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Academic literature on the topic 'Latent class method'

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Published: 9 March 2023

Last updated: 10 March 2023

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Journal articles on the topic "Latent class method"

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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Dissertations / Theses on the topic "Latent class method"

DALMARTELLO, MICHELA. "A LATENT VARIABLE APPROACH TO DIETARY PATTERNS RESEARCH." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/612183.

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INTRODUCTION The dietary pattern approach is useful to study the effect of the overall diet on health outcomes, through considering the network of complex interactions between foods or nutrients. The main methods traditionally used to identify dietary patterns are principal components analysis, factor analysis, principal components factor analysis and cluster analysis. Latent class analysis (LCA) is a latent variable approach, that has some advantages in comparison to the previous methods. Unlike principal component, factor and principal component factor analysis, it can be used to classify individuals into mutually exclusive groups conceived as dietary patterns and differently from cluster analysis, which has the same aim of grouping subjects, it permits quantification of the uncertainty of class membership, and assessment of goodness of fit. Moreover, it allows for adjustment for covariates directly in the pattern identification. OBJECTIVES As latent class analysis has rarely been applied in dietary pattern studies, the aim of this research is to apply the recent developments of the techniques to this area of research. We targeted to address the issue of dietary pattern identification in the case-control setting using latent class analysis and latent class trees. We provided estimation of pattern sizes and their characterization, taking into account correlations between dietary variables (local dependencies), and covariate adjustment. We also evaluated the robustness of the identified dietary patterns to total non-alcoholic energy intake adjustment, for different types of correction. Finally, we illustrated the method’s properties in the assessment of the relation between the identified dietary patterns and selected health outcomes, given the all the above. DIETARY PATTERNS AND THE RISK OF ORAL AND PHARYNGEAL CANCER We analyzed data from a multicentric case-control study on oral and pharyngeal cancer (OPC) carried out between 1992 and 2009, including 946 cases and 2492 hospital controls. Information on diet was collected through a food frequency questionnaire (FFQ). Using LCA, we identified 4 dietary patterns, conceived as mutually exclusive groups of people who shared a common dietary behaviour within groups. The first pattern, labelled ‘Prudent pattern’, showed higher probability of consuming more leafy and fruiting vegetables, citrus fruit and all other kinds of fruits, tea while showing lower probability of consuming red meat. The second pattern, that we named ‘Western pattern’, reported higher consumption of red meat and lower consumption of fruits, cruciferous and fruiting vegetables. We termed the third pattern ‘Lower consumers-combination pattern’ as people in it were less likely to eat fruits, leafy and fruiting vegetables, pulses, potatoes, fish, white and red meat, bread and tea/decaffeinated coffee. The last pattern had higher probability to eating fruiting, leafy and other vegetables, white and red meat and bread, while showed a lower probability to consume coffee, tea, processed meat, cheese, fish, sugary drinks and desserts. We called this last pattern ‘Higher consumers-combination pattern’. Dietary patterns were adjusted for total non-alcoholic energy intake and correlation between certain foods item (sugar-coffee, soups-pulses) was allowed during classes identification. Compared to the Prudent pattern, the Western and the Lower consumers-combination ones were positively related to the risk of OPC (OR=2.56, 95% CI: 1.90 – 3.45 and OR=2.23, 95% CI: 1.64 – 3.02). Higher consumers-combination pattern didn’t differ significantly from the Prudent pattern (OR=1.28, 95% CI: 0.92 – 1.77). ENERGY INTAKE ADJUSTMENT IN DIETARY PATTERN RESEARCH USING LATENT CLASS ANALYSIS Using data from the multicentric case-control study on OPC (Italy, 1992-2009), we identified and compared dietary patterns adjusting or not for total non-alcoholic energy intake in the classes identification phase of the analysis. Three possible ways to correct for total energy intake in class identification were presented, corresponding to different hypothesis on the effect of this variable. In general unadjusted and adjusted solutions were comparable. The main difference was related to the patterns that showed highest/lowest non-alcoholic energy intake, that resulted in a variation of number of classes (4/5/7 patterns for the different adjusted solutions and 5 patterns for the unadjusted one). Then, to determine the effect of adjustment in predicting an health outcome, we compared the effect of unadjusted dietary patterns, unadjusted dietary patterns with non-alcoholic energy intake variable also included in the model as a confounder, and adjusted dietary patterns on the risk of OPC . Differences in the estimations for the distinct solutions were found when Odds Ratios (ORs) were not corrected for known/potential risk factors. In general, adjustments for non-alcoholic energy intake results in a mitigation of the effects, thus remaining in the same order. When adjusting for known/potential risk factors, estimations of ORs and related confidence intervals (CIs) remained consistent in all the models we fitted. In the end, specific suggestions on how to perform energy correction in dietary patterns research using LCA were delivered, basing on the results of the current analysis. DIETARY PATTERNS INSPECTION THROUGH LATENT CLASS TREE We analyzed data from two Italian case–control studies, the first included 946 cases with OPC and 2492 hospital controls, and the second included 304 cases with squamous cell carcinoma of the esophagus (ESCC) and 743 hospital controls. In our application of latent class analysis on the combined dataset of the two case-control studies (Italy, 1992-2009), we found the best fit for a solution that was difficult to interpret and included minor differences between clusters. To address these issues, the Latent Class Tree method was proposed. Three fit statistics (AIC, AIC3, BIC) were used for their different level of penalty that resulted in different lengths of the tree and consequently, different granularity in the analysis. For the first split we allowed for a 4-class solution which identified a pattern characterized by high intake of leafy and fruiting vegetable and fruits (‘Prudent pattern’), a pattern with a high intake of red meat and low intake of certain fruits and vegetables (‘Western pattern’) and two patterns which showed a combination-type of diet. The first ‘combination’ pattern showed a low intake of the majority of foods (‘Lower consumers-combination pattern’), and the other one high intake of various foods (‘Higher consumers-combination pattern’). Compared to the Prudent pattern, the Western one was positively related to OPC (OR=1.91, 95% CI: 1.41-2.58) and to ESCC (OR=3.22, 95% CI: 1.78 – 5.82). The Lower consumers-combination pattern was positively associated to OPC (OR=2.14, 95% CI: 1.58-2.91) and to ESCC (OR=2.85, 95% CI: 1.47-5.55). No significant association was found between the Higher consumers-combination pattern and OPC (1.04, 95% CI: 0.74-1.46) and ESCC (OR=0.89, 95% CI: 0.39-1.99). In the ‘Prudent pattern’ branch of the tree, at the third level, we found two classes that differed in the risk of both cancer types. These two classes differed mainly for the intake of citrus fruit, showing respectively, OR=1.85, 95% CI:1.07-3.19 for OPC and OR=5.37, 95% CI: 1.48-19.44 for ESCC for the class that reported low intake of citrus fruit with respect to the class which exhibit a high intake of citrus fruit. No other significant differences were found between the other pairs of classes at any other level of the tree. CONCLUSION We presented latent class methods as powerful tools to determine dietary patterns conceived as mutually exclusive homogeneous groups of subjects which shared common dietary habits. These methods exhibit some advantages, with respect to classical approaches, that can address important issues in dietary pattern research. For example, it is possible to obtain estimation for pattern prevalence in the population, and to perform energy intake adjustment in the pattern identification phase of the analysis. Moreover, class formation inspection, comparison between different solutions and the analysis of subgroups that may be relevant for the research at hand are features offered by the newly developed latent class tree approach.

