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Journal articles on the topic 'Multinomial logit model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Tutz, Gerhard. "Uncertain Choices: The Heterogeneous Multinomial Logit Model." Sociological Methodology 51, no. 1 (2021): 86–111. http://dx.doi.org/10.1177/0081175020979689.

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In this article, a modeling strategy is proposed that accounts for heterogeneity in nominal responses that is typically ignored when using common multinomial logit models. Heterogeneity can arise from unobserved variance heterogeneity, but it may also represent uncertainty in choosing from alternatives or, more generally, result from varying coefficients determined by effect modifiers. It is demonstrated that the bias in parameter estimation in multinomial logit models can be substantial if heterogeneity is present but ignored. The modeling strategy avoids biased estimates and allows researche

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13

Oppenheim, P. P., and T. R. L. Fry. "A MULTINOMIAL LOGIT MODEL OF FLORAL CHOICE." Acta Horticulturae, no. 524 (March 2000): 131–40. http://dx.doi.org/10.17660/actahortic.2000.524.15.

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14

Camminatie, I., and A. Lucadamo. "Estimating Multinomial Logit Model with Multicollinear Data." Asian Journal of Mathematics & Statistics 3, no. 2 (2010): 93–101. http://dx.doi.org/10.3923/ajms.2010.93.101.

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15

Fomby, Thomas B., and James E. Pearce. "Standard errors in the multinomial logit model." Communications in Statistics - Theory and Methods 15, no. 8 (1986): 2555–68. http://dx.doi.org/10.1080/03610928608829268.

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16

Wulff, Jesper N. "Interpreting Results From the Multinomial Logit Model." Organizational Research Methods 18, no. 2 (2014): 300–325. http://dx.doi.org/10.1177/1094428114560024.

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17

Łukawska, Mirosława, Anders Fjendbo Jensen, and Filipe Rodrigues. "Context-aware Bayesian mixed multinomial logit model." Journal of Choice Modelling 54 (March 2025): 100536. https://doi.org/10.1016/j.jocm.2024.100536.

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18

Rekkas, M. "Approximate inference for the multinomial logit model." Statistics & Probability Letters 79, no. 2 (2009): 237–42. http://dx.doi.org/10.1016/j.spl.2008.08.004.

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19

Cramer, J. S., and G. Ridder. "Pooling states in the multinomial logit model." Journal of Econometrics 47, no. 2-3 (1991): 267–72. http://dx.doi.org/10.1016/0304-4076(91)90102-j.

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20

Castro, Marisol, Francisco Martínez, and Marcela A. Munizaga. "Estimation of a constrained multinomial logit model." Transportation 40, no. 3 (2012): 563–81. http://dx.doi.org/10.1007/s11116-012-9435-4.

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21

Adams, Raymond J., Mark Wilson, and Wen-chung Wang. "The Multidimensional Random Coefficients Multinomial Logit Model." Applied Psychological Measurement 21, no. 1 (1997): 1–23. http://dx.doi.org/10.1177/0146621697211001.

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22

Aggarwal, Manish. "Preferences-based learning of multinomial logit model." Knowledge and Information Systems 59, no. 3 (2018): 523–38. http://dx.doi.org/10.1007/s10115-018-1215-9.

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23

Chu, Chia-Shang J., Nan Liu, and Lina Zhang. "Significance test in nonstationary multinomial logit model." Economics Letters 143 (June 2016): 94–98. http://dx.doi.org/10.1016/j.econlet.2016.03.022.

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24

van Ophem, Hans, and Arthur Schram. "Sequential and multinomial logit: A nested model." Empirical Economics 22, no. 1 (1997): 131–52. http://dx.doi.org/10.1007/bf01188174.

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25

Wan, Alan T. K., Xinyu Zhang, and Shouyang Wang. "Frequentist model averaging for multinomial and ordered logit models." International Journal of Forecasting 30, no. 1 (2014): 118–28. http://dx.doi.org/10.1016/j.ijforecast.2013.07.013.

