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Journal articles on the topic 'Probit multinomial'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Horowitz, Joel L. "Reconsidering the multinomial probit model." Transportation Research Part B: Methodological 25, no. 6 (1991): 433–38. http://dx.doi.org/10.1016/0191-2615(91)90036-i.

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2

Breslaw, Jon A. "Multinomial probit estimation without nuisance parameters." Econometrics Journal 5, no. 2 (2002): 417–34. http://dx.doi.org/10.1111/1368-423x.00091.

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3

Dokumacı, Emin, and William H. Sandholm. "Large deviations and multinomial probit choice." Journal of Economic Theory 146, no. 5 (2011): 2151–58. http://dx.doi.org/10.1016/j.jet.2011.06.013.

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4

Bunch, David S. "Estimability in the multinomial probit model." Transportation Research Part B: Methodological 25, no. 1 (1991): 1–12. http://dx.doi.org/10.1016/0191-2615(91)90009-8.

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5

Kindo, Bereket P., Hao Wang, and Edsel A. Peña. "Multinomial probit Bayesian additive regression trees." Stat 5, no. 1 (2016): 119–31. http://dx.doi.org/10.1002/sta4.110.

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6

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

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7

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

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8

Myatt, David P., and Chris Wallace. "A multinomial probit model of stochastic evolution." Journal of Economic Theory 113, no. 2 (2003): 286–301. http://dx.doi.org/10.1016/s0022-0531(03)00069-3.

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9

Ding, Yunfei, and Robert F. Harrison. "A sparse multinomial probit model for classification." Pattern Analysis and Applications 14, no. 1 (2010): 47–55. http://dx.doi.org/10.1007/s10044-010-0177-7.

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10

Dansie, B. R. "Parameter estimability in the multinomial probit model." Transportation Research Part B: Methodological 19, no. 6 (1985): 526–28. http://dx.doi.org/10.1016/0191-2615(85)90047-5.

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11

Ziegler, Andreas. "Simulated classical tests in multinomial probit models." Statistical Papers 48, no. 4 (2007): 655–81. http://dx.doi.org/10.1007/s00362-007-0362-3.

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12

Wardani, Ika Nurwanitantya, and A'yunin Sofro. "ESTIMASI PARAMETER MODEL REGRESI PROBIT MULTINOMIAL DENGAN METODE MAXIMUM LIKELIHOOD." MATHunesa: Jurnal Ilmiah Matematika 8, no. 2 (2020): 209–15. http://dx.doi.org/10.26740/mathunesa.v8n2.p209-215.

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Regresi Probit Multinomial merupakan model non linier yang digunakan untuk menganalisis hubungan antara satu variabel dependen (terikat) dengan beberapa variabel independen (bebas), dimana variabel dependen nya berupa data kualitatif biner yaitu bernilai 0 dan 1. Penelitian ini mengkaji tentang model regresi Probit Multinomial untuk mengetahui faktor yang mempengaruhi jumlah pendapatan terhadap pilihan memancing seseorang. Dalam mengestimasi parameter digunakan metode Maximum Likelihood Estimation (MLE). Kemudian dilanjutkan dengan uji serentak menggunakan Likelihood Ratio test, dan uji parsia

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13

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 (2004): 107–22. http://dx.doi.org/10.1016/s0261-3794(03)00040-4.

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14

YAI, Tetsuo, and Takahiro NAKAGAWA. "Applicability of Multinomial Probit Models with Structured Covariance." INFRASTRUCTURE PLANNING REVIEW 13 (1996): 563–70. http://dx.doi.org/10.2208/journalip.13.563.

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15

Mori, Harunori. "Bayes Estimation in the Hierarchical Multinomial Probit Model." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 44, no. 2 (2014): 135–55. http://dx.doi.org/10.14490/jjss.44.135.

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16

Fares, M’hand. "Using a multinomial probit to test for complementarity." Applied Economics Letters 21, no. 3 (2013): 180–84. http://dx.doi.org/10.1080/13504851.2013.848014.

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17

Glewwe, Paul. "The Three-Choice Multinomial Probit with Selectivity Corrections." Econometric Theory 9, no. 2 (1993): 316–22. http://dx.doi.org/10.1017/s0266466600007623.

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18

Glewwe, Paul. "The Three-Choice Multinomial Probit with Selectivity Corrections." Econometric Theory 8, no. 2 (1992): 302–4. http://dx.doi.org/10.1017/s0266466600012846.

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19

Paleti, Rajesh. "Generalized multinomial probit Model: Accommodating constrained random parameters." Transportation Research Part B: Methodological 118 (December 2018): 248–62. http://dx.doi.org/10.1016/j.trb.2018.10.019.

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20

Geweke, John F., Michael P. Keane, and David E. Runkle. "Statistical inference in the multinomial multiperiod probit model." Journal of Econometrics 80, no. 1 (1997): 125–65. http://dx.doi.org/10.1016/s0304-4076(97)00005-5.

