fentanyl associated with propofol. Respiratory AEs are increased by BMI, ASA and Mallampati scores, emergency, in-hospital, procedure duration, midazolam/fentanyl associated with propofol. Hemorrhagic AEs are increased by age, in-hospital, procedure duration, midazolam/fentanyl associated with propofol. The risk of suspension of the endoscopic procedure before accomplishment is increased by female gender, ASA and Mallampati scores, and in-hospital, and it is reduced by emergency and procedure duration. Conclusions Age, BMI, ASA score, Mallampati score, in-hospital, procedure duration, other sedatives with propofol increase the risk for AEs during sedation for GI endoscopy.

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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 (April 1, 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 computationally demanding because it requires estimating many LC models: first models with different number of classes should be estimated to determine the required number of classes and subsequently the selected model is reestimated for multiple bootstrap samples to take into account parameter uncertainty during the imputation stage. Whereas the Bayesian Dirichlet process models perform the model selection and the handling of the parameter uncertainty automatically, the disadvantage of this method is that it tends to use a too small number of clusters during the Gibbs sampling, leading to an underfitting model yielding invalid imputations. In this paper, we propose an alternative approach which combined the strengths of the two existing approaches; that is, we use the Bayesian standard latent class model as an imputation model. We show how model selection can be performed prior to the imputation step using a single run of the Gibbs sampler and, moreover, show how underfitting is prevented by using large values for the hyperparameters of the mixture weights. The results of two simulation studies and one real-data study indicate that with a proper setting of the prior distributions, the Bayesian latent class model yields valid imputations and outperforms competing methods.

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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 (May 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 sales elasticities to be decomposed by segment and purchase behavior (i.e., choice, incidence, and quantity). The authors apply the approach to scanner panel data for the yogurt category and find substantial differences across segments in the relative impact of the choice, incidence, and quantity decisions on overall sales response to price.

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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 (December 24, 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 distributions as the model for the true latent values of the data, truncated to allow only households that satisfy all edit constraints, (ii) a model for the location of errors, and (iii) a reporting model for the observed responses in error. The approach propagates uncertainty due to unknown locations of errors and missing values, generates plausible datasets that satisfy all edit constraints, and can preserve multivariate relationships within and across individuals in the same household. We illustrate the approach using data from the 2012 American Community Survey.

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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 (February 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 development was motivated by the need to quantify catches of individual skate (Rajidae) species in fisheries for which landing and discard data are only reliable at the family level. The model was validated by applying it to flatfishes (Pleuronectidae), whose catches are generally reliably and consistently disaggregated by species. The predicted species composition of flatfish matched the composition observed in fishery catches well. The present approach should be applicable to other well-surveyed ecosystems where assemblage composition is structured by one or more key environmental variables of known spatial distribution.

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Honda, Katsuhiro, Nami Yamamoto, Seiki Ubukata, and Akira Notsu. "Noise Rejection in MMMs-Induced Fuzzy Co-Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 7 (November 20, 2017): 1144–51. http://dx.doi.org/10.20965/jaciii.2017.p1144.

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Noise rejection is an important issue in practical application of FCM-type fuzzy clustering, and noise clustering achieves robust estimation of cluster prototypes with an additional noise cluster for dumping noise objects into it. Noise objects having larger distances from all clusters are designed to be assigned to the noise cluster, which is located in an equal (fixed) distance from all objects. Fuzzy co-clustering is an extended version of FCM-type clustering for handling cooccurrence information among objects and items, where the goal of analysis is to extract pair-wise clusters of familiar objects and items. This paper proposes a novel noise rejection model for fuzzy co-clustering induced by multinomial mixture models (MMMs), where a noise cluster is defined with homogeneous item memberships for drawing noise objects having dissimilar cooccurrence features from all general clusters. The noise rejection scheme can be also utilized in selecting the optimal cluster number through a sequential implementation with different cluster numbers.

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Puig, Xavier, Marti Font, and Josep Ginebra. "Bayesian Analysis of the Heterogeneity of Literary Style." Revista Colombiana de Estadística 39, no. 2 (July 18, 2016): 205. http://dx.doi.org/10.15446/rce.v39n2.50151.