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Rankin, Lela Antoinette. "Ideal Dating Styles and Meanings of Romantic Relationships Among White and Latino High School Students: A Multi-Method Approach." Diss., Tucson, Arizona : University of Arizona, 2006. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1554%5F1%5Fm.pdf&type=application/pdf.

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Reneker, Jennifer Christine. "Differential Diagnosis of Dizziness Following a Sports-Related Concussion." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1445530345.

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Otter, Thomas, Regina Tüchler, and Sylvia Frühwirth-Schnatter. "Bayesian latent class metric conjoint analysis. A case study from the Austrian mineral water market." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1012/1/document.pdf.

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This paper presents the fully Bayesian analysis of the latent class model using a new approach towards MCMC estimation in the context of mixture models. The approach starts with estimating unidentified models for various numbers of classes. Exact Bayes' factors are computed by the bridge sampling estimator to compare different models and select the number of classes. Estimation of the unidentified model is carried out using the random permutation sampler. From the unidentified model estimates for model parameters that are not class specific are derived. Then, the exploration of the MCMC output from the unconstrained model yields suitable identifiability constraints. Finally, the constrained version of the permutation sampler is used to estimate group specific parameters. Conjoint data from the Austrian mineral water market serve to illustrate the method. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Frühwirth-Schnatter, Sylvia, Thomas Otter, and Regina Tüchler. "Fully Bayesian Analysis of Multivariate Latent Class Models with an Application to Metric Conjoint Analysis." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/378/1/document.pdf.

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In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract)
Series: Forschungsberichte / Institut für Statistik

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Frühwirth-Schnatter, Sylvia, Thomas Otter, and Regina Tüchler. "A Fully Bayesian Analysis of Multivariate Latent Class Models with an Application to Metric Conjoint Analysis." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1470/1/document.pdf.

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Atalar, Deniz. "Functional failure sequences in traffic accidents." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/32727.

