Relevant bibliographies by topics / Logit

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Logit'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Logit"

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Li, Mengyang, Fengguang Su, Ou Wu, and Ji Zhang. "Logit Perturbation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1359–66. http://dx.doi.org/10.1609/aaai.v36i2.20024.

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Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches. For example, (adversarial) feature perturbation can improve the robustness or even generalization capability of learned models. However, limited studies have explicitly explored for the perturbation of logit vectors. This work discusses several existing methods related to logit perturbation. Based on a unified viewpoint between
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Bushway, Shawn D. "Is There Any Logic to Using Logit." Criminology & Public Policy 12, no. 3 (2013): 563–67. http://dx.doi.org/10.1111/1745-9133.12059.

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LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (2011): 309–24. http://dx.doi.org/10.1142/s1793536911000738.

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For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions
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Weng, Juanjuan, Zhiming Luo, Shaozi Li, Nicu Sebe, and Zhun Zhong. "Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration." IEEE Transactions on Information Forensics and Security 18, no. 6 (2023): 3561–74. https://doi.org/10.1109/TIFS.2023.3284649.

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Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted adversarial examples with higher transferability than non-targeted ones. Recent studies reveal that the traditional Cross-Entropy (CE) loss function is insufficient to learn transferable targeted adversarial examples due to the issue of vanishing gradient. In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly ob
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Baser, O., and S. Long. "PMC11 WHICH ONE IS LOGICAL? LOGIT OR RARE EVENT LOGIT (RE-LOGIT)." Value in Health 8, no. 3 (2005): 378. http://dx.doi.org/10.1016/s1098-3015(10)63015-x.

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Ferraioli, Diodato. "Logit dynamics." ACM SIGecom Exchanges 12, no. 1 (2013): 34–37. http://dx.doi.org/10.1145/2509013.2509018.

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Halim, Asma, Irshad Ahmad Arshad, Abdullah Mohammed Alomair, and Mohammed Ahmed Alomair. "Estimation of Hidden Logits Using Several Randomized Response Techniques." Symmetry 15, no. 9 (2023): 1636. http://dx.doi.org/10.3390/sym15091636.

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In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden lo
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Washington, Simon, Peter Congdon, Matthew G. Karlaftis, and Fred L. Mannering. "Bayesian Multinomial Logit." Transportation Research Record: Journal of the Transportation Research Board 2136, no. 1 (2009): 28–36. http://dx.doi.org/10.3141/2136-04.

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Gatignon, Hubert, and David J. Reibstein. "Pooling Logit Models." Journal of Marketing Research 23, no. 3 (1986): 281. http://dx.doi.org/10.2307/3151486.

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Uberti, Luca J. "Interpreting logit models." Stata Journal: Promoting communications on statistics and Stata 22, no. 1 (2022): 60–76. http://dx.doi.org/10.1177/1536867x221083855.

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The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata. I consider marginal effects, partial effects, (contrasts of) predictive margins, elasticities, and odds and risk ratios. I also show that interaction terms are typically easier to interpret in practice than implied by the recent literature on this topic.
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Dissertations / Theses on the topic "Logit"

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Aboutaleb, Youssef Medhat. "Learning structure in nested logit models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123208.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 67-68).<br>This work is about developing an estimation procedure for nested logit models that optim
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Mount, Robert E. (Robert Earl). "Measurement Disturbance Effects on Rasch Fit Statistics and the Logit Residual Index." Thesis, University of North Texas, 1997. https://digital.library.unt.edu/ark:/67531/metadc279376/.

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The effects of random guessing as a measurement disturbance on Rasch fit statistics (unweighted total, weighted total, and unweighted ability between) and the Logit Residual Index (LRI) were examined through simulated data sets of varying sample sizes, test lengths, and distribution types. Three test lengths (25, 50, and 100), three sample sizes (25, 50, and 100), two item difficulty distributions (normal and uniform), and three levels of guessing (no guessing (0%), 25%, and 50%) were used in the simulations, resulting in 54 experimental conditions. The mean logit person ability for each exper
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Bartels, Knut. "Testen der Spezifikation von multinomialen Logit-Modellen." Universität Potsdam, 2000. http://opus.kobv.de/ubp/volltexte/2006/845/.

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Eine Klasse von statistischen Tests zur Spezifikation von Logit-Modellen wird vorgestellt. Diese Tests sind Adaptionen von allgemeineren Spezifikationstests nichtlinearer Modelle. Die theoretische Anwendbarkeit der Tests wird dargelegt und die praktische Anwendbarkeit in Simulationsstudien erörtert. Eine konkrete Anwendung auf einen Datensatz und ein multinomiales Logit-Modell zur Produktwahl wird präsentiert.
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Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Bayesian Inference in the Multinomial Logit Model." Austrian Statistical Society, 2012. http://epub.wu.ac.at/5629/1/186%2D751%2D1%2DSM.pdf.