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<p>We proposed statistical analysis of the heterogeneity of literary style in a set of texts that simultaneously use different stylometric characteristics, like word length and the frequency of function words. The data set consists of several tables with the same number of rows, with the i-th row of all tables corresponding to the i-th text. The analysis proposed clusters the rows of all these tables simultaneously into groups with homogeneous style, based on a finite mixture of sets of multinomial models, one set for each table. Different from the usual heuristic cluster analysis approaches, our method naturally incorporates the text size, the discrete nature of the data, and the dependence between categories in the analysis. The model is checked and chosen with the help of posterior predictive checks, together with the use of closed form expressions for the posterior probabilities that each of the models considered to be appropriate. This is illustrated through an analysis of the heterogeneity in Shakespeare’s plays, and by revisiting the authorshipattributionproblem of Tirant lo Blanc.</p>

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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 process. We present a Bayesian approach to handle attrition in two-wave panels with one refreshment sample and many categorical survey variables. The approach includes (1) an additive non-ignorable selection model for the attrition process; and (2) a Dirichlet process mixture of multinomial distributions for the categorical survey variables. We present Markov chain Monte Carlo algorithms for sampling from the posterior distribution of model parameters and missing data. We apply the model to correct attrition bias in an analysis of data from the 2007–08 Associated Press/Yahoo News election panel study.

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Li, Linwei, Liangchen Guo, Zhenying He, Yinan Jing, and X. Sean Wang. "X-DMM: Fast and Scalable Model Based Text Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4197–204. http://dx.doi.org/10.1609/aaai.v33i01.33014197.

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Text clustering is a widely studied problem in the text mining domain. The Dirichlet Multinomial Mixture (DMM) model based clustering algorithms have shown good performance to cope with high dimensional sparse text data, obtaining reasonable results in both clustering accuracy and computational efficiency. However, the time complexity of DMM model training is proportional to the average document length and the number of clusters, making it inefficient for scaling up to long text and large corpora, which is common in realworld applications such as documents organization, retrieval and recommendation. In this paper, we leverage a symmetric prior setting for Dirichlet distribution, and build indices to decrease the time complexity of the sampling-based training for DMM from O(K∗L) to O(K∗U), where K is the number of clusters, L the average length of document, and U the average number of unique words in each document. We introduce a Metropolis-Hastings sampling algorithm, which further reduces the sampling time complexity from O(K∗U) to O(U) in the nearly-to-convergence training stages. Moreover, we also parallelize the DMM model training to obtain a further acceleration by using an uncollapsed Gibbs sampler. We combine all these optimizations into a highly efficient implementation, called X-DMM, which enables the DMM model to scale up for long and large-scale text clustering. We evaluate the performance of X-DMM on several real world datasets, and the experimental results show that XDMM achieves substantial speed up compared with existing state-of-the-art algorithms without clustering accuracy degradation.

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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 (January 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 data with the severity level given for each bug reports. Then, the training of classifiers is performed to predict the severity of new bug reports submitted by the user using Multinomial Naïve Bayes Classifier, Logistic Regression Classifier and Stochastic Gradient Descent Classifier. Using these methods, around 85% accuracy is obtained. More accurate predictions can be done using the authors approach.

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Lee, Eun Hak, Inmook Lee, Shin-Hyung Cho, Seung-Young Kho, and Dong-Kyu Kim. "A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data." Sustainability 11, no. 10 (May 16, 2019): 2791. http://dx.doi.org/10.3390/su11102791.

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This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.

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Nguyen, Hai, Dario Moreno-Agostino, Kia-Chong Chua, Silia Vitoratou, and A. Matthew Prina. "Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts." PLOS ONE 16, no. 4 (April 6, 2021): e0248844. http://dx.doi.org/10.1371/journal.pone.0248844.

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Objectives In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. Setting and participants Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). Methods Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. Results Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). Conclusion Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.

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Yang, Hongyan, Jun Ma, Hongwei Hu, and Fanjie Li. "Identification, Trend Analysis and Influencing Factors of Mental Health Status of the Chinese Older Adults." International Journal of Environmental Research and Public Health 17, no. 21 (November 8, 2020): 8251. http://dx.doi.org/10.3390/ijerph17218251.

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This study aimed to analyse the classification, development trends and the influencing factors of the Chinese older adults’ mental health state. Based on longitudinal data of Chinese older adults from 2005 to 2014, 2077 older adults aged 64 to 105 were included and the Latent Class Model, Latent Growth Mixture Model and Multinomial Logit models were employed in this study. We find that there are three types of mental health state of the Chinese older adults: negative, positive and contradictory; and the contradictory type could easily turn into negative or positive mental health state. There are four types of dynamic trends of mental health state: persistently negative, persistently positive, pro-negative, and pro-positive. About 40% of the older adults could maintain positive mental health state, and the pro-negative accounts for larger proportion than the pro-positive. Better economic status, good living habits, cohabitation with family members and pension coverage are beneficial for positive mental health state of the Chinese older adults. There is significant heterogeneity in the state as well as development trends of mental health of the older adults. The older adults with contradictory and negative types of mental health state should get timely psychological help to avoid turning into negative state. A series of polices are needed to promote mental health for the older adults in China.

