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Journal articles on the topic 'Multilevel Analysi'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

George, Alan, Wei-Pai Tang, and Ya Dan Wu. "Multilevel One-Way Dissection Factorization." SIAM Journal on Matrix Analysis and Applications 22, no. 3 (January 2001): 752–71. http://dx.doi.org/10.1137/s0895479898332564.

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12

van den Eeden, Pieter, and Joop J. Hox. "Introduction - Multilevel Analysis." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 51, no. 1 (June 1996): 5–9. http://dx.doi.org/10.1177/075910639605100101.

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13

Zhang, Weihua, Jiaxin Li, Yi Li, and Haibo Chen. "Multilevel Phase Analysis." ACM Transactions on Embedded Computing Systems 14, no. 2 (March 25, 2015): 1–29. http://dx.doi.org/10.1145/2629594.

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14

Caldwell, Alex B. "Simultaneous multilevel analysis." American Psychologist 49, no. 2 (1994): 144–45. http://dx.doi.org/10.1037/0003-066x.49.2.144.

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15

HOX, JOOP J., and ITA G. G. KREFT. "Multilevel Analysis Methods." Sociological Methods & Research 22, no. 3 (February 1994): 283–99. http://dx.doi.org/10.1177/0049124194022003001.

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16

Blau, Peter M. "Multilevel structural analysis." Social Networks 15, no. 2 (June 1993): 201–15. http://dx.doi.org/10.1016/0378-8733(93)90005-6.

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17

Timmerman, Marieke E. "Multilevel component analysis." British Journal of Mathematical and Statistical Psychology 59, no. 2 (November 2006): 301–20. http://dx.doi.org/10.1348/000711005x67599.

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18

Buchholz, Peter. "Multilevel Solutions for Structured Markov Chains." SIAM Journal on Matrix Analysis and Applications 22, no. 2 (January 2000): 342–57. http://dx.doi.org/10.1137/s0895479898342419.

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19

Axelsson, O., and P. S. Vassilevski. "Algebraic Multilevel Preconditioning Methods, II." SIAM Journal on Numerical Analysis 27, no. 6 (December 1990): 1569–90. http://dx.doi.org/10.1137/0727092.

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20

Rai, Ranjan Kumar, and Umashankar Patel. "An Analysis and Modeling of Grid Connected Multiple-Pole Multilevel Unity Power Factor Rectifier." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 1010–16. http://dx.doi.org/10.31142/ijtsrd7137.

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21

Bollhöfer, Matthias, and Volker Mehrmann. "Algebraic Multilevel Methods and Sparse Approximate Inverses." SIAM Journal on Matrix Analysis and Applications 24, no. 1 (January 2002): 191–218. http://dx.doi.org/10.1137/s0895479899364441.

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22

Leung, A. Y. T. "Multilevel dynamic substructures." International Journal for Numerical Methods in Engineering 28, no. 1 (January 1989): 181–91. http://dx.doi.org/10.1002/nme.1620280113.

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23

Muniz, Joanna Rocha. "A rede organizacional dedicada às migrações forçadas no Brasil: uma análise a partir da governança multinível." REMHU: Revista Interdisciplinar da Mobilidade Humana 30, no. 65 (August 2022): 219–36. http://dx.doi.org/10.1590/1980-85852503880006514.

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Resumo Este artigo se propõe a refletir, através das lentes da governança multinível, como se configura a rede organizacional do refúgio no Brasil. Para tal, inicialmente se debruça sobre o conceito de governança multinível, suas duas tipologias e como são compreendidos os níveis territoriais dentro desta teoria. Em sequência, analisa como esta configuração se aplica ao cenário brasileiro, qual o percurso do país para a regulamentação do refúgio e o envolvimento de organizações públicas e privadas. Ao observar os atores e os laços que compõem a rede organizacional do refúgio no Brasil, pretende-se localizar as entidades em níveis territoriais, compreender seu papel e sua centralidade. Ao final, são feitas reflexões sobre a governança do refúgio no contexto brasileiro desde 2017, os limites e os desafios enfrentados, a importância da interdependência e independência das organizações e a relevância da localidade para o acolhimento.

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24

Kobayashi, Yuichiro. "Identifying L2 Developmental Indices while Controlling for L1 Effects: A Multilevel Ordinal Logistic Regression Analysis." Journal of Pan-Pacific Association of Applied Linguistics 25, no. 2 (December 31, 2021): 87–104. http://dx.doi.org/10.25256/paal.25.2.5.

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25

Bořivoj, Šarapatka, Bednář Marek, and Netopil Patrik. "Multilevel soil degradation analysis focusing on soil erosion as a basis for agrarian landscape optimization." Soil and Water Research 13, No. 3 (July 2, 2018): 119–28. http://dx.doi.org/10.17221/118/2017-swr.

