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Thematische Bibliographien / Logit / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Logit“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

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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2

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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3

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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6

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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7

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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9

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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10

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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Gatignon, Hubert, and David J. Reibstein. "Pooling Logit Models." Journal of Marketing Research 23, no. 3 (1986): 281–85. http://dx.doi.org/10.1177/002224378602300308.

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The typical testing for equality of parameters across several response functions cannot be performed when the dependent variable is a probability. The authors investigate the pooling issues of the response function when the model is specified as a transformational logit. Various estimation methods are compared and an iterative generalized least squares procedure is proposed for testing the poolability of transformational logit response functions.

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COSTAIN, JAMES, and ANTON NAKOV. "Logit Price Dynamics." Journal of Money, Credit and Banking 51, no. 1 (2018): 43–78. http://dx.doi.org/10.1111/jmcb.12559.

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13

Kim, Dae-hak, and Hyeong-Chul Jeong. "Bootstrapping Logit Model." Communications for Statistical Applications and Methods 9, no. 1 (2002): 281–89. http://dx.doi.org/10.5351/ckss.2002.9.1.281.

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14

Gerken, J. "Generalized logit model." Transportation Research Part B: Methodological 25, no. 2-3 (1991): 75–88. http://dx.doi.org/10.1016/0191-2615(91)90015-b.

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15

Gilbert, François, Patrice Marcotte, and Gilles Savard. "Logit network pricing." Computers & Operations Research 41 (January 2014): 291–98. http://dx.doi.org/10.1016/j.cor.2013.05.010.

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16

Walker, Joan. "Mixed Logit (or Logit Kernel) Model: Dispelling Misconceptions of Identification." Transportation Research Record: Journal of the Transportation Research Board 1805, no. 1 (2002): 86–98. http://dx.doi.org/10.3141/1805-11.

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17

NAGAKURA, DAISUKE, and MASAHITO KOBAYASHI. "TESTING THE SEQUENTIAL LOGIT MODEL AGAINST THE NESTED LOGIT MODEL." Japanese Economic Review 60, no. 3 (2009): 345–61. http://dx.doi.org/10.1111/j.1468-5876.2008.00458.x.

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18

Sa'diyah, Halimatus, and Riza Yuli Rusdiana. "MEMAHAMI PENGGUNAAN REGRESI PADA DATA RESPON MULTINOMIAL UNTUK PENELITIAN SOSIAL DAN KEPENDIDIKAN." FIBONACCI: Jurnal Pendidikan Matematika dan Matematika 7, no. 2 (2022): 109. http://dx.doi.org/10.24853/fbc.7.2.109-126.

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Model logit multinomial digunakan untuk memodelkan sifat hubungan antara peubah respon politomus dan peubah penjelas. Ada dua model logit multinomial untuk peubah respon politomus yang strukturnya tak berurut: model logit terampat dan model logit bersyarat. Kedua model mempunyai struktur serupa, , j = 1, …, k, tetapi berbeda dalam karakteristik peubah penjelasnya. Logit terampat menggunakan karakteristik dari individu (subyek) sebagai peubah penjelas, sedang logit bersyarat menggunakan karakteristik dari pilihan individu. Tulisan ini ingin menyajikan penggunaan keduanya dalam model regresi yan

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Chintagunta, Pradeep K. "Heterogeneous Logit Model Implications for Brand Positioning." Journal of Marketing Research 31, no. 2 (1994): 304–11. http://dx.doi.org/10.1177/002224379403100212.

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The author discusses the implications of a heterogeneous logit model for brand positioning. The methodology presented is a restricted version of a mixture-of-logits model and obtains brand positions on a product-market map and the distribution of preferences across households while accounting for the effects of marketing variables on household brand choice behavior. The restriction involves imposing a factor structure on the covariance matrix of the distribution of intrinsic brand preferences. An empirical application of the methodology is presented using A.C. Nielsen household-level scanner p

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Small, Kenneth A., and Cheng Hsiao. "Multinomial Logit Specification Tests." International Economic Review 26, no. 3 (1985): 619. http://dx.doi.org/10.2307/2526707.

