Добірка наукової літератури з теми "Latent Covariates"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Latent Covariates".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Latent Covariates"
Mäkikangas, Anne, Asko Tolvanen, Kaisa Aunola, Taru Feldt, Saija Mauno, and Ulla Kinnunen. "Multilevel Latent Profile Analysis With Covariates." Organizational Research Methods 21, no. 4 (February 22, 2018): 931–54. http://dx.doi.org/10.1177/1094428118760690.
Повний текст джерелаPoon, Wai-Yin, and Hai-Bin Wang. "Latent variable models with ordinal categorical covariates." Statistics and Computing 22, no. 5 (October 12, 2011): 1135–54. http://dx.doi.org/10.1007/s11222-011-9290-8.
Повний текст джерелаLagishetty, Chakradhar V., Carolyn V. Coulter, and Stephen B. Duffull. "Design of pharmacokinetic studies for latent covariates." Journal of Pharmacokinetics and Pharmacodynamics 39, no. 1 (December 10, 2011): 87–97. http://dx.doi.org/10.1007/s10928-011-9231-3.
Повний текст джерелаSchofield, Lynne Steuerle, Brian Junker, Lowell J. Taylor, and Dan A. Black. "Predictive Inference Using Latent Variables with Covariates." Psychometrika 80, no. 3 (September 18, 2014): 727–47. http://dx.doi.org/10.1007/s11336-014-9415-z.
Повний текст джерелаFerrari, Diogo. "Modeling Context-Dependent Latent Effect Heterogeneity." Political Analysis 28, no. 1 (May 20, 2019): 20–46. http://dx.doi.org/10.1017/pan.2019.13.
Повний текст джерелаLi, Ming, and Jeffrey R. Harring. "Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study." Educational and Psychological Measurement 77, no. 5 (June 15, 2016): 766–91. http://dx.doi.org/10.1177/0013164416653789.
Повний текст джерелаNguyen, Trang Quynh, and Elizabeth A. Stuart. "Propensity Score Analysis With Latent Covariates: Measurement Error Bias Correction Using the Covariate’s Posterior Mean, aka the Inclusive Factor Score." Journal of Educational and Behavioral Statistics 45, no. 5 (April 8, 2020): 598–636. http://dx.doi.org/10.3102/1076998620911920.
Повний текст джерелаZhang, 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.
Повний текст джерелаAlhadabi, Amal. "Latent Heterogeneity in High School Academic Growth: A Comparison of the Performance of Growth Mixture Model, Structural Equation Modeling Tree, and Forest." Journal of Educational and Psychological Studies [JEPS] 16, no. 4 (November 30, 2022): 355–472. http://dx.doi.org/10.53543/jeps.vol16iss4pp355-472.
Повний текст джерелаAlhadabi, Amal. "Latent Heterogeneity in High School Academic Growth: A Comparison of the Performance of Growth Mixture Model, Structural Equation Modeling Tree, and Forest." Journal of Educational and Psychological Studies [JEPS] 16, no. 4 (December 4, 2022): 355–72. http://dx.doi.org/10.53543/jeps.vol16iss4pp355-372.
Повний текст джерелаДисертації з теми "Latent Covariates"
Ren, Chunfeng. "LATENT VARIABLE MODELS GIVEN INCOMPLETELY OBSERVED SURROGATE OUTCOMES AND COVARIATES." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3473.
Повний текст джерелаRockwood, Nicholas John. "Estimating Multilevel Structural Equation Models with Random Slopes for Latent Covariates." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478681581538.
Повний текст джерелаWang, Junhua. "Large-Sample Logistic Regression with Latent Covariates in a Bayesian Networking Context." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/103.
Повний текст джерелаWang, Yan. "Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7715.
Повний текст джерелаHarman, David M. "Stochastic process customer lifetime value models with time-varying covariates." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2221.
Повний текст джерелаFlory, Felix [Verfasser], Rolf [Gutachter] Steyer, Michael [Gutachter] Eid, and Andreas [Gutachter] Klein. "Average treatment effects in regression models with interactions between treatment and manifest or latent covariates / Felix Flory ; Gutachter: Rolf Steyer, Michael Eid, Andreas Klein." Jena : Friedrich-Schiller-Universität Jena, 2008. http://d-nb.info/1178544117/34.
