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Journal articles on the topic 'Model comparisons'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Anderson-Sprecher, Richard. "Model Comparisons andR2." American Statistician 48, no. 2 (1994): 113–17. http://dx.doi.org/10.1080/00031305.1994.10476036.

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2

Sugden, A. M. "Climate Model Comparisons." Science 336, no. 6078 (2012): 133. http://dx.doi.org/10.1126/science.336.6078.133-c.

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3

Riyanto, Agus, and Gabriel Sianturi. "PROSEDUR PROSES LAYANAN INDUSTRI KEUANGAN MENGGUNAKAN MODEL IDEF0." Jurnal Ilmiah Bisnis dan Ekonomi Asia 15, no. 2 (2021): 172–78. http://dx.doi.org/10.32815/jibeka.v15i2.219.

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This research aims to provide an overview of the procedure of the financial comparison service process in the form of credit card comparisons and mortgage loan comparisons conducted digitally in Indonesia. This research used a qualitative method using the IDEF0 model. Current financial services have used digital technology. A large amount of financial services competition conducted by Fintech has been able to trim the existing process procedures. Credit card comparisons and mortgage loan comparisons can be done online. Innovations made by Fintech for financial services have cut downtime and a

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4

Petrilli, Sara, Miriam Parise, Silvio Carlo Ripamonti, and Silvia Donato. "Couple Social Comparisons and Relationship Quality: A Path Analysis Model." Informing Science: The International Journal of an Emerging Transdiscipline 27 (2024): 003. http://dx.doi.org/10.28945/5281.

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Aim/Purpose: This study offers an important contribution to the literature on couple social comparisons by showing how different aspects of comparisons are related to relationship quality. Background: Making social comparisons is a daily tendency of human beings that does not only occur on an individual level but also in the context of romantic relationships. This phenomenon is widespread among couples, though partners differ in terms of their propensity to make couple social comparisons. The literature has shown that all these facets of couple social comparison play an important role in relat

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5

Suls, Jerry, René Martin, and Ladd Wheeler. "Three Kinds of Opinion Comparison: The Triadic Model." Personality and Social Psychology Review 4, no. 3 (2000): 219–37. http://dx.doi.org/10.1207/s15327957pspr0403_2.

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This article introduces the triadic model, which proposes that the social comparison of opinion is best considered in terms of 3 different evaluative questions: preference assessment (i.e., “Do I like X?”), belief assessment (i.e., “Is X correct?”), and preference prediction (i.e., “Will I like X?”). Each evaluative question is associated with a different comparison dynamic. The triadic model proposes that comparisons with persons similar in related attributes have special importance for preference assessment. For belief assessment, comparisons with persons of more advantaged status (or “exper

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6

Peteet, Dorothy M. "Paleoclimate Data-Model Comparisons." Ecology 76, no. 2 (1995): 672–73. http://dx.doi.org/10.2307/1941232.

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7

Harms, Travis W., and Z. Christopher Mercer. "S Corporation Model Comparisons." Business Valuation Review 27, no. 1 (2008): 38–54. http://dx.doi.org/10.5791/0882-2875-27.1.38.

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8

Brierley, Chris, and Kira Rehfeld. "Paleovariability: Data Model Comparisons." Past Global Changes Magazine 22, no. 2 (2014): 102. http://dx.doi.org/10.22498/pages.22.2.102.

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9

O'Neill, Brian C., Jean-Francois Lamarque, and David Lawrence. "Developing Climate Model Comparisons." Eos, Transactions American Geophysical Union 95, no. 49 (2014): 462. http://dx.doi.org/10.1002/2014eo490008.

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10

Cherkassky, Vladimir, and Yunqian Ma. "Comparison of Model Selection for Regression." Neural Computation 15, no. 7 (2003): 1691–714. http://dx.doi.org/10.1162/089976603321891864.

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We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the best method for finite-sample estimation problems, even for the simple case of linear estimators. This article presents empirical comparisons between classical statistical methods—Akaike information criterion (AIC) and Bayesian information criterion (BIC)—and the structural risk minimization (SRM) method, basedon Vapnik-Chervonenkis (VC) theory, for regression problems. Our study is motivated by empirical comparisons in Hastie, Tibshirani, and Friedman (2001), which claims that th

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11

Kim, Yong-Jea, and Dong-hyuk Shin. "Comparisons of Flame Stretch Calculation Models and Their Applications Part I: Model Comparisons." Journal of The Korean Society of Combustion 28, no. 2 (2023): 22–37. http://dx.doi.org/10.15231/jksc.2023.28.2.022.