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21

Chintagunta, Pradeep K., and Alfred Demaris. "Logit Modeling: Practical Applications." Journal of Marketing Research 30, no. 3 (1993): 391. http://dx.doi.org/10.2307/3172890.

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22

ZHANG, JUNSEN, and SAUL D. HOFFMAN. "Discrete-Choice Logit Models." Sociological Methods & Research 22, no. 2 (1993): 193–213. http://dx.doi.org/10.1177/0049124193022002002.

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23

de Grange, Louis, Felipe González, Ignacio Vargas, and Juan Carlos Muñoz. "A polarized logit model." Transportation Research Part A: Policy and Practice 53 (July 2013): 1–9. http://dx.doi.org/10.1016/j.tra.2013.06.003.

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24

Shi, Haolun, and Guosheng Yin. "Boosting conditional logit model." Journal of Choice Modelling 26 (March 2018): 48–63. http://dx.doi.org/10.1016/j.jocm.2017.07.002.

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25

Andersen, Steffen, Glenn W. Harrison, Arne Risa Hole, Morten Lau, and E. Elisabet Rutström. "Non-linear mixed logit." Theory and Decision 73, no. 1 (2011): 77–96. http://dx.doi.org/10.1007/s11238-011-9277-0.

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26

Lo, Andrew W. "Logit versus discriminant analysis." Journal of Econometrics 31, no. 2 (1986): 151–78. http://dx.doi.org/10.1016/0304-4076(86)90046-1.

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27

de Palma, André, and Karim Kilani. "Switching in the logit." Economics Letters 88, no. 2 (2005): 196–202. http://dx.doi.org/10.1016/j.econlet.2005.01.018.

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28

Howel, Denise, and A. Demaris. "Logit Modelling: Practical Applications." Statistician 43, no. 1 (1994): 206. http://dx.doi.org/10.2307/2348949.

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29

Alós-Ferrer, Carlos, and Nick Netzer. "The logit-response dynamics." Games and Economic Behavior 68, no. 2 (2010): 413–27. http://dx.doi.org/10.1016/j.geb.2009.08.004.

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30

Daly, Andrew. "Estimating “tree” logit models." Transportation Research Part B: Methodological 21, no. 4 (1987): 251–67. http://dx.doi.org/10.1016/0191-2615(87)90026-9.

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31

Gilbert, François, Patrice Marcotte, and Gilles Savard. "Mixed-logit network pricing." Computational Optimization and Applications 57, no. 1 (2013): 105–27. http://dx.doi.org/10.1007/s10589-013-9585-0.

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32

Hsieh, Sung-Lin, Shaowei Ke, Zhaoran Wang, and Chen Zhao. "Logit neural-network utility." Journal of Economic Behavior & Organization 236 (August 2025): 107054. https://doi.org/10.1016/j.jebo.2025.107054.

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33

Nugraha, Jaka. "Studi Simulasi Model Nested Logit dan Paired Combinatorial Logit pada Respon Multinomial." Eksakta 13, no. 1-2 (2016): 63–71. http://dx.doi.org/10.20885/eksakta.vol13.iss1-2.art8.

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34

Sarkar, S. K., Habshah Midi, and Sohel Rana. "Adequacy of Multinomial Logit Model with Nominal Responses over Binary Logit Model." Trends in Applied Sciences Research 6, no. 8 (2011): 900–909. http://dx.doi.org/10.3923/tasr.2011.900.909.

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35

Wang, Jian, Srinivas Peeta, Xiaozheng He, and Jinbao Zhao. "Combined multinomial logit modal split and paired combinatorial logit traffic assignment model." Transportmetrica A: Transport Science 14, no. 9 (2018): 737–60. http://dx.doi.org/10.1080/23249935.2018.1431701.

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36

Ding, Hao, Ziwei Su, and Xiaoqian Liu. "A modified multinomial baseline logit model with logit functions having different covariates." Communications in Statistics - Simulation and Computation 49, no. 11 (2019): 2861–75. http://dx.doi.org/10.1080/03610918.2018.1529238.

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37

Yang, Heeyoon, Gahyung Kim, and Jee-Hyoung Lee. "Logit Averaging: Capturing Global Relation for Session-Based Recommendation." Applied Sciences 12, no. 9 (2022): 4256. http://dx.doi.org/10.3390/app12094256.