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21

Bolduc, Denis. "Generalized autoregressive errors in the multinomial probit model." Transportation Research Part B: Methodological 26, no. 2 (1992): 155–70. http://dx.doi.org/10.1016/0191-2615(92)90005-h.

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22

Kwon, Soonil, Mark O. Goodarzi, Kent D. Taylor, et al. "A Multinomial Ordinal Probit Model with Singular Value Decomposition Method for a Multinomial Trait." Journal of Probability and Statistics 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/419832.

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We developed a multinomial ordinal probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously for association with multidisease status when sample size is much smaller than the number of SNPs. The validity and performance of the method was evaluated via simulation. We applied the method to our real study sample recruited through the Mexican-American Coronary Artery Disease study. We found 3 genes (SORCS1, AMPD1, and PPARα) to be associated with the development of both IGT and IFG, while 5 genes (AMPD2, PRKAA2, C5, TCF7L2,

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23

Velandia, Margarita, Roderick M. Rejesus, Thomas O. Knight, and Bruce J. Sherrick. "Factors Affecting Farmers' Utilization of Agricultural Risk Management Tools: The Case of Crop Insurance, Forward Contracting, and Spreading Sales." Journal of Agricultural and Applied Economics 41, no. 1 (2009): 107–23. http://dx.doi.org/10.1017/s1074070800002583.

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Factors affecting the adoption of crop insurance, forward contracting, and spreading sales are analyzed using multivariate and multinomial probit approaches that account for simultaneous adoption and/or correlation among the three risk management adoption decisions. Our empirical results suggest that the decision to adopt crop insurance, forward contracting, and/or spreading sales are correlated. Richer insights can be drawn from our multivariate and multinomial probit analysis than from separate, single-equation probit estimation that assumes independence of adoption decisions. Some factors s

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24

Keane, Michael P. "A Note on Identification in the Multinomial Probit Model." Journal of Business & Economic Statistics 10, no. 2 (1992): 193. http://dx.doi.org/10.2307/1391677.

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25

Autant-Bernard, Lesage, and Parent. "Firm Innovation Strategies: A Spatial Cohort Multinomial Probit Approach." Annales d'Économie et de Statistique, no. 87/88 (2007): 63. http://dx.doi.org/10.2307/27650042.

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26

Keane, Michael P. "A Note on Identification in the Multinomial Probit Model." Journal of Business & Economic Statistics 10, no. 2 (1992): 193–200. http://dx.doi.org/10.1080/07350015.1992.10509898.

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27

Yai, Tetsuo, Seiji Iwakura, and Shigeru Morichi. "Multinomial probit with structured covariance for route choice behavior." Transportation Research Part B: Methodological 31, no. 3 (1997): 195–207. http://dx.doi.org/10.1016/s0191-2615(96)00025-2.

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28

Nobile, Agostino. "Comment: Bayesian multinomial probit models with a normalization constraint." Journal of Econometrics 99, no. 2 (2000): 335–45. http://dx.doi.org/10.1016/s0304-4076(00)00035-x.

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29

McCulloch, Robert, and Peter E. Rossi. "An exact likelihood analysis of the multinomial probit model." Journal of Econometrics 64, no. 1-2 (1994): 207–40. http://dx.doi.org/10.1016/0304-4076(94)90064-7.

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30

Girolami, Mark, and Simon Rogers. "Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors." Neural Computation 18, no. 8 (2006): 1790–817. http://dx.doi.org/10.1162/neco.2006.18.8.1790.

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It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting.1 The model augmentation with additional latent variables ensures full a po

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31

Burda, Martin, Matthew Harding, and Jerry Hausman. "A Bayesian mixed logit–probit model for multinomial choice." Journal of Econometrics 147, no. 2 (2008): 232–46. http://dx.doi.org/10.1016/j.jeconom.2008.09.029.

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32

Geweke, John, Michael Keane, and David Runkle. "Alternative Computational Approaches to Inference in the Multinomial Probit Model." Review of Economics and Statistics 76, no. 4 (1994): 609. http://dx.doi.org/10.2307/2109766.

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33

Kamakura, Wagner A. "The Estimation of Multinomial Probit Models: A New Calibration Algorithm." Transportation Science 23, no. 4 (1989): 253–65. http://dx.doi.org/10.1287/trsc.23.4.253.

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34

Bolduc, Denis. "A practical technique to estimate multinomial probit models in transportation." Transportation Research Part B: Methodological 33, no. 1 (1999): 63–79. http://dx.doi.org/10.1016/s0191-2615(98)00028-9.

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35

Cripps, Edward, Denzil G. Fiebig, and Robert Kohn. "Parsimonious Estimation of the Covariance Matrix in Multinomial Probit Models." Econometric Reviews 29, no. 2 (2009): 146–57. http://dx.doi.org/10.1080/07474930903382158.

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36

Winzar, Hume F., and Lester W. Johnson. "A Multinomial Probit Conjoint Preference Simulator Incorporating Varying Importance Weights." Australasian Marketing Journal (AMJ) 7, no. 2 (1999): 49–57. http://dx.doi.org/10.1016/s1441-3582(99)70215-6.

