Relevant bibliographies by topics / Multinomial logit

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Multinomial logit'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Multinomial logit"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Multinomial logit"

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Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Bayesian Inference in the Multinomial Logit Model." Austrian Statistical Society, 2012. http://epub.wu.ac.at/5629/1/186%2D751%2D1%2DSM.pdf.

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The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.
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Powell, R. G. "Modelling take-over targets : a multinomial logit analysis." Thesis, University of Essex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307833.

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Kropko, Jonathan Rabinowitz George. "Choosing between multinomial logit and multinomial probit models for analysis of unordered choice data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1680.

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Thesis (M.A.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Sep. 16, 2008). "... in partial full̄lment of the requirements for the degree of Master of Arts in the Department of Political Science." Discipline: Political Science; Department/School: Political Science.
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Park, Seong Yong. "Modeling dynamic choice behavior : empirical analysis using multinomial logit and multiperiod multinomial probit models /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/8727.

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

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Klockare, Mikael. "Logit, oddskvot och sannolikhet : En analys av multinomial logistisk regression." Thesis, Karlstads universitet, Avdelningen för nationalekonomi och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-74575.

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Den här uppsatsen inleds med att studera de moment som används för multinomial logistisk regression och hur resultaten mäts. Teorin tar sin avsats i den binomiala logistiska regression, för att stegvis ta sig vidare till den multinomiala logistiska regressionen. Begreppen logit, oddskvoten och sannolikheterna förtydligas, effekterna av de oberoende variablerna diskuteras och kopplingen till vanlig linjär regression åskådliggörs. Det blir även en fördjupning av matematiken bakom den logistiska funktionen. Därefter tillämpas den multinomial logistisk regressionsanalysen med ett praktiskt exempel. Analysmodellen är användbar inom flertalet områden och den här uppsatsen ligger inom ramen för sportanalys. Matchstatistik från ishockey och närmare bestämt Örebro Hockeys matcher från säsongerna 2012/13 till 2017/18 nyttjas och den slutgiltiga modellen använder sig av tre förklarande variabler. Resultatet visar att utfallet efter ordinarie tid kan förklaras till 60,9% med hjälp av matchstatistiken, vilket tyder på att den multinomiala regressionsmodellen presterar likvärdigt med andra metoder som tillämpar kategorisk dataanalys inom sportanalys.
This thesis starts by studying the multinomial logistic regression and its moments and how the results are measured. The theory begins with the binomial logistics regression and gradually moves on towards the multinomial logistics regression. Concepts as logit, odds ratio and probabilities are explained, the effects of the independent variables discussed and the link to ordinary linear regression is illustrated. There will also be a deeper, mathematical look at the function of logistic growth. Thereafter the multinomial logistic regression model will be applied. The model is useful within several domains and this thesis lies within sportsanalytics. For this thesis matchstatistics from ice hockey, that is Örebro Hockey’s matches from season 2012/13 to 2017/18, has been used and the final model has three exploratory variables. The outcome of the result performs equivalent to other methods, which applies categorical data analysis within sportsanalytics.
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Alzubaidi, Samirah Hamid. "A case study on cumulative logit models with low frequency and mixed effects." Kansas State University, 2017. http://hdl.handle.net/2097/38252.

