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Journal articles on the topic 'Multilevel analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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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

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Jain, Narendra Kumar, K. Gopala Krishna, Vipin Solanki, and Savita. "Mathematical Analysis for Power Loss Analysis of a Seven Level Aligned Multilevel Inverter." E3S Web of Conferences 540 (2024): 06011. http://dx.doi.org/10.1051/e3sconf/202454006011.

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Multilevel inverters are widely used in many applications due to their superior performance over conventional inverter. However, due to increasing number of switches in existed multilevel inverts, the cost, size and losses are increasing continuously and these factors are directly proportional to number of inverter level. Hence, aligned multilevel invert is developed and enhanced the analysis on power loses is presented in this paper. The number of switches are used in aligned multilevel inverter is very less compared to existing multilevel inverters. Moreover, space vector pulse width modulation technique is developed for generating pulses to inverter. The mathematical calculation of power loss analysis for 7 level aligned multilevel inverteris evaluated and presented in this paper.

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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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33

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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34

Di, Chong-Zhi, Ciprian M. Crainiceanu, Brian S. Caffo, and Naresh M. Punjabi. "Multilevel functional principal component analysis." Annals of Applied Statistics 3, no. 1 (March 2009): 458–88. http://dx.doi.org/10.1214/08-aoas206.

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35

van Oyen, Herman. "Multilevel analysis of survey data." International Journal of Public Health 54, no. 3 (May 2009): 129–30. http://dx.doi.org/10.1007/s00038-009-7075-z.

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36

Diez Roux, Ana V., and Allison E. Aiello. "Multilevel Analysis of Infectious Diseases." Journal of Infectious Diseases 191, s1 (February 2005): S25—S33. http://dx.doi.org/10.1086/425288.

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37

Kersten, Astrid. "Multilevel Analysis in Critical Research." Annals of the International Communication Association 10, no. 1 (January 1987): 709–29. http://dx.doi.org/10.1080/23808985.1987.11678670.

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38

Beard, John R. "New Approaches to Multilevel Analysis." Journal of Urban Health 85, no. 6 (September 16, 2008): 805–6. http://dx.doi.org/10.1007/s11524-008-9314-7.

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39

Beard, John R. "New Approaches to Multilevel Analysis." Journal of Urban Health 86, no. 2 (January 23, 2009): 280. http://dx.doi.org/10.1007/s11524-009-9342-y.

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40

Ceulemans, Eva, Mia Hubert, and Peter Rousseeuw. "Robust multilevel simultaneous component analysis." Chemometrics and Intelligent Laboratory Systems 129 (November 2013): 33–39. http://dx.doi.org/10.1016/j.chemolab.2013.06.016.

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41

Caron, Pier-Olivier. "Multilevel analysis of matching behavior." Journal of the Experimental Analysis of Behavior 111, no. 2 (February 13, 2019): 183–91. http://dx.doi.org/10.1002/jeab.510.

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42

Madhuresh, Sontakke. "Analysis & Comparison Multilevel Inverter Topologies." Journal of Electrical and Power System Engineering 4, no. 3 (December 2, 2018): 13–25. https://doi.org/10.5281/zenodo.1843058.

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<em>As the multilevel inverters synthesize waveforms with better harmonic spectrum and less harmonic distortion, are considered as an optimal choice for high voltage and high power applications, but the choice of suitable topology for respective application is the major concern. The paper gives detail analysis and comparison of three major topologies of multilevel inverter i.e.</em> <em> Diode clamped MLI, Flying capacitor MLI &cascade MLI on the basis of complexity of the circuit, power quality, required component and implementation cost.</em>  

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43

Iti, Mahashabdey* Prof. Pawan Pandey. "HARMONIC ANALYSIS OF MULTILEVEL INVERTER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (August 15, 2016): 659–66. https://doi.org/10.5281/zenodo.60113.

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This paper present the different levels of cascaded H-bridge inverters and the minimization of total harmonic distortion by increasing levels. Selective Harmonic Elimination technique is used as a switching scheme. The analysis of three, five and seven level cascaded H-bridge inverter simulation work is done by using the MATLAB software and experimental results have been presented to validate the theory. The simulation results show the improvement of output voltage waveform and reduction of the THD by increasing the levels in inverter. 

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44

Chiu, Ming Ming, and Carly A. Roberts. "Improved analyses of single cases: Dynamic multilevel analysis." Developmental Neurorehabilitation 21, no. 4 (February 10, 2016): 253–65. http://dx.doi.org/10.3109/17518423.2015.1119904.

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45

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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46

Lee, Jung-Hwan, Hyang-Sig Jun, Hee-Mun Park, Jin-Hyun Park, and Sang-Kil Lim. "Reliability Analysis of a Multilevel Inverter Applied to Stratospheric Drones Using Fault Tree Analysis." Electronics 12, no. 24 (December 9, 2023): 4952. http://dx.doi.org/10.3390/electronics12244952.

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Stratospheric drones operating in extreme environments are very important for predicting reliability and are high-efficiency, high-performance, and lightweight power units. Multilevel inverters are suitable for application as power conversion units for stratospheric drones. A guideline is needed to evaluate whether it is suitable for practical application from a reliability perspective among various multilevel topologies. Existing reliability prediction models cannot reflect the operating characteristics of multilevel inverters. In this paper, we analyze the driving characteristics of each topology from the perspective of half-bride, which is the basic configuration of multilevel inverters, and we propose a fault tree analysis (FTA) design with three operating modes. The proposed method has the advantage of being able to easily analyze the failure rate by expanding to single-phase and three-phase and to analyze the failure rate according to changes in modulation index (MI) and power factor (PF). The failure rates of the proposed method and the part count method are analyzed using MIL-HDBK-217F. We also analyze the impact of different various operating characteristics on the failure rate. From a reliability perspective, we provide a variety of guidelines for selecting a multilevel topology that fits the operation conditions.

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47

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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48

Janiga, Srinivas Rao, Suresh Kumar Tummala, Ibrahim H. Al-Kharsan, and Ruby Faizan. "Bibliography analysis of Multilevel Inverters (2021-2022)." E3S Web of Conferences 391 (2023): 01173. http://dx.doi.org/10.1051/e3sconf/202339101173.

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49

Coromina, Lluís, Germà Coenders, and Tina Kogovšek. "Multilevel multitrait multimethod model." Advances in Methodology and Statistics 1, no. 2 (July 1, 2004): 323–49. http://dx.doi.org/10.51936/ukft4267.

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Our goal in this paper is to assess reliability and validity of egocentered network data using multilevel analysis (Muthén, 1989, Hox, 1993) under the multitrait-multimethod approach. The confirmatory factor analysis model for multitrait-multimethod data (Werts & Linn, 1970; Andrews, 1984) is used for our analyses. In this study we reanalyse a part of data of another study (Kogovšek et al., 2002) done on a representative sample of the inhabitants of Ljubljana. The traits used in our article are the name interpreters. We consider egocentered network data as hierarchical; therefore a multilevel analysis is required. We use Muthén’s partial maximum likelihood approach, called pseudobalanced solution (Muthén, 1989, 1990, 1994) which produces estimations close to maximum likelihood for large ego sample sizes (Hox & Mass, 2001). Several analyses will be done in order to compare this multilevel analysis to classic methods of analysis such as the ones made in Kogovšek et al. (2002), who analysed the data only at group (ego) level considering averages of all alters within the ego. We show that some of the results obtained by classic methods are biased and that multilevel analysis provides more detailed information that much enriches the interpretation of reliability and validity of hierarchical data. Within and between-ego reliabilities and validities and other related quality measures are defined, computed and interpreted.

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50

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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