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Journal articles on the topic 'Multinomial Logit Model (MNLM)'

Author: Grafiati

Published: 11 December 2022

Last updated: 19 July 2025

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1

Nurlaela, S. "An application of Multinomial Logit Model (MNL) on tourist destination choices." IOP Conference Series: Earth and Environmental Science 202 (November 27, 2018): 012021. http://dx.doi.org/10.1088/1755-1315/202/1/012021.

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Irianti, S., and P. Prasetyoputra. "The struggle for water in Indonesia: the role of women and children as household water fetcher." Journal of Water, Sanitation and Hygiene for Development 9, no. 3 (2019): 540–48. http://dx.doi.org/10.2166/washdev.2019.005.

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AbstractGlobally, billions of people are still without access to safe water. Every day they need to travel far to fetch water, and most of them are women. The gender and water fetching issue in Indonesia is under-researched. Hence, this article addresses the spatial, environmental, and socio-demographic correlates of women or children as the household water fetcher in Indonesia. Using data from the 2013 Baseline Health Research (Riskesdas) from the Ministry of Health, we fitted a multivariable multinomial logit regression model (MNLM) to examine the relationship between women and children as w

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Yingcui, Du, Liu Benxing, Dong Shulong, Wang Xiaoqing, and Zhao Pengsheng. "Research on Travel Mode Selection Decision Method Based on Discrete Choice Model." Journal of Scientific and Engineering Research 9, no. 8 (2022): 59–67. https://doi.org/10.5281/zenodo.10527716.

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<strong>Abstract</strong> With the development of Sweden’s economy, new modes of transportation have gradually developed. In order to understand the acceptance of new modes of transportation by the Swedish people, this paper takes the Swedish people as the research object. The data are obtained by filling out questionnaires on the spot, mailing questionnaires and SP surveys. BIOGEME is used to establish a Multinomial Logit (MNL) model to analyze the travel choice behavior of the Swedish people under various modes of transportation, helping the government to further optimize and improve t

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Davis, Peter, and Pasquale Schiraldi. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products." RAND Journal of Economics 45, no. 1 (2014): 32–63. http://dx.doi.org/10.1111/1756-2171.12041.

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Yugihartiman, Masrono, B. Budiono, Maman Setiawan, and Achmad Kemal Hidayat. "Estimating Travel Choice Probability of Link-Based Congestion Charging Scheme for Car Commuter Trips in Jakarta." Sustainability 15, no. 10 (2023): 8104. http://dx.doi.org/10.3390/su15108104.

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The likely effects on car commuters of enforcing congestion charging using the link-based corridor method include that they may shift to public transport, divert their route of travel, or decide not to travel to the related area. However, most recent research has focused mainly on the choice of modes. This paper examined the travel choices of private car commuters resulting from the congestion charging scheme set to be implemented in Jakarta, Indonesia. The scheme is intended to replace the current odd-even strategy. It is imperative to study all possible mutually exclusive alternatives faced

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Xie, Feng, Ming Xie, and Cheng Wang. "Using the MNL Model in a Mobile Device’s Indoor Positioning." Biomimetics 8, no. 2 (2023): 252. http://dx.doi.org/10.3390/biomimetics8020252.

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Indoor Positioning Services (IPS) allow mobile devices or bionic robots to locate themselves quickly and accurately in large commercial complexes, shopping malls, supermarkets, exhibition venues, parking garages, airports, or train hubs, and access surrounding information. Wi-Fi-based indoor positioning technology can use existing WLAN networks, and has promising prospects for broad market applications. This paper presents a method using the Multinomial Logit Model (MNL) to generate Wi-Fi signal fingerprints for positioning in real time. In an experiment, 31 locations were randomly selected an

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Lalisa, Wendimu Biyena. "Perception of effect of climate change and adaptation strategies of beekeepers of Welmera district, Ethiopia." International Journal of Environmental & Agriculture Research 6, no. 9 (2020): 01–09. https://doi.org/10.5281/zenodo.4059140.

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<strong><em>Abstract</em></strong><strong>—</strong> <em>This study identifies factors affecting smallholder beekeepers’ decisions to choose strategies to adapt to climate change in Welmera District, Oromia regional state, Ethiopia. Accordingly, quantitative data analysis and a multinomial logit model was used to identify  perception of effects of climate change and adaptation strategies, and factors influencing beekeepers’ choice of adaptation strategies to climate change, respectively. Results signified that skip honey harvesting, additional feeding, bee hive shade and

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Yan, Yongnan, Xiangdong Xu, and Anthony Chen. "Is it Necessary to Relax the IID Assumptions in the Logsum-Based Accessibility Analysis?" Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (2019): 84–96. http://dx.doi.org/10.1177/0361198119839972.

