Relevant bibliographies by topics / Multinomial logit model

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

Academic literature on the topic 'Multinomial logit model'

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

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Journal articles on the topic "Multinomial logit model"

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Bonett, Douglas G. "The negative multinomial logit model." Communications in Statistics - Theory and Methods 14, no. 7 (1985): 1713–17. http://dx.doi.org/10.1080/03610928508829007.

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Jakaitienė, Audronė. "Multinomial logit death forecasting model." Lietuvos matematikos rinkinys, no. III (December 17, 1999): 367–69. http://dx.doi.org/10.15388/lmd.1999.35662.

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The multinomial regression logit model is analyzed. The algorithms and software are made for this model in order to get estimation of parameters. Calculations are made using generated population of 1000 cases.
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Marsili, Matteo. "On the multinomial logit model." Physica A: Statistical Mechanics and its Applications 269, no. 1 (1999): 9–15. http://dx.doi.org/10.1016/s0378-4371(99)00074-6.

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Kim, Jin-Hyung, and Mijung Kim. "Two-stage multinomial logit model." Expert Systems with Applications 38, no. 6 (2011): 6439–46. http://dx.doi.org/10.1016/j.eswa.2010.11.057.

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Su, Peng, Yuan Liu, and Lingyun Zhao. "General Deep Multinomial Logit Model." Computing and Informatics 41, no. 5 (2022): 1240–59. http://dx.doi.org/10.31577/cai_2022_5_1240.

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Bashir, Shaheena, and Edward M. Carter. "Penalized multinomial mixture logit model." Computational Statistics 25, no. 1 (2009): 121–41. http://dx.doi.org/10.1007/s00180-009-0165-9.

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김병우. "Knowledge Protection Selection: Multinomial Logit Model." Journal of Product Research 33, no. 6 (2015): 55–62. http://dx.doi.org/10.36345/kacst.2015.33.6.006.

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Rajaonarison, Dominique, Denis Bolduc, and Hubert Jayet. "The K-deformed multinomial logit model." Economics Letters 86, no. 1 (2005): 13–20. http://dx.doi.org/10.1016/j.econlet.2004.05.002.

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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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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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Dissertations / Theses on the topic "Multinomial logit model"

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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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Hendricks, Nathan. "Estimating irrigation water demand with a multinomial logit selectivity model." Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/326.

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Nagel, Herbert, and Reinhold Hatzinger. "Diagnostics in some Discrete Choice Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1990. http://epub.wu.ac.at/506/1/document.pdf.

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Discrete choice models form a class of models widely used in econometrics for modelling the individual choice from a finite set of alternatives. The most widely used model is the multinomial logit model, implicitly assuming independence of irrelevant alternatives. A generalization is the nested multinomial logit model, relaxing this strong assurnp tion. Viewing both models as nonlinear regression models a set of diagnostics is derived. This includes a hat matrix, measures of leverage, influence and residuals and an approximation to the parameters for case deletion. In an example for the multin
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Sikder, Sujan. "An Analysis of the Travel Patterns and Preferences of the Elderly." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3469.

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The number of elderly is increasing; to meet their transportation needs, it is important to clearly understand their travel patterns and preferences. Since travel patterns and preferences depend on socio-demographic and other factors, it is essential to identify these factors first to understand the travel behavior of the elderly. The main purpose of this thesis is to analyze the travel patterns and preferences of the elderly age 65 and above using 2009 National Household Travel Survey (NHTS) data. This thesis presents a detailed descriptive analysis of 2009 NHTS data to understand the travel
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Choi, Eugene. "Adaptive Reuse of Religious Buildings in the U.S: Determinants of Project Outcomes and the Role of Tax Credits." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1276711021.

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Reyes, Delfos Enrique López. "Um procedimento para determinação de matriz origem-destino para diferentes modos: método indireto baseado em modelo de escolha discreta." Universidade de São Paulo, 1999. http://www.teses.usp.br/teses/disponiveis/18/18137/tde-26012018-184716/.