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The article demonstrates a multilevel method of soil degradation analysis on land within South Moravia (Czech Republic (CZ)), in the Hodonín region, which is among the highest producing agricultural regions in CZ. The analysis takes a top-down approach, from a regional scale, through cadastres, to individual blocks of land. In the initial (rough) phase, selection was based on the Soil Degradation Model created for the Czech Republic, which classifies the extent of soil degradation to a cadastral level. Within the chosen region, the Čejkovice cadastre is the most burdened in terms of the combination of various degradation factors, and was therefore chosen for a further level of analysis in the form of remote sensing. The results of remote sensing and image classification identify areas with a high level of water erosion, which is the most significant degradation factor within CZ. Pedological research was then carried out in these identified areas. The results of both approaches were compared, and showed significant differences between erosional areas and depositional areas of slopes, which confirms their suitability for the given form of research and analysis. A combination of the given general (Degradation Model) and more detailed methods (erosion modelling, image classification and soil sample analysis) can find practical application in the optimization of farm production in the rural landscape.

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26

Hoppe, R. H. W., and R. Kornhuber. "Adaptive Multilevel Methods for Obstacle Problems." SIAM Journal on Numerical Analysis 31, no. 2 (April 1994): 301–23. http://dx.doi.org/10.1137/0731016.

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27

Yamana, Hayato. "Introduction to Multilevel Analysis." Annals of Clinical Epidemiology 3, no. 1 (2021): 5–9. http://dx.doi.org/10.37737/ace.3.1_5.

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28

Muthén, Bengt, and Tihomir Asparouhov. "Multilevel regression mixture analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 3 (June 2009): 639–57. http://dx.doi.org/10.1111/j.1467-985x.2009.00589.x.

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29

Leckie, George. "Handbook of Multilevel Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 3 (June 2009): 694–95. http://dx.doi.org/10.1111/j.1467-985x.2009.00595_3.x.

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30

Serban, Nicoleta, and Huijing Jiang. "Multilevel Functional Clustering Analysis." Biometrics 68, no. 3 (February 7, 2012): 805–14. http://dx.doi.org/10.1111/j.1541-0420.2011.01714.x.

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31

MUTHÉN, BENGT O. "Multilevel Covariance Structure Analysis." Sociological Methods & Research 22, no. 3 (February 1994): 376–98. http://dx.doi.org/10.1177/0049124194022003006.

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32

Nguyen, Duc T. "Multilevel substructuring sensitivity analysis." Computers & Structures 25, no. 2 (January 1987): 191–202. http://dx.doi.org/10.1016/0045-7949(87)90142-8.

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33

Hutchison, Dougal. "Handbook of multilevel analysis." Journal of Applied Statistics 37, no. 8 (August 2010): 1417–18. http://dx.doi.org/10.1080/02664760902899741.

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34

Zhao, Wei, and Dmitry E. Pelinovsky. "Multilevel computations of dispersed drug release." Numerical Methods for Partial Differential Equations 29, no. 4 (December 26, 2012): 1391–415. http://dx.doi.org/10.1002/num.21761.

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35

Elssel, Kolja, and Heinrich Voss. "An A Priori Bound for Automated Multilevel Substructuring." SIAM Journal on Matrix Analysis and Applications 28, no. 2 (January 2006): 386–97. http://dx.doi.org/10.1137/040616097.

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36

Pestana, J. "Preconditioners for Symmetrized Toeplitz and Multilevel Toeplitz Matrices." SIAM Journal on Matrix Analysis and Applications 40, no. 3 (January 2019): 870–87. http://dx.doi.org/10.1137/18m1205406.

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37

de Noord, Onno E., and Eugene H. Theobald. "Multilevel component analysis and multilevel PLS of chemical process data." Journal of Chemometrics 19, no. 5-7 (May 2005): 301–7. http://dx.doi.org/10.1002/cem.933.

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38

Kelley, C. T. "A Fast Multilevel Algorithm for Integral Equations." SIAM Journal on Numerical Analysis 32, no. 2 (April 1995): 501–13. http://dx.doi.org/10.1137/0732021.

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39

Maas, Cora J. M., Gerty J. L. M. Lensvelt-Mulders, and Joop J. Hox. "A Multilevel Multitrait-Multimethod Analysis." Methodology 5, no. 3 (January 2009): 72–77. http://dx.doi.org/10.1027/1614-2241.5.3.72.

