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Journal articles on the topic 'Latent class method'
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Baker, Stuart G. "The latent class twin method." Biometrics 72, no. 3 (January 11, 2016): 827–34. http://dx.doi.org/10.1111/biom.12460.
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Sun, Ming, Xiaoduan Sun, and Donghui Shan. "Pedestrian crash analysis with latent class clustering method." Accident Analysis & Prevention 124 (March 2019): 50–57. http://dx.doi.org/10.1016/j.aap.2018.12.016.
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Scotto Rosato, N., and J. C. Baer. "Latent Class Analysis: A Method for Capturing Heterogeneity." Social Work Research 36, no. 1 (March 1, 2012): 61–69. http://dx.doi.org/10.1093/swr/svs006.
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Zhang, Ningshan, and Jeffrey S. Simonoff. "Joint latent class trees: A tree-based approach to modeling time-to-event and longitudinal data." Statistical Methods in Medical Research 31, no. 4 (February 18, 2022): 719–52. http://dx.doi.org/10.1177/09622802211055857.
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In this paper, we propose a semiparametric, tree-based joint latent class model for the joint behavior of longitudinal and time-to-event data. Existing joint latent class approaches are parametric and can suffer from high computational cost. The most common parametric approach, the joint latent class model, further restricts analysis to using time-invariant covariates in modeling survival risks and latent class memberships. The proposed tree method (joint latent class tree) is fast to fit, and permits time-varying covariates in all of its modeling components. We demonstrate the prognostic value of using time-varying covariates, and therefore the advantage of joint latent class tree over joint latent class model on simulated data. We apply joint latent class tree to a well-known data set (the PAQUID data set) and confirm its superior prediction performance and orders-of-magnitude speedup over joint latent class model.
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Vermunt, Jeroen K. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches." Political Analysis 18, no. 4 (2010): 450–69. http://dx.doi.org/10.1093/pan/mpq025.
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Researchers using latent class (LC) analysis often proceed using the following three steps: (1) an LC model is built for a set of response variables, (2) subjects are assigned to LCs based on their posterior class membership probabilities, and (3) the association between the assigned class membership and external variables is investigated using simple cross-tabulations or multinomial logistic regression analysis. Bolck, Croon, and Hagenaars (2004) demonstrated that such a three-step approach underestimates the associations between covariates and class membership. They proposed resolving this problem by means of a specific correction method that involves modifying the third step. In this article, I extend the correction method of Bolck, Croon, and Hagenaars by showing that it involves maximizing a weighted log-likelihood function for clustered data. This conceptualization makes it possible to apply the method not only with categorical but also with continuous explanatory variables, to obtain correct tests using complex sampling variance estimation methods, and to implement it in standard software for logistic regression analysis. In addition, a new maximum likelihood (ML)—based correction method is proposed, which is more direct in the sense that it does not require analyzing weighted data. This new three-step ML method can be easily implemented in software for LC analysis. The reported simulation study shows that both correction methods perform very well in the sense that their parameter estimates and their SEs can be trusted, except for situations with very poorly separated classes. The main advantage of the ML method compared with the Bolck, Croon, and Hagenaars approach is that it is much more efficient and almost as efficient as one-step ML estimation.
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Porcu, Mariano, and Francesca Giambona. "Introduction to Latent Class Analysis With Applications." Journal of Early Adolescence 37, no. 1 (July 27, 2016): 129–58. http://dx.doi.org/10.1177/0272431616648452.
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Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence researchers. We provide an application of LCA to empirical data collected from a national survey carried out in 2010 in Italy to assess mathematics and reading skills of fifth-grade primary school pupils (10 years in age). The data were used to measure pupils’ supplies of cultural capital by specifying a latent class model. This article aims to describe and interpret results of LCA, allowing users to replicate the analysis. All LCA examples included in the text are illustrated using the Latent GOLD package, and command files needed to reproduce all analyses with SAS and R are available as supplemental online appendix files along with the example data files.
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Dziak, John J., Bethany C. Bray, Jieting Zhang, Minqiang Zhang, and Stephanie T. Lanza. "Comparing the Performance of Improved Classify-Analyze Approaches for Distal Outcomes in Latent Profile Analysis." Methodology 12, no. 4 (October 2016): 107–16. http://dx.doi.org/10.1027/1614-2241/a000114.
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Abstract. Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004) , Vermunt’s (2010) maximum likelihood (ML) approach, and the inclusive three-step approach of Bray, Lanza, and Tan (2015) . These methods have been studied in the related case of latent class analysis (LCA) with categorical indicators, but not as well studied for LPA with continuous indicators. We investigated the performance of these approaches in LPA with normally distributed indicators, under different conditions of distal outcome distribution, class measurement quality, relative latent class size, and strength of association between latent class and the distal outcome. The modified BCH implemented in Latent GOLD had excellent performance. The maximum likelihood and inclusive approaches were not robust to violations of distributional assumptions. These findings broadly agree with and extend the results presented by Bakk and Vermunt (2016) in the context of LCA with categorical indicators.
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NOWAKOWSKA, Marzena, and Michał PAJĘCKI. "Applying latent class analysis in the identification of occupational accident patterns." Scientific Papers of Silesian University of Technology. Organization and Management Series 2020, no. 146 (2020): 339–55. http://dx.doi.org/10.29119/1641-3466.2020.146.25.