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Apresenta-se um procedimento para estimativa da matriz origem-destino (O/D) a partir de um modelo de escolha discreta em combinação com uma matriz O/D de transporte público, a qual pode ser estimada de maneira relativamente fácil e rápida. O modelo de escolha discreta utilizado para realizar a divisão modal é o modelo logit multinomial. A calibração do modelo logit é realizada com base em 505 observações e considerando três situações: a população de viajantes sem segmentação, com segmentação segundo posse de automóvel no domicílio, e com segmentação segundo a distância de viagem. Analisa-se a
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ZHANG, LEI, and XI YOU. "The Choice of STIGA Table Tennis Blades : Evidence from China." Thesis, Högskolan Dalarna, Nationalekonomi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4879.

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The purpose of this paper is to investigate how individuals with different characteristics make their choice-decisions when consuming STIGA table tennis blades, which are combinations of various attributes, such as price, control, attack, etc. It is expected that the general trend of choice behavior on this special commodity can be, at least to some extent, revealed. Data were collected using questionnaires sent to registered members of a table tennis club in China. The questionnaires included information and questions about individuals’ monthly income levels, ages, technique styles, etc. A mu
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WANG, Yanan. "Exploring online brand choice at the SKU level : the effects of internet-specific attributes." Digital Commons @ Lingnan University, 2004. https://commons.ln.edu.hk/mkt_etd/13.

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E-Commerce research shows that existing studies on online consumer choice behavior has focused on comparative studies of channel or store choice (online or offline), or online store choice (different e-tailers). Relatively less effort has been devoted to consumers’ online brand choice behavior within a single e-tailer. The goal of this research is to model online brand choice, including generating loyalty variables, setting up base model, and exploring the effects of Internet-specific attributes, i.e., order delivery, webpage display and order confirmation, on online brand choice at the SKU le
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Barus, Lita Sari. "Contribution to the intercity modal choise considering the intracity transport systems : application of an adapted mixed multinomial Logit model for the Jakarta-Bandung corridor." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2223/document.

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Ce travail de recherche traite de la problématique des transports dans les villes d’Indonésie, Jakarta et Bandung, mais également de la grande concurrence modale du trajet Jakarta-Bandung et Bandung-Jakarta. Les préférences des passagers sont des variables très importantes à connaître en raison de leurs impacts pour choisir un mode de transport parmi d’autres. Dans les transports, le modèle Logit est largement utilisé comme une méthode pour aborder la problématique du choix de transport multimodal comportant de multiples variables, mais dans la présente recherche, ces modèles ne sont pas appro
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Augustin, Bertho. "Comparison of Different Approaches to Estimating Budgets for Kuhn-Tucker Demand Systems: Applications for Individuals' Time-Use Analysis and Households' Vehicle Ownership and Utilization Analysis." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5174.

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This thesis compares different approaches to estimating budgets for Kuhn-Tucker (KT) demand systems, more specifically for the multiple discrete-continuous extreme value (MDCEV) model. The approaches tested include: (1) The log-linear regression approach (2) The stochastic frontier regression approach, and (3) arbitrarily assumed budgets that are not necessarily modeled as a function of decision maker characteristics and choice-environment characteristics. The log-linear regression approach has been used in the literature to model the observed total expenditure as way of estimating budgets for
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Books on the topic "Multinomial logit model"

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

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

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Wright, Peter. Union membership and coverage: A study using the nested multinomial logit model. University of Nottingham, 1994.

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Poterba, James M. Unemployment benefits, labor market transitions, and spurious flows: A multinomial logit model with errors in classification. National Bureau of Economic Research, 1993.

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Otok, Bambang Widjanarko. Multinomial logit model pada faktor-faktor yang mempengaruhi tingkat hidup pekerja di sektor industri pengolahan Propinsi Jawa Tengah: Laporan penelitian. Jurusan Statistik, Fakultas Matematika dan Ilmu Pengatahuan [i.e. Pengetahuan] Alam, Lembaga Penelitian, Institut Teknologi Sepuluh Nopember, 1996.

