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Journal articles on the topic 'Wrong models'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

So, Richard Jean. "“All Models Are Wrong”." PMLA/Publications of the Modern Language Association of America 132, no. 3 (May 2017): 668–73. http://dx.doi.org/10.1632/pmla.2017.132.3.668.

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Several years ago, the first thing i learned in my introductory statistics class was the following declaration, which the instructor had written in capital letters on the blackboard: “all models are wrong.” Models are statistical, graphic, or physical objects, and their primary quality is that they can be manipulated. Scientists and social scientists use them to think about the social or natural worlds and to represent those worlds in a simplified manner. Statistical models, which dominate the social sciences, particularly in economics, are typically equations with response and predictor variables. Specifically, a researcher seeks to understand some social phenomenon, such as the relation between students' scores on a math test and how many hours the students spent preparing for the exam. To predict or describe this relation, the researcher constructs a quantitative model with quantitative inputs (the number of hours each student spent studying) and outputs (each student's test score). The researcher hopes that the number of hours a student spent preparing for the exam will correlate with the student's score. If it does, this quantified relation can help describe the overall dynamics of test taking.

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2

Shafer, Steven L. "All Models Are Wrong." Anesthesiology 116, no. 2 (February 1, 2012): 240–41. http://dx.doi.org/10.1097/aln.0b013e318242a4a7.

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3

Hickerson, Michael J. "All models are wrong." Molecular Ecology 23, no. 12 (June 2014): 2887–89. http://dx.doi.org/10.1111/mec.12794.

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4

Steelman, Brandon. "Trump: polls right, models wrong." Nature 540, no. 7631 (November 30, 2016): 39. http://dx.doi.org/10.1038/540039e.

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5

Zilla, Peter, Deon Bezuidenhout, and Paul Human. "Prosthetic vascular grafts: Wrong models, wrong questions and no healing." Biomaterials 28, no. 34 (December 2007): 5009–27. http://dx.doi.org/10.1016/j.biomaterials.2007.07.017.

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6

Grabinski, Michael, and Galiya Klinkova. "Wrong Use of Averages Implies Wrong Results from Many Heuristic Models." Applied Mathematics 10, no. 07 (2019): 605–18. http://dx.doi.org/10.4236/am.2019.107043.

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7

Bakker, Mark. "Are All Models Wrong? Absolutely Not." Groundwater 51, no. 3 (February 26, 2013): 313. http://dx.doi.org/10.1111/gwat.12037.

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8

Parnell, Laura K. S. "Wound Models: Wrong, Right, or Irrelevant?" Advances in Wound Care 8, no. 12 (December 1, 2019): 644. http://dx.doi.org/10.1089/wound.2019.1099.

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9

Kamb, Alexander. "What's wrong with our cancer models?" Nature Reviews Drug Discovery 4, no. 2 (February 2005): 161–65. http://dx.doi.org/10.1038/nrd1635.

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10

Remington, Patrick L. "All Models Are Wrong; Some Are Useful." American Journal of Public Health 107, no. 8 (August 2017): e28-e28. http://dx.doi.org/10.2105/ajph.2017.303892.

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11

Lefebvre, J., H. Roussel, E. Walter, D. Lecointe, and W. Tabbara. "Prediction from wrong models: the Kriging approach." IEEE Antennas and Propagation Magazine 38, no. 4 (1996): 35–45. http://dx.doi.org/10.1109/74.537364.

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12

Mogil, Jeffrey S. "What’s wrong with animal models of pain?" Scandinavian Journal of Pain 1, no. 3 (July 1, 2010): 168. http://dx.doi.org/10.1016/j.sjpain.2010.05.004.

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13

Stiglitz, Joseph E. "Where modern macroeconomics went wrong." Oxford Review of Economic Policy 34, no. 1-2 (2018): 70–106. http://dx.doi.org/10.1093/oxrep/grx057.