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Master of Science
Department of Statistics
Perla E. Reyes Cuellar
Data with ordinal responses may be encountered in many research fields, such as social, medical, agriculture or financial sciences. In this paper, we present a case study on cumulative logit models with low frequency and mixed effects and discuss some strengths and limitations of the current methodology. Two plant pathologists requested our statistical advice to fit a cumulative logit mixed model seeking for the effect of six commercial products on the control of a seed and seedling disease in soybeans in vitro. In their attempt to estimate the model parameters using a generalized linear mixed model approach with PROC GLIMMIX, the model failed to converge. Three alternative approaches to solve the problem were examined: 1) stratifying the data searching for the random effect; 2) assuming the random effect would be small and reducing the model to a fixed model; and 3) combining the original categories of the response variable to a lower number of categories. In addition, we conducted a power analysis to evaluate the required sample size to detect treatment differences. The results of all the proposed solutions were similar. Collapsing categories for a cumulative/proportional odds model has little effect on estimation. The sample size used in the case study is enough to detect a large shift of frequencies between categories, but not for moderated changes. Moreover, we do not have enough information to estimate a random effect. Even when it is present, the results regarding the fixed factors: pathogen, evaluation day, and treatment effects are the same as the obtained by the fixed model alternatives. All six products had a significant effect in slowing the effect of the pathogen, but the effects vary between pathogen species and assessment timing or date.
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Firmino, Costa da Silva Diego. "Escolha de cursos de graduação na Universidade Federal de Pernambuco : um estudo de seus determinantes." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/4139.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Este trabalho teve como objetivo principal analisar como os estudantes candidatos às vagas na Universidade Federal de Pernambuco tem escolhido qual carreira seguir. Desta forma foi estudado como as características sócio-econômicas dos candidatos influenciam na escolha e, em seguida, foi analisado como os retornos salariais correspondentes ao grupos de cursos disponíveis se associavam às características individuais para influenciar na decisão de qual profissão seguir. Para isto, foram utilizados os dados da Covest, que é a comissão organizadora do vestibular da Universidade Federal de Pernambuco, para o vestibular 2009. Além disso também foram utilizados dados da PNAD 2008 para a estimação dos salário médio de cada carreira. Os cursos oferecidos pela UFPE foram divididos em 9 grupos, obedecendo a razões de proximidade profissional das carreiras, e para realizar as estimações foram utilizados os modelos econométricos de logit multinomial e logit condicional. O primeiro modelo utiliza apenas variáveis explicativas relacionadas aos indivíduos e o segundo é quando se utiliza alguma variável explicativa característica das alternativas. Os resultados apontam para a influência que as variáveis pessoais, background familiar e variáveis educacionais exercem sobre a probabilidade de escolha dos grupos de cursos disponíveis. Também foi constatado, que o retorno salarial não tem significância sobre a probabilidade de escolha quando se leva em conta as variáveis que representam as características individuais
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Oliveira, Mara Janaina Gomes de [UNESP]. "Um perfil de concluintes do curso superior com base no ENADE (2005)." Universidade Estadual Paulista (UNESP), 2011. http://hdl.handle.net/11449/94775.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A Educação Superior pode ser considerada uma ferramenta que possibilita ao indivíduo, através do investimento em seu Capital humano, ampliação de sua capacidade produtiva, maiores salários, proporcionando menor iniqüidade na distribuição de renda entre os indivíduos. O objetivo deste trabalho é traçar um perfil sócio-econômico do recém formado no ensino superior brasileiro com base em dados do Exame Nacional do Desempenho dos Estudantes (ENADE). Como se sabe, a escolha do curso superior no Brasil tem forte influência de fatores sociais e econômicos. Para estimar probabilidades com que um graduado seja de determinado curso, dado seu perfil, um modelo logit multinomial foi estimado. Conclui-se que quando a expectativa em relação ao curso é de ganhos futuros, homens brancos, negros e mulatos tendem a escolher cursos de exatas e engenharias. Em contrapartida, os cursos de humanas, em sua maioria, são demandados por mulheres brancas, negras e mulatas. Mesmo assim, há mulheres brancas, em certas regiões como Sudeste e Sul, que tem preferência por curso de engenharias e exatas
Higher education can be considered a tool that enables an individual, through investment in human capital, expanding his or her production abilities, higher wages, providing less unequal distribution of income among individuals.This work aims to build a profile of the Brazilian college graduated based on data from ENADE (Student Performance National Exam). As fairly known, college career choice in Brazil is strongly affected by social and economic factors. In order to estimate the probability of choosing a career, given the student profile, a multinomial logit model will be estimated. It is possible to conclude that when the expectation is over the course of future earnings, white males, blacks and mulattoes tend choose the exact and engineering courses. In contrast, the humanities courses, mostly, are demanded by white, black and mulatto. Still, there are white women in certain regions such as Southeast and South, which has a preference for engineering courses and exact
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Rodrigues, Diego da Silva. "Uma análise dos determinantes da migração entre estados do trabalhador informal brasileiro." Universidade Federal de Juiz de Fora (UFJF), 2009. https://repositorio.ufjf.br/jspui/handle/ufjf/3974.