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The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.
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Williams, Andre. "Stereotype Logit Models for High Dimensional Data." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/147.

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Gene expression studies are of growing importance in the field of medicine. In fact, subtypes within the same disease have been shown to have differing gene expression profiles (Golub et al., 1999). Often, researchers are interested in differentiating a disease by a categorical classification indicative of disease progression. For example, it may be of interest to identify genes that are associated with progression and to accurately predict the state of progression using gene expression data. One challenge when modeling microarray gene expression data is that there are more genes (variables) t
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Casari, Priscila. "Retorno Esperado e Escolha Profissional: fatores associados à escolha da carreira dos alunos da Universidade de São Paulo." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-24082006-151746/.

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Nessa dissertação, procura-se avaliar se o retorno esperado do ensino superior é determinante para a escolha profissional e explicar como os salários, as habilidades e as características sócio-econômicas dos alunos da Universidade de São Paulo (USP), em conjunto, se associam à escolha da carreira. Para atingir esses objetivos, são utilizados dados da Fundação Universitária para o Vestibular (Fuvest) de 1995 e de 1996 e do Censo 2000. As carreiras são divididas em seis áreas de atuação – educação, ciências humanas, negócios, saúde, engenharia e matemática/ciências – e são estimados dois modelos
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Dogan, Deniz. "Numerical optimization for mixed logit models and an application." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28190.

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Thesis (M. S.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Anton Kleywegt; Committee Co-Chair: Alexander Shapiro; Committee Member: Charles Rosa; Committee Member: Shabbir Ahmed; Committee Member: Sigrun Andradottir.
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Powell, R. G. "Modelling take-over targets : a multinomial logit analysis." Thesis, University of Essex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307833.

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Xiao, Weizhong. "Nested logit model analysis of aggregate sales response." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10543.

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Chopra, Sameer. "Efficient scenario evaluations using the nested logit model." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11036.

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Books on the topic "Logit"

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Demaris, Alfred. Logit Modeling. SAGE Publications, Inc., 1992. http://dx.doi.org/10.4135/9781412984836.

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Borooah, Vani. Logit and Probit. SAGE Publications, Inc., 2002. http://dx.doi.org/10.4135/9781412984829.

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Chatterji, Monojit. Logit analysis and voluntary accounting disclosures. University of Essex, Dept. of Economics, 1985.

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Grønhaug, Kjell. Exploring income nonresponse: A logit model analysis. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1987.

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Steenburgh, Thomas. Substitution pattersn of the random coefficients logit. Harvard Business School, 2010.

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Vanhonacker, Wilfried R. What does the multinomial logit model really measure. INSEAD, 1993.

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Küchenhoff, Helmut. Logit- und Probitregression mit Fehlern in den Variabeln. A. Hain, 1990.

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Kannan, P. K. Modeling and testing structured markets: A nested logit approach. Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1990.

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Barros, Carlos Pestana. Low-wage employment in Portugal: A mixed logit approach. IZA, 2005.

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Stratton, Leslie S. A multinomial logit model of college stopout and dropout behavior. IZA, 2005.

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Book chapters on the topic "Logit"

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Páez, Antonio, and Geneviève Boisjoly. "Logit." In Use R! Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20719-8_4.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, et al. "Logit Model." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_494.

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Galata, Robert, Markus Wessler, Rita Augustin, and Sandro Scheid. "Logit-Modell." In Empirische Wirtschaftsforschung. Carl Hanser Verlag GmbH & Co. KG, 2013. http://dx.doi.org/10.3139/9783446437838.004.

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Grilli, Leonardo, and Carla Rampichini. "Ordered Logit Model." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2023.

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Grilli, Leonardo, and Carla Rampichini. "Ordered Logit Model." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_2023.

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Behnke, Joachim. "Das Logit-Modell." In Logistische Regressionsanalyse. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05082-5_3.

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Andersen, Erling B. "The Logit Model." In The Statistical Analysis of Categorical Data. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78817-8_8.

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Andersen, Erling B. "The Logit Model." In The Statistical Analysis of Categorical Data. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97225-6_8.

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Andersen, Erling B. "The Logit Model." In The Statistical Analysis of Categorical Data. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97353-6_8.

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Scheid, Sandro, and Stefanie Vogl. "Das Logit-Modell." In Data Science. Carl Hanser Verlag GmbH & Co. KG, 2021. http://dx.doi.org/10.3139/9783446470019.013.