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Purpose: The objective of the study is to use selected data mining techniques to discover patterns of certain recurring mechanisms related to the occurrence of occupational accidents in relation to production processes. Design/methodology/approach: The latent class analysis (LCA) method was employed in the investigation. This statistical modeling technique enables discovering mutually exclusive homogenous classes of objects in a multivariate data set on the basis of observable qualitative variables, defining the class homogeneity in terms of probabilities. Due to a bilateral agreement, Statistics Poland provided individual record-level real data for the research. Then the data were preprocessed to enable the LCA model identification. Pilot studies were conducted in relation to occupational accidents registered in production plants in 2008-2017 in the Wielkopolskie voivodeship. Findings: Three severe accident patterns and two light accident patterns represented by latent classes were obtained. The classes were subjected to descriptive characteristics and labeling, using interpretable results presented in the form of probabilities classifying categories of observable variables, symptomatic for a given latent class. Research limitations/implications: The results from the pilot studies indicate the necessity to continue the research based on a larger data set along with the analysis development, particularly as regards selecting indicators for the latent class model characterization. Practical implications: The identification of occupational accident patterns related to the production process can play a vital role in the elaboration of efficient safety countermeasures that can help to improve the prevention and outcome mitigation of such accidents among workers. Social implications: Creating a safe work environment comprises the quality of life of workers, their families, thus affirming the enterprises' principles and values in the area of corporate social responsibility. Originality/value: The investigation showed that latent class analysis is a promising tool supporting the scientific research in discovering the patterns of occupational accidents. The proposed investigation approach indicates the importance for the research both in terms of the availability of non-aggregated occupational accident data as well as the type of value aggregation of the variables taken for the analysis.
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Iwata, Tomoharu, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, and Joshua B. Tenenbaum. "Parametric Embedding for Class Visualization." Neural Computation 19, no. 9 (September 2007): 2536–56. http://dx.doi.org/10.1162/neco.2007.19.9.2536.
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We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.
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Sánchez-Monedero, J., Pedro A. Gutiérrez, Peter Tiňo, and C. Hervás-Martínez. "Exploitation of Pairwise Class Distances for Ordinal Classification." Neural Computation 25, no. 9 (September 2013): 2450–85. http://dx.doi.org/10.1162/neco_a_00478.
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Ordinal classification refers to classification problems in which the classes have a natural order imposed on them because of the nature of the concept studied. Some ordinal classification approaches perform a projection from the input space to one-dimensional (latent) space that is partitioned into a sequence of intervals (one for each class). Class identity of a novel input pattern is then decided based on the interval its projection falls into. This projection is trained only indirectly as part of the overall model fitting. As with any other latent model fitting, direct construction hints one may have about the desired form of the latent model can prove very useful for obtaining high-quality models. The key idea of this letter is to construct such a projection model directly, using insights about the class distribution obtained from pairwise distance calculations. The proposed approach is extensively evaluated with 8 nominal and ordinal classifiers methods, 10 real-world ordinal classification data sets, and 4 different performance measures. The new methodology obtained the best results in average ranking when considering three of the performance metrics, although significant differences are found for only some of the methods. Also, after observing other methods of internal behavior in the latent space, we conclude that the internal projections do not fully reflect the intraclass behavior of the patterns. Our method is intrinsically simple, intuitive, and easily understandable, yet highly competitive with state-of-the-art approaches to ordinal classification.
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Zhu, Qiuyu, Liheng Hu, and Rui Wang. "Image Clustering Algorithm Based on Predefined Evenly-Distributed Class Centroids and Composite Cosine Distance." Entropy 24, no. 11 (October 26, 2022): 1533. http://dx.doi.org/10.3390/e24111533.
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The clustering algorithms based on deep neural network perform clustering by obtaining the optimal feature representation. However, in the face of complex natural images, the cluster accuracy of existing clustering algorithms is still relatively low. This paper presents an image clustering algorithm based on predefined evenly-distributed class centroids (PEDCC) and composite cosine distance. Compared with the current popular auto-encoder structure, we design an encoder-only network structure with normalized latent features, and two effective loss functions in latent feature space by replacing the Euclidean distance with a composite cosine distance. We find that (1) contrastive learning plays a key role in the clustering algorithm and greatly improves the quality of learning latent features; (2) compared with the Euclidean distance, the composite cosine distance can be more suitable for the normalized latent features and PEDCC-based Maximum Mean Discrepancy (MMD) loss function; and (3) for complex natural images, a self-supervised pretrained model can be used to effectively improve clustering performance. Several experiments have been carried out on six common data sets, MNIST, Fashion-MNIST, COIL20, CIFAR-10, STL-10 and ImageNet-10. Experimental results show that our method achieves the best clustering effect compared with other latest clustering algorithms.
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Shim, Dongsub, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, and Jongseong Jang. "Online Class-Incremental Continual Learning with Adversarial Shapley Value." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9630–38. http://dx.doi.org/10.1609/aaai.v35i11.17159.
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As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. To this end, we contribute a novel Adversarial Shapley value scoring method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that our proposed ASER method provides competitive or improved performance compared to state-of-the-art replay-based continual learning methods on a variety of datasets.
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van der Palm, Daniël W., L. Andries van der Ark, and Jeroen K. Vermunt. "Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data." Journal of Classification 33, no. 1 (February 23, 2016): 52–72. http://dx.doi.org/10.1007/s00357-016-9195-5.
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Rijmen, Frank, Kristof Vansteelandt, and Paul De Boeck. "Latent Class Models for Diary Method Data: Parameter Estimation by Local Computations." Psychometrika 73, no. 2 (October 4, 2007): 167–82. http://dx.doi.org/10.1007/s11336-007-9001-8.
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Peschel, Anne O., Carola Grebitus, Mohammed Hussen Alemu, and Renée S. Hughner. "Personality traits and preferences for production method labeling – A latent class approach." Food Quality and Preference 74 (June 2019): 163–71. http://dx.doi.org/10.1016/j.foodqual.2019.01.014.
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Miller, William E. "A latent class method for the selection of prototypes using expert ratings." Statistics in Medicine 31, no. 1 (November 15, 2011): 80–92. http://dx.doi.org/10.1002/sim.4399.
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Böckenholt, Ulf, and William R. Dillon. "Inferring Latent Brand Dependencies." Journal of Marketing Research 37, no. 1 (February 2000): 72–87. http://dx.doi.org/10.1509/jmkr.37.1.72.18726.
