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Relevant bibliographies by topics / Multilevel Analysi

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Academic literature on the topic 'Multilevel Analysi'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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Journal articles on the topic "Multilevel Analysi"

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Augusto Arbugeri, Cesar, Tiago Kommers Jappe, Telles Brunelli Lazzarin, Domingo A. Ruiz-Caballero, Reynaldo R. Astudillo, Luis Martinez, and Samir Ahmad Mussa. "Asymetrical Multilevel Hybrid Inverter - Analysis And Experimentation." Eletrônica de Potência 24, no. 3 (September 30, 2019): 296–305. http://dx.doi.org/10.18618/rep.2019.3.0031.

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Ahmed, Wondimu. "Motivation and Self-Regulated Learning: A Multivariate Multilevel Analysis." International Journal of Psychology and Educational Studies 4, no. 3 (September 1, 2017): 1–11. http://dx.doi.org/10.17220/ijpes.2017.03.001.

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Shende, Ms Komal, Dr HariKumar Naidu, and Prof Vaishali Pawade. "Performance Analysis of Higher Order Cascaded H-Bridge Multilevel Inverters." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1850–56. http://dx.doi.org/10.31142/ijtsrd14456.

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Le Borne, Sabine. "Multilevel Hierarchical Matrices." SIAM Journal on Matrix Analysis and Applications 28, no. 3 (January 2006): 871–89. http://dx.doi.org/10.1137/040607964.

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Rodríguez-Godoy, Mauricio, Laura Marcela Navarro-Saiz, Juan Pablo Alzate, and Carol Cristina Guarnizo-Herreño. "Inequidad en el ingreso y caries de la infancia temprana en Colombia: un análisis multinivel." Ciência & Saúde Coletiva 27, no. 6 (June 2022): 2325–36. http://dx.doi.org/10.1590/1413-81232022276.18452021.

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Resumen Evaluamos la asociación entre inequidad en los ingresos y caries de la infancia temprana en Colombia, utilizando un análisis multinivel. Analizamos datos del último estudio nacional de salud bucal (2014) e información sobre ingresos en términos absolutos y relativos a nivel departamental. Los desenlaces fueron experiencia de caries y caries no tratada. Se utilizó un modelo de regresión logística multinivel con dos niveles: niños/familias (nivel 1) anidados en departamentos (nivel 2). En el nivel 1 se consideraron variables de edad, sexo, posición socioeconómica (PSE) de la vivienda, ingresos del hogar y régimen de aseguramiento en salud. Para el nivel 2 las variables fueron coeficiente Gini, Necesidades Básicas Insatisfechas (NBI) y Producto Interno Bruto (PIB). Se evaluaron datos de 5.250 niños de 1, 3 y 5 años, 36.9% tenían experiencia de caries y 33.0% caries no tratada. Los desenlaces mostraron asociaciones significativas con edad, PSE baja del hogar y pertenecer al régimen subsidiado de salud. Para caries no tratada se encontraron asociaciones con PSE baja o muy baja (OR: 1.72; IC95% 1.42, 2.07 y OR: 1.69; IC95% 1.36, 2.09 respectivamente) y régimen subsidiado de salud (OR: 1.58; IC95% 1.11, 2.24). No se encontraron asociaciones significativas con indicadores de coeficiente Gini, PIB y NBI.

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Kehl, René, Reinhard Nabben, and Daniel B. Szyld. "Adaptive Multilevel Krylov Methods." ETNA - Electronic Transactions on Numerical Analysis 51 (2019): 512–28. http://dx.doi.org/10.1553/etna_vol51s512.

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Kuo, C. C. Jay, Tony F. Chan, and Charles Tong. "Multilevel Filtering Elliptic Preconditioners." SIAM Journal on Matrix Analysis and Applications 11, no. 3 (July 1990): 403–29. http://dx.doi.org/10.1137/0611029.

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Diaz, J. C., and K. Komara. "Incomplete Multilevel Cholesky Factorizations." SIAM Journal on Matrix Analysis and Applications 22, no. 3 (January 2001): 895–911. http://dx.doi.org/10.1137/s0895479896311128.