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This thesis examines the interactions between road users and the factors that contribute to the occurrence of traffic accidents, and discusses the implications of these interactions with regards to driver behaviour and accident prevention measures. Traffic accident data is collected on a macroscopic level by local police authorities throughout the UK. This data provides a description of accident related factors on a macroscopic level which does not allow for a complete understanding of the interaction between the various road users or the influence of errors made by active road users. Traffic accident data collected on a microscopic level analysis of real world accident data, explaining why and how an accident occurred, can further contribute to a data driven approach to provide safety measures. This data allows for a better understanding of the interaction of factors for all road users within an accident that is not possible with other data collection methods. In the first part of the thesis, a literature review presents relevant research in traffic accident analysis and accident causation research, afterwards three accident causation models used to understand behaviour and factors leading to traffic accidents are introduced. A comparison study of these accident causation coding models that classify road user error was carried out to determine a model that would be best suited to code the accident data according to the thesis aims. Latent class cluster analyses were made of two separate datasets, the UK On the Spot (OTS) in-depth accident investigation study and the STATS19 national accident database. A comparison between microscopic (in-depth) accident data and macroscopic (national) accident data was carried out. This analysis allowed for the interactions between all relevant factors for the road users involved in the accident to be grouped into specific accident segmentations based on the cluster analysis results. First, all of the cases that were collected by the OTS team between the years 2000 to 2003 were analysed. Results suggested that for single vehicle accidents males and females typically made failures related to detection and execution issues, whereas male road users made diagnosis failures with speed as a particularly important factor. In terms of the multiple vehicle accidents the interactions between the first two road users and the subsequent accident sequence were demonstrated. A cluster analysis of all two vehicle accidents in Great Britain in the year 2005 and recorded within the STATS19 accident database was carried out as a comparison to the multiple vehicle accident OTS data. This analysis demonstrated the necessity of in-depth accident causation data in interpreting accident scenarios, as the resulting accident clusters did not provide significant differences between the groups to usefully segment the crash population. Relevant human factors were not coded for these cases and the level of detail in the accident cases did not allow for a discussion of countermeasure implications. An analysis of 428 Powered Two Wheeler accidents that were collected by the OTS team between the years 2000 to 2010 was carried out. Results identified 7 specific scenarios, the main types of which identified two particular looked but did not see accidents and two types of single vehicle PTW accidents. In cases where the PTW lost control, diagnosis failures were more common, for road users other than the PTW rider, detection issues were of particular relevance. In these cases the interaction between all relevant road users was interpreted in relation to one another. The subsequent study analysed 248 Pedestrian accidents that were collected by the OTS team between the years 2000 to 2010. Results identified scenarios related to pedestrians as being in a hurry and making detection errors, impairment due to alcohol, and young children playing in the roadside. For accidents that were initiated by the other road user s behaviour pedestrians were either struck after an accident had already occurred or due to the manoeuvre that a road user was making, older pedestrians were over-represented in this accident type. This thesis concludes by discussing how (1) microscopic in-depth accident data is needed to understand accident mechanisms, (2) a data mining approach using latent class clustering can benefit the understanding of failure mechanisms, (3) accident causation analysis is necessary to understand the types of failures that road users make and (4) accident scenario development helps quantify accidents and allows for accident countermeasure implication discussion. The original contribution to knowledge is the demonstration that when relevant data is available there is a possibility to understand the interactions that are occurring between road users before the crash, that is not possible otherwise. This contribution has been demonstrated by highlighting how latent class cluster analysis combined with accident causation data allows for relevant interactions between road users to be observed. Finally implications for this work and future considerations are outlined.

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Petri, Svetlana [Verfasser]. "Wählen und politische Performanz in Transformationsländern : Theorie, Methoden und empirische Anwendung der Latent-Class-Modelle [[Elektronische Ressource]] / Svetlana Petri." Kiel : Universitätsbibliothek Kiel, 2016. http://d-nb.info/1081077603/34.

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CHEN, Wei. "Predicting customer responses to direct marketing : a Bayesian approach." Digital Commons @ Lingnan University, 2007. https://commons.ln.edu.hk/mkt_etd/11.

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Direct marketing problems have been intensively reviewed in the marketing literature recently, such as purchase frequency and time, sales profit, and brand choices. However, modeling the customer response, which is an important issue in direct marketing research, remains a significant challenge. This thesis is an empirical study of predicting customer response to direct marketing and applies a Bayesian approach, including the Bayesian Binary Regression (BBR) and the Hierarchical Bayes (HB). Other classical methods, such as Logistic Regression and Latent Class Analysis (LCA), have been conducted for the purpose of comparison. The results of comparing the performance of all these techniques suggest that the Bayesian methods are more appropriate in predicting direct marketing customer responses. Specifically, when customers are analyzed as a whole group, the Bayesian Binary Regression (BBR) has greater predictive accuracy than Logistic Regression. When we consider customer heterogeneity, the Hierarchical Bayes (HB) models, which use demographic and geographic variables for clustering, do not match the performance of Latent Class Analysis (LCA). Further analyses indicate that when latent variables are used for clustering, the Hierarchical Bayes (HB) approach has the highest predictive accuracy.