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Esse trabalho tem por finalidade estimar a probabilidade de migração interestadual dos trabalhadores que se destinam ao setor informal. Especificamente, busca-se analisar a probabilidade de migração conforme o nível de renda dos estados de destino. Esse objetivo é motivado pelo fato do Brasil apresentar intensa migração interna, o que leva à necessidade de compreender se as características dessa migração são as mesmas se considerarmos apenas o setor informal, que apresenta peculiaridades. Para isso, será elaborado um modelo probabilístico de migração com base em um banco de dados montado a partir da Ecinf (IBGE/2003). Inicialmente, é estimado um modelo probit simples, visando entender o impacto de características observadas pessoais, de trabalho e da região na decisão do indivíduo migrar. Depois, é estimado um modelo multinomial, buscando entender o impacto que essas características observadas têm quando o destino da migração é diferenciado pelo nível de renda dos estados. Os principais resultados obtidos indicam que, entre os informais, a migração segue características semelhantes às observadas na literatura, como ser mais propensa entre as mulheres, e apresentar renda maior entre os migrantes em comparação com os não-migrantes, sendo esta uma variável importante para a migração às regiões mais ricas. Por outro lado, os resultados também mostram que o aumento do nível de instrução tende a diminuir a probabilidade de um trabalhador informal migrar, indo de encontro ao que se observa noutros mercados de trabalho.
This paper aims to estimate the probability of interstate migration of informal workers. More specifically, it has the objective to analyze the probability of migration according to the income level of the destination states. This goal is motivated by the fact that Brazil has a strong internal migration, which leads to the need of understanding if the characteristics of the internal migration are the same if one considers only the informal sector, which presents peculiarities. This way, a probabilistic model of migration is made based on a database from the Ecinf (IBGE/2003). Initially, it is estimated a simple probit model, in order to understand the impact of observed personal, job and regional characteristics on the individual's decision to migrate. After that, it is estimated a multinomial model, trying to understand the impact that these observed characteristics have when the destination of the migration is differentiated by the income level of the states. The main results show that, among informal workers, the migration has characteristics similar to those observed in the literature and in the proposed model, as being more likely among women, and presenting higher incomes among the migrants when compared with non-migrants, and that being an important variable for migration to richer regions. On the other hand, the results also show that, among the informal workers, increasing of the educational level of the individuals tends to reduce the probability to migrate, against what is observed in other job markets.
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Books on the topic "Multinomial logit"

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

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

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

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Shi, Feng. Learn About Multinomial Logit Regression in R With Data From the General Social Survey (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526473479.

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Reid, Abigail-Kate, and Nick Allum. Learn About Multinomial Logit in Stata With Data From the Cooperative Congressional Election Study (2012). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710465.

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Reid, Abigail-Kate, and Nick Allum. Learn About Multinomial Logit in Stata With Data From the Behavioral Risk Factor Surveillance System (2013). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529712452.

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

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

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

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Multinomial Logit and the Behavioral Risk Factor Surveillance System (2013): Strenuousness of Recent Activity. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473961951.

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Book chapters on the topic "Multinomial logit"

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Bartels, K., Y. Boztug, and M. Müller. "Testing the Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization, 296–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57280-7_32.

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Börsch-Supan, Axel. "The Nested Multinomial Logit Model." In Lecture Notes in Economics and Mathematical Systems, 41–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-45633-6_4.

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Toyomane, Norimichi. "Multinomial Logit Models of Trade Coefficients." In Studies in Operational Regional Science, 80–119. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2782-7_5.

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Christ, Steffen. "Multinomial Logit Model for Low-Cost Travel Choice." In Operationalizing Dynamic Pricing Models, 253–301. Wiesbaden: Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_11.