Повний текст джерелаHatzinger, Reinhold, and Walter Katzenbeisser. "Log-linear Rasch-type models for repeated categorical data with a psychobiological application." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/126/1/document.pdf.
Повний текст джерелаSeries: Research Report Series / Department of Statistics and Mathematics
Jay, Flora. "Méthodes bayésiennes en génétique des populations : relations entre structure génétique des populations et environnement." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS026/document.
Повний текст джерелаWe introduce a new method to study the relationships between population genetic structure and environment. This method is based on Bayesian hierarchical models which use both multi-loci genetic data, and spatial, environmental, and/or cultural data. Our method provides the inference of population genetic structure, the evaluation of the relationships between the structure and non-genetic covariates, and the prediction of population genetic structure based on these covariates. We present two applications of our Bayesian method. First, we used human genetic data to evaluate the role of geography and languages in shaping Native American population structure. Second, we studied the population genetic structure of 20 Alpine plant species and we forecasted intra-specific changes in response to global warming. STAR
Crespo, Cuaresma Jesus, Bettina Grün, Paul Hofmarcher, Stefan Humer, and Mathias Moser. "Unveiling Covariate Inclusion Structures In Economic Growth Regressions Using Latent Class Analysis." Elsevier, 2016. http://dx.doi.org/10.1016/j.euroecorev.2015.03.009.
Повний текст джерелаPereira, Gilberto de Araujo. "Avaliação de testes diagnósticos na ausência de padrão ouro considerando relaxamento da suposição de independência condicional, covariáveis e estratificação da população: uma abordagem Bayesiana." Universidade Federal de São Carlos, 2011. https://repositorio.ufscar.br/handle/ufscar/4486.
Повний текст джерелаFinanciadora de Estudos e Projetos
The application of a gold standard reference test in all or part of the sample under investigation is often not feasible for the majority of diseases affecting humans, either by a lack of consensus on which testing may be considered a gold standard, the high level of invasion of the gold standard technique, the high cost of financially large-scale application, or by ethical questions, so to know the performance of existing tests is essential for the process of diagnosis of these diseases. In statistical modeling aimed to obtain robust estimates of the prevalence of the disease (x ) and the performance parameters of diagnostic tests (sensitivity (Se) and specificity (Sp)), various strategies have been considered such as the stratification of the population, the relaxation of the assumption of conditional independence, the inclusion of covariates, the verification type (partial or total) and the techniques to replace the gold standard. In this thesis we propose a new structure of stratification of the population considering both the prevalence rates and the parameters of test performance among the different strata (EHW). A Bayesian latent class modeling to estimate these parameters was developed for the general case of K diagnostic tests under investigation, relaxation of the assumption of conditional independence according to the formulations of the fixed effect (FECD) and random (RECD) with dependent order (h _ k) and M covariates. The application of models to two data sets about the performance evaluation of diagnostic tests used in screening for Chagas disease in blood donors showed results consistent with the sensitivity studies. Overall, we observed for the structure of stratification proposal (EHW) superior performance and estimates closer to the nominal values when compared to the structure of stratification when only the prevalence rates are different between the strata (HW), even when we consider data set with rates of Se, Sp and x close among the strata. Generally, the structure of latent class, when we have low or high prevalence of the disease, estimates of sensitivity and specificity rates have higher standard errors. However, in these cases, when there is high concordance of positive or negative results of the tests, the error pattern of these estimates are reduced. Regardless of the structure of stratification (EHW, HW), sample size and the different scenarios used to model the prior information, the model of conditional dependency from the FECD and RECD had, from the information criteria (AIC, BIC and DIC), superior performance to the structure of conditional independence (CI) and to FECD with improved performance and estimates closer to the nominal values. Besides the connection logit, derived from the logistic distribution with symmetrical shape, find in the link GEV, derived from the generalized extreme value distribution which accommodates symmetric and asymmetric shapes, a interesting alternative to construct the conditional dependence structure from the RECD. As an alternative to the problem of identifiability, present in this type of model, the criteria adopted to elicit the informative priors by combining descriptive analysis of data, adjustment models from simpler structures, were able to produce estimates with low standard error and very close to the nominal values.