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Abstract This paper provides a critique of the DSGE models that have come to dominate macroeconomics during the past quarter-century. It argues that at the heart of the failure were the wrong microfoundations, which failed to incorporate key aspects of economic behaviour, e.g. incorporating insights from information economics and behavioural economics. Inadequate modelling of the financial sector meant they were ill-suited for predicting or responding to a financial crisis; and a reliance on representative agent models meant they were ill-suited for analysing either the role of distribution in fluctuations and crises or the consequences of fluctuations on inequality. The paper proposes alternative benchmark models that may be more useful both in understanding deep downturns and responding to them.

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14

Field, E. H. ""All Models Are Wrong, but Some Are Useful"." Seismological Research Letters 86, no. 2A (March 1, 2015): 291–93. http://dx.doi.org/10.1785/02201401213.

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15

Yang, Z. "How often do wrong models produce better phylogenies?" Molecular Biology and Evolution 14, no. 1 (January 1, 1997): 105–8. http://dx.doi.org/10.1093/oxfordjournals.molbev.a025695.

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16

Curchoe, Carol Lynn. "All Models Are Wrong, but Some Are Useful." Journal of Assisted Reproduction and Genetics 37, no. 10 (October 2020): 2389–91. http://dx.doi.org/10.1007/s10815-020-01895-3.

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17

Chin, Brian, and Christopher J. Coroneos. "All Models Are Wrong, but Some Are Useful." Journal of the American College of Surgeons 230, no. 5 (May 2020): 794–95. http://dx.doi.org/10.1016/j.jamcollsurg.2020.03.012.

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18

Ghamami, Samim, and Lisa R. Goldberg. "Stochastic Intensity Models of Wrong Way Risk: Wrong Way CVA Need Not Exceed Independent CVA." Journal of Derivatives 21, no. 3 (February 28, 2014): 24–35. http://dx.doi.org/10.3905/jod.2014.21.3.024.

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19

Ghamami, Samim, and Lisa R. Goldberg. "Stochastic intensity models of wrong way risk : wrong way CVA need not exceed independent CVA." Finance and Economics Discussion Series 2014, no. 054 (2014): 1–18. http://dx.doi.org/10.17016/feds.2014.054.

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20

Wit, Ernst, Edwin van den Heuvel, and Jan-Willem Romeijn. "‘All models are wrong...’: an introduction to model uncertainty." Statistica Neerlandica 66, no. 3 (July 3, 2012): 217–36. http://dx.doi.org/10.1111/j.1467-9574.2012.00530.x.

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21

McDonald, J. C. "Editorial - All Models are Wrong but Some are Useful." Radiation Protection Dosimetry 71, no. 4 (June 2, 1997): 243–44. http://dx.doi.org/10.1093/oxfordjournals.rpd.a032060.

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22

TELFORD, R., E. HEEGAARD, and H. BIRKS. "All age–depth models are wrong: but how badly?" Quaternary Science Reviews 23, no. 1-2 (January 2004): 1–5. http://dx.doi.org/10.1016/j.quascirev.2003.11.003.

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23

Simard, J. Marc. "Essentially all models are wrong, but some are useful." Neurology 85, no. 3 (June 24, 2015): 210–11. http://dx.doi.org/10.1212/wnl.0000000000001769.

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24

Mogil, J. "1-10 WHAT'S WRONG WITH ANIMAL MODELS OF PAIN?" Osteoarthritis and Cartilage 19 (September 2011): S3. http://dx.doi.org/10.1016/s1063-4584(11)60016-2.

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25

Stouffer, Daniel B. "All ecological models are wrong, but some are useful." Journal of Animal Ecology 88, no. 2 (February 2019): 192–95. http://dx.doi.org/10.1111/1365-2656.12949.

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26

Kujansivu, Paula. "Is there something wrong with intellectual capital management models?" Knowledge Management Research & Practice 7, no. 4 (December 2009): 300–307. http://dx.doi.org/10.1057/kmrp.2009.23.