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Liu, Jie. "Novel Bayesian Methods for Disease Mapping: An Application to Chronic Obstructive Pulmonary Disease." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0501102-110350.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: latent class model; Poisson regression model; Metropolis-Hastings sampler; order restriction; disease mapping. Includes bibliographical references.

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Books on the topic "Latent class method"

Heinen, Ton. Latent class and discrete latent trait models: Similarities and differences. Thousand Oaks, Calif: Sage Publications, 1996.

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Evans, Michael J. Latent class analysis of two-way contingency tables by Bayesian methods. Toronto: University of Toronto, Dept. of Statistics, 1988.

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Magidson, Jay, and Jeroen W. Vermunt. Introduction to Latent Class and Finite Mixture Modeling (Statistics in the Social and Behavioral Sciences). Chapman & Hall/CRC, 2009.

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Heinen, Ton. Latent Class and Discrete Latent Trait Models: Similarities and Differences. SAGE Publications, Incorporated, 2012.

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Eshima, Nobuoki. Introduction to Latent Class Analysis: Methods and Applications. Springer Singapore Pte. Limited, 2022.

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Churchman, GJ, RW Fitzpatrick, and RA Eggleton, eds. Clays: Controlling the Environment. CSIRO Publishing, 1995. http://dx.doi.org/10.1071/9780643104969.

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Proceedings of the 10th International Clays Conference, Adelaide, Australia, July 18 to 23, 1993. Clays have provided us with the most active ingredients in soils, with building materials, with pottery and ceramics for both utility and decoration, and with coatings and fillers for paper, among other uses. The unique properties of these apparently everyday materials are being studied and used in an increasing range of industrial and environmental applications. Clays: Controlling the Environment provides a valuable compendium of the latest results from the complete range of clay-related scientific research. It includes coverage of the economic and environmental issues as well as directions for further research and development in many vital and expanding industries. All papers in these proceedings were subject to peer review. The topics discussed are: Clays in industry and the environment Surface and interlayer reactions Clay mineral structures and chemistry Methods of investigation Clays in geology Soil mineralogy The emphasis of this book reflects the vital role that clays play in controlling natural, polluted and technological environments.

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Richards, Ronnie. “What’s Your Name, Where Are You From, and What Have You Had?”. Edited by Roger Mantie and Gareth Dylan Smith. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190244705.013.18.

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This discussion considers the utopian/dystopian dialect in relation to Acid House culture in Leeds during the late 1980s. It utilizes an ethnographic, autoethnographic, and fictional/nonfictional narrative method to illustrate the key signifiers and relations of Acid House culture, including utopian ideals, social class, and the significance of geographical location. Overall the chapter serves to illustrate the significance of individual and group identities, the importance of embodiment and the changing intersection of social constructs such as class. Chas Critcher had defined Acid House as “no more that music associated with LSD,” but this chapter highlights the richly textured sense of feeling, space, place, and social relations that demonstrate Acid House was something much more than that. This chapter also has a direct association with the themes of agency, identity, meaning, and cultural appropriation.

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MacLean, Hs. High School Book-Keeping, Containing Illustrations of the Latest and Best Methods of Keeping Accounts by Single and Double Entry; Business Forms, Correspondence, and Numerous Class Exercises; Also Precis-writing and Indexing. Creative Media Partners, LLC, 2018.

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High School Book-Keeping, Containing Illustrations of the Latest and Best Methods of Keeping Accounts by Single and Double Entry; Business Forms, Correspondence, and Numerous Class Exercises; Also Precis-writing and Indexing. Creative Media Partners, LLC, 2018.

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Fuchsel, Catherine. Sí, Yo Puedo Curriculum, Weekly Sessions, Instruction, and Activities. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190672829.003.0006.

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This chapter describes how to facilitate the weekly classes for the Sí, Yo Puedo program for immigrant Latina women in detail. The chapter provides an overview of the weekly goals, objectives, methods for instruction, self-reflection drawing and writing exercises, and in-between class exercises. It also provides group facilitators with a step-by-step guide and instruction on how to facilitate the weekly classes. For each of the weekly classes, background information is provided to understand the topic being addressed, additional suggested reading is recommended to help group facilitators, and sample and blank handouts are provided in English and Spanish to use in the self-reflection drawing and writing weekly activities.