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In this article, the authors develop a class of models to reconstruct brand-transition probabilities when individual brand purchase sequence information is not available. The authors introduce two general model forms by assuming different underlying mechanisms for individual heterogeneity in brand switching. The first model form captures individual heterogeneity by a latent class structure. The second model form captures individual heterogeneity by postulating that the brand-choice probabilities follow a Dirichlet distribution, which yields the popular Dirichlet multinomial formulation. Monte Carlo simulations are performed with a view toward assessing whether individual transition probabilities can be captured from knowledge of only aggregated brand choices. Results indicate that the proposed method can indeed capture individual brand-transition probabilities under several different conditions. An empirical application illustrates how these models can be used to provide important information on individual brand transitions and the role of marketing-related covariates.
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Boeschoten, Laura, Daniel Oberski, and Ton de Waal. "Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)." Journal of Official Statistics 33, no. 4 (December 1, 2017): 921–62. http://dx.doi.org/10.1515/jos-2017-0044.
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Abstract Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.
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Lin, Kaiyi, Xing Xu, Lianli Gao, Zheng Wang, and Heng Tao Shen. "Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11515–22. http://dx.doi.org/10.1609/aaai.v34i07.6817.
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Zero-Shot Cross-Modal Retrieval (ZS-CMR) is an emerging research hotspot that aims to retrieve data of new classes across different modality data. It is challenging for not only the heterogeneous distributions across different modalities, but also the inconsistent semantics across seen and unseen classes. A handful of recently proposed methods typically borrow the idea from zero-shot learning, i.e., exploiting word embeddings of class labels (i.e., class-embeddings) as common semantic space, and using generative adversarial network (GAN) to capture the underlying multimodal data structures, as well as strengthen relations between input data and semantic space to generalize across seen and unseen classes. In this paper, we propose a novel method termed Learning Cross-Aligned Latent Embeddings (LCALE) as an alternative to these GAN based methods for ZS-CMR. Unlike using the class-embeddings as the semantic space, our method seeks for a shared low-dimensional latent space of input multimodal features and class-embeddings by modality-specific variational autoencoders. Notably, we align the distributions learned from multimodal input features and from class-embeddings to construct latent embeddings that contain the essential cross-modal correlation associated with unseen classes. Effective cross-reconstruction and cross-alignment criterions are further developed to preserve class-discriminative information in latent space, which benefits the efficiency for retrieval and enable the knowledge transfer to unseen classes. We evaluate our model using four benchmark datasets on image-text retrieval tasks and one large-scale dataset on image-sketch retrieval tasks. The experimental results show that our method establishes the new state-of-the-art performance for both tasks on all datasets.
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Riyanto, Andreas, Heri Kuswanto, and Dedy Dwi Prastyo. "Mutual Information-Based Variable Selection on Latent Class Cluster Analysis." Symmetry 14, no. 5 (April 29, 2022): 908. http://dx.doi.org/10.3390/sym14050908.
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Machine learning techniques are becoming indispensable tools for extracting useful information. Among many machine learning techniques, variable selection is a solution used for converting high-dimensional data into simpler data while still preserving the characteristics of the original data. Variable selection aims to find the best subset of variables that produce the smallest generalization error; it can also reduce computational complexity, storage, and costs. The variable selection method developed in this paper was part of a latent class cluster (LCC) analysis—i.e., it was not a pre-processing step but, instead, formed part of LCC analysis. Many studies have shown that variable selection in LCC analysis suffers from computational problems and has difficulty meeting local dependency assumptions—therefore, in this study, we developed a method for selecting variables using mutual information (MI) in LCC analysis. Mutual information (MI) is a symmetrical measure of information that is carried by two random variables. The proposed method was applied to MI-based variable selection in LCC analysis, and, as a result, four variables were selected for use in LCC-based village clustering.
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Chrisinta, Debora, I. Made Sumertajaya, and Indahwati Indahwati. "EVALUASI KINERJA METODE CLUSTER ENSEMBLE DAN LATENT CLASS CLUSTERING PADA PEUBAH CAMPURAN." Indonesian Journal of Statistics and Its Applications 4, no. 3 (November 30, 2020): 448–61. http://dx.doi.org/10.29244/ijsa.v4i3.630.
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Most of the traditional clustering algorithms are designed to focus either on numeric data or on categorical data. The collected data in the real-world often contain both numeric and categorical attributes. It is difficult for applying traditional clustering algorithms directly to these kinds of data. So, the paper aims to show the best method based on the cluster ensemble and latent class clustering approach for mixed data. Cluster ensemble is a method to combine different clustering results from two sub-datasets: the categorical and numerical variables. Then, clustering algorithms are designed for numerical and categorical datasets that are employed to produce corresponding clusters. On the other side, latent class clustering is a model-based clustering used for any type of data. The numbers of clusters base on the estimation of the probability model used. The best clustering method recommends LCC, which provides higher accuracy and the smallest standard deviation ratio. However, both LCC and cluster ensemble methods produce evaluation values that are not much different as the application method used potential village data in Bengkulu Province for clustering.
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Valliyammai, C., Nanthini M, and Ephina Thendral S. "Dimensionality Reduction Using Latent Variable across the Domains in Recommender System." International Research Journal of Electronics and Computer Engineering 2, no. 2 (June 15, 2016): 33. http://dx.doi.org/10.24178/irjece.2016.2.2.33.
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Dimensionality reduction plays an important role in big data analytics and machine learning for the past decades. While exploring the large volumes of data, it is necessary to perform the larger computation. In order to overcome this, a novel latent variable based dimensionality reduction across the domains in Recommender System (RS) is proposed. Firstly, we define the latent class corresponding to the attributes from two domains and user profiles. Then many-to-one mapping of attributes to a latent class variable is achieved. Finally, the entire data variables are reduced to five latent class variables and sharing the knowledge across the domains. The overall dimensionality reduction is very useful for easy processing of data and reducing the processing time in various applications. Compared with the traditional dimensionality reduction method, the proposed method discovers the hidden variable from the observed variable without any loss of information.
