Dissertations / Theses on the topic 'Latent class method'
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DALMARTELLO, MICHELA. "A LATENT VARIABLE APPROACH TO DIETARY PATTERNS RESEARCH." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/612183.
Full textRankin, 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.
Full textReneker, 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.
Full textOtter, 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.
Full textSeries: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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.
Full textSeries: Forschungsberichte / Institut für Statistik
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.
Full textSeries: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Atalar, Deniz. "Functional failure sequences in traffic accidents." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/32727.
Full textPetri, 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.
Full textCHEN, Wei. "Predicting customer responses to direct marketing : a Bayesian approach." Digital Commons @ Lingnan University, 2007. https://commons.ln.edu.hk/mkt_etd/11.
Full textLiu, 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.
Full textKeywords: latent class model; Poisson regression model; Metropolis-Hastings sampler; order restriction; disease mapping. Includes bibliographical references.
Tozlu, Ceren. "Classification et modélisation statistique intégrant des données cliniques et d’imagerie par résonance magnétique conventionnelle et avancée." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1043/document.
Full textStroke and multiple sclerosis are two of the most destructive neurological diseases of the central nervous system. Stroke is the second most common cause of death and the major cause of disability worldwide whereas multiple sclerosis is the most common non-traumatic disabling neurological disease of adulthood. Magnetic resonance imaging is an important tool to distinguish healthy from pathological brain tissue in diagnosis, monitoring disease evolution, and decision-making in personalized treatment of patients with stroke or multiple sclerosis.Predicting disease evolution in patients with stroke or multiple sclerosis is a challenge for clinicians that are about to decide on an appropriate individual treatment. The etiology, pathophysiology, symptoms, and evolution of stroke and multiple sclerosis are highly different. Therefore, in this thesis, the statistical methods used for the study of the two neurological diseases are different.The first aim was the identification of the tissue at risk of infarction in patients with stroke. For this purpose, the classification methods (including machine learning methods) have been used on voxel-based imaging data. The data measured at hospital admission is performed to predict the infarction risk at one month. Next, the performances of the classification methods in identifying the tissue at a high risk of infarction were compared. The second aim was to cluster patients with multiple sclerosis using an unsupervised method based on individual clinical and imaging trajectories plotted over five 5 years. Clusters of trajectories would help identifying patients who may have an important progression; thus, to treat them with more effective drugs irrespective of the clinical subtypes. The third and final aim of this thesis was to develop a predictive model for individual evolution of patients with multiple sclerosis based on demographic, clinical, and imaging data taken at study onset. The heterogeneity of disease evolution in patients with multiple sclerosis is an important challenge for the clinicians who seek to predict the disease evolution and decide on an appropriate individual treatment. For this purpose, the latent class linear mixed model was used to predict disease evolution considering individual and unobserved subgroup' variability in multiple sclerosis
Zhao-Xian, Zhuo, and 卓昭賢. "Constrained Latent class model by using Bayesian method." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/37417313413010936742.
Full textWu, Cheng-Ken, and 吳丞根. "A Comparison of Imputation Methods for Incomplete Categorical Data Using Latent Class Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/48069704306645691207.
Full text國立臺北大學
統計學系
96
Survey is a popular research tool, but often causes missing values for some reasons. When the proportion of the missing value is high, it can seriously affect the conclusion. Imputation is an alternative is to handle missing data. For categorical missing data, both model-based and non- model based imputation methods have been proposed, for example, hot deck imputation and loglinear models. However, there are still some problems for these methods. Latent class model (LCM) is a popularly used method for categorical variable. We extended the research of Vermunt al (2007) to study what are the important factors on accuracy rate of imputation for categorical data. Four imputation methods and 6 other independent variables were examined for their effects on accuracy of imputation. The imputation methods were evaluated in terms of accuracy rates. The result shows the significant factors are conditional probability, latent class proportions, number of manifest variables, imputation method, sample size, missing data mechanism. The accuracy rate of imputation is higher with substantially different conditional probability and latent class proportions, more manifest variables, method2 or method3, larger sample sizes, MCAR, and lower missing rate.
Wu, Cheng-Ken. "A Comparison of Imputation Methods for Incomplete Categorical Data Using Latent Class Model." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0023-1507200817501200.
Full textLi, Xiaodong. "Methods and theory for joint estimation of incidental and structural parameters in latent class models /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3242917.
Full textSource: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6485. Adviser: Jeffrey Douglas. Includes bibliographical references (leaf 58) Available on microfilm from Pro Quest Information and Learning.
Hsing, Hung Chao, and 洪兆祥. "Comparison on clustering methods in mixed distribution: Simulation and empirical analysis of two-step cluster, latent class modeling, and self-organizing map." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/95827416726936019568.
Full text輔仁大學
心理學系
98
Clustering is an important multivariate technique in social science which assigns individuals into group under particular rules. Due to the difference on the use of variable, there are different approaches in clustering methods. The purpose of this study is to apply three clustering methods: Two-Step Cluster (TSC), Latent Class Modeling (LCM), and Self-Organizing Map (SOM) work on the separation of mixture distribution. This study includes two sub-researches, simulation and empirical study. In the first study, the 2000 Monte Carlo simulation data is created by R software, including three variables which are normal distributed. In the second study, survey data regarding occupation interest of 617 undergraduates students were selected from Taiwan Integrated Postsecondary Education Database (TIPED). The occupation interest consists of six dimensions: interests in mathematics, machinery, creation, social, leading, and administration. The TSC was processed by using SPSS 17, the LCM was processed by using LatentGOLD 4.5, and the SOM was processed by Clementine 12. At first, the characteristics of the sample were examined and then followed by the TSC, LCM, and SOM analysis. Finally, the association of true score and discrimination among the outcome cluster of methods were applied. The results of simulation study showed that the same outcome of clustering by TSC and LCM was reported. However, SOM tend to cluster data in more detail. Moreover, the outcomes of all methods have high association and good discrimination. In the other hand, the results of empirical study showed difference in outcomes, but the structure of occupation interest was confirmed. In association test, middle association between TSC outcome and department, high association between LCM outcome and department, and low association between SOM outcome and department. In discrimination test, good discrimination exists in TSC and LCM outcome, but bad discrimination exists in SOM outcome. In the end, the difference in algorithm which might affect the performance of methods and their value in application were discussed. The research shown the importance and significance of clustering methods.