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Journal articles on the topic 'Multinomial logit'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

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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2

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

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3

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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4

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

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5

Hartzel, J., A. Agresti, and B. Caffo. "Multinomial logit random effects models." Statistical Modelling 1, no. 2 (February 1, 2001): 81–102. http://dx.doi.org/10.1191/147108201128104.

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6

Marsili, Matteo. "On the multinomial logit model." Physica A: Statistical Mechanics and its Applications 269, no. 1 (July 1999): 9–15. http://dx.doi.org/10.1016/s0378-4371(99)00074-6.

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7

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

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8

Hanson, Ward, and Kipp Martin. "Optimizing Multinomial Logit Profit Functions." Management Science 42, no. 7 (July 1996): 992–1003. http://dx.doi.org/10.1287/mnsc.42.7.992.

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9

Hartzel, Jonathan, Alan Agresti, and Brian Caffo. "Multinomial logit random effects models." Statistical Modelling: An International Journal 1, no. 2 (July 2001): 81–102. http://dx.doi.org/10.1177/1471082x0100100201.

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10

Bashir, Shaheena, and Edward M. Carter. "Penalized multinomial mixture logit model." Computational Statistics 25, no. 1 (August 14, 2009): 121–41. http://dx.doi.org/10.1007/s00180-009-0165-9.

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11

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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12

Schaak, Henning, and Oliver Mußhoff. "Public Preferences for Pasture Landscapes and the Role of Scale Heterogeneity." German Journal of Agricultural Economics 70, no. 3 (September 1, 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 Multinomial Logit Model. This highlights the potential impact of common model specifications in the results in landscape preference studies.

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13

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

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14

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

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15

Hsu, Arthur, and Ronald T. Wilcox. "Stochastic Prediction in Multinomial Logit Models." Management Science 46, no. 8 (August 2000): 1137–44. http://dx.doi.org/10.1287/mnsc.46.8.1137.12028.

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16

Fader, Peter S. "Integrating the Dirichlet-multinomial and multinomial logit models of brand choice." Marketing Letters 4, no. 2 (April 1993): 99–112. http://dx.doi.org/10.1007/bf00994069.

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17

Stern, Steven. "Rules of thumb for comparing multinomial logit and multinomial probit coefficients." Economics Letters 31, no. 3 (December 1989): 235–38. http://dx.doi.org/10.1016/0165-1765(89)90006-2.

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18

Nabila, Rifda, Risdiana Himmati, and Rendra Erdkhadifa. "PERBANDINGAN REGRESI LOGISTIK MULTINOMINAL DAN ANALISIS DISKRIMINAN." Journal of Islamic Tourism, Halal Food, Islamic Traveling, and Creative Economy 1, no. 2 (October 21, 2021): 135–50. http://dx.doi.org/10.21274/ar-rehla.v1i2.4820.

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Abstrak: Tujuan dari penelitian ini adalah untuk membandingkan analisis regresi logistik multinomial dan analisis diskriminan untuk mengelompokkan keputusan kunjungan wisata halal di Jawa Tengah berdasarkan ketepatan pengelompokan. Analisis statistik yang digunakan adalah regresi logistik multinomial dan analisis diskriminan. Kedua analisis tersebut dapat digunakan sebagai metode pengelompokan objek, sehingga keduanya dapat dibandingkan berdasarkan ketepatan pengelompokkannya. Penelitian ini membandingkan analisis regresi logistik multinomial dan analisis diskriminan dalam pengelompokan keputusan kunjungan wisata halal. Data yang digunakan adalah worship facilities, halalness, general Islamic mortality, dan tourism destination image. Hasil analisis menggunakan metode regresi logistik multinomial menunjukkan faktor-faktor yang secara signifikan mempengaruhi pengelompokan keputusan kunjungan wisata halal adalah variabel tourism destination image, variabel halalness, dan variabel general Islamic morality. Sedangkan dengan analisis diskriminan menunjukkan bahwa semua variabel prediktor yakni worship facilities, halalness, general Islamic mortality, dan tourism destination image memberikan pengaruh secara signifikan terhadap pengklasifikasian keputusan mengunjungi destinasi wisata halal. Penelitian ini menunjukkan bahwa metode regresi logistik multinomial lebih baik untuk pengelompokkan keputusan kunjungan wisata halal dibandingan metode analisis diskriminan, dengan presetnase ketepatan pengelompokkan pada metode regresi logit multinomial sebesar 59,5% dan analisis diskriminan sebesar 53,5%. Analisis regresi logistik multinominal lebih mudah digunakan dalam proses pengelompokan keputusan kunjuangan wisata halal karena tidak mempertimbangkan asumsi yang harus dipenuhi. Kata Kunci: Analisis Diskriminan; Regresi Logistik Multinominal; Keputusan Mengunjungi Abstract: The purpose of this study is to compare multinomial logistic regression analysis and discriminant analysis to classify decisions on halal tourism visits in Central Java based on grouping accuracy. Statistical analysis used is multinomial logistic regression and discriminant analysis. The two analyzes can be used as a method of grouping objects, so that they can be compared based on the accuracy of the grouping. This study compares multinomial logistic regression analysis and discriminant analysis in grouping decisions for halal tourism visits. The data used are worship facilities, halalness, general Islamic mortality, and tourism destination image. The results of the analysis using the multinomial logistic regression method show that the factors that significantly influence the grouping of decisions for halal tourism visits are the tourism destination image variable, the halalness variable, and the general Islamic morality variable. Meanwhile, discriminant analysis shows that all predictor variables namely worship facilities, halalness, general Islamic mortality, and tourism destination image have a significant influence on the classification of decisions to visit halal tourist destinations. This study shows that the multinomial logistic regression method is better for grouping decisions on halal tourist visits than the discriminant analysis method, with a preset percentage of grouping accuracy in the multinomial logit regression method of 59.5% and discriminant analysis of 53.5%. Multinominal logistic regression analysis is easier to use in the process of grouping halal tourism travel decisions because it does not consider the assumptions that must be met. Keywords: Discriminant Analysis; Multinomial Logistic Regression; Visiting decision.