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Book chapters on the topic "Latent class method"

Samuelsen, Karen M., and C. Mitchell Dayton. "Latent Class Analysis." In The Reviewer’s Guide to Quantitative Methods in the Social Sciences, 164–77. Second Edition. | New York : Routledge, 2019. | Revised edition of The reviewer’s guide to quantitative methods in the social sciences, 2010.: Routledge, 2018. http://dx.doi.org/10.4324/9781315755649-12.

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Harrison, Wendy, Robert M. West, Amy Downing, and Mark S. Gilthorpe. "Multilevel Latent Class Modelling." In Modern Methods for Epidemiology, 117–40. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3024-3_7.

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Flaherty, Brian P., and Cara J. Kiff. "Latent class and latent profile models." In APA handbook of research methods in psychology, Vol 3: Data analysis and research publication., 391–404. Washington: American Psychological Association, 2012. http://dx.doi.org/10.1037/13621-019.

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Bauer, Johannes. "A Primer to Latent Profile and Latent Class Analysis." In Methods for Researching Professional Learning and Development, 243–68. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08518-5_11.

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Biemer, Paul P., and Marcus Berzofsky. "Some Issues in the Application of Latent Class Models for Questionnaire Design." In Question Evaluation Methods, 151–85. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118037003.ch11.

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Muthén, Bengt. "Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class–latent growth modeling." In New methods for the analysis of change., 291–322. Washington: American Psychological Association, 2001. http://dx.doi.org/10.1037/10409-010.

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van Houwelingen, Hans C. "Latent Class Models to Describe Changes Over Time: A Case Study." In Statistical Methods for Quality of Life Studies, 245–59. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3625-0_19.

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Kim, Jee-Seon, and Peter M. Steiner. "Multilevel Propensity Score Methods for Estimating Causal Effects: A Latent Class Modeling Strategy." In Quantitative Psychology Research, 293–306. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19977-1_21.

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Kreuter, Frauke. "Response 1 to Biemer and Berzofsky's Chapter: Some Issues in the Application of Latent Class Models for Questionnaire Design." In Question Evaluation Methods, 187–97. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118037003.ch12.

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Harkness, Janet A., and Timothy P. Johnson. "Response 2 to Biemer and Berzofsky's Chapter: Some Issues in the Application of Latent Class Models for Questionnaire Design." In Question Evaluation Methods, 199–212. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118037003.ch13.

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Conference papers on the topic "Latent class method"

Wang, Xiao-Li, and Wei-Yi Liu. "An incremental learning method for hierarchical latent class models." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019786.

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Pilatti, Angelina, Adrian Bravo, Yanina Michelini, Gabriela Rivarola Montejano, and Ricardo Pautassi. "Contexts of Marijuana Use: A Latent Class Analysis among Argentinean College Students." In 2020 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2021. http://dx.doi.org/10.26828/cannabis.2021.01.000.23.

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Background: Substance use and the association between substance-related variables and outcomes seem to be context dependent. We employed Latent Class Analysis (LCA), a person-centered approach, to identify distinct subpopulations based on contexts of marijuana use. We also examined whether the resulting classes differ in a set of marijuana-related variables that hold promise as potential targets of interventions. Method: A sample of 1083 Argentinean college students (64% women; M age = 19.73±3.95) completed an online survey that assessed substance use and related variables (motives for substance use, protective behavioral strategies [PBS] and internalization of the college marijuana use culture). For the present study, only data from students that reported last month (i.e., past 30-day) marijuana use (n = 158) were included in the analysis. Participants reported whether or not they used marijuana in different places (i.e., own house, party at home, friends’ house, parties at friends' house, university party, non-university party, bar, dance-club, outside [street, park], or pregaming) or social contexts (i.e., alone, with family members, strangers, boyfriend/girlfriend, close friend, small group of same-sex friends, ≥10 same-sex friends, small co-ed group of friends, ≥10 co-ed friends). Results: LCA identified a 2-classes model for marijuana use context. Class 1 comprised 40% of last-month marijuana users. Students within this class endorsed a high probability of consuming marijuana across different places (e.g., at home, at parties, outdoors) and social contexts (e.g., close friend and in small same sex and coed groups). Participants in Class 2 exhibited a low endorsement of marijuana use across contexts, yet they reported a moderate to high probability of using marijuana with a small group of same-sex friends or with the close friend, at a friend’s home. The two classes significantly differed, as shown by Student’s t, on all marijuana outcomes (i.e., use and negative consequences) and marijuana-related variables (motives, PBS and internalization of the college marijuana use culture). Students in class 2 exhibited significantly less marijuana use, both in terms of frequency and quantity, and less marijuana-related negative consequences than those in class 1. The latter class exhibited more normative perceptions about marijuana use in college, more marijuana use motives -particularly social, coping and expansion motives- and less use of PBS than students in class 2 did. Conclusions: Our findings revealed subpopulations of college students that are heterogeneous regarding contexts of marijuana use, patterns of use and in a number of relevant variables. These distinctive subpopulations require different targeted interventions.