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Jacques Maia de Sousa, Gean, Luiz H. T. Schmidt, Marcelo Lobo Heldwein, and Daniel Juan Pagano. "PASSIVE VOLTAGE BALANCING IN MODULAR MULTILEVEL CONVERTER DURING PRECHARGE: ANALYSIS AND DESIGN." Eletrônica de Potência 25, no. 4 (December 18, 2020): 1–12. http://dx.doi.org/10.18618/rep.2020.4.0042.

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Jang, Tae Youn. "Traffic Accident Damage Severity of Old Age Drivers by Multilevel Analysis Model." Journal of the Korean Society of Civil Engineers 34, no. 2 (2014): 561. http://dx.doi.org/10.12652/ksce.2014.34.2.0561.

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Dissertations / Theses on the topic "Multilevel Analysi"

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DI, MARIA Chiara. "Longitudinal mediation analysis with structural and multilevel models: associational and causal perspectives." Doctoral thesis, Università degli Studi di Palermo, 2022. http://hdl.handle.net/10447/533485.

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Greco, T. "NETWORK META-ANALYSIS: A NOVEL APPROACH BASED ON A HIERARCHICAL DATA STRUCTURE." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/344198.

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INTRODUCTION Meta-analysis is a powerful tool to cumulate and summarize the knowledge in a research field through statistical instruments, and to identify the overall measure of a treatment’s effect by combining several study-specific results. However, it is a controversial tool, because even small violations of certain rules can lead to misleading conclusions. Pooling data through meta-analysis can create problems, such as non linear correlations, multifactorial rather than unifactorial effects, limited coverage, or inhomogeneous data that fails to connect with the hypothesis. When head-to-head treatment comparisons are not available or conclusive, the limitations of standard (i.e. pairwise) meta-analyses can be overcome by network meta-analyses (NMA) which can provide estimates of treatment efficacy or safety of multiple treatment regimens. Different treatment strategies are analyzed by statistical inference methods rather than simply summing up trials that evaluated the same intervention compared to another intervention, standard care, or placebo. If a first trial compares drug A to drug B, showing that drug A is significantly superior to drug B, and a second trial investigates the same or a similar patient population comparing drug B versus drug C (demonstrating that drug B is equivalent to drug C), NMA may allow to infer that drug A is also potentially superior to drug C for this given patient population, even though there was no direct test of drug A against drug C. CONTENTS In this thesis we provided and discussed methods to overcome the limits of standard (univariate) meta-analysis, focusing on the ability to cope with multiple treatments and to deal with correlated data where correlation can derive from multiple endpoints, time-varying responses or from clustered observation. In the first chapter we explore the principal steps (from writing a prospective protocol of analysis to results’ interpretation) in order to minimize the risk of conducting a mediocre meta-analysis and to support researchers to accurately evaluate the published findings. The second chapter represents an overview of conceptual and practical issues of a network meta-analysis. We start from general considerations on network meta-analysis to specifically appraise how to collect study data, structure the analytical network, and specify the requirements for different models and parameter interpretations. Specifically, we outline the key steps, from literature search to sensitivity analysis, necessary to perform a valid network meta-analysis on binomial data. In the third party of this work, we focus our attention on data which can be analyzed with a binomial model applying the Bayesian hierarchical approach and using Markov Chain Monte Carlo approach. We also apply this analytical approach to a case study on the beneficial effects of anesthetic agents in order to further clarify the statistical details of the models, diagnostics, and computations. We presented a practical guide with the actual WinBUGS and SAS codes to allow transparency and ease of replication of all steps that are required when carrying out such quantitative syntheses. In the fourth chapter we propose an alternative frequentist approach to estimate consistency and inconsistency models for a network meta-analysis. We discuss the multilevel network meta-analysis which include a three-level data structure: subjects within studies at the first level, studies within study designs at the second level and design configuration at the third level. We discuss multilevel modeling which may be carried out within widely available statistical programs such as SAS software, and we compare the results of a published Bayesian network meta-analysis on a binary endpoint which examines the effect on mortality of desflurane, isoflurane, sevoflurane, and total intravenous anaesthetics at the longest follow-up available. In the final chapter we compare the Bayesian and the novel frequentist-multilevel approach in performing network meta-analysis on publicly available data and we investigate the descriptive characteristics that may contribute to decrease or increase the potential difference between the estimates derived from the two approaches. The two approaches were compared in terms of the difference between the pooled estimates or their standardized values, and of the Euclidean distance. BAYESIAN NETWORK META-ANALYSIS Suppose that J trials provide mixed comparisons among K treatments and that a is the trial-specific reference treatment. The random effect model is defined by: yja= β0+eja for j=1,2,...,J; a=1,2,…,K-1 yjk= β0j+δj,ak+ejk for j=1,2,...,J; a=2,3,…,K; b

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CONSOLAZIO, DAVID. "Social and Spatial Inequalities in Health in Milan: the Case of Type 2 Diabetes Mellitus." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/263136.