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12

Tkachenko, Maksim, and Hady W. Lauw. "CompareLDA: A Topic Model for Document Comparison." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7112–19. http://dx.doi.org/10.1609/aaai.v33i01.33017112.

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A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison obs

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13

Bonomo, Anthony L., and Marcia J. Isakson. "Model/data comparisons and numerical experiments for the comparison of sandy sediment models." Journal of the Acoustical Society of America 144, no. 3 (2018): 1958–59. http://dx.doi.org/10.1121/1.5068559.

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14

Cribbie, Robert A., and H. J. Keselman. "Pairwise multiple comparisons: A model comparison approach versus stepwise procedures." British Journal of Mathematical and Statistical Psychology 56, no. 1 (2003): 167–82. http://dx.doi.org/10.1348/000711003321645412.

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15

Anderson-Sprecher, Richard. "Model Comparisons and R 2." American Statistician 48, no. 2 (1994): 113. http://dx.doi.org/10.2307/2684259.

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16

Dayton, C. Mitchell. "Model Comparisons Using Information Measures." Journal of Modern Applied Statistical Methods 2, no. 2 (2003): 281–92. http://dx.doi.org/10.22237/jmasm/1067644920.

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17

Ouzounis, Christos, Georg Casari, Chris Sander, Javier Tamames, and Alfonso Valencia. "Computational comparisons of model genomes." Trends in Biotechnology 14, no. 8 (1996): 280–85. http://dx.doi.org/10.1016/0167-7799(96)10043-3.

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18

Giacomini, Raffaella, and Barbara Rossi. "MODEL COMPARISONS IN UNSTABLE ENVIRONMENTS." International Economic Review 57, no. 2 (2016): 369–92. http://dx.doi.org/10.1111/iere.12161.

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19

Qian, Jiahe. "MODEL-BASED WEIGHTING AND COMPARISONS." ETS Research Report Series 2008, no. 1 (2008): i—24. http://dx.doi.org/10.1002/j.2333-8504.2008.tb02103.x.

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20

Rapee, Ronald M., Miriam K. Forbes, Ella L. Oar, et al. "Testing a concurrent model of social anxiety in preadolescence." International Journal of Behavioral Development 44, no. 6 (2020): 505–14. http://dx.doi.org/10.1177/0165025420912014.

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Social anxiety is a common mental disorder with an average age of onset in early adolescence. Current theories focus largely on risk factors that are present from early in life, but reasons for onset of the disorder as youth move into adolescence are rarely discussed. We recently proposed a model of the onset of certain mental disorders during the adolescent years based on characteristics of adolescent development. While this model will require longitudinal testing, the current article establishes concurrent associations between relevant variables in a cohort of 528 preadolescents ( M age = 11

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21

Zhang, Wen Fu, Dan Xie, Jing Ji, and Ying Chun Liu. "Comparisons of AR Model and Wavelet Analysis Model." Applied Mechanics and Materials 166-169 (May 2012): 3246–49. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.3246.

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Comparisons of AR model and wavelet analysis model are presented. Similarities and differences between the two models are firstly indicated with respect to its mathematic expression and physical meaning. Furthermore, range of frequency domain characterized by samples, length of samples and algorithms of models, accuracy and efficiency are also discussed. This can be used as reference to choose model reasonably in the application of wind field simulation.

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22

Trevisan, Marco, Ettore Capri, and Attilio A. M. Del Re. "Pesticide soil transport models: Model comparisons and field evaluation." Toxicological & Environmental Chemistry 40, no. 1-4 (1993): 71–81. http://dx.doi.org/10.1080/02772249309357932.

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23

Lee, Jeong‐Eun, Neal J. Evans II, and Edwin A. Bergin. "Comparisons of an Evolutionary Chemical Model with Other Models." Astrophysical Journal 631, no. 1 (2005): 351–60. http://dx.doi.org/10.1086/432531.