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Aitken, Madison, Rhonda Martinussen, Ruth Childs, and Rosemary Tannock. "Profiles of Co-Occurring Difficulties Identified Through School-Based Screening." Journal of Attention Disorders 24, no. 9 (December 22, 2016): 1355–65. http://dx.doi.org/10.1177/1087054716684377.
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Objective: This study used latent class analysis to identify patterns of co-occurrence among common childhood difficulties (inattention/hyperactivity, internalizing, externalizing, peer problems, and reading difficulties). Method: Parents and teachers of 501 children ages 6 to 9 provided mental health and social ratings, and children completed a reading task. Results: Four latent classes were identified in the analysis of parent ratings and reading: one with inattention/hyperactivity, externalizing, peer problems, and internalizing difficulties; one with inattention/hyperactivity and reading difficulties; one with internalizing and peer problems; and one normative class. The analysis of teacher ratings and reading also identified four latent classes: one with inattention/hyperactivity and externalizing, one with inattention/hyperactivity and reading difficulties, one with internalizing problems, and one normative class. Children in latent classes characterized by one or more difficulties were more impaired than children in the normative latent class 1 year later. Conclusion: The results highlight the need for multifaceted interventions.
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Alvarez, Antonio, Julio del Corral, and Loren W. Tauer. "Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models." Agricultural and Resource Economics Review 41, no. 3 (December 2012): 275–85. http://dx.doi.org/10.1017/s1068280500001258.
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Agricultural production estimates have often differentiated and estimated different technologies within a sample of farms. The common approach is to use observable farm characteristics to split the sample into groups and subsequently estimate different functions for each group. Alternatively, unique technologies can be determined by econometric procedures such as latent class models. This paper compares the results of a latent class model with the use of a priori information to split the sample using dairy farm data. Latent class separation appears to be a superior method of separating heterogeneous technologies and suggests that technology differences are multifaceted.
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Beall, Jonathan, Elizabeth G. Hill, Kent Armeson, Kendrea L. (Focht) Garand, Kate (Humphries) Davidson, and Bonnie Martin-Harris. "Classification of Physiologic Swallowing Impairment Severity: A Latent Class Analysis of Modified Barium Swallow Impairment Profile Scores." American Journal of Speech-Language Pathology 29, no. 2S (July 10, 2020): 1001–11. http://dx.doi.org/10.1044/2020_ajslp-19-00080.
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Purpose Our objectives were to (a) identify oral and pharyngeal physiologic swallowing impairment severity classes based on latent class analyses (LCAs) of the Modified Barium Swallow Impairment Profile (MBSImP) swallow task scores and (b) quantify the probability of severity class membership given composite MBSImP oral total (OT) and pharyngeal total (PT) scores. Method MBSImP scores were collected from a patient database of 319 consecutive modified barium swallow studies. Because of missing swallow task scores, LCA was performed using 25 multiply imputed data sets. Results LCA revealed a three-class structure for both oral and pharyngeal models. We identified OT and PT score intervals to assign subjects to oral and pharyngeal impairment latent severity classes, respectively, with high probability (probability of class membership ≥ 0.9 given OT or PT scores within specified ranges) and high confidence (95% credible interval [CI] widths ≤ 0.24 for all total scores within specified ranges). OT scores ranging from 0 to 10 and from 14 to 18 yielded assignments in Oral Latent Classes 1 and 2, respectively, while OT = 22 was assigned to Oral Latent Class 3. PT scores ranging from 0 to 13 and from 18 to 24 yielded assignments in Pharyngeal Latent Classes 1 and 2, respectively, while PT = 26 was assigned to Pharyngeal Latent Class 3. Conclusions LCA of MBSImP task-level data revealed significant underlying oral and pharyngeal ordinal class structures representing increasingly severe gradations of physiologic swallow impairment. Clinically meaningful OT and PT score ranges were derived facilitating latent class assignment. Supplemental Material https://doi.org/10.23641/asha.12315677
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Jedidi, Kamel, Venkatram Ramaswamy, and Wayne S. Desarbo. "A maximum likelihood method for latent class regression involving a censored dependent variable." Psychometrika 58, no. 3 (September 1993): 375–94. http://dx.doi.org/10.1007/bf02294647.
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Baker, Stuart G. "A latent class method for diagnostic tests: the new, reference, gold standard problem." Statistics in Medicine 33, no. 24 (October 2, 2014): 4320. http://dx.doi.org/10.1002/sim.6283.
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Park, Seohee, Seongeun Kim, and Ji Hoon Ryoo. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis." Mathematics 8, no. 11 (November 20, 2020): 2076. http://dx.doi.org/10.3390/math8112076.
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Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approach for LCA with covariates, also known as latent class regression (LCR), using a fuzzy clustering method and generalized structured component analysis (GSCA). This new approach is free from the distributional assumption and stable in estimating parameters. Parallel to the three-step approach used in the MLE-based LCA, we extend an algorithm of fuzzy clusterwise GSCA into LCR. This proposed algorithm has been demonstrated with an empirical data with both categorical and continuous covariates. Because the proposed algorithm can be used for a relatively small sample in LCR without requiring a multivariate normality assumption, the new algorithm is more applicable to social, behavioral, and health sciences.
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Ghanem, Khadoudja. "Local and Global Latent Semantic Analysis for Text Categorization." International Journal of Information Retrieval Research 4, no. 3 (July 2014): 1–13. http://dx.doi.org/10.4018/ijirr.2014070101.
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In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.
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Zhang, Sheng, Meiqian Gong, Wenyan Li, Wanxin Wang, Ruipeng Wu, Lan Guo, and Ciyong Lu. "Patterns of Bullying Victimization and Associations with Mental Health Problems in Chinese Adolescents: A Latent Class Analysis." International Journal of Environmental Research and Public Health 17, no. 3 (January 27, 2020): 779. http://dx.doi.org/10.3390/ijerph17030779.