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Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommen

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Gao, Zijian, Shanhao Han, Xingxing Zhang, et al. "Maintaining Fairness in Logit-based Knowledge Distillation for Class-Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16763–71. https://doi.org/10.1609/aaai.v39i16.33842.

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Logit-based knowledge distillation (KD) is commonly used to mitigate catastrophic forgetting in class-incremental learning (CIL) caused by data distribution shifts. However, the strict match of logit values between student and teacher models conflicts with the cross-entropy (CE) loss objective of learning new classes, leading to significant recency bias (i.e. unfairness). To address this issue, we rethink the overlooked limitations of KD-based methods through empirical analysis. Inspired by our findings, we introduce a plug-and-play pre-process method that normalizes the logits of both the stu

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Wei, Fulu, Zhenggan Cai, Zhenyu Wang, Yongqing Guo, Xin Li, and Xiaoyan Wu. "Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model." Applied Sciences 11, no. 17 (2021): 7819. http://dx.doi.org/10.3390/app11177819.

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The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit mo

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Jones, Stewart, and David A. Hensher. "Predicting Firm Financial Distress: A Mixed Logit Model." Accounting Review 79, no. 4 (2004): 1011–38. http://dx.doi.org/10.2308/accr.2004.79.4.1011.

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Over the past three decades the literature on financial distress prediction has largely been confined to simple multiple discriminant analysis, binary logistic or probit analysis, or rudimentary multinomial logit models (MNL). There has been a conspicuous absence of modeling innovation in this literature as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. In particular, there has been no recognition of major advances in discrete choice modeling over the last 15 years, which has increasingly relaxed behaviorally question

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Deauvieau, Jérôme. "Comparer les résultats d’un modèle logit dichotomique ou polytomique entre plusieurs groupes à partir des probabilités estimées." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 142, no. 1 (2019): 7–31. http://dx.doi.org/10.1177/0759106319834657.

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Il est très fréquent en sociologie de chercher à comparer les effets d’une variable d’un même modèle logit réalisé sur plusieurs groupes. La manière la plus courante de réaliser cette opération a souvent consisté à comparer directement la valeur du coefficient logit de la variable d’intérêt. Or, cette pratique soulève de sérieuses objections méthodologiques dans le cas de la modélisation logit. Ce point a été mis en évidence dans la littérature sociologique par Allison à la fin des années 1990. De la fin des années 2000 jusqu’à aujourd’hui, un nombre conséquent de travaux ont été consacrés à c

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Opic, Sinisa. "Specifics of logit and probit regression in education sciences - why wouldn't we use it?" Cypriot Journal of Educational Sciences 15, no. 6 (2020): 1557–68. http://dx.doi.org/10.18844/cjes.v15i6.5305.

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Regression is one of the dominant analysis methods used in the social sciences and educational sciences. There are different regression methods based on the type of research that is being conducted. The probit and logit regression models are regression methods which are being used recently by most researchers. However, their interpretations are not straightfoward and most researchers end up misinterepreting the results from the probit and logit regression models. This research therefore aims to examine the differences between the probit and logit models, in comparison with other linear regress

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Liu, Bingquan, Yonghong Zhang, and Wei Du. "A Simplified C-Logit Stochastic User Equilibrium Model on Bimodal Transportation Network." Mathematical Problems in Engineering 2020 (April 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/3702965.

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This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to sa

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Amalahu, Christian Chinenye, Joy Chioma Nwabueze, and Chibueze Barnabas Ekeadinotu. "EGG QUALITY ASSESSMENT: A MODEL COMPARISON APPROACH USING BAYESIAN MIXED LOGIT, MIXED LOGIT, LOGISTIC REGRESSION AND MULTINOMIAL REGRESSION MODELS." FUDMA JOURNAL OF SCIENCES 9, no. 5 (2025): 110–13. https://doi.org/10.33003/fjs-2025-0905-3656.