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La presente tesi di dottorato si propone di indagare lo stato delle disuguaglianze di salute nella città di Milano. Si parla di disuguaglianze di salute in presenza di differenze negli stati di salute delle persone all’interno di una popolazione, o tra gruppi di individui, quando queste sono attribuibili alle condizioni socioeconomiche delle persone, in virtù dell’iniqua distribuzione di risorse sociali, economiche, culturali e relazionali che consentono a ciascuno di raggiungere il proprio potenziale di salute. In aggiunta, il raggiungimento di uno stato di salute ottimale può essere influenzato anche dalle caratteristiche materiali e psicosociali del contesto di residenza, esponendo coloro che vivono in contesti svantaggiati a maggiori rischi per la loro. Muovendo dai presupposti teorici e concettuali della Fundamental Causes Theory e dall’approccio alla salute basato sui determinanti sociali questo lavoro si pone l’obiettivo di fornire una mappatura della distribuzione delle condizioni di salute all’interno del territorio milanese, contribuendo altresì al dibattito circa la presenza di neighbourhood effects sulla salute. Il lavoro svolto si basa sull’utilizzo di un approccio interdisciplinare, nel quale si fa ricorso a metodi e strumenti di tipo sociologico, epidemiologico, e geografico. Uno studio dettagliato della distribuzione sociale e territoriale di una patologia nei diversi quartieri della città è ad oggi assente, abbiamo dunque deciso di concentrarci sul Diabete Mellito di Tipo 2 alla luce della sua tipica associazione sia con le condizioni socioeconomiche individuali che con le caratteristiche dell’ambiente di vita. Facendo ricorso all’utilizzo inedito di dati amministrativi del sistema sanitario forniti dall’Unità di Epidemiologia dell’Agenzia di Tutela della Salute della Città Metropolitana di Milano, in combinazione con i dati provenienti dall’ultimo censimento della popolazione italiana, abbiamo condotto uno studio caso-controllo multilivello, con l’obiettivo di esaminare l’impatto relativo delle condizioni socioeconomiche individuali e del quartiere di residenza sul rischio di sviluppare la patologia in esame. I risultati hanno confermato la presenza di un gradiente sociale nella patologia, con una più alta prevalenza rintracciabile nelle persone con titolo di studio più basso. È stata inoltre riscontrata un’eterogeneità nella distribuzione territoriale della patologia, la quale non viene tuttavia spiegata unicamente dalle condizioni socioeconomiche individuali: l’associazione tra condizioni socioeconomiche del quartiere di residenza e rischio di sviluppo del Diabete Mellito di Tipo 2 risulta infatti essere statisticamente significativa anche controllando per le variabili individuali, suggerendo un ruolo del contesto di residenza nel plasmare l’esposizione al rischio indipendentemente dalla concentrazione di individui con caratteristiche simili nelle stesse aree. In linea con la letteratura di riferimento, è stato riscontrato che le caratteristiche individuali giocano un ruolo predominate nel determinare l’esposizione, ciononostante il quartiere dove le persone vivono esercita un effetto non trascurabile sulla salute e necessita di essere tenuto in considerazione nello sviluppo di politiche volte a contrastare l’incidenza della patologia e a ridurre le disuguaglianze sociali connaturate alla sua insorgenza. Pur essendo parzialmente in grado di mitigare le disparità in ambito di gestione della patologia e qualità delle cure, è evidente che il sistema sanitario da solo non può essere in grado di porre rimedio alle disuguaglianze sociali esistenti nel Diabete Mellito di Tipo 2, evidenziando il bisogno di interventi più ampi capaci di agire sulla struttura che contribuisce a generare e perpetuare le disuguaglianze sociali e territoriali in relazione alla patologia.
This PhD dissertation is aimed at studying health inequalities in the Italian city of Milan. Health inequalities can be defined as differences in people’s health across the population and between population groups, which are attributable to individuals’ socioeconomic status as a consequence of the uneven distribution of social, economic, cultural, and relational resources that enable people to reach their health potential (Sarti et al., 2011). Moreover, people’s health may also be affected by psychosocial and physical characteristics of the local environment in which they live, so that those living in disadvantaged areas may be at a higher risk of being subjected to worse health conditions (Macintyre and Ellaway, 2000; 2003). Moving from the theoretical and conceptual foundations of the Fundamental Causes Theory (Link and Phelan 1995; Phelan et al., 2010) and the Social Determinants of Health approach ( Solar and Irwin, 2010; Wilkinson and Marmot, 2003) this work intends to provide both an accurate mapping of the distribution of health conditions within the Milanese territory – and its association with individual and contextual socioeconomic status – and to contribute to the debate on the presence of neighbourhood effects on health (Diez-Roux, 2004; Galster, 2012). We thus relied on an interdisciplinary approach, making use of tools and methods from sociology, epidemiology, and geography. A fine-grained study of disease distribution among the neighbourhoods of the city of Milan was missing, and we opted to focus on Type 2 Diabetes Mellitus in light of its typical association with both individual socioeconomic conditions (Agardh et al., 2011) and environmental characteristics (Den Braver et al., 2018). Relying on the unprecedented use of administrative healthcare data provided by the Epidemiology Unit of the Health Protection Agency of the Metropolitan City of Milan, linked with data from the most recent Italian census, we performed a multilevel case-control study, aimed at assessing the relative impact of individual and neighbourhood socioeconomic status on the risk of developing the disease. Our results confirmed the presence of a social gradient in the distribution of the disease, with an increasing prevalence in correspondence with lower educational attainment. Moreover, we found evidence of a spatial heterogeneity in the distribution of the disease, which was not entirely explained by individual socioeconomic status: the association between neighbourhood socioeconomic status and the risk of developing Type 2 Diabetes Mellitus remained statistically significant even after accounting for individual-level variables, suggesting a role of the context in shaping risk exposure independently of the clustering of individuals with similar characteristics in the same areas. In line with the existing literature, we found that individual characteristics still play a major role in explaining risk exposure, but also that the context where people live has a non-negligible effect and should be encompassed in the design of policies aimed at tackling the disease and reducing social inequalities at its onset. Despite playing a role in mitigating disparities in relation to disease management and quality of care, there is evidence that the healthcare system alone is not able to effectively tackle existing inequalities, and that broader actions intervening in the structure that contribute to the generation and perpetuation of social and spatial inequalities are needed.