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19

Désir, Antoine, Vineet Goyal, and Jiawei Zhang. "Technical Note—Capacitated Assortment Optimization: Hardness and Approximation." Operations Research 70, no. 2 (March 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 the number of mixtures is not constant. In view of these hardness results, the authors present near-optimal algorithms for a large class of parametric choice models including the mixture of multinomial logit, Markov chain, nested logit, and d-level nested logit choice models. In fact, their approach extends to a large class of objective functions that depend only on a small number of linear functions.

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20

Tutz, Gerhard. "Uncertain Choices: The Heterogeneous Multinomial Logit Model." Sociological Methodology 51, no. 1 (January 4, 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 researchers to investigate which variables determine uncertainty in choice behavior. Several applications demonstrate the usefulness of the model.

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21

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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22

Camminatie, I., and A. Lucadamo. "Estimating Multinomial Logit Model with Multicollinear Data." Asian Journal of Mathematics & Statistics 3, no. 2 (March 15, 2010): 93–101. http://dx.doi.org/10.3923/ajms.2010.93.101.

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23

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

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24

Winship, Christopher. "Logit and Probit: Ordered and Multinomial Models." Journal of the American Statistical Association 98, no. 463 (September 2003): 775–76. http://dx.doi.org/10.1198/jasa.2003.s301.

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25

Bonnet, C. "Le modele logit multinomial a coefficients aleatoires." Recherche et Applications en Marketing 19, no. 3 (September 1, 2004): 61–72. http://dx.doi.org/10.1177/076737010401900304.

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26

Crawford, David L., Robert A. Pollak, and Francis Vella. "Simple inference in multinomial and ordered logit." Econometric Reviews 17, no. 3 (January 1998): 289–99. http://dx.doi.org/10.1080/07474939808800417.

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27

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

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28

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

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29

Zahid, Faisal Maqbool, and Gerhard Tutz. "Multinomial logit models with implicit variable selection." Advances in Data Analysis and Classification 7, no. 4 (July 6, 2013): 393–416. http://dx.doi.org/10.1007/s11634-013-0136-4.

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30

Khasnabis, S., and M. J. Cynecki. "Development of parameters of multinomial logit models." Mathematical and Computer Modelling 10, no. 5 (1988): 315–20. http://dx.doi.org/10.1016/0895-7177(88)90134-3.

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31

Tutz, Gerhard, Wolfgang Pößnecker, and Lorenz Uhlmann. "Variable selection in general multinomial logit models." Computational Statistics & Data Analysis 82 (February 2015): 207–22. http://dx.doi.org/10.1016/j.csda.2014.09.009.

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32

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

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33

Zahid, Faisal Maqbool, and Christian Heumann. "Response shrinkage estimation in multinomial logit models." Journal of Statistical Planning and Inference 142, no. 1 (January 2012): 95–109. http://dx.doi.org/10.1016/j.jspi.2011.06.027.

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34

Castro, Marisol, Francisco Martínez, and Marcela A. Munizaga. "Estimation of a constrained multinomial logit model." Transportation 40, no. 3 (August 30, 2012): 563–81. http://dx.doi.org/10.1007/s11116-012-9435-4.

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35

Khasnabis, Snehamay, and Michael J. Cynecki. "Development of parameters of multinomial logit models." Mathematical and Computer Modelling 11 (1988): 962–68. http://dx.doi.org/10.1016/0895-7177(88)90636-x.