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This study compares the performance of Bayesian mixed logit, mixed logit, logistic regression, and multinomial regression models in analyzing egg quality. The results show that the Bayesian mixed logit model outperforms traditional models, with egg weights, shell thickness, and shape index emerging as significant determinants of egg quality. The Bayesian mixed logit model's superior performance is evident in its lower AIC, DIC, RMSE, and MAE values. These findings have implications for the poultry industry, highlighting the importance of considering complex relationships between egg quality tr

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Prashker, J. N., and S. Bekhor. "Investigation of Stochastic Network Loading Procedures." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (1998): 94–102. http://dx.doi.org/10.3141/1645-12.

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The network loading process of stochastic traffic assignment is investigated. A central issue in the assignment problem is the behavioral assumption governing route choice, which concerns the definition of available routes and the choice model. These two problems are addressed and reviewed. Although the multinomial logit model can be implemented efficiently in stochastic network loading algorithms, the model suffers from theoretical drawbacks, some of them arising from the independence of irrelevant alternatives property. As a result, the stochastic loading on routes that share common links is

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Sándor, Zsolt. "Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums." Acta Universitatis Sapientiae, Economics and Business 1, no. 1 (2013): 85–108. http://dx.doi.org/10.2478/auseb-2014-0006.

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Abstract We study Monte Carlo simulation in some recent versions of random coefficient logit models that contain large sums of expressions involving multivariate integrals. Such large sums occur in the random coefficient logit with demographic characteristics, the random coefficient logit with limited consumer information and the design of choice experiments for the panel mixed logit. We show that certain quasi-Monte Carlo methods, that is, so-called (t, m, s)-nets, provide improved performance over pseudo-Monte Carlo methods in terms of bias, standard deviation and root mean squared error.

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Schmidheiny, Kurt, and Marius Brülhart. "On the equivalence of location choice models: Conditional logit, nested logit and Poisson." Journal of Urban Economics 69, no. 2 (2011): 214–22. http://dx.doi.org/10.1016/j.jue.2010.09.004.

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Czine, Péter. "A diszkrét választási kísérlet elméleti áttekintése." International Journal of Engineering and Management Sciences 5, no. 1 (2020): 62–73. http://dx.doi.org/10.21791/ijems.2020.1.6.

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Jelen tanulmány egy preferenciaértékelési módszer, a diszkrét választási kísérlet bemutatását célozza. Az olvasó információt nyerhet a módszer hátterével, alkalmazási területeivel (külön hangsúlyt fektetve az egészség-gazdaságosság területére), folyamatával és modelljei közül hárommal (multinomiális logit, random paraméterű logit, nested logit) kapcsolatosan. A szakirodalmi elemzés eredményei alapján elmondható, hogy a módszer rendkívül ígéretes, viszont számos területen még fejlesztésre szorul.

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Kajita, Yoshitaka. "Proposals of AHP-Type of Disaggregate Logit Model and Improved AHP Model in Modal Choice." Applied Mechanics and Materials 409-410 (September 2013): 1166–72. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1166.

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This paper aims at constructing an improved model of modal choice by the use of both of AHP model and disaggregate logit model. Firstly, using AHP structure model, the importance of various factors in modal choice is investigated, and relevant choosing consciousness is scored. Secondly, a disaggregate logit model for modal choice with AHP scores and physical factors is proposed. Thirdly, weights of items in AHP model for modal choice are reversibly estimated by the use of the proposed AHP-type of disaggregate logit model. Direct AHP model, AHP-type of disaggregate logit model and improved AHP

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Schaak, Henning, and Oliver Mußhoff. "Public Preferences for Pasture Landscapes and the Role of Scale Heterogeneity." German Journal of Agricultural Economics 70, no. 3 (2021): 182–91. http://dx.doi.org/10.30430/70.2021.3.182-191.

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The paper investigates the influence of different model specifications for interpreting the results of discrete choice experiments when investigating heterogeneous public landscape preferences. Comparing model specifications based on the Mixed Multinomial Logit and the Generalized Multinomial Logit Model reveals that the parameter estimates appear qualitatively comparable. Still, a more in-depth investigation of the conditional estimate distributions of the sample show that parameter interactions in the Generalized Multinomial Logit Model lead to different interpretations compared to the Mixed

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