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Venkatasubramanian, S. "Illiteracy in India : a multilevel analysis." Thesis, University of Portsmouth, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302445.

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Sy, Oumar Sekou. "Multilevel mediation analysis estimation and applications /." Search for this dissertation online, 2004. http://wwwlib.umi.com/cr/ksu/main.

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Feng, Yuanjian. "Detection and Characterization of Multilevel Genomic Patterns." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/38577.

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DNA microarray has become a powerful tool in genetics, molecular biology, and biomedical research. DNA microarray can be used for measuring the genotypes, structural changes, and gene expressions of human genomes. Detection and characterization of multilevel, high-throughput microarray genomic data pose new challenges to statistical pattern recognition and machine learning research. In this dissertation, we propose novel computational methods for analyzing DNA copy number changes and learning the trees of phenotypes using DNA microarray data. DNA copy number change is an important form of structural variations in human genomes. The copy number signals measured by high-density DNA microarrays usually have low signal-to-noise ratios and complex patterns due to inhomogeneous composition of tissue samples. We propose a robust detection method for extracting copy number changes in a single signal profile and consensus copy number changes in the signal profiles of a population. We adapt a solution-path algorithm to efficiently solve the optimization problems associated with the proposed method. We tested the proposed method on both simulation and real CGH and SNP microarray datasets, and observed competitively improved performance as compared to several widely-adopted copy number change detection methods. We also propose a chromosome instability measure to summarize the extracted copy number changes for assessing chromosomal instabilities of tumor genomes. The proposed measure demonstrates distinct patterns between different subtypes of ovarian serous carcinomas and normal samples. Among active research on complex human diseases using genomic data, little effort and progress have been made in discovering the relational structural information embedded in the molecular data. We propose two stability analysis based methods to learn stable and highly resolved trees of phenotypes using microarray gene expression data of heterogeneous diseases. In the first method, we use a hierarchical, divisive visualization approach to explore the tree of phenotypes and a leave-one-out cross validation to select stable tree structures. In the second method, we propose a node bandwidth constraint to construct stable trees that can balance the descriptive power and reproducibility of tree structures. Using a top-down merging procedure, we modify the binary tree structures learned by hierarchical group clustering methods to achieve a given node bandwidth. We use a bootstrap based stability analysis to select stable tree structures under different node bandwidth constraints. The experimental results on two microarray gene expression datasets of human diseases show that the proposed methods can discover stable trees of phenotypes that reveal the relationships between multiple diseases with biological plausibility.
Ph. D.