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26

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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27

Breen, Richard, and Jan O. Jonsson. "Analyzing Educational Careers: A Multinomial Transition Model." American Sociological Review 65, no. 5 (2000): 754–72. http://dx.doi.org/10.1177/000312240006500507.

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The logit model of educational transitions has become standard in research in educational stratification. One limitation of the model, however, is the assumption that individuals progress through the educational system in a unilinear sequential mode. Many school systems contain parallel branches of study that are most fruitfully seen as qualitatively different alternative pathways with different probabilities of school continuation attached to them. This study tests a multinomial model of educational careers, that takes previous paths and grade-point averages into account. Applied to a large S

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28

Çakmak Şahin, Senem, and İbrahim Engin Kılıç. "Poverty Dynamics in Turkey: A Multinomial Logit Model." Ekonomika 100, no. 2 (2021): 133–43. http://dx.doi.org/10.15388/ekon.2021.100.2.6.

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The availability of longitudinal data allows researchers to analyse the dynamics of poverty. By using the Turkish Statistical Institute’s (TurkStat) Income and Living Conditions Survey micro dataset, we analyse the households’ long-term monetary poverty conditions. We categorise poverty as transitory and chronic and employ the multinomial logit method to analyse determinants of each types of poverty. Results indicate that education and household size are the most effective factors for reducing transitory poverty, and for chronic poverty, the most effective factors are having a regular job and

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29

Tse, Y. K. "A Diagnostic Test for the Multinomial Logit Model." Journal of Business & Economic Statistics 5, no. 2 (1987): 283. http://dx.doi.org/10.2307/1391909.

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30

Tse, Y. K. "A Diagnostic Test for the Multinomial Logit Model." Journal of Business & Economic Statistics 5, no. 2 (1987): 283–86. http://dx.doi.org/10.1080/07350015.1987.10509587.

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31

Fader, Peter S., James M. Lattin, and John D. C. Little. "Estimating Nonlinear Parameters in the Multinomial Logit Model." Marketing Science 11, no. 4 (1992): 372–85. http://dx.doi.org/10.1287/mksc.11.4.372.

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32

Głuszak, Michał. "Multinomial Logit Model Of Housing Demand In Poland." Real Estate Management and Valuation 23, no. 1 (2015): 84–89. http://dx.doi.org/10.1515/remav-2015-0008.

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Abstract When compared to mature housing markets, little has been done to understand the nature of demand on emerging housing markets in Central and Eastern Europe and to develop testable models for post-socialist economies. With the exception of Bazyl 2009 and Głuszak 2010, there is hardly any econometric evidence on factors behind housing tenure choices in Poland. The article focus mainly on: permanent (and current) income, household structure, lifecycle, and differences between local market characteristics. In the research, multinomial logistic regression is used to analyze factors that inc

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33

Gu, Yuanyuan, Arne Risa Hole, and Stephanie Knox. "Fitting the Generalized Multinomial Logit Model in Stata." Stata Journal: Promoting communications on statistics and Stata 13, no. 2 (2013): 382–97. http://dx.doi.org/10.1177/1536867x1301300213.

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34

Cheng, Simon, and J. Scott Long. "Testing for IIA in the Multinomial Logit Model." Sociological Methods & Research 35, no. 4 (2007): 583–600. http://dx.doi.org/10.1177/0049124106292361.

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35

Wang, Xuesong, Shikai You, and Ling Wang. "Classifying road network patterns using multinomial logit model." Journal of Transport Geography 58 (January 2017): 104–12. http://dx.doi.org/10.1016/j.jtrangeo.2016.11.013.

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36

Flores, Alvaro, Gerardo Berbeglia, and Pascal Van Hentenryck. "Assortment optimization under the Sequential Multinomial Logit Model." European Journal of Operational Research 273, no. 3 (2019): 1052–64. http://dx.doi.org/10.1016/j.ejor.2018.08.047.