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Giesen, Joachim, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum, Christoph Staudt, and Sina Zarrieß. "Method of Moments for Topic Models with Mixed Discrete and Continuous Features." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/333.

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Topic models are characterized by a latent class variable that represents the different topics. Traditionally, their observable variables are modeled as discrete variables like, for instance, in the prototypical latent Dirichlet allocation (LDA) topic model. In LDA, words in text documents are encoded by discrete count vectors with respect to some dictionary. The classical approach for learning topic models optimizes a likelihood function that is non-concave due to the presence of the latent variable. Hence, this approach mostly boils down to using search heuristics like the EM algorithm for parameter estimation. Recently, it was shown that topic models can be learned with strong algorithmic and statistical guarantees through Pearson's method of moments. Here, we extend this line of work to topic models that feature discrete as well as continuous observable variables (features). Moving beyond discrete variables as in LDA allows for more sophisticated features and a natural extension of topic models to other modalities than text, like, for instance, images. We provide algorithmic and statistical guarantees for the method of moments applied to the extended topic model that we corroborate experimentally on synthetic data. We also demonstrate the applicability of our model on real-world document data with embedded images that we preprocess into continuous state-of-the-art feature vectors.

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Wang, Liwei, Siyu Tao, Ping Zhu, and Wei Chen. "Data-Driven Multiscale Topology Optimization Using Multi-Response Latent Variable Gaussian Process." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22595.

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Abstract The data-driven approach is emerging as a promising method for the topological design of the multiscale structure with greater efficiency. However, existing data-driven methods mostly focus on a single class of unit cells without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of inherent ordering or “distance” measure between different classes of unit cells in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) to creating multi-response LVGP (MRLVGP) for the unit cell libraries of metamaterials, taking both qualitative unit cell concepts and quantitative unit cell design variables as mixed-variable inputs. The MRLVGP embeds the mixed variables into a continuous design space based on their collective effect on the responses, providing substantial insights into the interplay between different geometrical classes and unit cell materials. With this model, we can easily obtain a continuous and differentiable transition between different unit cell concepts that can render gradient information for multiscale topology optimization. While the proposed approach has a broader impact on the concurrent topological and material design of engineered systems, we demonstrate its benefits through multiscale topology optimization with aperiodic unit cells. Design examples reveal that considering multiple unit cell types can lead to improved performance due to the consistent load-transferred paths for micro- and macrostructures.

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Jensen, David C., Christopher Hoyle, and Irem Y. Tumer. "Clustering Function-Based Failure Analysis Results to Evaluate and Reduce System-Level Risks." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70180.

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For complex, safety-critical systems failures due to component faults and system interactions can be catastrophic. One aspect of ensuring a safe system design is the analysis of the impact and risk of potential faults early in the system design process. This early design-stage analysis can be accomplished through function-based reasoning on a qualitative behavior simulation of the system. Reasoning on the functional effect of failures provides designers with the information needed to understand the potential impact of faults. This paper proposes three different methods for evaluating and grouping the results of a function failure analysis and their use in design decision-making. Specifically, a method of clustering failure analysis results based on consequence is presented to identify groups of critical failures. A method of clustering using Latent Class Analysis provides characterization of high-level, emergent system failure behavior. Finally, a method of identifying functional similarity provides lists of similar and identical functional effects to a system state of interest. These three methods are applied to the function-based failure analysis results of 677 single and multiple fault scenarios in an electrical power system. The risk-based clustering found three distinct levels of scenario functional impact. The Latent Class Analysis identified five separate failure modes of the system. Finally, the similarity grouping identified different groups of scenarios with identical and similar functional impact to specific scenarios of interest. The overall goal of this work is to provide a framework for making design decisions that decrease system risks.

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Retamosa, Marta, Ángel Millán, and Juan Antonio García. "Thinking about going to university? Segmenting undergraduates." In Fifth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/head19.2019.9208.

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Choosing a university is a crucial decision in a person's life because, most of the time, the acquisition of a university degree allows him access to better working conditions. Universities are interested in knowing the factors that students cite as impacting their choice of university. This study aims to classify future university students according to different evaluation criteria that could help university administrators to improve their recruitment and positioning strategies. Building on the growing body of knowledge related to the marketing of Higher Education Institutions, the current study seeks to further explore the existence of segments featuring different selection patterns. The main goal of this study was tested by applying Latent Class Analysis as a segmentation method, also referred to as Latent Class Cluster Analysis. This study found that students have different sets of motivations for their choice of Higher Education Institutions, and also found significant differences in the motivations of males and females with regard to university selection. All of these findings are of great importance to the managers of university brands, particularly at the university under study.