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Woodhouse, Geoffrey M. "Adjustment for measurement error in multilevel analysis." Thesis, University College London (University of London), 1998. http://discovery.ucl.ac.uk/10019113/.

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Measurements in educational research are often subject to error. Where it is desired to base conclusions on underlying characteristics rather than on the raw measurements of them, it is necessary to adjust for measurement error in the modelling process. In this thesis it is shown how the classical model for measurement error may be extended to model the more complex structures of error variance and covariance that typically occur in multilevel models, particularly multivariate multilevel models, with continuous response. For these models parameter estimators are derived, with adjustment based on prior values of the measurement error variances and covariances among the response and explanatory variables. A straightforward method of specifring these prior values is presented. In simulations using data with known characteristics the new procedure is shown to be effective in reducing the biases in parameter estimates that result from unadjusted estimation. Improved estimates of the standard errors also are demonstrated. In particular, random coefficients of variables with error are successfully estimated. The estimation procedure is then used in a two-level analysis of an educational data set. It is shown how estimates and conclusions can vary, depending on the degree of measurement error that is assumed to exist in explanatory variables at level 1 and level 2. The importance of obtaining satisfactory prior estimates of measurement error variances and covariances, and of correctly adjusting for them during analysis, is demonstrated.

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Pham, Thanh Vinh. "The performance of Multilevel Structural Equation Modeling (MSEM) in comparison to Multilevel Modeling (MLM) in multilevel mediation analysis with non-normal data." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7077.

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The mediation analysis has been used to test if the effect of one variable on another variable is mediated by the third variable. The mediation analysis answers a question of how a predictor influences an outcome variable. Such information helps to gain understanding of mechanism underlying the variation of the outcome. When the mediation analysis is conducted on hierarchical data, the structure of data needs to be taken into account. Krull and MacKinnon (1999) recommended using Multilevel Modeling (MLM) with nested data and showed that the MLM approach has more power and flexibility over the standard Ordinary Least Squares (OLS) approach in multilevel data. However the MLM mediation model still has some limitations such as incapability of analyzing outcome variables measured at the upper level. Preacher, Zyphur, and Zhang (2010) proposed that the Multilevel Structural Equation Modeling (MSEM) will overcome the limitation of MLM approach in multilevel mediation analysis. The purpose of this study was to examine the performance of the MSEM approach on non-normal hierarchical data. This study also aimed to compare the MSEM method with the MLM method proposed by MacKinnon (2008) and Zhang, Zyphur, and Preacher (2009). The study focused on the null hypothesis testing which were presented by Type I error, statistical power, and convergence rate. Using Monte Carlo method, this study systematically investigates the effect of several factors on the performance of the MSEM and MLM methods. Designed factors considered were: the magnitude of the population indirect effect, the population distribution shape, sample size at level 1 and level 2, and the intra-class correlation (ICC) level. The results of this study showed no significant effect of the degree of non-normality on any performance criteria of either MSEM or MLM models. While the Type I error rates of the MLM model reached the expected alpha level as the group number was 300 or higher, the MSEM model showed very conservative performance in term of controlling for the Type I error with the rejection rates of null conditions were zero or closed to zero across all conditions. It was evident that the MLM model outperformed the MSEM model in term of power for most simulated conditions. Among the simulation factors examined in this dissertation, the mediation effect size emerged as the most important one since it is highly associated with each of the considered performance criteria. This study also supported the finding of previous studies (Preacher, Zhang, & Zyphur, 2011; Zhang, 2005) about the relationship between sample size, especially the number of group, and the performance of either the MLM or MSEM models. The accuracy and precision of the MLM and MSEM methods were also investigated partially in this study in term of relative bias and confidence interval (CI) width. The MSEM model outperformed the MLM model in term of relative bias while the MLM model had better CI width than the MSEM model. Sample size, effect size, and ICC value were the factors that significantly associate with the performance of these methods in term of relative bias and CI width.