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The classical multitrait-multimethod (MTMM) matrix can be viewed as a two-dimensional cross-classification of traits and methods. Beside commonly used analysis methods such as structural equation modeling and generalizability theory, multilevel analysis offers attractive possibilities. If the focus is only on analyzing classical MTMM data, the multilevel approach has no surplus value, because the resulting model is equivalent to a confirmatory factor model with additional restrictions imposed by the multilevel parameterization. However, if the data contain further complexities, such as additional information on the traits or persons, multilevel analysis of MTMM data offers a flexible analysis tool with more possibilities than the other approaches.

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40

Fish, J., and V. Belsky. "Generalized Aggregation Multilevel solver." International Journal for Numerical Methods in Engineering 40, no. 23 (December 15, 1997): 4341–61. http://dx.doi.org/10.1002/(sici)1097-0207(19971215)40:23<4341::aid-nme261>3.0.co;2-c.

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41

BANAEI, M. R., and E. SALARY. "ANALYSIS OF A GENERALIZED SYMMETRICAL MULTILEVEL INVERTER." Journal of Circuits, Systems and Computers 20, no. 02 (April 2011): 299–311. http://dx.doi.org/10.1142/s0218126611007268.

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The cascade multilevel converter requires the least number of main components among traditional multilevel converter. In this paper, comparison of main components between proposed and traditional converter is shown. In addition, a generalized symmetrical multilevel inverter is proposed. The proposed multilevel inverter can generate DC voltage levels similar to other topologies with less number of components. The proposed topology results in reduction of installation area and cost and has simplicity of control system. This converter has been used in an AC drive system. The operation and performance of the proposed generalized multilevel converter has been verified by the simulation results using SIMULINK/MATLAB. The simulation results show its capability in voltage generation and speed control.

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42

Haji-Ali, Abdul-Lateef, Fabio Nobile, Raúl Tempone, and Sören Wolfers. "Multilevel weighted least squares polynomial approximation." ESAIM: Mathematical Modelling and Numerical Analysis 54, no. 2 (March 2020): 649–77. http://dx.doi.org/10.1051/m2an/2019045.

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Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi-optimal approximation in a given polynomial subspace scales, up to a logarithmic factor, linearly in the dimension of this space. However, in many applications, the computation of samples includes a numerical discretization error. Thus, obtaining polynomial approximations with a single level method can become prohibitively expensive, as it requires a sufficiently large number of samples, each computed with a sufficiently small discretization error. As a solution to this problem, we propose a multilevel method that utilizes samples computed with different accuracies and is able to match the accuracy of single-level approximations with reduced computational cost. We derive complexity bounds under certain assumptions about polynomial approximability and sample work. Furthermore, we propose an adaptive algorithm for situations where such assumptions cannot be verified a priori. Finally, we provide an efficient algorithm for the sampling from optimal distributions and an analysis of computationally favorable alternative distributions. Numerical experiments underscore the practical applicability of our method.

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43

Canuto, Claudio, and Anita Tabacco. "Multilevel decompositions of functional spaces." Journal of Fourier Analysis and Applications 3, no. 6 (November 1997): 715–42. http://dx.doi.org/10.1007/bf02648264.

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44

Hwang, Heungsun, Yoshio Takane, and Naresh Malhotra. "Multilevel Generalized Structured Component Analysis." Behaviormetrika 34, no. 2 (July 2007): 95–109. http://dx.doi.org/10.2333/bhmk.34.95.

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45

Leckie, George. "Handbook of Advanced Multilevel Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 3 (July 2011): 844–45. http://dx.doi.org/10.1111/j.1467-985x.2011.00709_6.x.

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46

Longford, N. "Multilevel analysis with messy data." Statistical Methods in Medical Research 10, no. 6 (December 1, 2001): 429–44. http://dx.doi.org/10.1191/096228001682157643.

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47

Robinson, Timothy J. "Multilevel Analysis: Techniques and Applications." Journal of the American Statistical Association 98, no. 462 (June 2003): 496. http://dx.doi.org/10.1198/jasa.2003.s281.

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48

Longford, N. T. "Multilevel analysis with messy data." Statistical Methods in Medical Research 10, no. 6 (December 2001): 429–44. http://dx.doi.org/10.1177/096228020101000605.

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49

Watson, Carl B., Martin M. Chemers, and Natalya Preiser. "Collective Efficacy: A Multilevel Analysis." Personality and Social Psychology Bulletin 27, no. 8 (August 2001): 1057–68. http://dx.doi.org/10.1177/0146167201278012.

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50

Diez Roux, A. V. "A glossary for multilevel analysis." Journal of Epidemiology & Community Health 56, no. 8 (August 1, 2002): 588–94. http://dx.doi.org/10.1136/jech.56.8.588.

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