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Accessibility is an important link between transportation and land use. As a typical measure of accessibility, logsum or a utility-based measure has been widely used in project appraisal, urban transit accessibility evaluation, destination choice, and network vulnerability analysis. Since the logsum term is the log of the denominator of the choice probability expression, it inherits the independently and identically distributed (IID) assumptions of the classical multinomial logit (MNL) route choice model. This paper aims to explore whether the IID assumptions have a significant effect on the l

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Erokhin, Viktor V. "FORECASTING THE ECONOMY OF THE SCIENTIFIC SECTOR BASED ON DISCRETE CHOICE MODELS USING ARTIFICIAL NEURAL NETWORKS IN CROSS-COUNTRY ANALYSIS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 5/7, no. 146 (2024): 6–20. http://dx.doi.org/10.36871/ek.up.p.r.2024.05.07.001.

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The article presents a study of the influence of artificial deep neural networks (AGNN) on increasing the accuracy and reliability of predictive regression models in the economics of the scientific sector of 61 countries, including Russia. We integrated the feedforward IGNS into the econometric discrete choice model (DCM), which represents a multinomial logit model (MNLR). AGNN in regression models built on statistical data with a large range of standard deviation lead to an increase in forecasting accuracy by at least 32% within 0,8 periods of the analyzed data. The use of integration of AGNN

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10

Lipovetsky, Stan, and Michael Conklin. "MaxDiff Priority Estimations with and without HB-MNL." Advances in Adaptive Data Analysis 07, no. 01n02 (2015): 1550002. http://dx.doi.org/10.1142/s1793536915500028.

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Maximum difference (MaxDiff) is a discrete choice modeling approach widely used in marketing research for finding utilities and preference probabilities among multiple alternatives. It can be seen as an extension of the paired comparison in Thurstone and Bradley–Terry techniques for the simultaneous presenting of three, four or more items to respondents. A respondent identifies the best and the worst ones, so the remaining are deemed intermediate by preference alternatives. Estimation of individual utilities is usually performed in a hierarchical Bayesian (HB)-multinomial-logit (MNL) modeling.

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Erokhin, Victor V., and Elena V. Eliseeva. "DISCRETE CHOICE MODELS BASED ON ARTIFICIAL NEURAL NETWORKS FOR SOLVING REGRESSION PROBLEMS." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 72–81. http://dx.doi.org/10.36871/2618-9976.2022.11.007.

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An integrated structure from a discrete choice model with an embedded artificial neural network is proposed: TN-MNLR, which allows more flexible and accurate solving of regression problems with good predictability. The TN-MNLR framework allows you to more accurately evaluate Multinomial logit regression (MNLR) built on nonlinear input parameters. An artificial neural network with feedforward is used to predict the parameters of the studied regression in the form of a nonlinear function. Further, these found parameters from the neural network are transferred to the parametric logical data selec

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Etebari, Farhad, Abdolah Aghaei, and Ammar Jalalimanesh. "Network revenue management under Specific choice Models." Scientia Iranica 20, no. 6 (2013): 2161–76. https://doi.org/10.5281/zenodo.14013745.

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New challenges in the business environment such as increasing competition and influence of Internet as a main distribution channel lead to fundamental changes in traditional revenue management models.&lrm; Within these conditions, modeling individual’s decisions more accurately is becoming a key factor.&lrm; Nearly all research studies about the choice-based revenue management models used the well-known multinomial logit model.&lrm; This model has one important restriction that is called independence of irrelevant alternatives, a property which states that the ratio of choice probabiliti

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13

Shafique, Muhammad Awais, and Eiji Hato. "Incorporating MNL Model into Random Forest for Travel Mode Detection." July 2021 40, no. 3 (2021): 496–501. http://dx.doi.org/10.22581/muet1982.2103.04.

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Mode choice models have been used widely to forecast the relative probabilities of using available travel modes. These depend on mode-related and traveler-related characteristics. On the other hand, smartphones are increasingly being used to collect sensors’ data relating to trips made after selection of a suitable mode. Such sensors’ data may be correlated with decision-making process of travelers regarding travel mode selection. Discrete Choice Modelling is used to simulate this decision-making process by computing utilities of various travel alternatives, and then calculating their respecti

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Agrawal, Shipra, Vashist Avadhanula, Vineet Goyal, and Assaf Zeevi. "MNL-Bandit: A Dynamic Learning Approach to Assortment Selection." Operations Research 67, no. 5 (2019): 1453–85. http://dx.doi.org/10.1287/opre.2018.1832.