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Galliat, Tobias. "Adaptive multilevel cluster analysis by self organizing box maps." [S.l.] : [s.n.], 2002. http://www.diss.fu-berlin.de/2002/125/index.html.

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Wong, Chun-mei May. "Multilevel models for survival analysis in dental research." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B3637216X.

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Books on the topic "Multilevel Analysi"

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J, Bosker R., ed. Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publications, 1999.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. Multilevel Analysis. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982.

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Hox, J. J. Applied multilevel analysis. 2nd ed. Amsterdam: TT-Publikaties, 1995.

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Leeuw, Jan de, and Erik Meijer, eds. Handbook of Multilevel Analysis. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-73186-5.

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de, Leeuw Jan, and Meijer Erik 1963-, eds. Handbook of multilevel analysis. New York: Springer, 2008.

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de, Leeuw Jan, and Meijer Erik 1963-, eds. Handbook of multilevel analysis. New York: Springer, 2008.

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de, Leeuw Jan, and Meijer Erik 1963-, eds. Handbook of multilevel analysis. New York: Springer, 2008.

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Bosker, R. J. (Roel J.), ed. Multilevel analysis: An introduction to basic and advanced multilevel modeling. 2nd ed. Los Angeles: Sage, 2012.

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Gelman, Andrew. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press, 2007.

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Paul, Reise Steven, and Duan Naihua 1949-, eds. Multilevel modeling: Methodological advances, issues, and applications. Mahwah, N.J: Lawrence Erlbaum Associates, 2003.

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Book chapters on the topic "Multilevel Analysi"

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Introduction to Multilevel Analysis." In Multilevel Analysis, 1–7. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-1.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Multivariate Multilevel Regression Models." In Multilevel Analysis, 173–88. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-10.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "The Multilevel Approach to Meta-Analysis." In Multilevel Analysis, 189–211. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-11.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Sample Sizes and Power Analysis in Multilevel Regression." In Multilevel Analysis, 212–34. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-12.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Assumptions and Robust Estimation Methods." In Multilevel Analysis, 235–68. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-13.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Multilevel Factor Models." In Multilevel Analysis, 269–83. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-14.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Multilevel Path Models." In Multilevel Analysis, 284–93. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-15.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Latent Curve Models." In Multilevel Analysis, 294–304. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-16.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "The Basic Two-Level Regression Model." In Multilevel Analysis, 8–26. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-2.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Estimation and Hypothesis Testing in Multilevel Regression." In Multilevel Analysis, 27–40. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-3.

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Conference papers on the topic "Multilevel Analysi"

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Striz, A., and Jaroslaw Sobieszczanski-Sobieski. "Displacement based multilevel structural optimization." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4098.

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Kyrola, Erkki, and Markus Lindberg. "Spectra of ladder systems." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.my5.

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A strong laser field interacting with an atom or a molecule can induce a coherent process which includes a large number of energy levels. If we can find a transformation which eliminates the explicit time dependence of the interaction Hamiltonian (for example, the rotating-wave approximation), we can reduce the dynamic problem to the consideration of eigenvalues and eigenvectors of the Hamiltonian. However, the complexity of an arbitrary multilevel Hamiltonian usually forces us to use numerical methods, and, therefore, not much general understanding about multilevel systems can be achieved. We consider the multilevel problems in a more general setting. Our approach is based on the observation that an arbitrary Hamiltonian matrix can be transformed into a tridiagonal form by unitary transformation.1 A tridiagonal form represents a ladder-type multilevel system. From all W-level systems the ladder configuration is the simplest in the sense that the number of level connections is the smallest possible. This means that the most effective analysis of a given multilevel system can be achieved using the transformation to ladder configuration. The spectral analysis of ladders is facilitated by the connections to orthogonal polynomials and continued fractions. The investigations about the spectral properties of ladders are reported.

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Setianingsih, Triseu. "Performance Analysis of Children's Health Program in Indonesia: A Multilevel Analysis." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.85.