Na área da saúde a aplicação de teste de referência padrão ouro na totalidade ou parte da amostra sob investigação é, muitas vezes, impraticável devido à inexistência de consenso sobre o teste a ser considerado padrão ouro, ao elevado nível de invasão da técnica, ao alto custo da aplicação em grande escala ou por questões éticas. Contudo, conhecer o desempenho dos testes é fundamental no processo de diagnóstico. Na modelagem estatística voltada à estimação da taxa de prevalência da doença (x ) e dos parâmetros de desempenho de testes diagnósticos (sensibilidade (S) e especificidade (E)), a literatura tem explorado: estratificação da população, relaxamento da suposição de independência condicional, inclusão de covariáveis, tipo de verificação pelo teste padrão ouro e técnicas para substituir o teste padrão ouro inexistente ou inviável de ser aplicado em toda a amostra. Neste trabalho, propomos uma nova estrutura de estratificação da população considerando taxas de prevalências e parâmetros de desempenho diferentes entre os estratos (HWE). Apresentamos uma modelagem bayesiana de classe latente para o caso geral de K testes diagnósticos sob investigação, relaxamento da suposição de independência condicional segundo as formulações de efeito fixo (DCEF) e efeito aleatório (DCEA) com dependência de ordem (h _ K) e inclusão de M covariáveis. A aplicação dos modelos a dois conjuntos de dados sobre avaliação do desempenho de testes diagnósticos utilizados na triagem da doença de Chagas em doadores de sangue apresentou resultados coerentes com os estudos de sensibilidade. Observamos, para a estrutura de estratificação proposta, HWE, desempenho superior e estimativas muito próximas dos valores nominais quando comparados à estrutura de estratificação na qual somente as taxas de prevalências são diferentes entre os estratos (HW), mesmo quando consideramos dados com taxas de S, E e x muito próximas entre os estratos. Geralmente, na estrutura de classe latente, quando temos baixa ou alta prevalência da doença, as estimativas das sensibilidades e especificidades apresentam, respectivamente, erro padrão mais elevado. No entanto, quando há alta concordância de resultados positivos ou negativos, tal erro diminui. Independentemente da estrutura de estratificação (HWE, HW), do tamanho amostral e dos diferentes cenários utilizados para modelar o conhecimento a priori, os modelos de DCEF e de DCEA apresentaram, a partir dos critérios de informação (AIC, BIC e DIC), desempenhos superiores à estrutura de independência condicional (IC), sendo o de DCEF com melhor desempenho e estimativas mais próximas dos valores nominais. Além da ligação logito, derivada da distribuição logística com forma simétrica, encontramos na ligação VEG , derivada da distribuição de valor extremo generalizada a qual acomoda formas simétricas e assimétricas, interessante alternativa para construir a estrutura de DCEA. Como alternativa ao problema de identificabilidade, neste tipo de modelo, os critérios para elicitar as prioris informativas, combinando análise descritiva dos dados com ajuste de modelos de estruturas mais simples, contribuíram para produzir estimativas com baixo erro padrão e muito próximas dos valores nominais.
Книги з теми "Latent Covariates"
Brian, Everitt, and Pickles Andrew, eds. Modelling covariances and latent variables using EQS. London: Chapman & Hall, 1993.
Знайти повний текст джерелаDunn, G. Modelling Covariances and Latent Variables Using Eqs. Taylor & Francis Group, 2020.
Знайти повний текст джерелаЧастини книг з теми "Latent Covariates"
Marshall, Adele H., Hannah Mitchell, and Mariangela Zenga. "Modelling the Length of Stay of Geriatric Patients in Emilia Romagna Hospitals Using Coxian Phase-Type Distributions with Covariates." In Advances in Latent Variables, 127–39. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/10104_2014_21.
Повний текст джерелаGalluccio, Carla, Rosa Fabbricatore, and Daniela Caso. "Exploring the intention to walk: a study on undergraduate students using item response theory and theory of planned behaviour." In Proceedings e report, 153–58. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.30.