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Luo, Chen, and Shiliang Sun. "Variational Mixtures of Gaussian Processes for Classification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/642.

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Gaussian Processes (GPs) are powerful tools for machine learning which have been applied to both classification and regression. The mixture models of GPs were later proposed to further improve GPs for data modeling. However, these models are formulated for regression problems. In this work, we propose a new Mixture of Gaussian Processes for Classification (MGPC). Instead of the Gaussian likelihood for regression, MGPC employs the logistic function as likelihood to obtain the class probabilities, which is suitable for classification problems. The posterior distribution of latent variables is approximated through variational inference. The hyperparameters are optimized through the variational EM method and a greedy algorithm. Experiments are performed on multiple real-world datasets which show improvements over five widely used methods on predictive performance. The results also indicate that for classification MGPC is significantly better than the regression model with mixtures of GPs, different from the existing consensus that their single model counterparts are comparable.

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Syrová, Lenka. "ASSOCIATION BETWEEN FIRM SIZE AND ENTERPRISE RISK MANAGEMENT LEVEL." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.810.

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The paper aims to investigate if firm size plays a role as a driver for the ERM method and as an ERM de-terminant. A comprehensive literature review (conducted 2010–2021) and primary data (SMEs in Czech Republic, research conducted in 2021) were applied. Latent class analysis and contingency tables were employed. The results show that firm size predicts the adequate ERM method and has positive effects on the ERM level. The contribution is in identifying significant differences between micro- and medium-sized enterprises with respect to the ERM level. At the conclusion, the author discusses other possible ERM drivers and ERM determinants.

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Banerjee, Sayan, Avik Hati, Subhasis Chaudhuri, and Rajbabu Velmurugan. "CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/95.

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The objective in image co-segmentation is to jointly segment unknown common objects from a given set of images. In this paper, we propose a novel deep convolution neural network based end-to-end co-segmentation model. It is composed of a metric learning and decision network leading to a novel conditional siamese encoder-decoder network for estimating a co-segmentation mask. The role of the metric learning network is to find an optimum latent feature space where objects of the same class are closer and that of different classes are separated by a certain margin. Depending on the extracted features, the decision network decides whether input images have common objects or not and the encoder-decoder network produces a cosegmentation mask accordingly. Key aspects of the architecture are as follows. First, it is completely class agnostic and does not require any semantic information. Second, in addition to producing masks, the decoder network also learns similarity across image pairs that improves co-segmentation significantly. Experimental results reflect an excellent performance of our method compared to state of-the-art methods on challenging co-segmentation datasets.

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Davoudi Kakhki, Fatemeh, Steven A. Freeman, and Gretchen A. Mosher. "Unsupervised Machine Learning for Pattern Identification in Occupational Accidents." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001089.

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Creating safe work environment is significant in saving workers’ lives, improving corporates’ social responsibility and sustainable development. Pattern identification in occupational accidents is vital in elaborating efficient safety countermeasures aiming at improving prevention and mitigating outcomes of future incidents. The objective of this study is to identify patterns related to the occurrence of occupational accidents in non-farm agricultural work environments based on workers’ compensation claims data, using latent class clustering method as an unsupervised machine learning modeling approach. The result showed injury profiles and incident dynamics have low, average, and high levels of risks based on the main causes and outcomes of the injuries and the affected body part(s).

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Reports on the topic "Latent class method"

Tokarieva, Anastasiia V., Nataliia P. Volkova, Inesa V. Harkusha, and Vladimir N. Soloviev. Educational digital games: models and implementation. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3242.

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Nowadays, social media, ICT, mobile technologies and applications are increasingly used as tools for communication, interaction, building up social skills and unique learning environments. One of the latest trends observed in education is an attempt to streamline the learning process by applying educational digital games. Despite numerous research data, that confirms the positive effects of digital games, their integration into formal educational contexts is still relatively low. The purpose of this article is to analyze, discuss and conclude what is necessary to start using games as an instructional tool in formal education. In order to achieve this aim, a complex of qualitative research methods, including semi-structured expert interviews was applied. As the result, the potential of educational digital games to give a unique and safe learning environment with a wide spectrum of build-in assistive features, be efficient in specific training contexts, help memorize studied material and incorporate different learning styles, as well as to be individually adaptable, was determined. At the same time, the need for complex approach affecting the administration, IT departments, educators, students, parents, a strong skill set and a wide spectrum of different roles and tasks a teacher carries out in a digital game-based learning class were outlined. In conclusion and as a vector for further research, the organization of Education Design Laboratory as an integral part of a contemporary educational institution was proposed.

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Mazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.