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ABSTRACT Background: Most of the neonatal deaths that occur after 6-48 hours postpartum can be prevented with appropriate newborn care and initiated immediately after delivery through adequate and standardized first neonatal visit. In Indonesia, it is still not in accordance with the expected target. This study aimed to analyze the factors that influence the first neonatal visit from various levels through a multilevel analysis approach. Subjects and Method: This was a cross sectional study with samples of individual level (level 1) was 1014 mother babies, village Level (level 2) as many as 95 village managers, public health center (level 3) was 51 manager of child health programs, and district level (level 4) was 13 managers of health programs in eight provinces Indonesia. The dependent variable was first neonatal visits. The independent variables were birth attendant and pregnancy counselling (level 1), number of village midwives and community control in development (level 2), partnership and resources (level 3), and policy strategy (level 4). Data analysis was performed through univariate, bivariate, multivariate analysis with logistic regression and multilevel modeling using multilevel regression logistic random intercept analysis run on Stata 14.0. Results: At level 1, birth attendants increased first neonatal visits (OR = 3.21; 95% CI 95% = 1,984 to 5,182; p <0.001), pregnancy counselling (OR = 1,705; 95% CI 95% = 1,162 to 2,503; p = 0.007) significantly. At level 2 modelling, the number of village midwives increased the risk of the first neonatal visit (OR = 1,815; 95% CI = 0.950 to 3,467; p = 0.049), community control (OR = 2,659; 95% CI = 1.396 to 5.066; p = 0.009) significantly. At level 3 modelling, partnerships and resources significantly increased the first neonatal visit (OR = 2,131; 95% CI = 1,114 to 4,078; p = 0.012) significantly. At level 4 modelling, birth attendants significantly increased the first neonatal visits (OR = 3.056; 95% CI = 1.901 to 4,914; p = 0.029) significantly. Conclusion: Birth attendants, pregnancy counselling, the number of village midwives, community control, partnerships and resources, birth attendants increase the risk of first neonatal visit. Contextual village areas, public health center, and district health offices have contextual effects on the first neonatal visit. Keywords: first neonatal visit, multilevel analysis Correspondence: Triseu Setianingsih. Drg. Suherman Medical Institut. Jl. Industri Pasirgombong Jababeka Cikarang, Bekasi, West Java. Email: triseu.pantyarso@imds.ac.id. Mobile: +6281299192199. DOI: https://doi.org/10.26911/the7thicph.03.85

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Alexandrov, Natalia. "Multilevel and multiobjective optimization in multidisciplinary design." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4122.

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Tianxing, Man, Nataly Zhukova, and Georgi Tsochev. "A Multilevel Intelligent Assistant for Multilevel Social Network Analysis." In 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE, 2020. http://dx.doi.org/10.1109/is48319.2020.9199840.

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YATES, K., Z. GURDAL, and S. THANGJITHAM. "Multilevel optimization using a continuum model for structures." In 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4786.

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Shiau, T. N., J. R. Chang, and W. B. Lu. "Multilevel Optimization of the Geared Rotor-Bearing System for Multi-Objectives With Critical Speed Constraints." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-54066.

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This paper presents the multi-objective optimization of a geared rotor-bearing system with the critical speeds constraints by using an efficient multilevel algorithm. The weight of each rotor shaft, the unbalance response, and the response due to the transmission error are minimized simultaneously under the critical speed constraints. The design variables are the inner radii of the shaft, the stiffness of bearings, and the gear mesh stiffness. The finite element method (FEM) is employed to analyze the dynamic characteristics and the method of feasible direction (MFD) is applied in the optimization of the single objective stage. Based on the sensitivity analysis that gear mesh stiffness has almost no influences on the critical speeds of the uncoupled modes of two shafts, an efficient multilevel algorithm composed of system and subsystem levels is developed. In the cycle of iteration, the minimization of the shaft weight is performed in the subsystem level with the critical speed constraints of only uncoupled modes of two shafts and the unbalance response and the transmission error response are reduced in the system level with the critical speed constraints of only coupled modes. It is indicated from the numerical results that the shaft weight, the unbalance response, and the transmission error response via the multilevel technique (ML) are all reduced much more than those via the weighting method (WM) and the goal programming method (GPM).

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Shiau, T. N., H. J. Lee, and Y. J. Tsai. "Multilevel Optimization of Rotor Bearing System With Dynamic Behavior Constraints." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0392.