Повний текст джерелаSarra, Annalina, Adelia Evangelista, and Tonio Di Battista. "Assessment of visitors’ perceptions in protected areas through a model-based clustering." In Proceedings e report, 245–50. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.46.
Повний текст джерелаDayton, C. Mitchell, and George B. Macready. "A Latent Class Covariate Model with Applications to Criterion-Referenced Testing." In Latent Trait and Latent Class Models, 129–43. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4757-5644-9_7.
Повний текст джерелаMarasco, Emanuela, Mengling He, Larry Tang, and Sumanth Sriram. "Accounting for Demographic Differentials in Forensic Error Rate Assessment of Latent Prints via Covariate-Specific ROC Regression." In Communications in Computer and Information Science, 338–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1086-8_30.
Повний текст джерелаSchneeweiss, Hans, Chi-Lun Cheng, and Roland Wolf. "On the Bias of Structural Estimation Methods in a Polynomial Regression with Measurement Error When the Distribution of the Latent Covariate is Misspecified." In Contributions to Modern Econometrics, 209–22. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3602-1_14.
Повний текст джерелаXiang, Brian, and Abdelrahman Abdelmonsef. "Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift." In HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction, 617–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17615-9_44.
Повний текст джерела"Multilevel Models With Latent Variables and Covariates." In An Introduction to Multilevel Modeling Techniques, 204–38. Routledge, 2015. http://dx.doi.org/10.4324/9781315746494-14.
Повний текст джерелаDayton, C. Mitchell, and George B. Macready. "Use of Categorical and Continuous Covariates in Latent Class Analysis." In Applied Latent Class Analysis, 213–33. Cambridge University Press, 2002. http://dx.doi.org/10.1017/cbo9780511499531.009.
Повний текст джерела"- Including individual covariates and relaxing basic model assumptions." In Latent Markov Models for Longitudinal Data, 130–59. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b13246-9.
Повний текст джерелаТези доповідей конференцій з теми "Latent Covariates"
Chung, Tammy, Marc Steinberg, Mary Bridgeman, and YingYing Chen. "Driving Under the Influence of Cannabis: Associations with Latent Profiles of Substance Use and Executive Cognitive Functioning." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.53.
Повний текст джерелаLeno da Silva, Felipe, Raphael Cobe, and Renato Vicente. "A Tree-Adaptation Mechanism for Covariate and Concept Drift." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2022. http://dx.doi.org/10.52591/2021072414.
Повний текст джерелаThanoon, Thanoon Y., and Robiah Adnan. "Improve the Bayesian generalized latent variable models with non-linear variable and covariate of dichotomous data." In SECOND INTERNATIONAL CONFERENCE OF MATHEMATICS (SICME2019). Author(s), 2019. http://dx.doi.org/10.1063/1.5097806.
Повний текст джерелаAntonio Delgado-Guerrero, Juan, Adria Colome, and Carme Torras. "Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9340709.
Повний текст джерелаLiu, Ziquan, Lei Yu, Janet H. Hsiao, and Antoni B. Chan. "Parametric Manifold Learning of Gaussian Mixture Models." 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.2019/426.
Повний текст джерелаWong, C. F., O. Odejimi, B. M. Conn, J. Davis, J. Ataiants, E. V. Fedorova, M. Suen, S. J. Lee, A. Osornio, and S. E. Lankenau. "Gender by Ethnicity Differences in Trajectory of Cannabis Use Among Cannabis-Using Young Adults during Pre- and Post-Recreational Cannabis Legalization (RCL) in Los Angeles." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.22.
Повний текст джерелаBritton, Mark, Eric Porges, Ronald Cohen, Yan Wang, Gladys Ibanez, Charurut Somboonwit, and Robert Cook. "Adolescent-Onset Cannabis Use Disorder Is Associated With Greater Self-Reported Apathy Among Adults Living with HIV in Florida." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.41.
Повний текст джерелаЗвіти організацій з теми "Latent Covariates"
Bang, Minji, Wayne Gao, Andrew Postlewaite, and Holger Sieg. Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates. Cambridge, MA: National Bureau of Economic Research, February 2021. http://dx.doi.org/10.3386/w28436.
Повний текст джерела