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We consider a dynamic assortment selection problem where in every round the retailer offers a subset (assortment) of N substitutable products to a consumer, who selects one of these products according to a multinomial logit (MNL) choice model. The retailer observes this choice, and the objective is to dynamically learn the model parameters while optimizing cumulative revenues over a selling horizon of length T. We refer to this exploration–exploitation formulation as the MNL-Bandit problem. Existing methods for this problem follow an explore-then-exploit approach, which estimates parameters to

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Etebari, Farhad, Abdolah Aaghaie, and Ammar Jalalimanesh. "Intelligent Decision Support System in the Choice-based Network Revenue Management." Australian Journal of Basic and Applied Sciences 6, no. 9 (2012): 346–52. https://doi.org/10.5281/zenodo.13988435.

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During recent years, independent demand assumption in traditional revenue management models has been faced with serious doubts and has been replaced with choice-based models. More accurate modeling of individual’s decisions is a key factor in these models. Although multinomial logit model (MNL) is the most well-known choice model, it has a limitation which causes the ratio of choice probabilities of two distinct alternatives to be independent from the attributes of any other alternatives and is called independence of irrelevant alternative (IIA). Nested logit model can be replaced with M

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16

Wan, Peng, Jie Zhang, Yiwei Liu, and Xiaoxuan Jiang. "Location Optimization of Offline Physical Stores Based on MNL Model under BOPS Omnichannel." Journal of Theoretical and Applied Electronic Commerce Research 17, no. 4 (2022): 1633–54. http://dx.doi.org/10.3390/jtaer17040083.

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With the continuous upgrading of consumer demand and retail modes, more and more retailers are switching to an omnichannel retail mode. In order to study the location problem of offline physical stores of online retail enterprises that plan to implement the BOPS (Buy Online and Pickup in Store) omnichannel retail model, this paper considers consumers’ choice behavior under the omnichannel retail model; uses the MNL (Multinomial Logit) model to depict customers’ choice behavior between the online channel, BOPS channel, and offline physical channel; and constructs a location optimization model o

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17

Shao, Mengru, Chao Chen, Qingchang Lu, Xinyu Zuo, Xueling Liu, and Xiaoning Gu. "The Impacts of Low-Carbon Incentives and Carbon-Reduction Awareness on Airport Ground Access Mode Choice under Travel Time Uncertainty: A Hybrid CPT-MNL Model." Sustainability 15, no. 16 (2023): 12610. http://dx.doi.org/10.3390/su151612610.

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Developing strategies to incentivize travelers towards adopting sustainable mobility options is one of the effective approaches to mitigate carbon emissions. Using Xi’an Xianyang International Airport as a case study, this study aims to explore the effects of low-carbon incentives and carbon-reduction awareness on airport ground access mode choices. In addition, to account for the complex road environment, an innovative stated preference choice experiment was designed, integrating the factor of travel time uncertainty. Then, a hybrid cumulative prospect theory–Multinomial Logit (CPT-MNL) model

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18

Cui, Hongjun, Mingzhi Li, Minqing Zhu, and Xinwei Ma. "Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model." Sustainability 15, no. 15 (2023): 11950. http://dx.doi.org/10.3390/su151511950.

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The high-quality development of urban–rural buses is an important way to narrow the gap between urban and rural areas. However, studies on the quality of bus services and its impact on bus travel-mode choice have mostly focused on urban buses, with less attention paid to urban–rural buses. This paper aims to explore how urban–rural bus service quality affects rural residents’ travel-mode-choice behavior based on survey data from rural residents in Henan Province, China. A structural equation model (SEM) is applied to examine the critical factors affecting rural residents’ satisfaction with urb

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19

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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20

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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21

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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22

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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23

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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24

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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25

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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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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27

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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28

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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29

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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30

Habib, Khandker Nurul. "Rational inattention in discrete choice models: Estimable specifications of RI-multinomial logit (RI-MNL) and RI-nested logit (RI-NL) models." Transportation Research Part B: Methodological 172 (June 2023): 53–70. http://dx.doi.org/10.1016/j.trb.2023.03.010.

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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

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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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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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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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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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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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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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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49

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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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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