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Genge, Ewa. "LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position." Sustainability 13, no. 8 (April 7, 2021): 4130. http://dx.doi.org/10.3390/su13084130.

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One of the pillars of sustainable development is related to the elimination of poverty and improvement of quality of life. Financial situation of the households plays a crucial role in the subjective well-being, quality of life and overall satisfaction. According to most recent Eurostat data, Poland is one of the countries with the lowest level of subjective material well-being. In this paper we aim to find latent structures of Poles with similar tendency of self-reporting their income position additionally influenced by the socio-economic features and analyze the item characteristics of the questionnaire as well. To address the questions at hand, first we apply the variant of finite mixture models, i.e., latent class (LC) model for data from all, eight waves (2000–2015) of the Polish Household Panel. We are especially focused on the constrained version of the model under IRT parameterization. We compare the results for LC and LC-IRT models and show the influence of the covariates such as family type, socio-economic status and place of living on the probability of belonging to the three identified classes of Polish households, based both on multinomial and global logit parameterization. In this way we can show which types of families tend to be more satisfied with their financial position and those whose members are prone to belong to the worst situated group of Poles. Note that, we present the results for the data including survey weighs, being omitted in most of the studies concerning Polish financial well-being.

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Record, Nicholas R., John D. O’Brien, Karen Stamieszkin, and Jeffrey A. Runge. "Omic-style statistical clustering reveals old and new patterns in the Gulf of Maine ecosystem." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 7 (July 2017): 973–79. http://dx.doi.org/10.1139/cjfas-2016-0151.

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The burgeoning of omic technology has spawned a new subfield of statistics aimed at interpreting the complex information contained in omic data. Some of these statistical methods can be applied to any data set with taxonomic counts, and they have the potential to provide additional insights over traditional approaches. We test this potential by reanalyzing a well-studied zooplankton data set — the Gulf of Maine continuous plankton recorder series — using a modified Dirichlet-multinomial mixture (DMM) model. The data set has ∼50 years of approximately monthly samples along a transect from Boston, USA, to Yarmouth, Canada. The results from the DMM analysis were largely consistent with previous analyses but also provided new insights. Notably, the Calanus-dominated communities that returned following a reduction in the 1990s showed a loss of background diversity, suggesting a shift in sources and possibly higher vulnerability of these communities. The DMM analysis also revealed a breakdown of seasonal ecological succession in the 1990s. These changes could be a precursor to similar changes in other Calanus-dominated systems. The approach demonstrates a path toward linking traditional analyses with recent omic-style analyses.

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Barnett, Adrian G., Paul McElwee, Andrea Nathan, Nicola W. Burton, and Gavin Turrell. "Identifying patterns of item missing survey data using latent groups: an observational study." BMJ Open 7, no. 10 (October 2017): e017284. http://dx.doi.org/10.1136/bmjopen-2017-017284.

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ObjectivesTo examine whether respondents to a survey of health and physical activity and potential determinants could be grouped according to the questions they missed, known as ‘item missing’.DesignObservational study of longitudinal data.SettingResidents of Brisbane, Australia.Participants6901 people aged 40–65 years in 2007.Materials and methodsWe used a latent class model with a mixture of multinomial distributions and chose the number of classes using the Bayesian information criterion. We used logistic regression to examine if participants’ characteristics were associated with their modal latent class. We used logistic regression to examine whether the amount of item missing in a survey predicted wave missing in the following survey.ResultsFour per cent of participants missed almost one-fifth of the questions, and this group missed more questions in the middle of the survey. Eighty-three per cent of participants completed almost every question, but had a relatively high missing probability for a question on sleep time, a question which had an inconsistent presentation compared with the rest of the survey. Participants who completed almost every question were generally younger and more educated. Participants who completed more questions were less likely to miss the next longitudinal wave.ConclusionsExamining patterns in item missing data has improved our understanding of how missing data were generated and has informed future survey design to help reduce missing data.

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Shahbazi, Zeinab, and Yung-Cheol Byun. "Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 2441–57. http://dx.doi.org/10.3233/jifs-202545.

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Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.

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Chen, Yu-Chih, Sojung Park, and Nancy Morrow-Howell. "PATTERNS OF WEALTH TRAJECTORY IN LATER LIFE: CRITICAL PERIOD, ACCUMULATION, AND SOCIAL MOBILITY MODELS." Innovation in Aging 3, Supplement_1 (November 2019): S382. http://dx.doi.org/10.1093/geroni/igz038.1403.