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Theil, Henri. "A Multinomial Extension of the Linear Logit Model." In Advanced Studies in Theoretical and Applied Econometrics, 181–91. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2546-8_11.

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Street, Deborah J., and Leonie Burgess. "Designs for Choice Experiments for the Multinomial Logit Model." In Design and Analysis of Experiments, 331–78. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118147634.ch10.

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Kasper, Daniel, Ali Ünlü, and Bernhard Gschrey. "Sensitivity Analyses for the Mixed Coefficients Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization, 389–96. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01595-8_42.

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Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Data Augmentation and MCMC for Binary and Multinomial Logit Models." In Statistical Modelling and Regression Structures, 111–32. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2413-1_7.

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Anh, Tran-Thi P., Phan Cao Tho, and Fumihiko Nakamura. "Determinants of Bus Passengers’ Loyalty: A Multinomial Logit Regression Approach." In Lecture Notes in Civil Engineering, 1593–601. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7160-9_161.

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Aguilera, Ana, and Manuel Escabias. "Solving Multicollinearity in Functional Multinomial Logit Models for Nominal and Ordinal Responses." In Contributions to Statistics, 7–13. Heidelberg: Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2062-1_2.

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Conference papers on the topic "Multinomial logit"

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Ranganathan, Ananth. "Semantic Scene Segmentation using Random Multinomial Logit." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.59.

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Ou, Mingdong, Nan Li, Shenghuo Zhu, and Rong Jin. "Multinomial Logit Bandit with Linear Utility Functions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/361.

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Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a K-cardinality subset from N candidate items, and receives a reward which is governed by a multinomial logit (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon T. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, there have developed some algorithms by exploiting specific characteristics of the MNL model, but all of them estimate the parameters of MNL model separately and incur a regret bound which is not preferred for large candidate set size N. In this paper, we consider the linear utility MNL choice model whose item utilities are represented as linear functions of d-dimension item features, and propose an algorithm, titled LUMB, to exploit the underlying structure. It is proven that the proposed algorithm achieves regret which is free of candidate set size. Experiments show the superiority of the proposed algorithm.
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Meng, Jie. "Multinomial logit PLS regression of compositional data." In 2010 Second International Conference on Communication Systems, Networks and Applications (ICCSNA). IEEE, 2010. http://dx.doi.org/10.1109/iccsna.2010.5588855.

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

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Fang, Boli. "Fixed-Budget Pure Exploration in Multinomial Logit Bandits." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/409.

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In this paper we investigate pure exploration problem in Multinomial Logit bandit(MNL-bandit) under fixed budget settings, a problem motivated by real-time applications in online advertising and retailing. Given an MNL-bandit instance and a fixed exploration budget, our goal is to minimize the misidentification error of the optimal assortment. Towards such an end we propose an algorithm that achieve gap-dependent complexities, and complement our investigation with a discussion on recent studies and a minimax lower bound on misidentification probability. To the best of our knowledge, our paper is the first to address the recently proposed open problem of fixed-budget pure exploration problem for MNL-bandits.
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Suhua, Chen, Zhang Xiaojun, and Ding Jianming. "Feasibility on HOV lanes based on multinomial logit model." In 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE). IEEE, 2010. http://dx.doi.org/10.1109/icife.2010.5609269.

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

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

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

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

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Reports on the topic "Multinomial logit"

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Burda, Martin, Matthew C. Harding, and Jerry Hausman. A Bayesian mixed logit-probit model for multinomial choice. Institute for Fiscal Studies, August 2008. http://dx.doi.org/10.1920/wp.cem.2008.2308.

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

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Nesheim, Lars, and Joel L. Horowitz. Using penalized likelihood to select parameters in a random coefficients multinomial logit model. The IFS, April 2018. http://dx.doi.org/10.1920/wp.cem.2018.2918.

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Greene, D. L. RUMS: a PC-based Fortran program for estimating consumer surplus charges using multinomial logic and hedonic demand models. Office of Scientific and Technical Information (OSTI), August 1986. http://dx.doi.org/10.2172/5381050.

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