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Bullying victimization in school students is a serious public health concern and has been linked to a wide range of mental health problems. The current study aims to examine patterns of involvement in different types of bullying victimization among Chinese adolescents and evaluate the associations between bullying victimization and mental health problems. Cross-sectional data from 20,722 middle school students from Guangdong Province were sampled using a multistage, stratified cluster-randomized sampling method. Latent class analysis (LCA) was performed on seven items representing bullying victimization. Levels of mental health outcomes were compared across each latent class. Four latent classes were identified for boys: the high victimization class (0.6%), the moderate victimization class (2.8%), the verbal victimization class (12.4%), and the low victimization class (84.2%). For girls, three latent classes were identified: the high victimization class (0.7%), the moderate victimization class (5.6%), and the low victimization class (93.7%). Characteristics of the item probabilities were different between boys and girls. For both genders, a graded relationship was found between bullying victimization class membership and mental health outcomes. These findings underline the complexity of bullying victimization patterns among Chinese adolescents. Students with higher involvement in bullying victimization have more severe mental health problems.
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Bartlett, Christopher W., Brett G. Klamer, Steven Buyske, Stephen A. Petrill, and William C. Ray. "Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features." Genes 10, no. 9 (September 19, 2019): 727. http://dx.doi.org/10.3390/genes10090727.
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Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization collecting data makes unique decisions on instruments, protocols, and methods of processing the data. This typically denies literal concatenation of the raw data since constituent cohorts do not have the same measurements (i.e., columns of data). When measurements across datasets are similar prima facie, there is a desire to combine the data to increase power, but mixing non-identical measurements could greatly reduce the sensitivity of the downstream analysis. Here, we discuss a statistical method that is applicable when certain patterns of missing data are found; namely, it is possible to combine datasets that measure the same underlying constructs (or latent traits) when there is only partial overlap of measurements across the constituent datasets. Our method, ROSETTA empirically derives a set of common latent trait metrics for each related measurement domain using a novel variation of factor analysis to ensure equivalence across the constituent datasets. The advantage of combining datasets this way is the simplicity, statistical power, and modeling flexibility of a single joint analysis of all the data. Three simulation studies show the performance of ROSETTA on datasets with only partially overlapping measurements (i.e., systematically missing information), benchmarked to a condition of perfectly overlapped data (i.e., full information). The first study examined a range of correlations, while the second study was modeled after the observed correlations in a well-characterized clinical, behavioral cohort. Both studies consistently show significant correlations >0.94, often >0.96, indicating the robustness of the method and validating the general approach. The third study varied within and between domain correlations and compared ROSETTA to multiple imputation and meta-analysis as two commonly used methods that ostensibly solve the same data integration problem. We provide one alternative to meta-analysis and multiple imputation by developing a method that statistically equates similar but distinct manifest metrics into a set of empirically derived metrics that can be used for analysis across all datasets.
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Allman, Elizabeth, Hector Banos Cervantes, Serkan Hosten, Kaie Kubjas, Daniel Lemke, John Rhodes, and Piotr Zwiernik. "Maximum likelihood estimation of the Latent Class Model through model boundary decomposition." Journal of Algebraic Statistics 10, no. 1 (April 10, 2019): 51–84. http://dx.doi.org/10.18409/jas.v10i1.75.
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The Expectation-Maximization (EM) algorithm is routinely used for the maximum likelihood estimation in the latent class analysis. However, the EM algorithm comes with no guarantees of reaching the global optimum. We study the geometry of the latent class model in order to understand the behavior of the maximum likelihood estimator. In particular, we characterize the boundary stratification of the binary latent class model with a binary hidden variable. For small models, such as for three binary observed variables, we show that this stratification allows exact computation of the maximum likelihood estimator. In this case we use simulations to study the maximum likelihood estimation attraction basins of the various strata. Our theoretical study is complemented with a careful analysis of the EM fixed point ideal which provides an alternative method of studying the boundary stratification and maximizing the likelihood function. In particular, we compute the minimal primes of this ideal in the case of a binary latent class model with a binary or ternary hidden random variable.
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Mollah, Md Nurul Haque, Mihoko Minami, and Shinto Eguchi. "Exploring Latent Structure of Mixture ICA Models by the Minimum β-Divergence Method." Neural Computation 18, no. 1 (January 1, 2006): 166–90. http://dx.doi.org/10.1162/089976606774841549.
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Independent component analysis (ICA) attempts to extract original independent signals (source components) that are linearly mixed in a basic framework. This letter discusses a learning algorithm for the separation of different source classes in which the observed data follow a mixture of several ICA models, where each model is described by a linear combination of independent and nongaussian sources. The proposed method is based on a sequential application of the minimum β-divergence method to separate all source classes sequentially. The proposed method searches the recovering matrix of each class on the basis of a rule of sequential change of the shifting parameter. If the initial choice of the shifting parameter vector is close to the mean of a data class, then all of the hidden sources belonging to that class are recovered properly with independent and nongaussian structure considering the data in other classes as outliers. The value of the tuning parameter β is a key in the performance of the proposed method. A cross-validation technique is proposed as an adaptive selection procedure for the tuning parameter β for this algorithm, together with applications for both real and synthetic data analysis.
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Lee, Heeyoung, Kyeongra Yang, Joshua Palmer, Brayden Kameg, Lin Clark, and Brian Greene. "Substance Use Patterns Among Adolescents: A Latent Class Analysis." Journal of the American Psychiatric Nurses Association 26, no. 6 (June 28, 2019): 586–94. http://dx.doi.org/10.1177/1078390319858658.