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36

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

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37

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

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38

Aggarwal, Manish. "Preferences-based learning of multinomial logit model." Knowledge and Information Systems 59, no. 3 (June 5, 2018): 523–38. http://dx.doi.org/10.1007/s10115-018-1215-9.

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39

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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40

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 (January 9, 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 yang sering dibutuhkan dalam penelitian-penelitian sosial dan kependidikan. Ilustrasi melalui data hipotetik digunakan untuk memperjelas kebutuhan, kegunaan, metode analisis data sampai pada interpretasi hasil model regrgesi. Sajian komputasi yang ringkas dilakukan melalui dua software yang populer yaitu SAS untuk model regresi generalized-logit. Sedangkan untuk model regresi logit bersyarat dapat dilakukan dengan SAS dan SPSS. Baik SAS dan SPSS menyajikan hasil analsis regresi yang sama untuk model regresi logit bersyarat.

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41

Cook, Scott J., John Niehaus, and Samantha Zuhlke. "A warning on separation in multinomial logistic models." Research & Politics 5, no. 2 (April 2018): 205316801876951. http://dx.doi.org/10.1177/2053168018769510.

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Oppenheim et al. (2015) provides the first empirical analysis of insurgent defection during armed rebellion, estimating a series of multinomial logit models of continued rebel participation using a survey of ex-combatants in Colombia. Unfortunately, many of the main results from this analysis are an artifact of separation in these data – that is, one or more of the covariates perfectly predicts the outcome. We demonstrate that this can be identified using simple cross tabulations. Furthermore, we show that Oppenheim et al.’s (2015) results are not supported when separation is explicitly accounted for. Using a generalization of Firth’s (1993) penalized-likelihood estimator – a well-known solution for separation – we are unable to reproduce any of their conditional results. While our (re-)analysis focuses on Oppenheim et al. (2015), this problem appears in other research using multinomial logit models as well. We believe that this is both because the discussion on separation in political science has primarily focused on binary-outcome models, and because software (Stata and R) does not warn researchers about seperation in multinomial logit models. Therefore, we encourage researchers using multinomial logit models to be especially vigilant about separation, and discuss simple red flags to consider.

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42

Changpetch, Pannapa. "Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset." Symmetry 13, no. 2 (February 8, 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 used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods.

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43

Nugraha, Jaka. "Studi Simulasi Model Nested Logit dan Paired Combinatorial Logit pada Respon Multinomial." Eksakta 13, no. 1-2 (February 3, 2016): 63–71. http://dx.doi.org/10.20885/eksakta.vol13.iss1-2.art8.

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44

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 (August 1, 2011): 900–909. http://dx.doi.org/10.3923/tasr.2011.900.909.

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45

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 (February 2, 2018): 737–60. http://dx.doi.org/10.1080/23249935.2018.1431701.

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46

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 (January 21, 2019): 2861–75. http://dx.doi.org/10.1080/03610918.2018.1529238.

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47

Dow, Jay K., and James W. Endersby. "Multinomial probit and multinomial logit: a comparison of choice models for voting research." Electoral Studies 23, no. 1 (March 2004): 107–22. http://dx.doi.org/10.1016/s0261-3794(03)00040-4.

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48

Park, Kang H., and Peter M. Kerr. "Determinants of Academic Performance: A Multinomial Logit Approach." Journal of Economic Education 21, no. 2 (1990): 101. http://dx.doi.org/10.2307/1181978.

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49

Oh, Min-hwan, and Garud Iyengar. "Multinomial Logit Contextual Bandits: Provable Optimality and Practicality." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9205–13. http://dx.doi.org/10.1609/aaai.v35i10.17111.

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We consider a sequential assortment selection problem where the user choice is given by a multinomial logit (MNL) choice model whose parameters are unknown. In each period, the learning agent observes a d-dimensional contextual information about the user and the N available items, and offers an assortment of size K to the user, and observes the bandit feedback of the item chosen from the assortment. We propose upper confidence bound based algorithms for this MNL contextual bandit. The first algorithm is a simple and practical method that achieves an O(d√T) regret over T rounds. Next, we propose a second algorithm which achieves a O(√dT) regret. This matches the lower bound for the MNL bandit problem, up to logarithmic terms, and improves on the best-known result by a √d factor. To establish this sharper regret bound, we present a non-asymptotic confidence bound for the maximum likelihood estimator of the MNL model that may be of independent interest as its own theoretical contribution. We then revisit the simpler, significantly more practical, first algorithm and show that a simple variant of the algorithm achieves the optimal regret for a broad class of important applications.

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50

Çakmak Şahin, Senem, and İbrahim Engin Kılıç. "Poverty Dynamics in Turkey: A Multinomial Logit Model." Ekonomika 100, no. 2 (October 13, 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 having a skilled occupation; insurance, home ownership, and number of children are important determinants for both types of poverty.

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