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24

López-Vázquez, Carlos, and Francisco Javier Ariza-López. "Global Digital Elevation Model Comparison Criteria: An Evident Need to Consider Their Application." ISPRS International Journal of Geo-Information 12, no. 8 (2023): 337. http://dx.doi.org/10.3390/ijgi12080337.

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From an extensive search of papers related to the comparison of Global Digital Elevation Models (hereinafter GDEMs), an analysis is carried out that aims to answer several questions such as: Which GDEMs have been compared? Where have the comparisons been made? How many comparisons have been made? How have the assessments been carried out? Which is the GDEM option with the lowest RMSE? Analysis shows that SRTM and ASTER are the most popular GDEMs, that the countries where more comparisons have been made are Brazil, India, and China, and that the main type of reference data for evaluations is th

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25

Aslam, Muhammad, and Syed Haider Shah. "Lindley-Shannon Information for Comparison of Priors Under Paired Comparisons Model." Pakistan Journal of Statistics and Operation Research 11, no. 3 (2015): 317. http://dx.doi.org/10.18187/pjsor.v11i3.779.

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26

Zhao, Jinxin, Xingsong Li, Jinhua Yu, and Yuanyuan Wang. "Nonlinear Ultrasound Simulation Based on Full-Wave Model and Comparisons with KZK." International Journal of Bioscience, Biochemistry and Bioinformatics 4, no. 5 (2014): 322–25. http://dx.doi.org/10.7763/ijbbb.2014.v4.363.

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27

Jameel, Abbood M. "Developing Multiple Paired Comparisons Model for Analysis of Variance Technique." Cihan University-Erbil Journal of Humanities and Social Sciences 9, no. 1 (2025): 183–87. https://doi.org/10.24086/cuejhss.v9n1y2025.pp183-187.

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A class of multiple paired comparison models can be developed for taste-testing experiments to evaluate several objects on a seven-point preference scale. This allows for calculating a preference score for each object using the two-way analysis of variance technique (ANOVAT). The primary objective of this research is to distinguish between different types of objects or items by calculating degrees of preference (scores), denoted as S j. These objects, represented by L, are evaluated through paired comparisons. Specifically, we consider six types of potato chips, labeled as A, B, C, D, E, and F

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28

O’Loughlin, Ryan. "Robustness reasoning in climate model comparisons." Studies in History and Philosophy of Science Part A 85 (February 2021): 34–43. http://dx.doi.org/10.1016/j.shpsa.2020.12.005.

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29

Hunt, J. D., Robert Johnston, John E. Abraham, et al. "Comparisons from Sacramento Model Test Bed." Transportation Research Record: Journal of the Transportation Research Board 1780, no. 1 (2001): 53–63. http://dx.doi.org/10.3141/1780-07.

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30

Peacock, R. D., S. Davis, and V. Babrauskas. "Data for room fire model comparisons." Journal of Research of the National Institute of Standards and Technology 96, no. 4 (1991): 411. http://dx.doi.org/10.6028/jres.096.022.

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31

McElroy, T. S. "Nonnested model comparisons for time series." Biometrika 103, no. 4 (2016): 905–14. http://dx.doi.org/10.1093/biomet/asw048.

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32

Cao, Bihua, Heming Gao, and Fuhong Li. "Hybrid model of price pair comparisons." NeuroReport 26, no. 14 (2015): 838–41. http://dx.doi.org/10.1097/wnr.0000000000000433.

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33

Salis, Fiora. "The Nature of Model-World Comparisons." Monist 99, no. 3 (2016): 243–59. http://dx.doi.org/10.1093/monist/onw003.

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34

Neath, Andrew A., Javier E. Flores, and Joseph E. Cavanaugh. "Bayesian multiple comparisons and model selection." Wiley Interdisciplinary Reviews: Computational Statistics 10, no. 2 (2017): e1420. http://dx.doi.org/10.1002/wics.1420.

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35

Rashid, Junaid, Waqar Mehmood, and Muhammad Wasif Nisar. "A Survey of Model Comparison Strategies and Techniques in Model Driven Engineering." International Journal of Software Engineering and Technologies (IJSET) 1, no. 3 (2016): 165. http://dx.doi.org/10.11591/ijset.v1i3.4579.