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37

Chiew, Esther, and Ricardo A. Daziano. "A Bayes multinomial probit model for random consumer-surplus maximization." Journal of Choice Modelling 21 (December 2016): 56–59. http://dx.doi.org/10.1016/j.jocm.2015.09.007.

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38

Patil, Priyadarshan N., Subodh K. Dubey, Abdul R. Pinjari, Elisabetta Cherchi, Ricardo Daziano, and Chandra R. Bhat. "Simulation evaluation of emerging estimation techniques for multinomial probit models." Journal of Choice Modelling 23 (June 2017): 9–20. http://dx.doi.org/10.1016/j.jocm.2017.01.007.

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39

Natarajan, Ranjini, Charles E. McCulloch, and Nicholas M. Kiefer. "A Monte Carlo EM method for estimating multinomial probit models." Computational Statistics & Data Analysis 34, no. 1 (2000): 33–50. http://dx.doi.org/10.1016/s0167-9473(99)00073-0.

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40

Carolevschi, Laura. "Monetary Policy Choice in Developing Countries: A Multinomial Probit Model." Journal of Developing Areas 52, no. 3 (2018): 125–38. http://dx.doi.org/10.1353/jda.2018.0041.

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41

Chakir, Raja, and Olivier Parent. "Determinants of land use changes: A spatial multinomial probit approach." Papers in Regional Science 88, no. 2 (2009): 327–44. http://dx.doi.org/10.1111/j.1435-5957.2009.00239.x.

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42

Liesenfeld, Roman, and Jean-François Richard. "The dynamic invariant multinomial probit model: Identification, pretesting and estimation." Journal of Econometrics 155, no. 2 (2010): 117–27. http://dx.doi.org/10.1016/j.jeconom.2009.09.021.

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43

이서구. "A Multinomial Probit Analysis of Korean Automotive Market Using Bayesian Estimation." Journal of Product Research 34, no. 5 (2016): 65–74. http://dx.doi.org/10.36345/kacst.2016.34.5.008.

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44

Dubey, Subodh, Oded Cats, Serge Hoogendoorn, and Prateek Bansal. "A multinomial probit model with Choquet integral and attribute cut-offs." Transportation Research Part B: Methodological 158 (April 2022): 140–63. http://dx.doi.org/10.1016/j.trb.2022.02.007.

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45

Yai, Tetsuo, Takahiro Nakagawa, and Jun-ichi Ishitsuka. "Estimation of Multinomial Probit Model with Structured Covariance Using Simulation Method." Doboku Gakkai Ronbunshu, no. 604 (1998): 11–21. http://dx.doi.org/10.2208/jscej.1998.604_11.

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46

Yai, Tetsuo, and Tetsuo Shimizu. "Multinomial Probit with Structured Covariance for Choice Situations with Similar Alternatives." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (1998): 69–75. http://dx.doi.org/10.3141/1645-09.

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The estimatability of the multinomial probit (MNP) model has been improved greatly by the modeling of the error structure. A modeling idea for travelers’ choice behaviors using the MNP with structured covariances (MNPSC) model is proposed. Choice of route, destination, and time of day are considered. The covariance of the error term of the utility function is structured by introducing the overlapped length between any two routes and the overlapped time length between any two departure times. Simultaneous models such as route and train class, route and mode, and time of day and mode choice mode

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47

Garrido, Rodrigo A., and Hani S. Mahmassani. "Forecasting freight transportation demand with the space–time multinomial probit model." Transportation Research Part B: Methodological 34, no. 5 (2000): 403–18. http://dx.doi.org/10.1016/s0191-2615(99)00032-6.

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48

Liu, Yu-Hsin, and Hani S. Mahmassani. "Global maximum likelihood estimation procedure for multinomial probit (MNP) model parameters." Transportation Research Part B: Methodological 34, no. 5 (2000): 419–49. http://dx.doi.org/10.1016/s0191-2615(99)00033-8.

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49

Zhang, Xiao, W. John Boscardin, and Thomas R. Belin. "Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models." Computational Statistics & Data Analysis 52, no. 7 (2008): 3697–708. http://dx.doi.org/10.1016/j.csda.2007.12.012.

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50

Çelik, Ali Kemal, and Betül Tamer. "Household’s Fuel Type Choice for Space Heating in Türkiye: A Comparison of Multinomial Logit and Multinomial Probit Models." International Journal of Energy Economics and Policy 14, no. 6 (2024): 651–64. http://dx.doi.org/10.32479/ijeep.17253.

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Better understanding of households’ fuel type choice behaviour for residential heating, cooking, or lighting purposes would provide valuable information in estimating households’ energy use and in developing efficient fuel switching and energy saving policies; these solutions could include reducing consumption and utilising renewable energy sources. This paper aims to explore potential determinants of household’s fuel choice for residential heating in Türkiye. Using nineteenth wave of Household Budget Survey which was administered to 11,828 households and 40,688 individuals throughout the cou

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