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Conference papers on the topic "Logit"

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Sun, Shangquan, Wenqi Ren, Jingzhi Li, Rui Wang, and Xiaochun Cao. "Logit Standardization in Knowledge Distillation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01489.

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Tan, Zhengtao, Jimin Bao, Zhuangzhuang He, and Dacao Zhang. "Logit Adjustment for Imbalanced Cognitive Diagnosis." In 2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2025. https://doi.org/10.1109/cacml64929.2025.11010969.

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Fang, Zhehao, and Siyuan Meng. "Estimating travel mode choices of residents in Stockholm region based on multinomial logit model and mixed logit model." In International Conference on Smart Transportation and City Engineering (STCE 2024), edited by Zhengang Feng and Miroslava Mikusova. SPIE, 2025. https://doi.org/10.1117/12.3060925.

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Hu, Zixuan, Xiaotong Li, Shixiang Tang, Jun Liu, Yichun Hu, and Ling-Yu Duan. "LEAD: Exploring Logit Space Evolution for Model Selection." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02708.

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Dong, Ninghua, Zhen Liu, Xiang He, and Yiran Li. "Ternary Logit Distillation via Non-Target Classes Decomposition." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825011.

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Gan, Chong-Xin, Youzhi Tu, Zezhong Jin, Man-Wai Mak, and Kong Aik Lee. "Grouped Knowledge Distillation with Adaptive Logit Softening for Speaker Recognition." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887814.

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Gavin, Rory, Ming Cao, and Keith Paarporn. "An Analysis of Logit Learning with the r-Lambert Function." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886861.

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Luo, Yiyang, Ke Lin, Chao Gu, Jiahui Hou, Lijie Wen, and Luo Ping. "Lost in Overlap: Exploring Logit-based Watermark Collision in LLMs." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.37.

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Messner, Craig, and Tom Lippincott. "Transferring Extreme Subword Style Using Ngram Model-Based Logit Scaling." In Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.nlp4dh-1.24.

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Chen, Dubing, Yuming Shen, Haofeng Zhang, and Philip H. S. Torr. "Zero-Shot Logit Adjustment." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/114.

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Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses challenges in recognizing novel classes in the test phase. The development of generative models enables current GZSL techniques to probe further into the semantic-visual link, culminating in a two-stage form that includes a generator and a classifier. However, existing generation-based methods focus on enhancing the generator's effect while neglecting the improvement of the classifier. In this paper, we first analyze of two properties of the generated pseudo unseen samples: bias and homogeneity. Then, we perform variational
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Reports on the topic "Logit"

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Kirchhoff, William H. Logistic function data analysis program LOGIT. National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.88-3803.

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Lustig, Josh, Jerry Hausman, and Jinyong Hahn. Specification test on mixed logit models. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.5817.

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Bajari, Patrick, Jeremy Fox, Kyoo il Kim, and Stephen Ryan. The Random Coefficients Logit Model Is Identified. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w14934.

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Burda, Martin, Matthew C. Harding, and Jerry Hausman. A Bayesian mixed logit-probit model for multinomial choice. Institute for Fiscal Studies, 2008. http://dx.doi.org/10.1920/wp.cem.2008.2308.

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Dubé, Jean-Pierre, Ali Hortaçsu, and Joonhwi Joo. Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w26795.

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Weidner, Martin, and Bo E. Honoré. Moment Conditions for Dynamic Panel Logit Models with Fixed Effects. The IFS, 2020. http://dx.doi.org/10.1920/wp.cem.2020.3820.

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Weidner, Martin, Hyungsik Roger Moon, and Matthew Shum. Estimation of random coefficients logit demand models with interactive fixed effects. Institute for Fiscal Studies, 2012. http://dx.doi.org/10.1920/wp.cem.2012.0812.

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Shum, Matthew, Hyungsik Roger Moon, and Martin Weidner. Estimation of random coefficients logit demand models with interactive fixed effects. Institute for Fiscal Studies, 2014. http://dx.doi.org/10.1920/wp.cem.2014.2014.

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Moon, Hyungsik Roger, Matthew Shum, and Martin Weidner. Estimation of random coefficients logit demand models with interactive fixed effects. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.1217.

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Tirapat, Sunti. An investigation of default probability in Thailand. Chulalongkorn University, 2001. https://doi.org/10.58837/chula.res.2001.21.

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Using the sample of 100 most liquid companies listed in the Stock Exchange of Thailand during 1992-1999, the default probabilities from two approaches, the logit model and the KMV model, are calculated and compared. The results from the KMV model suggest that the default probabilities of financial institutions are higher than the probabilities of industrial companies. Moreover, the results from the KMV model confirm that the average default probabilities of the financial distressed firms in the 1997 financial crisis are higher than the average default probabilities of non-distressed firms. Com
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