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Abstract Wealth, an important financial cushion for older adults to buffer economic stress, requires a longer time to accumulate and develop in one’s course of life. However, little is known about the trajectories of wealth in later life, and how the life course socioeconomic status (SES) may contribute to the development of wealth at old-age. This study investigated longitudinal patterns of wealth trajectory and whether SES across the life course affects these trajectories using critical period, accumulation, and social mobility models. Using data from 16,189 adults aged 51 and older from the 2004-2014 Health and Retirement Study, a growth mixture model was used to explore distinct wealth trajectories. Impacts of life course models were studied using multinomial logistic regression. Results showed that four heterogeneous latent classes of wealth were identified: Stable high (reference group), Low and increasing, Stable low, and High but decline. Disadvantaged adulthood SES, accumulated exposure to socioeconomic risks, and downward or persistent socioeconomic disadvantage over the life course were associated with Stable low, Low and increasing, and High but decline, supporting all three life course mechanisms on wealth development in later life. Evidence suggests that wealth development is heterogeneous across individuals, and a strong gradient effect of life-course SES on wealth trajectories are clearly observed. Programs and policies should address the effects of life course on wealth development to strengthen the economic well-being in later life.

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Haines, Linda M. "Multinomial N ‐mixture models for removal sampling." Biometrics 76, no. 2 (October 29, 2019): 540–48. http://dx.doi.org/10.1111/biom.13147.

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Rashid, Amir, Lis Cordingley, Roberto Carrasco, Helen E. Foster, Eileen M. Baildam, Alice Chieng, Joyce E. Davidson, et al. "Patterns of pain over time among children with juvenile idiopathic arthritis." Archives of Disease in Childhood 103, no. 5 (November 25, 2017): 437–43. http://dx.doi.org/10.1136/archdischild-2017-313337.

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ObjectivesPain is a very common symptom of juvenile idiopathic arthritis (JIA). Disease activity alone cannot explain symptoms of pain in all children, suggesting other factors may be relevant. The objectives of this study were to describe the different patterns of pain experienced over time in children with JIA and to identify predictors of which children are likely to experience ongoing pain.MethodsThis study used longitudinal-data from patients (aged 1–16 years) with new-onset JIA. Baseline and up to 5-year follow-up pain data from the Childhood Arthritis Prospective Study (CAPS) were used. A two-step approach was adopted. First, pain trajectories were modelled using a discrete mixture model. Second, multinomial logistic regression was used to determine the association between variables and trajectories.ResultsData from 851 individuals were included (4 years, median follow-up). A three-group trajectory model was identified: consistently low pain (n=453), improved pain (n=254) and consistently high pain (n=144). Children with improved pain or consistently high pain differed on average at baseline from consistently low pain. Older age at onset, poor function/disability and longer disease duration at baseline were associated with consistently high pain compared with consistently low pain. Early increases in pain and poor function/disability were also associated with consistently high pain compared with consistently low pain.ConclusionsThis study has identified routinely collected clinical factors, which may indicate those individuals with JIA at risk of poor pain outcomes earlier in disease. Identifying those at highest risk of poor pain outcomes at disease onset may enable targeted pain management strategies to be implemented early in disease thus reducing the risk of poor pain outcomes.

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Jorgensen, Murray. "Using Multinomial Mixture Models to Cluster Internet Traffic." Australian New Zealand Journal of Statistics 46, no. 2 (June 2004): 205–18. http://dx.doi.org/10.1111/j.1467-842x.2004.00325.x.

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de Jong, Lea, Marika Plöthner, Jona Theodor Stahmeyer, Sveja Eberhard, Jan Zeidler, and Kathrin Damm. "Informal and formal care preferences and expected willingness of providing elderly care in Germany: protocol for a mixed-methods study." BMJ Open 9, no. 1 (January 2019): e023253. http://dx.doi.org/10.1136/bmjopen-2018-023253.