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27

Goodwin, Barry K., Ashok K. Mishra, and François N. Ortalo‐Magné. "What's Wrong with Our Models of Agricultural Land Values?" American Journal of Agricultural Economics 85, no. 3 (August 2003): 744–52. http://dx.doi.org/10.1111/1467-8276.00479.

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28

Féménias, Jean-Louis. "Fitting models to correlated data II (small samples, large correlations, and wrong models)." Journal of Molecular Spectroscopy 230, no. 1 (March 2005): 22–42. http://dx.doi.org/10.1016/j.jms.2004.10.005.

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29

Wainer, Howard, and David Thissen. "Estimating Ability With the Wrong Model." Journal of Educational Statistics 12, no. 4 (December 1987): 339–68. http://dx.doi.org/10.3102/10769986012004339.

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No model is ever a perfect reflection of the data it is to summarize. There are always errors of fit. This is as true with modern item response theory (IRT) as with all other models. It is important to know to what extent the accuracy of measurement made with these models is influenced by misfit and what can be done to minimize the inaccuracy. First, a detailed general model was fit to data to provide the framework for a realistic simulation structure. Then three of the most commonly used IRT models were fit in this simulation. A variety of robust estimators of ability were used and the accuracy and efficiency of each estimator was determined. With short tests, a simple model coupled with a robust estimator seemed to be the methodology of choice for describing the data. As test length increased, so too did the benefits of utilizing a more complex parameterization. An unexpected finding was that coupling robust estimators with a Bayesian prior yielded substantial shrinkage. Future work on ability estimation, especially for practical applications of adaptive testing, is required to “unshrink” ability estimates.

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30

Louie, A. H. "A Living System Must Have Noncomputable Models." Artificial Life 13, no. 3 (July 2007): 293–97. http://dx.doi.org/10.1162/artl.2007.13.3.293.

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Chu and Ho's recent article in Artificial Life is riddled with errors. In particular, they use a wrong definition of Robert Rosen's mechanism. This renders their “critical assessment” of Rosen's central proof null and void.

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31

de Bree, Elise, and Madelon van den Boer. "Wrong place, wrong time: Children’s sensitivity to present tense spelling conventions." Applied Psycholinguistics 42, no. 5 (July 14, 2021): 1221–42. http://dx.doi.org/10.1017/s0142716421000254.

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AbstractSpelling has been found to be influenced by the frequency with which certain orthographic patterns occur. We examined whether Grades 2–5 children were already sensitive to orthographic frequency in spelling present tense verb inflections that sound the same but are spelled differently. Children were asked to spell present tenses in two homophonous forms; both inflections are pronounced with final /t/ but are spelled with final -d (“ik vind,” I find) or -dt (“hij/zij vindt,” he/she finds). Previous research has shown that adolescents and adults make inflection errors based on the relative frequency within a pair; as “vind’ is more frequent than “vindt,” “vind” is often used incorrectly. The children showed low correct scores for third person singular spellings, and overall better performance for -d dominant verbs. Surprisingly, they did make errors related to homophone inflection but in the wrong place, marking the wrong time: homophone-based errors occurred in present tense non-homophone verbs and in past tenses. We take our findings to mean that the children were not sensitive to homophone dominance. Furthermore, the findings illustrate the importance of specific graphotactic patterns in literacy development and call for attention to these patterns in models and teaching of spelling.

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32

Butterworth, Karl T., and Jacqueline P. Williams. "Animal Models for Radiotherapy Research: All (Animal) Models Are Wrong but Some Are Useful." Cancers 13, no. 6 (March 16, 2021): 1319. http://dx.doi.org/10.3390/cancers13061319.

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33

Arnold, Antje, Fausto Rodriguez, Charles G. Eberhart, and Eric H. Raabe. "Response to letter to the editor: “All models are wrong; some models are useful”." Neuro-Oncology 22, no. 9 (June 29, 2020): 1406–7. http://dx.doi.org/10.1093/neuonc/noaa137.