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This report is one of a series of reports documenting the methods and findings of a multi-year, multi-disciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER) and funded by the California Earthquake Authority (CEA). The overall project is titled “Quantifying the Performance of Retrofit of Cripple Walls and Sill Anchorage in Single-Family Wood-Frame Buildings,” henceforth referred to as the “PEER–CEA Project.” The overall objective of the PEER–CEA Project is to provide scientifically based information (e.g., testing, analysis, and resulting loss models) that measure and assess the effectiveness of seismic retrofit to reduce the risk of damage and associated losses (repair costs) of wood-frame houses with cripple wall and sill anchorage deficiencies as well as retrofitted conditions that address those deficiencies. Tasks that support and inform the loss-modeling effort are: (1) collecting and summarizing existing information and results of previous research on the performance of wood-frame houses; (2) identifying construction features to characterize alternative variants of wood-frame houses; (3) characterizing earthquake hazard and ground motions at representative sites in California; (4) developing cyclic loading protocols and conducting laboratory tests of cripple wall panels, wood-frame wall subassemblies, and sill anchorages to measure and document their response (strength and stiffness) under cyclic loading; and (5) the computer modeling, simulations, and the development of loss models as informed by a workshop with claims adjustors. This report is a product of Working Group 3 (WG3), Task 3.1: Selecting and Scaling Ground-motion records. The objective of Task 3.1 is to provide suites of ground motions to be used by other working groups (WGs), especially Working Group 5: Analytical Modeling (WG5) for Simulation Studies. The ground motions used in the numerical simulations are intended to represent seismic hazard at the building site. The seismic hazard is dependent on the location of the site relative to seismic sources, the characteristics of the seismic sources in the region and the local soil conditions at the site. To achieve a proper representation of hazard across the State of California, ten sites were selected, and a site-specific probabilistic seismic hazard analysis (PSHA) was performed at each of these sites for both a soft soil (Vs30 = 270 m/sec) and a stiff soil (Vs30=760 m/sec). The PSHA used the UCERF3 seismic source model, which represents the latest seismic source model adopted by the USGS [2013] and NGA-West2 ground-motion models. The PSHA was carried out for structural periods ranging from 0.01 to 10 sec. At each site and soil class, the results from the PSHA—hazard curves, hazard deaggregation, and uniform-hazard spectra (UHS)—were extracted for a series of ten return periods, prescribed by WG5 and WG6, ranging from 15.5–2500 years. For each case (site, soil class, and return period), the UHS was used as the target spectrum for selection and modification of a suite of ground motions. Additionally, another set of target spectra based on “Conditional Spectra” (CS), which are more realistic than UHS, was developed [Baker and Lee 2018]. The Conditional Spectra are defined by the median (Conditional Mean Spectrum) and a period-dependent variance. A suite of at least 40 record pairs (horizontal) were selected and modified for each return period and target-spectrum type. Thus, for each ground-motion suite, 40 or more record pairs were selected using the deaggregation of the hazard, resulting in more than 200 record pairs per target-spectrum type at each site. The suites contained more than 40 records in case some were rejected by the modelers due to secondary characteristics; however, none were rejected, and the complete set was used. For the case of UHS as the target spectrum, the selected motions were modified (scaled) such that the average of the median spectrum (RotD50) [Boore 2010] of the ground-motion pairs follow the target spectrum closely within the period range of interest to the analysts. In communications with WG5 researchers, for ground-motion (time histories, or time series) selection and modification, a period range between 0.01–2.0 sec was selected for this specific application for the project. The duration metrics and pulse characteristics of the records were also used in the final selection of ground motions. The damping ratio for the PSHA and ground-motion target spectra was set to 5%, which is standard practice in engineering applications. For the cases where the CS was used as the target spectrum, the ground-motion suites were selected and scaled using a modified version of the conditional spectrum ground-motion selection tool (CS-GMS tool) developed by Baker and Lee [2018]. This tool selects and scales a suite of ground motions to meet both the median and the user-defined variability. This variability is defined by the relationship developed by Baker and Jayaram [2008]. The computation of CS requires a structural period for the conditional model. In collaboration with WG5 researchers, a conditioning period of 0.25 sec was selected as a representative of the fundamental mode of vibration of the buildings of interest in this study. Working Group 5 carried out a sensitivity analysis of using other conditioning periods, and the results and discussion of selection of conditioning period are reported in Section 4 of the WG5 PEER report entitled Technical Background Report for Structural Analysis and Performance Assessment. The WG3.1 report presents a summary of the selected sites, the seismic-source characterization model, and the ground-motion characterization model used in the PSHA, followed by selection and modification of suites of ground motions. The Record Sequence Number (RSN) and the associated scale factors are tabulated in the Appendices of this report, and the actual time-series files can be downloaded from the PEER Ground-motion database Portal (https://ngawest2.berkeley.edu/)(link is external).

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Academic literature on the topic 'Latent class method'

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Reports on the topic "Latent class method"