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The main purpose of this study is to investigate the multilevel optimization of rotor-bearing systems. The design variables include the shaft inner radius, the bearings stiffness, and the axial position of the bearings and disks. The design objectives are minimization of the shaft weight and transmitted force to the bearings. Constraints are placed on the critical speeds, the maximum shaft bending stress, and the maximum amplitude of the steady state response. In the multilevel optimization, three levels are considered and each level includes a single objective and/or multiobjective with various design variables and constraints. For each level, the method of feasible direction (MFD) is used. In addition, the weighting method (WM) is used for multiobjective optimization. The dynamic analysis is carried out using the generalized polynomial expansion method. The results show that the shaft weight and transmitted forces can be simultaneously reduced with the multilevel technique and are better than those obtained using the multiobjective optimization technique with only a single level.

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Hu, Daohang, Xin Zhao, and Jinlun Cai. "Impacts of Supplemental Viscous Damping on Seismic Responses of Building Structures for Construction Extended Renovation Projects under Multilevel Decomposition Design Model Frame." In IABSE Congress, Nanjing 2022: Bridges and Structures: Connection, Integration and Harmonisation. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2022. http://dx.doi.org/10.2749/nanjing.2022.1031.

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<p>The installation of supplemental viscous damping devices can effectively reduce the seismic responses of the engineering structure, so as to reduce the construction quantity of the renovation project. This paper addresses the impacts of supplemental viscous damping devices on the seismic responses of the construction extended renovation projects under the multilevel decomposition design model frame. Firstly, the multilevel decomposition design model of the seismic design of engineering structures is introduced. Secondly, the typical driving factors of the construction extended renovation projects are discussed. The impacts of supplemental damping on the seismic response of the structure are then investigated from the perspective of response spectrum. This paper takes a 250m ultra-tall structure as engineering case to analyse the multilevel decomposition design model, the driving factors of the construction extended renovation projects and the impacts of supplemental damping on the seismic responses of the structure. The results show that the supplemental viscous damping can effectively reduce the construction quantity of renovation projects.</p>

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KUMAR, V., M. ACIKGOZ, H. CAKAL, and O. ARI. "Multilevel optimization with multiple objectives and mixed design variables." In 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4757.

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Reports on the topic "Multilevel Analysi"

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Zeigler-Johnson, Charnita. Multilevel Analysis of Neighborhood Characteristics and Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574693.

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Zeigler-Johnson, Charnita. Multilevel Analysis of Neighborhood Characteristics and Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada554191.

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de Saussure, G., N. M. Larson, J. A. Harvey, and N. W. Hill. Multilevel resonance analysis of sup 59 Co neutron transmission measurements. Office of Scientific and Technical Information (OSTI), May 1991. http://dx.doi.org/10.2172/5697452.

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Manteuffel, T. Development and Analysis of Optimal Multilevel Solvers on Advanced Computers. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1898297.

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Stratton, R. C., and D. B. Jarrell. Towards the development of multilevel-multiagent diagnostic aids. [Root-cause analysis]. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/6128963.

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Adams, Jennifer, and Emily C. Hannum. Under Attack: A Multilevel Analysis of Peer Victimization in Rural Chinese Middle Schools. Unknown, 2013. http://dx.doi.org/10.35648/20.500.12413/11781/ii138.

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Prabaharan, Natarajan, Vijayakumar Arun, Thangam Chinnadurai, Kalyanasundaram Arulkumar, Amalorpavaraj Rini Ann Jerin, and Kaliannan Palanisamy. Analysis of Symmetric Multilevel Inverter Using Unipolar Pulse Width Modulation for Photovoltaic Application. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/crabs.2018.02.13.

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Prabaharan, Natarajan, Vijayakumar Arun, Thangam Chinnadurai, Kalyanasundaram Arulkumar, Amalorpavaraj Rini Ann Jerin, and Kaliannan Palanisamy. Analysis of Symmetric Multilevel Inverter Using Unipolar Pulse Width Modulation for Photovoltaic Application. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/grabs2018.2.13.

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Kresta, David. Can Churches Change a Neighborhood? A Census Tract, Multilevel Analysis of Churches and Neighborhood Change. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6862.

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Norman, Oliver M., George J. Stukenborg, and Kristen M. Wells. African Americans and Prostate Cancer: A Spatial and Multilevel Analysis of Post-treatment Care and Outcomes. Fort Belvoir, VA: Defense Technical Information Center, August 2008. http://dx.doi.org/10.21236/ada502564.

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