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IntroductionIn Germany, the number of elderly people in need of care is expected to increase from 2.4 million in 2015 to 3.2 million in 2030. The subsequent rise in demand for long-term care facilities is unlikely to be met by the current care structures and available staff. Additionally, many Germans still prefer to be cared for at home for as long as possible. In light of recent changes, such as increasing employment rates of women and growing geographical distances of family members, informal caregiving becomes more challenging in the future. The aim of this study is to explore preferences for informal and formal care services in the German general population, as well as the expected willingness of providing elderly care.Methods and analysisA mixed-methods approach will be used to explore care preferences and expected willingness of providing elderly care in the German general population. A systematic literature review will be performed to provide an overview of the current academic literature on the topic. Qualitative interviews will be conducted with informal caregivers, care consultants and people with no prior caregiving experiences. A labelled discrete choice experiment will be designed and conducted to quantitatively measure the preferences for informal and formal care in the German general population. People between 18 and 65 years of age will be recruited in cooperation with a (regional) statutory health insurance (AOK Lower Saxony). A mixed multinomial logit regression model and a latent class finite mixture model will be used to analyse the data and test for subgroup differences in care preferences.Ethics and disseminationThe study has been approved by the Committee for Clinical Ethics of the Medical School in Hannover. Data will be treated confidential to ensure the participants' anonymity. The results will be discussed and disseminated to relevant stakeholders in the field.Trial registration numberDRKS00012266.

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Butter, Sarah, Mark Shevlin, and Jamie Murphy. "Negative self-evaluation and the genesis of internal threat: beyond a continuum of suicidal thought and behaviour." Psychological Medicine 49, no. 15 (December 3, 2018): 2591–99. http://dx.doi.org/10.1017/s0033291718003562.

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AbstractBackgroundDeath by suicide is often preceded by attempted suicide, suicidal ideation and non-suicidal self-injury. These extreme thoughts and behaviours have been considered in terms of a continuum of suicidality. Little known research, however, has considered a suicide continuum that extends beyond these extreme thoughts and behaviours and incorporates a much wider array of phenomena that may vary in severity and may constitute a broader negative self-evaluation (NSE) continuum.MethodHarvesting key indicators of NSE from a British epidemiological survey (N = 8580), the current study used exploratory factor analysis, confirmatory factor analysis and factor mixture modelling to (i) identify the dimensional structure of NSE in the general population and (ii) profile the distribution of the resultant NSE dimensions. Multinomial logistic regression was then used to differentiate between classes using an array of risk variables, psychopathology outcome variables and a suicide attempt indicator.ResultsA 4-factor model that reflected graded levels of NSE was identified; (F1) Low self-worth & subordination (F2) depression, (F3) suicidal thoughts, (F4) self-harm (SH). Seven classes suggested a clear pattern of NSE severity. Classes characterised by higher levels across the dimensions exhibited greater risk and poorer outcomes. The greatest risk for suicide attempt was associated with a class characterised by engagement in SH behaviour.ConclusionsLow self-worth, subordination and depression, while representative of distinct groups in the population are also highly prevalent in those who entertain suicidal thoughts and engage in SH behaviour. The findings promote further investigation into the genesis and evolution of suicidality and internal threat.

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Lee, Ji Hyun. "SOCIAL RELATIONS AND FRAILTY TRAJECTORY IN LATER ADULTHOOD: EVIDENCE FROM HEALTH AND RETIREMENT STUDY." Innovation in Aging 3, Supplement_1 (November 2019): S972—S973. http://dx.doi.org/10.1093/geroni/igz038.3526.

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Abstract Frailty is a state of heightened vulnerability due to the cumulative declines across multiple physiological systems. Growing attention is given to identifying social environmental factors associated with the risk of frailty. It is not yet known how different aspects of social relationships (structure and quality) are linked to frailty, and whether the strength and directions of links may differ by relationship types. Current study aims to 1) to identify sub-populations that follows distinctive trajectory of frailty and 2) to examine the multidimensional social relationship predictors of frailty trajectory. Data came from six waves of the Health and Retirement Study (2006-2016). Sample was older adults aged over 65 (n = 8,892; Mage = 74, SD = 6.96). Frailty index was created using 32 items each wave. Network size, frequency of contact, support, and strain with each relationship type (spouse, children, family, friends) was measured at baseline. Growth mixture model identified three distinctive subpopulations of older adults who share similar frailty trajectory, namely the average, high, and steep increase frailty group. Multinomial logistic regression results showed that frequent contact with friends were associated with lesser frailty. Perceived strain with the spouse, children, and family members all had additive influence on the membership to the higher or steep increase frailty group, compared to the average frailty group. Total network size or perceived support from ties were not significant factors for frailty progression. Interventions can target friendships and stress with kin members as modifiable factors to reduce the risk of frailty progression.

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Sabbaghi, Azam, Farzad Eskandari, and Hamid Reza Navabpoor. "A Robust High-Dimensional Estimation of Multinomial Mixture Models." Journal of Statistical Theory and Applications 20, no. 1 (2021): 21. http://dx.doi.org/10.2991/jsta.d.210126.001.

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Journal articles on the topic 'Multinomial mixture model'

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