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This Survey paper shows the recent state of model comparison as it’s applies to Model Driven engineering. In Model Driven Engineering to calculate the difference between the models is a very important and challenging task. There are number of tasks involved in Model differencing that firstly starts with identifying and matching the elements of the model. In this paper we discuss how model matching is accomplished, the strategies, techniques and the types of the model. In this paper we also discuss the future direction. We find out that many of the latest model comparison strategies are geared

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36

Fageot, Julien, Sadegh Farhadkhani, Lê-Nguyên Hoang, and Oscar Villemaud. "Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20379–86. http://dx.doi.org/10.1609/aaai.v38i18.30020.

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Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model considers binary comparisons (victory/defeat) between alternatives, while more recent developments allow finer comparisons to be taken into account. In this article, we introduce a probabilistic model encompassing a broad variety of paired comparisons that can take discrete or continuous values. We do so by considering a well-behaved subset of the e

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37

Li, Qie, and Junfeng Shang. "A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models." Communications in Statistics - Theory and Methods 44, no. 23 (2015): 5071–90. http://dx.doi.org/10.1080/03610926.2013.813042.

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38

Yue, XinAn, WeiXing Wan, LiBo Liu, BaiQi Ning, BiQiang Zhao, and ManLian Zhang. "TIME-IGGCAS model validation: Comparisons with empirical models and observations." Science in China Series E: Technological Sciences 51, no. 3 (2008): 308–22. http://dx.doi.org/10.1007/s11431-008-0026-8.

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39

Von Eye, Alexander, and Wolfgang Wiedermann. "Multigroup Comparisons with Configural Frequency Analysis." Journal for Person-Oriented Research 11, no. 1 (2025): 1–9. https://doi.org/10.17505/jpor.2025.27572.

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Lienert’s (1973) original approach to comparing groups with Configural Frequency Analysis (CFA) cannot straightforwardly be generalized to the comparison of multiple groups. The present article proposes a new base model for group comparison with CFA. This model allows researchers to compare multiple groups, to evaluate overall model fit, to take covariates into account, and to conduct exploratory and confirmatory analyses. In confirmatory group comparisons, base models need to be specified in which particular configurations are blanked out, and other configurations are explicitly set equal. Re

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40

Brandstätter, Eduard. "Affective Reactions to Social Income Comparisons." Zeitschrift für Sozialpsychologie 32, no. 3 (2001): 142–51. http://dx.doi.org/10.1024//0044-3514.32.3.142.

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Summary: The article investigates specific affective reactions to social comparison outcomes within the framework of the contrast-empathy model ( Brandstätter, 1998 , 2000 ). One hundred and one participants received vignettes that described social income comparisons between a target person and a comparison other. In these vignettes comparison direction, relationship quality and relevance were varied. Participants (1) freely expressed the target person's likely emotions and (2) rated the hedonic intensities of various emotions mentioned. Results support the predictions derived from the contras

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41

Margolis, Jaclyn A., and Scott B. Dust. "It’s All Relative: A Team-Based Social Comparison Model for Self-Evaluations of Effectiveness." Group & Organization Management 44, no. 2 (2016): 361–95. http://dx.doi.org/10.1177/1059601116682901.

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We apply social comparison theory (SCT) to the organizational context and develop a model explicating the social comparison process that occurs within organizational teams. In doing so, we highlight how individual, team, and managerial factors influence this process. First, we discuss how task-related (e.g., functional background and experience) and demographic-related (e.g., age, gender, and race) team characteristics affect social comparison target selection (i.e., the team as a whole, a subgroup, or a specific individual) and further explain the impact of metacognitive capacities on this re

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42

Joyce, Achola Ogundo 1., Peter Ki bas 2. Prof, and Robert Otuya 3. Dr. "NETWORKING AND RISK -TAKING ON GROWTH OF SELECTED KENYA EXPORT FIRMS: AN ANALYSIS." International Journal of Research - Granthaalayah 6, no. 4 (2018): 351–56. https://doi.org/10.5281/zenodo.1250500.

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Networking and Risk _Taking on Growth of of Export Firms have such an important role in global success and become more useful overtime. Networking and Risk _Taking on Growth of Export Firms have not been addressed in full, their challenges such as lack of Literature on Risk -Taking on Export Firms is very scanty and hence not exhaustive .This Research attempts to utilise Sample size of 169 ,adopted from thesis on in Selected Kenya Export Firms .The Research employed Analysis of Moment Structures to compare Networking and Risk _Taking to Analyse Growth of Export Firms Selected Kenya Export Firm

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43

Chandler, Mark, H. Dowsett, and A. Haywood. "The PRISM Model/Data Cooperative: Mid-Pliocene data-model comparisons." PAGES news 16, no. 2 (2008): 24–25. http://dx.doi.org/10.22498/pages.16.2.24.

