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: 11 March 2023

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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 (June 28, 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 positive/negative data augmentation and loss variations incurred by logit perturbation, a new method is proposed to explicitly learn to perturb logits. A comparative analysis is conducted for the perturbations used in our and existing methods. Extensive experiments on benchmark image classification data sets and their long-tail versions indicated the competitive performance of our learning method. In addition, existing methods can be further improved by utilizing our method.
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Bushway, Shawn D. "Is There Any Logic to Using Logit." Criminology & Public Policy 12, no. 3 (August 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 (July 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. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.
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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 (May 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 (June 2013): 34–37. http://dx.doi.org/10.1145/2509013.2509018.

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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 (January 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 (August 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 (March 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 (August 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 (October 7, 2018): 43–78. http://dx.doi.org/10.1111/jmcb.12559.

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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.
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-68).
This work is about developing an estimation procedure for nested logit models that optimizes over the nesting structure in addition to the model parameters. Current estimation practices require an a priori specification of a nesting structure. We formulate the problem of learning an optimal nesting structure as a mixed integer nonlinear programming (MINLP) optimization problem and solve it using a variant of the linear outer approximation algorithm. We demonstrate that it is indeed possible to recover the nesting structure directly from the data by applying our method to synthetic and real datasets.
by Youssef Medhat Aboutaleb.
S.M. in Transportation
S.M.
S.M.inTransportation Massachusetts Institute of Technology, Department of Civil and Environmental Engineering
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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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 experiment was +1. Each experimental condition was simulated once in an effort to approximate what could happen on the single administration of a four option per item multiple choice test to a group of relatively high ability persons. Previous research has shown that varying item and person parameters have no effect on Rasch fit statistics. Consequently, these parameters were used in the present study to establish realistic test conditions, but were not interpreted as effect factors in determining the results of this study.
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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) than there are observations. In addition, the genes usually demonstrate a complex variance-covariance structure. Therefore, modeling a categorical variable reflecting disease progression using gene expression data presents the need for methods capable of handling an ordinal outcome in the presence of a high dimensional covariate space. In this research we present a method that combines the stereotype regression model (Anderson, 1984) with an elastic net penalty (Friedman et al., 2010) as a method capable of modeling an ordinal outcome for high-throughput genomic datasets. Results from applying the proposed method to both simulated and gene expression data will be reported and the effectiveness of the proposed method compared to a univariable and heuristic approach will be discussed.
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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 de escolha discreta: logit multinomial e logit condicional. Na estimação do logit multinomial são utilizadas apenas variáveis relativas às características dos indivíduos e no logit condicional inclui-se o salário médio de cada área de atuação. Os resultados indicam que o retorno esperado não tem efeito sobre a escolha profissional.
This research evaluate if higher education expected return is determinant for the professional choice and how the interaction between wage, abilities and socio-economic characteristics of Universidade de São Paulo’s students, all together, are associated to the career choice. Data used is from Fundação Universitária para o Vestibular (Fuvest) 1995 and 1996’s questionnaires and from Censo 2000. The careers are grouped in six concentration areas – education, human sciences, management, health, engineer, math/sciences – and two discrete choice models are estimated: multinomial logit and conditional logit. Multinomial logit contains only variables specific to individuals and the average wage of each concentration area is included in conditional logit estimation. The results show that expected return doesn’t have effect over the professional choice.
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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.
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. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1992. http://dx.doi.org/10.4135/9781412984836.

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Borooah, Vani. Logit and Probit. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2002. http://dx.doi.org/10.4135/9781412984829.

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Logit modeling: Practical applications. Newbury Park: Sage Publications, 1992.

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

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Grønhaug, Kjell. Exploring income nonresponse: A logit model analysis. [Urbana, Ill.]: 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. [Boston]: Harvard Business School, 2010.

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The LOGIT model: An introduction for economists. London: E. Arnold, 1991.

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

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

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

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Book chapters on the topic "Logit"

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Páez, Antonio, and Geneviève Boisjoly. "Logit." In Use R!, 85–104. Cham: 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, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Logit Model." In Encyclopedia of Machine Learning, 631. Boston, MA: 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, 118–51. München: 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, 4510–13. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2023.

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Behnke, Joachim. "Das Logit-Modell." In Logistische Regressionsanalyse, 23–35. Wiesbaden: 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, 239–68. Berlin, Heidelberg: 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, 239–68. Berlin, Heidelberg: 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, 239–68. Berlin, Heidelberg: 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, 276–96. München: Carl Hanser Verlag GmbH & Co. KG, 2021. http://dx.doi.org/10.3139/9783446470019.013.

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Grilli, Leonardo, and Carla Rampichini. "Ordered Logit Model." In Encyclopedia of Quality of Life and Well-Being Research, 1–4. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-69909-7_2023-2.