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BACKGROUND: Substance use among adolescents remains a major public health concern, which is correlated with mortality. AIMS: The purpose of this study was to (1) examine risk factors predisposing adolescents to substance use and (2) identify patterns of simultaneous drug exploration among adolescents. METHOD: Data ( N = 15,624; collected in 2015) were drawn from the Centers for Disease Control and Prevention, National Youth Risk Behavior Survey, which is a national school-based survey of 9th- to 12th-grade students to monitor health risk behaviors. Substance use was assessed using self-reported questionnaires, and latent class analysis and logistic regression were used for data analysis. RESULTS: Five latent patterns of substance use were identified: (1) abstinent (64%); (2) 1st-step social experimenter (25%) (i.e., used alcohol, e-cigarettes, and/or marijuana); (3) 2nd-step social experimenter (6%) (i.e., used alcohol, cigarettes, e-cigarettes, marijuana, synthetic marijuana, and/or prescription pills); (4) pill experimenter (4%), (i.e., used prescription pills); (5) full experimenter (2%) (i.e., likely to use all assessed substances). Gender, race, grade, and depressive mood were strong predictors of membership in a particular substance use class. CONCLUSION: Adolescents presenting for care may possess symptoms associated with various substances beyond those being managed. Mental health nurses can leverage these results in reducing adolescent substance use through primary and secondary prevention. A longitudinal study of not only substance use patterns but also the progression to substance use disorders among adolescents is warranted.
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Dudley, Matthew Z., Rupali J. Limaye, Daniel A. Salmon, Saad B. Omer, Sean T. O’Leary, Mallory K. Ellingson, Christine I. Spina, et al. "Latent Class Analysis of Maternal Vaccine Attitudes and Beliefs." Health Education & Behavior 47, no. 5 (July 8, 2020): 765–81. http://dx.doi.org/10.1177/1090198120939491.
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Background. Maternal vaccine coverage is suboptimal, and a substantial proportion of parents have concerns about vaccines. Most parents seek out vaccine information during and immediately after their first pregnancy. No study to our knowledge has analyzed survey data to identify homogeneous groups of pregnant women based on their vaccine attitudes and beliefs. Aims. To identify homogeneity among groups of pregnant women based on their vaccine attitudes and beliefs to facilitate audience segmentation and targeting of tailored educational interventions. Method. Between June 2017 and July 2018, we surveyed 2,196 pregnant women recruited from geographically and sociodemographically diverse prenatal care practices in Georgia and Colorado. We then performed a latent class analysis to identify homogeneity among groups of pregnant women. Results. Our latent class analysis produced three groups of pregnant women: vaccine supporters (36% of women), vaccine acceptors (41%), and vaccine skeptics (23%). Discussion. The major difference between the supporters and the acceptors were whether they mostly “strongly agreed” or just “agreed” to Likert-type scale survey items assessing their vaccine attitudes and beliefs. The skeptics most frequently chose “disagree” or “don’t know” for items assessing attitudinal constructs such as confidence in vaccine safety and efficacy and disease susceptibility. However, even skeptics often chose “agree” for items assessing constructs such as disease severity and self-efficacy. Conclusions. This article provides useful insight into the homogeneity among groups of pregnant women based on their vaccine attitudes and beliefs. This knowledge should help facilitate audience segmentation and targeting of tailored educational interventions among this population.
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Lee, Haein, and In-Seo La. "Latent Class Analysis of Obesogenic Behaviors among Korean Adolescents: Associations with Weight-Related Outcomes." International Journal of Environmental Research and Public Health 18, no. 21 (October 21, 2021): 11059. http://dx.doi.org/10.3390/ijerph182111059.
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This study aimed to explore sex-specific latent class models of adolescent obesogenic behaviors (OBs), predictors of latent class membership (LCM), and associations between LCM and weight-related outcomes (i.e., weight status and unhealthy weight control behaviors). We analyzed nationally representative data from the 2019 Korea Youth Risk Behavior Survey. To identify latent classes for boys (n = 29,841) and girls (n = 27,462), we conducted a multiple-group latent class analysis using eight OBs (e.g., breakfast skipping, physical activity, and tobacco product use). Moreover, we performed a multinomial logistic regression analysis and a three-step method to examine associations of LCM with predictors and weight-related outcomes. Among both sexes, the 3-class models best fit the data: (a) mostly healthy behavior class, (b) poor dietary habits and high Internet use class, and (c) poor dietary habits and substance use class. School year, residential area, academic performance, and psychological status predicted the LCM for both sexes. In addition, perceived economic status predicted the LCM for girls. The distribution of weight-related outcomes differed across sex-specific classes. Our findings highlight the importance of developing obesity prevention and treatment interventions tailored to each homogeneous pattern of adolescent OBs, considering differences in their associations with predictors and weight-related outcomes.
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Davis, Sam. "Choosing the Right Baskets for Your Eggs: Deriving Actionable Customer Segments Using Supervised Genetic Algorithms." International Journal of Market Research 54, no. 5 (September 2012): 689–706. http://dx.doi.org/10.2501/ijmr-54-5-689-706.
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In the context of key driver analysis in applied customer satisfaction research, the assumption of sample homogeneity (that single models perform adequately over the entirety of a survey sample) can be shown to restrict the value of the insights derived. While latent class regression has been used as a method of circumventing some of these issues, it is proposed that there are major barriers to both uptake and successful practical usage of the technique. Several of these issues are common to any multivariate technique, while others are specific to latent class regression. Following an examination of these issues, we introduce an alternative technique for deriving discrete latent classes, using a combination of genetic algorithms and (bivariate) correlations. This paper concludes that the proposed approach outperforms latent class regression in its ability to deliver action-orientated insights, and is better placed to assist marketers facing real-world research questions and datasets.
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Kang, Yashu, and Aemal Khattak. "Cluster-Based Approach to Analyzing Crash Injury Severity at Highway–Rail Grade Crossings." Transportation Research Record: Journal of the Transportation Research Board 2608, no. 1 (January 2017): 58–69. http://dx.doi.org/10.3141/2608-07.
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The presence of unobserved heterogeneity in crash data can result in estimation of biased model parameters and incorrect inferences. The research presented in this paper investigated severity of crashes reported at highway–rail grade crossings by appropriately clustering the data, accounting for unobserved heterogeneity. A combination of data mining and statistical regression methods was used to cluster crash data into subsets and then to identify factors associated with crash injury severity levels. This research relied on highway–rail accident, incident, and crossing inventory databases for 2011 to 2015 obtained from FRA. Three clustering methods— K-means, traditional latent class cluster, and variational Bayesian latent class cluster—were considered, and the variational Bayesian latent class cluster method was chosen for partitioning the data set for model estimation. Unclustered data as well as the clustered subsets were used to estimate ordered logit models for crash injury severity. A comparison revealed that the cluster-based approach provided more relevant model parameters and identified factors relevant only to certain clusters of the data.