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34

Kozierski, Piotr, Marcin Lis, and Dariusz Horla. "Wrong transition and measurement models in power system state estimation." Archives of Electrical Engineering 65, no. 3 (September 1, 2016): 559–74. http://dx.doi.org/10.1515/aee-2016-0040.

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Abstract The influence of wrong information about transition and measurement models on estimation quality has been presented in the paper. Two methods of a particle filter, with and without the Population Monte Carlo modification, and also the extended and unscented Kalman filters methods have been compared. A small 5-bus power system has been used in simulations, which have been performed based on one data set, and this data set has been chosen from among 100 different - to draw the most general conclusions. Based on the obtained results it has been found that for the particle filter methods the implementation of the slightly higher standard deviation than the true value, usually increases the estimation quality. For the Kalman filters methods it has been concluded that optimal values of variances are equal to the true values.

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35

Bruno, W. J., and A. L. Halpern. "Topological bias and inconsistency of maximum likelihood using wrong models." Molecular Biology and Evolution 16, no. 4 (April 1, 1999): 564–66. http://dx.doi.org/10.1093/oxfordjournals.molbev.a026137.

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36

Posada, David, and Keith A. Crandall. "Simple (Wrong) Models for Complex Trees: A Case from Retroviridae." Molecular Biology and Evolution 18, no. 2 (February 1, 2001): 271–75. http://dx.doi.org/10.1093/oxfordjournals.molbev.a003802.

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37

Trachsel, Mathias, and Richard J. Telford. "All age–depth models are wrong, but are getting better." Holocene 27, no. 6 (November 3, 2016): 860–69. http://dx.doi.org/10.1177/0959683616675939.

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38

Mathieu, Pierre-Philippe, and Eric Deleersnijder. "What is wrong with isopycnal diffusion in world ocean models?" Applied Mathematical Modelling 22, no. 4-5 (April 1998): 367–78. http://dx.doi.org/10.1016/s0307-904x(98)10008-2.

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39

Conchello, José-Angel, Joanne Markham, and James G. McNally. "All Models are Wrong an Overview of 3D Deconvolution Methods." Microscopy and Microanalysis 3, S2 (August 1997): 375–76. http://dx.doi.org/10.1017/s143192760000876x.

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Three dimensional (3D)microscopy is a powerful toll for the visualization of biological specimens and processes. In 3D microscopy, a 3D image is collected by recording a series of two-dimensional (2D) images focusing the microscope at different planes through the specimen. Each 2D optical slice in this through focus series contains the in-focus information plus contributions from out-of-focus structures that obscure the image and reduce its contrast. There are two complementary approaches to reduce or ameliorate the effects of the out-of-focus contributions, optical and computational. In the optical approach a microscope is used that avoids collecting out-of-focus light, such as a confocal microscope (see and references therein), a two-photon or three-photon fluorescence excitation microscope, or atwo-sided microscope. In the computational approach, the through-focus series is processed in a computer using any of a number of debluring algorithms to reduce or ameliorate the out-of-focus contributions. In the past two decades, several methods for debluring microscopic images have been developed whose common aim is to undo the degradations introduced by the process of image formation and recording

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40

Peters, Susanne. "Germany's security policy after unification: Taking the wrong models’1." European Security 6, no. 1 (March 1997): 18–47. http://dx.doi.org/10.1080/09662839708407297.

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41

Sterman, John D. "All models are wrong: reflections on becoming a systems scientist." System Dynamics Review 18, no. 4 (2002): 501–31. http://dx.doi.org/10.1002/sdr.261.

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42

Legrenzi, Paolo, Vittorio Girotto, and P. N. Johnson-Laird. "Models of Consistency." Psychological Science 14, no. 2 (March 2003): 131–37. http://dx.doi.org/10.1111/1467-9280.t01-1-01431.