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Conference papers on the topic "Logit"

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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}. California: 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 Bayesian inference to back-derive the evaluation metrics, which reflects the balance of the seen and unseen classes. As a consequence of our derivation, the aforementioned two properties are incorporated into the classifier training as seen-unseen priors via logit adjustment. The Zero-Shot Logit Adjustment further puts semantic-based classifiers into effect in generation-based GZSL. Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative Zero-Shot Learning frameworks. Our codes are available on https://github.com/cdb342/IJCAI-2022-ZLA.
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Bang, Duhyeon, Kyungjune Baek, Jiwoo Kim, Yunho Jeon, Jin-Hwa Kim, Jiwon Kim, Jongwuk Lee, and Hyunjung Shim. "Logit Mixing Training for More Reliable and Accurate Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/390.

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When a person solves the multi-choice problem, she considers not only what is the answer but also what is not the answer. Knowing what choice is not the answer and utilizing the relationships between choices, she can improve the prediction accuracy. Inspired by this human reasoning process, we propose a new training strategy to fully utilize inter-class relationships, namely LogitMix. Our strategy is combined with recent data augmentation techniques, e.g., Mixup, Manifold Mixup, CutMix, and PuzzleMix. Then, we suggest using a mixed logit, i.e., a mixture of two logits, as an auxiliary training objective. Since the logit can preserve both positive and negative inter-class relationships, it can impose a network to learn the probability of wrong answers correctly. Our extensive experimental results on the image- and language-based tasks demonstrate that LogitMix achieves state-of-the-art performance among recent data augmentation techniques regarding calibration error and prediction accuracy.
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Mizutani, Kaori, and Takamasa Akiyama. "A Logit Model for Modal Choice with a Fuzzy Logic Utility Function." In Second International Conference on Transportation and Traffic Studies (ICTTS ). Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40503(277)49.

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Bai, Han, Fengrui Sun, Yuan Zhang, and Yangdong Zhao. "LOGIT-Based Road Network Optimization Model." In The Twelfth COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412442.018.

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Wu, Huiyu, and Diego Klabjan. "Logit-based Uncertainty Measure in Classification." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9672066.

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Keren, Gil, Sivan Sabato, and Björn Schuller. "A Walkthrough for the Principle of Logit Separation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/861.

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We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is a binary classification task of determining whether the given example belongs to a specific class. We define the Single Logit Classification (SLC) task: training the network so that at test-time, it would be possible to accurately identify whether the example belongs to a given class in a computationally efficient manner, based only on the output logit for this class. We propose a natural principle, the Principle of Logit Separation, as a guideline for choosing and designing loss functions that are suitable for SLC. We show that the Principle of Logit Separation is a crucial ingredient for success in the SLC task, and that SLC results in considerable speedups when the number of classes is large.
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Ranganathan, Ananth. "Semantic Scene Segmentation using Random Multinomial Logit." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.59.

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Auletta, Vincenzo, Diodato Ferraioli, Francesco Pasquale, and Giuseppe Persiano. "Metastability of Logit Dynamics for Coordination Games." In Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2012. http://dx.doi.org/10.1137/1.9781611973099.80.

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Ou, Mingdong, Nan Li, Shenghuo Zhu, and Rong Jin. "Multinomial Logit Bandit with Linear Utility Functions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/361.

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Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a K-cardinality subset from N candidate items, and receives a reward which is governed by a multinomial logit (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon T. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, there have developed some algorithms by exploiting specific characteristics of the MNL model, but all of them estimate the parameters of MNL model separately and incur a regret bound which is not preferred for large candidate set size N. In this paper, we consider the linear utility MNL choice model whose item utilities are represented as linear functions of d-dimension item features, and propose an algorithm, titled LUMB, to exploit the underlying structure. It is proven that the proposed algorithm achieves regret which is free of candidate set size. Experiments show the superiority of the proposed algorithm.
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Pan, Zaisheng, Xuanhao Zhou, and Peng Chen. "Characteristic of transportation network with LOGIT model." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7979113.

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Reports on the topic "Logit"

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Kirchhoff, William H. Logistic function data analysis program LOGIT. Gaithersburg, MD: 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, December 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. Cambridge, MA: National Bureau of Economic Research, April 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, August 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. Cambridge, MA: National Bureau of Economic Research, February 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, July 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, March 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, April 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, February 2017. http://dx.doi.org/10.1920/wp.cem.2017.1217.

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Nesheim, Lars, and Joel L. Horowitz. Using penalized likelihood to select parameters in a random coefficients multinomial logit model. The IFS, October 2019. http://dx.doi.org/10.1920/wp.cem.2019.5019.

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