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Alexopoulou, Maria. The use and evaluation of mathematical models for predicting consumer behaviour: Multinomial logit model versus conjoint analysis. Manchester Business School, MBA, 1994.

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Süzülmüs, Emre Yakut Seval. Multinominal Logit Model Analiziyle Konutlarda Isinma Sistemi Seçimi Osmaniye Ili Hanehalki Uygulamasi. Gazi Kitabevi, 2021.

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Logit and Probit: Ordered and Multinomial Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc, 2001.

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Glasgow, Garrett, and R. Michael Alvarez. Discrete Choice Methods. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0022.

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This article describes the statistical models commonly used to study discrete choices. It concentrates on the ‘basic’ discrete choice models, and the theoretical choice situations that lead to these models. Specifically the choice situation addressed include: the ordered choice situation and the unordered choice situation. In addition, the article discusses two extensions of the basic discrete choice models commonly seen in political science research — models allowing for heteroskedasticity in the choices made across political agents (such as the heteroskedastic probit), and models that estima
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Ciríaco, Juliane da Silva, Otoniel Rodrigues dos Anjos Junior, Sandro Pereira Silva Silva, Julyan Gleyvison Machado Gouveia Lins Lins, and Cinthia Barbosa Sousa Sousa. Texto para Discussão 3121. Instituto de Pesquisa Econômica Aplicada (Ipea), 2025. https://doi.org/10.38116/td3121-port.

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Este estudo tem como objetivo geral analisar a prevalência de insegurança alimentar e nutricional (IAN) na população brasileira, diferenciando sua incidência entre os ambientes urbanos e rurais. Para isso, utilizou-se um modelo logit multinomial com dados da Pesquisa Nacional por Amostra de Domicílios Contínua (PNAD Contínua) de 2023. Identificou-se que fatores como escolaridade do chefe do domicílio e renda per capita influenciam positivamente a condição alimentar da população, enquanto lares monoparentais chefiados por mulheres e com mais crianças residentes enfrentam níveis maiores de IAN.
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Book chapters on the topic "Multinomial logit model"

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Bartels, K., Y. Boztug, and M. Müller. "Testing the Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57280-7_32.

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Börsch-Supan, Axel. "The Nested Multinomial Logit Model." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-45633-6_4.

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Christ, Steffen. "Multinomial Logit Model for Low-Cost Travel Choice." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_11.

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Mariel, Petr, Danny Campbell, Erlend Dancke Sandorf, Jürgen Meyerhoff, Ainhoa Vega-Bayo, and Rebecca Blevins. "Random Utility Models: Theoretical Background." In The Economics of Non-Market Goods and Resources. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89338-4_3.

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Abstract This chapter provides an overview of the random utility maximisation (RUM) model, reviewing its assumptions and delving into its theoretical foundations. We explore the multinomial logit (MNL) model, which is widely used in DCE literature due to its many advantages. These include its robustness, ease of estimation, and straightforward interpretation, with closed-form choice probabilities that simplify calculations. We also review advanced specifications of the mixed logit model, including the random parameters logit (RP-MXL) and latent class (LC-MXL) models, and walk you through the m
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Theil, Henri. "A Multinomial Extension of the Linear Logit Model." In Advanced Studies in Theoretical and Applied Econometrics. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2546-8_11.

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Mariel, Petr, Danny Campbell, Erlend Dancke Sandorf, Jürgen Meyerhoff, Ainhoa Vega-Bayo, and Rebecca Blevins. "Estimation." In The Economics of Non-Market Goods and Resources. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89338-4_9.