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44

Busemeyer, Jerome R., and Yi-Min Wang. "Model Comparisons and Model Selections Based on Generalization Criterion Methodology." Journal of Mathematical Psychology 44, no. 1 (2000): 171–89. http://dx.doi.org/10.1006/jmps.1999.1282.

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45

Ridley, A. J., D. L. De Zeeuw, and L. Rastätter. "Rating global magnetosphere model simulations through statistical data-model comparisons." Space Weather 14, no. 10 (2016): 819–34. http://dx.doi.org/10.1002/2016sw001465.

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46

Jönsson, Bror F., Christopher L. Follett, Jacob Bien, et al. "Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll." Geoscientific Model Development 16, no. 16 (2023): 4639–57. http://dx.doi.org/10.5194/gmd-16-4639-2023.

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Abstract. Global biogeochemical ocean models are invaluable tools to examine how physical, chemical, and biological processes interact in the ocean. Satellite-derived ocean color properties, on the other hand, provide observations of the surface ocean, with unprecedented coverage and resolution. Advances in our understanding of marine ecosystems and biogeochemistry are strengthened by the combined use of these resources, together with sparse in situ data. Recent modeling advances allow the simulation of the spectral properties of phytoplankton and remote sensing reflectances, bringing model ou

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47

Miki, Hideo, De-bi Cao, and Yasushi Masuda. "STOCHASTIC COMPARISONS IN REVENUE MANAGEMENT UNDER A DISCRETE CHOICE MODEL OF CONSUMER BEHAVIOR." Journal of the Operations Research Society of Japan 53, no. 3 (2010): 207–19. http://dx.doi.org/10.15807/jorsj.53.207.

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48

Tosun, Leman Pınar, and Ezgi Kaşdarma. "Passive Facebook Use and Depression." Journal of Media Psychology 32, no. 4 (2020): 165–75. http://dx.doi.org/10.1027/1864-1105/a000269.

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Abstract. In the current study we examined a psychological mechanism linking Facebook use to depression. A survey was conducted with 319 undergraduates about their passive Facebook use, their frequency of making upward social comparisons on Facebook, the emotions evoked through these comparisons, and their levels of depression. Half of the participants were given questions about the Facebook comparisons they made with their close friends, while the other half were given questions about the Facebook comparisons they made with acquaintances. Analysis of the whole sample revealed that upward Face

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49

Zhang, Jun, Shaosong Zhang, and Zhongming Wang. "Analysis of WACSIS Data Using a Directional Hybrid Wave Model." Journal of Offshore Mechanics and Arctic Engineering 126, no. 1 (2004): 60–65. http://dx.doi.org/10.1115/1.1641797.

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The Wave Crest Sensor Inter-comparison Study (WACSIS) employed many different types of sensors to measure ocean surface waves. These sensors were attached to a steel jacket platform located in 18 m deep water about 9 km from the Dutch coast. To investigate the suitability and consistency of different wave sensors, wave characteristics at the locations of some sensors were deterministically predicted based on three other wave measurements using a Directional Hybrid Wave Model (DHWM). The comparisons between the predictions and related measurements were applied to the examination of the consiste

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50

Cobos, Marlon E., Hannah L. Owens, Jorge Soberón, and A. Townsend Peterson. "Detailed multivariate comparisons of environments with mobility oriented parity." Frontiers of Biogeography 17 (September 25, 2024): e132916. https://doi.org/10.21425/fob.17.132916.

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Multivariate model projections onto conditions that differ from those under which the models were calibrated can result in uncertainty owing to response extrapolation. Therefore, interpretations of models transferred to conditions not analogous to those of model calibration must be done carefully to avoid misleading conclusions. A good practice when producing model transfers is to assess the degree of dissimilarity between calibration and transfer conditions. The mobility oriented parity (MOP) metric is a tool commonly used to quantify such dissimilarities. Our study elucidated the details of

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