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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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Raykov, Tenko, George A. Marcoulides, and Tenglong Li. "Evaluation of Measurement Instrument Criterion Validity in Finite Mixture Settings." Educational and Psychological Measurement 76, no. 6 (July 19, 2016): 1026–44. http://dx.doi.org/10.1177/0013164415613542.
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A method for evaluating the validity of multicomponent measurement instruments in heterogeneous populations is discussed. The procedure can be used for point and interval estimation of criterion validity of linear composites in populations representing mixtures of an unknown number of latent classes. The approach permits also the evaluation of between-class validity differences as well as within-class validity coefficients. The method can similarly be used with known class membership when distinct populations are investigated, their number is known beforehand and membership in them is observed for the studied subjects, as well as in settings where only the number of latent classes is known. The discussed procedure is illustrated with numerical data.
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Guidotti, Riccardo, Anna Monreale, Stan Matwin, and Dino Pedreschi. "Explaining Image Classifiers Generating Exemplars and Counter-Exemplars from Latent Representations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13665–68. http://dx.doi.org/10.1609/aaai.v34i09.7116.
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We present an approach to explain the decisions of black box image classifiers through synthetic exemplar and counter-exemplar learnt in the latent feature space. Our explanation method exploits the latent representations learned through an adversarial autoencoder for generating a synthetic neighborhood of the image for which an explanation is required. A decision tree is trained on a set of images represented in the latent space, and its decision rules are used to generate exemplar images showing how the original image can be modified to stay within its class. Counterfactual rules are used to generate counter-exemplars showing how the original image can “morph” into another class. The explanation also comprehends a saliency map highlighting the areas that contribute to its classification, and areas that push it into another class. A wide and deep experimental evaluation proves that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability, besides providing the most useful and interpretable explanations.
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Marbaniang, Strong P., Hemkhothang Lhungdim, and Holendro Singh Chungkham. "Identifying the latent classes of modifiable risk behaviours among diabetic and hypertensive individuals in Northeastern India: a population-based cross-sectional study." BMJ Open 12, no. 2 (February 2022): e053757. http://dx.doi.org/10.1136/bmjopen-2021-053757.
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ObjectiveTo identify the latent classes of modifiable risk factors among the patients with diabetes and hypertension based on the observed indicator variables: smoking, alcohol, aerated drinks, overweight or obesity, diabetes and hypertension. We hypothesised that the study population diagnosed with diabetes or hypertension is homogeneous with respect to the modifiable risk factors.DesignA cross-sectional study using a stratified random sampling method and a nationally representative large-scale survey.Setting and participantsData come from the fourth round of the Indian National Family Health Survey, 2015–2016. Respondents aged 15–49 years who were diagnosed with either diabetes or hypertension or both were included. The total sample is 22 249, out of which 3284 were men and 18 965 were women.Primary and secondary outcome measuresThe observed variables used as latent indicators are the following: smoking, alcohol, aerated drinks, overweight or obesity, diabetes and hypertension. The concomitant variables include age, gender, education, marital status and household wealth index. Latent class model was used to simultaneously identify the latent class and to determine the association between the concomitant variables and the latent classes.ResultsThree latent classes were identified and labelled as class 1: ‘diabetic with low-risk lifestyle’ (21%), class 2: ‘high-risk lifestyle’ (8%) and class 3: ‘hypertensive with low-risk lifestyle’ (71%). Class 1 is characterised by those with a high probability of having diabetes and low probability of smoking and drinking alcohol. Class 2 is characterised by a high probability of smoking and drinking alcohol and class 3 by a high probability of having high blood pressure and low probability of smoking and drinking alcohol.ConclusionsCo-occurrence of smoking and alcohol consumption was prevalent in men, while excess body weight and high blood pressure were prevalent in women. Policy and programmes in Northeastern India should focus on targeting multiple modifiable risk behaviours that co-occur within an individual.
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Ken Cor, M., and Gaurav Sood. "Guessing and Forgetting: A Latent Class Model for Measuring Learning." Political Analysis 24, no. 2 (2016): 226–42. http://dx.doi.org/10.1093/pan/mpw010.
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Guessing on closed-ended knowledge items is common. Under likely-to-hold assumptions, in the presence of guessing, the most common estimator of learning, difference between pre- and postprocess scores, is negatively biased. To account for guessing-related error, we develop a latent class model of how people respond to knowledge questions and identify the model with the mild assumption that people do not lose knowledge over short periods of time. A Monte Carlo simulation over a broad range of informative processes and knowledge items shows that the simple difference score is negatively biased and the method we develop here is unbiased. To demonstrate its use, we apply our model to data from Deliberative Polls. We find that estimates of learning, once adjusted for guessing, are about 13% higher. Adjusting for guessing also eliminates the gender gap in learning, and halves the pre-deliberation gender gap on political knowledge.
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Liu, Taoran, Winghei Tsang, Fengqiu Huang, Oi Ying Lau, Yanhui Chen, Jie Sheng, Yiwei Guo, Babatunde Akinwunmi, Casper JP Zhang, and Wai-Kit Ming. "Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment." Journal of Medical Internet Research 23, no. 2 (February 23, 2021): e22841. http://dx.doi.org/10.2196/22841.
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Background Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. Objective This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. Methods A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. Results A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. Conclusions Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.
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Park, Kee Jeong, Hyun-Jeong Lee, and Hyo-Won Kim. "Latent Class Analysis of ADHD Symptoms in Korean Children and Adolescents." Journal of Attention Disorders 24, no. 8 (February 1, 2017): 1117–24. http://dx.doi.org/10.1177/1087054717691828.