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Abstract This chapter covers the estimation of discrete choice models. We begin with a simple Multinomial Logit (MNL) model (with and without unobserved preference heterogeneity) and highlight the importance of optimisation diagnostics, review goodness-of-fit indicators and the identification of outliers, and provide insights into the interpretation of estimates. Progressing to more advanced topics, we continue with the Random Parameters Mixed Logit (RP-MXL) model, addressing both uncorrelated and correlated coefficients, and discuss different parameterisation approaches such as the preference
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Street, Deborah J., and Leonie Burgess. "Designs for Choice Experiments for the Multinomial Logit Model." In Design and Analysis of Experiments. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118147634.ch10.

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Kasper, Daniel, Ali Ünlü, and Bernhard Gschrey. "Sensitivity Analyses for the Mixed Coefficients Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01595-8_42.

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Fiona, Sauerbier, Haase Knut, Kück Jannis, and Spindler Martin. "Choice-Based Optimization Under a High-Dimensional Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54468-2_11.

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Adams, Raymond J., and Margaret L. Wu. "The Mixed-Coefficients Multinomial Logit Model: A Generalized Form of the Rasch Model." In Multivariate and Mixture Distribution Rasch Models. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_4.

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Conference papers on the topic "Multinomial logit model"

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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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Li, Hua-Min, and Hai-Jun Huang. "The Multinomial Logit Model with Last Choice Feedback." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.308.

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Amin, Ahmed, Khaled Hamad, and Mohsin Balwan. "Modeling Sharjah's Travel Mode Choice Using Multinomial Logit Regression Model." In 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). IEEE, 2022. http://dx.doi.org/10.1109/icetsis55481.2022.9888851.

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Suhua, Chen, Zhang Xiaojun, and Ding Jianming. "Feasibility on HOV lanes based on multinomial logit model." In 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE). IEEE, 2010. http://dx.doi.org/10.1109/icife.2010.5609269.

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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}. 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, th
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Ali, Ismail Abou, Francesco Rouhana, Rasha Zein Eddine, and Samer al Zoer. "Estimating Travel Mode Choices of NDU Students Using Multinomial Logit Model." In 2019 Fourth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). IEEE, 2019. http://dx.doi.org/10.1109/actea.2019.8851118.

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Wen, Chengwei, Junhong Hu, Wenjie Zhang, and Rui Tang. "Research on shared parking intention based on multinomial logit model." In ICIT 2021: IoT and Smart City. ACM, 2021. http://dx.doi.org/10.1145/3512576.3512673.

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Pu, Davide, Antonio A. Trani, and Nicolas Hinze. "Zip Vehicle Commuter Aircraft Demand Estimate: a Multinomial Logit Mode Choice Model." In 14th AIAA Aviation Technology, Integration, and Operations Conference. American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-2411.

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Liang, Wei, Jianming Hu, Yi Zhang, and Ziwei Wang. "Multinomial logit model-based parking choice in a mall at city." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531002.

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Frühwirth-Schnatter, Sylvia, Christoph Pamminger, Rudolf Winter-Ebmer, et al. "Model-based Clustering of Categorical Time Series with Multinomial Logit Classification." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3498286.

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Reports on the topic "Multinomial logit model"

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

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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, 2018. http://dx.doi.org/10.1920/wp.cem.2018.2918.

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Zhang, Yongping, Carol Kachadoorian, Wen Cheng, and Edward Clay. Enhancing Older Adults’ Mobility in Active Living and Tiered Living Communities. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2159.

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The U.S. population is aging rapidly. As people get older, they increasingly face issues such as increased susceptibility to injuries and the need to be assisted with many day-to-day activities. Older adults have the opportunity to opt-in to live in an older adult community (OAC) based on their needs and capabilities. This study comprehensively reviews existing governing development regulations and design criteria related to the older adults’ communities, conducts surveys among people involved with some of these communities in California, and recommends improvements to community design for act
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Greene, D. L. RUMS: a PC-based Fortran program for estimating consumer surplus charges using multinomial logic and hedonic demand models. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/5381050.

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