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37

de Clercq, Gijsbert Koen, Arjan van Binsbergen, Bart van Arem, and Maaike Snelder. "Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data." Journal of Advanced Transportation 2022 (December 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/6816851.

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Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed

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38

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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39

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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40

Désir, Antoine, Vineet Goyal, and Jiawei Zhang. "Technical Note—Capacitated Assortment Optimization: Hardness and Approximation." Operations Research 70, no. 2 (2022): 893–904. http://dx.doi.org/10.1287/opre.2021.2142.

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Assortment optimization is an important problem arising in various applications. In many practical settings, the assortment is subject to a capacity constraint. In “Capacitated Assortment Optimization: Hardness and Approximation,” Désir, Goyal, and Zhang study the capacitated assortment optimization problem. The authors first show that adding a general capacity constraint makes the problem NP-hard even for the simple multinomial logit model. They also show that under the mixture of multinomial logit model, even the unconstrained problem is hard to approximate within any reasonable factor when

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41

Changpetch, Pannapa. "Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset." Symmetry 13, no. 2 (2021): 287. http://dx.doi.org/10.3390/sym13020287.

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A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is

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42

Li, Xiaowei, Siyu Zhang, Yao Wu, Yuting Wang, and Wenbo Wang. "Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain." Journal of Advanced Transportation 2021 (September 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/9454873.

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Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is q

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43

Gruca, Thomas S., and D. Sudharshan. "Equilibrium Characteristics of Multinomial Logit Market Share Models." Journal of Marketing Research 28, no. 4 (1991): 480–82. http://dx.doi.org/10.1177/002224379102800410.

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Previous research suggests that a multinomial logit model of market share can be used in optimization or equilibrium analyses of marketing decision making. However, there are significant problems with the solutions obtained from such an analysis. The authors show that for any firm with less than a 50% market share, the profit maximizing solution is to increase advertising as much as possible. The same holds true for any other positively signed marketing mix variable. The exponential property of the MNL model precludes reasonable descriptions of firm behavior in this case. Thus, the MNL market

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44

Waddell, Paul. "A MULTINOMIAL LOGIT MODEL OF RACE AND URBAN STRUCTURE." Urban Geography 13, no. 2 (1992): 127–41. http://dx.doi.org/10.2747/0272-3638.13.2.127.

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45

Bechtel, Gordon G. "Share-Ratio Estimation of the Nested Multinomial Logit Model." Journal of Marketing Research 27, no. 2 (1990): 232. http://dx.doi.org/10.2307/3172850.

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46

Malchow-Møller, Nikolaj, and Michael Svarer. "Estimation of the multinomial logit model with random effects." Applied Economics Letters 10, no. 7 (2003): 389–92. http://dx.doi.org/10.1080/1350485032000082018.

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47

Bechtel, Gordon G. "Share-Ratio Estimation of the Nested Multinomial Logit Model." Journal of Marketing Research 27, no. 2 (1990): 232–37. http://dx.doi.org/10.1177/002224379002700211.

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48

Grigolon, Anna B., Aloys W. J. Borgers, Astrid D. A. M. Kemperman, and Harry J. P. Timmermans. "Vacation length choice: A dynamic mixed multinomial logit model." Tourism Management 41 (April 2014): 158–67. http://dx.doi.org/10.1016/j.tourman.2013.09.002.

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49

Martínez, Francisco, Felipe Aguila, and Ricardo Hurtubia. "The constrained multinomial logit: A semi-compensatory choice model." Transportation Research Part B: Methodological 43, no. 3 (2009): 365–77. http://dx.doi.org/10.1016/j.trb.2008.06.006.

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50

McFadden, Daniel. "Regression-based specification tests for the multinomial logit model." Journal of Econometrics 34, no. 1-2 (1987): 63–82. http://dx.doi.org/10.1016/0304-4076(87)90067-4.

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