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Objective: The objective of this study was to conduct latent class analysis (LCA) of ADHD symptoms to characterize the underlying structure of ADHD. Method: Participants were recruited from September 2012 to January 2015 from the Department of Psychiatry of Asan Medical Center, Seoul, Korea. Diagnoses of ADHD and comorbid psychiatric disorders were confirmed with the Kiddie–Schedule for Affective Disorders and Schizophrenia–Present and Lifetime Version (K-SADS-PL). We performed LCA of ADHD symptoms in those who had ( n = 141, age = 8.1 ± 2.3 years, 106 boys) and did not have ( n = 82, age = 9.1 ± 2.5 years, 40 boys) ADHD. Results: A three-class solution was found to be the best model, revealing classes of children with mostly combined and hyperactive/impulsive subtypes of ADHD (Class 1), non-ADHD (Class 2), and inattentive subtype of ADHD (Class 3). Conclusion: The three-class solution with LCA supports a two-factor two-class structure of ADHD symptoms.
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Percy, Andrew, and Dorota Iwaniec. "The validity of a latent class typology of adolescent drinking patterns." Irish Journal of Psychological Medicine 24, no. 1 (March 2007): 13–18. http://dx.doi.org/10.1017/s0790966700010089.
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AbstractObjectives: This study examined the validity of a latent class typology of adolescent drinking based on four alcohol dimensions; frequency of drinking, quantity consumed, frequency of binge drinking and the number of alcohol related problems encountered.Method: Data used were from the 1970 British Cohort Study 16-year-old follow-up. Partial or complete responses to the selected alcohol measures were provided by 6,516 cohort members. The data were collected via a series of postal questionnaires.Results: A five class LCA typology was constructed. Around 12% of the sample were classified as ‘hazardous drinkers’ reporting frequent drinking, high levels of alcohol consumed, frequent binge drinking and multiple alcohol related problems. Multinomial logistic regression, with multiple imputation for missing data, was used to assess the covariates of adolescent drinking patterns. Hazardous drinking was associated with being white, being male, having heavy drinking parents (in particular fathers), smoking, illicit drug use, and minor and violent offending behaviour. Non-significant associations were found between drinking patterns and general mental health and attention deficient disorder.Conclusion: The latent class typology exhibited concurrent validity in terms of its ability to distinguish respondents across a number of alcohol and non-alcohol indicators. Notwithstanding a number of limitations, latent class analysis offers an alternative data reduction method for the construction of drinking typologies that addresses known weaknesses inherent in more tradition classification methods.
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Ünlü, Ali. "A Note on the Connection Between Knowledge Structures and Latent Class Models." Methodology 7, no. 2 (January 2011): 63–67. http://dx.doi.org/10.1027/1614-2241/a000023.
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Schrepp (2005) points out and builds upon the connection between knowledge space theory (KST) and latent class analysis (LCA) to propose a method for constructing knowledge structures from data. Candidate knowledge structures are generated, they are considered as restricted latent class models and fitted to the data, and the BIC is used to choose among them. This article adds additional information about the relationship between KST and LCA. It gives a more comprehensive overview of the literature and the probabilistic models that are at the interface of KST and LCA. KST and LCA are also compared with regard to parameter estimation and model testing methodologies applied in their fields. This article concludes with an overview of KST-related publications addressing the outlined connection and presents further remarks about possible future research arising from a connection of KST to other latent variable modeling approaches.
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Bartolucci, Francesco, Silvia Pandolfi, and Fulvia Pennoni. "Discrete Latent Variable Models." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 425–52. http://dx.doi.org/10.1146/annurev-statistics-040220-091910.
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We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation–maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications available in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested.
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Nowakowska, M., and M. Pajecki. "Latent class analysis for identification of occupational accident casualty profiles in the selected Polish manufacturing sector." Advances in Production Engineering & Management 16, no. 4 (December 18, 2021): 485–99. http://dx.doi.org/10.14743/apem2021.4.415.
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The objective of the analysis is identifying profiles of occupational accident casualties as regards production companies to provide the necessary knowledge to facilitate the preparation and management of a safe work environment. Qualitative data characterizing employees injured in accidents registered in Polish wood processing plants over a period of 10 years were the subject of the research. The latent class analysis (LCA) method was employed in the investigation. This statistical modelling technique, based on the values of selected indicators (observed variables) divides the data set into separate groups, called latent classes, which enable the definition of patterns. A procedure which supports the decision as regards the number of classes was presented. The procedure considers the quality of the LCA model and the distinguishability of the classes. Moreover, a method of assessing the importance of indicators in the patterns description was proposed. Seven latent classes were obtained and illustrated by the heat map, which enabled the profiles identification. They were labelled as follows: very serious, serious, moderate, minor (three latent classes), slight. Some recommendations were made regarding the circumstances of occupational accidents with the most severe consequences for the casualties.
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Gou, Xu, Wei Lu, Yi Wang, Binyu Yan, and Mulin Xin. "Fisher-discriminative regularized latent sparse transfer model." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 03 (May 2019): 1950011. http://dx.doi.org/10.1142/s0219691319500115.
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Top performing algorithms are trained on massive amounts of labeled data. Alternatively, domain adaptation (DA) provides an attractive way to address the few labeled tasks when the labeled data from a different but related domain are available. Motivated by Fisher criterion, we present the novel discriminative regularization term on the latent subspace which incorporates the latent sparse domain transfer (LSDT) model in a unified framework. The key underlying idea is to make samples from one class closer and farther away from different class samples. However, it is nontrivial to design the efficient optimization algorithm. Instead, we construct a convex surrogate relaxation optimization constraint to ease this issue by alternating direction method of multipliers (ADMM) algorithm. Subsequently, we generalize our model in the reproduced kernel Hilbert space (RKHS) for tracking the nonlinear domain shift. Empirical studies demonstrate the performance improvement on the benchmark vision dataset Caltech-4DA.