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This article presents a theory of how individuals detect whether descriptions of an entity are consistent or inconsistent. The theory postulates that individuals try to construct a mental model of the entity in which all the propositions are true. If they succeed, they infer that the description is consistent; otherwise, they infer that it is inconsistent. We report three experiments that corroborated the theory. Experiment 1 confirmed that evaluating consistency is easier when an initial model suffices than when reasoners have to find an alternative model. Experiment 2 established the occurrence of illusory inferences about the properties of entities. Experiment 3 showed that the illusions correspond to mental models of the assertions, even when these models are wrong because they fail to represent what is false.

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43

Wong, Zoie Shui-Yee, HY So, Belinda SC Kwok, Mavis WS Lai, and David TF Sun. "Medication-rights detection using incident reports: A natural language processing and deep neural network approach." Health Informatics Journal 26, no. 3 (December 10, 2019): 1777–94. http://dx.doi.org/10.1177/1460458219889798.

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Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models’ performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method). We also evaluated the effects on prediction outcomes of several deep neural network model settings, including number of layers, number of neurons and activation regularisation functions. The accuracy of the models was measured at 0.9 or above across model settings and algorithms. The average values obtained for accuracy and area under the curve were 0.940 (standard deviation: 0.011) and 0.911 (standard deviation: 0.019), respectively. It is shown that deep neural network models were more accurate than the other classifiers across all of the tested class labels (including wrong patient, wrong drug, wrong time, wrong dose and wrong route). The deep neural network method outperformed other binary classifiers and our default base case model, and parameter arguments setting generally performed well for the five medication-rights datasets. The medication-rights detection system developed in this study successfully uses a natural language processing and deep-learning approach to classify patient-safety incidents using the Advanced Incident Reporting System reports, which may be transferable to other mandatory and voluntary incident reporting systems worldwide.

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44

Welsing, Paco M. J. "Statistical modelling: essentially, all models are wrong, but some are useful." Rheumatology 54, no. 7 (May 13, 2015): 1133–34. http://dx.doi.org/10.1093/rheumatology/kev116.

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45

Ransohoff, Richard M. "All (animal) models (of neurodegeneration) are wrong. Are they also useful?" Journal of Experimental Medicine 215, no. 12 (November 20, 2018): 2955–58. http://dx.doi.org/10.1084/jem.20182042.

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Richard M. Ransohoff, Entrepreneur-in-Residence at Third Rock Ventures and Visiting Scientist at Harvard Medical School, provides his personal opinion on using animal models to address current challenges and opportunities in drug development for neurodegeneration.

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46

Thompson, David R., Chantal F. Ski, and Alexander M. Clark. "Cardiac rehabilitation and secondary prevention: Wrong terms, aims, models and outcomes?" European Journal of Preventive Cardiology 26, no. 9 (February 27, 2019): 995–97. http://dx.doi.org/10.1177/2047487319834385.

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47

Hafner, Christian M., Hans Manner, and Léopold Simar. "The “wrong skewness” problem in stochastic frontier models: A new approach." Econometric Reviews 37, no. 4 (April 20, 2016): 380–400. http://dx.doi.org/10.1080/07474938.2016.1140284.

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48

Jung, Robert C., and A. R. Tremayne. "Useful models for time series of counts or simply wrong ones?" AStA Advances in Statistical Analysis 95, no. 1 (October 1, 2010): 59–91. http://dx.doi.org/10.1007/s10182-010-0139-9.

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49

Besnard, Denis, David Greathead, and Gordon Baxter. "When mental models go wrong: co-occurrences in dynamic, critical systems." International Journal of Human-Computer Studies 60, no. 1 (January 2004): 117–28. http://dx.doi.org/10.1016/j.ijhcs.2003.09.001.

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50

Liddell, Torrin M., and John K. Kruschke. "Analyzing ordinal data with metric models: What could possibly go wrong?" Journal of Experimental Social Psychology 79 (November 2018): 328–48. http://dx.doi.org/10.1016/j.jesp.2018.08.009.

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