Easy and flexible mixture distributions
Author
Abstract
Suggested Citation
DOI: 10.1016/j.econlet.2013.03.050
Download full text from publisher
As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.
Other versions of this item:
- Fosgerau, Mogens & Mabit, Stefan, 2013. "Easy and flexible mixture distributions," MPRA Paper 46078, University Library of Munich, Germany.
References listed on IDEAS
- Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
- Fosgerau, Mogens & Bierlaire, Michel, 2007.
"A practical test for the choice of mixing distribution in discrete choice models,"
Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
- Mogens Fosgerau & Michel Bierlaire, 2005. "A practical test for the choice of mixing distribution in a discrete choice model," Econometrics 0512002, University Library of Munich, Germany.
- Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," MPRA Paper 42276, University Library of Munich, Germany.
- Vassilis A. Hajivassiliou & Paul A. Ruud, 1993. "Handbook of Econometrics: Classical Estimation Methods for LDV Models Using Simulation," Working Papers _021, Yale University.
- Fosgerau, Mogens & Nielsen, Søren Feodor, 2010.
"Deconvoluting Preferences And Errors: A Model For Binomial Panel Data,"
Econometric Theory, Cambridge University Press, vol. 26(6), pages 1846-1854, December.
- Fosgerau, Mogens & Nielsen, Søren Feodor, 2007. "Deconvoluting preferences and errors: a model for binomial panel data," MPRA Paper 3950, University Library of Munich, Germany.
- Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986.
"Classical estimation methods for LDV models using simulation,"
Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441,
Elsevier.
- Hajivassiliou, Vassilis A & Ruud, Paul A., 1993. "Classical Estimation Methods for LDV Models Using Simulation," Department of Economics, Working Paper Series qt3cg196fr, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
- Vassilis A. Hajivassiliou and Paul A. Ruud., 1993. "Classical Estimation Methods for LDV Models Using Simulation," Economics Working Papers 93-219, University of California at Berkeley.
- Vassilis A. Hajivassiliou & Paul A. Ruud, 1993. "Classical Estimation Methods for LDV Models Using Simulation," Cowles Foundation Discussion Papers 1051, Cowles Foundation for Research in Economics, Yale University.
- V.A. Hajivassiliou & P. A. Ruud, 1993. "Classical Estimation Methods for LDV Models Using Simulation," Econometrics 9311002, University Library of Munich, Germany.
- Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
- Train,Kenneth E., 2009.
"Discrete Choice Methods with Simulation,"
Cambridge Books,
Cambridge University Press, number 9780521747387, September.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
- Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2.
- Fosgerau, Mogens, 2006.
"Investigating the distribution of the value of travel time savings,"
Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 688-707, September.
- Mogens Fosgerau, 2004. "Investigating the distribution of the value of travel time savings," Urban/Regional 0410005, University Library of Munich, Germany, revised 25 Nov 2004.
- Mogens Fosgerau, 2004. "Investigating the distribution of the value of travel time savings," Urban/Regional 0411006, University Library of Munich, Germany.
- Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
- Bierens, Herman J., 2008. "Semi-Nonparametric Interval-Censored Mixed Proportional Hazard Models: Identification And Consistency Results," Econometric Theory, Cambridge University Press, vol. 24(3), pages 749-794, June.
- Headrick, Todd C., 2002. "Fast fifth-order polynomial transforms for generating univariate and multivariate nonnormal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 685-711, October.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Mogens Fosgerau, 2024.
"Nonparametric approaches to describing heterogeneity,"
Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 11, pages 308-318,
Edward Elgar Publishing.
- Mogens Fosgerau, 2014. "Nonparametric approaches to describing heterogeneity," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 11, pages 257-267, Edward Elgar Publishing.
- Fosgerau, Mogens & Hess, Stephane, 2008. "Competing methods for representing random taste heterogeneity in discrete choice models," MPRA Paper 10038, University Library of Munich, Germany.
- Ye, Xin & Garikapati, Venu M. & You, Daehyun & Pendyala, Ram M., 2017. "A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 173-192.
- Fosgerau, Mogens & Hjort, Katrine & Vincent Lyk-Jensen, Stéphanie, 2007. "An approach to the estimation of the distribution of marginal valuations from discrete choice data," MPRA Paper 3907, University Library of Munich, Germany.
- Fosgerau, Mogens & Hess, Stephane, 2009. "A comparison of methods for representing random taste heterogeneity in discrete choice models," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 42, pages 1-25.
- Bouscasse, Hélène & de Lapparent, Matthieu, 2019. "Perceived comfort and values of travel time savings in the Rhône-Alpes Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 370-387.
- Hong, Sung-Pil & Kim, Kyung min & Byeon, Geunyeong & Min, Yun-Hong, 2017. "A method to directly derive taste heterogeneity of travellers’ route choice in public transport from observed routes," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 41-52.
- Feo-Valero, María & Arencibia, Ana Isabel & Román, Concepción, 2016. "Analyzing discrepancies between willingness to pay and willingness to accept for freight transport attributes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 151-164.
- De Ayala Bilbao, Amaya & Hoyos Ramos, David & Mariel Chladkova, Petr, 2012. "Landscape valuation through discrete choice experiments: Current practice and future research reflections," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
- Fosgerau, Mogens & Nielsen, Søren Feodor, 2010.
"Deconvoluting Preferences And Errors: A Model For Binomial Panel Data,"
Econometric Theory, Cambridge University Press, vol. 26(6), pages 1846-1854, December.
- Fosgerau, Mogens & Nielsen, Søren Feodor, 2007. "Deconvoluting preferences and errors: a model for binomial panel data," MPRA Paper 3950, University Library of Munich, Germany.
- Steven M. Ramsey & Jason S. Bergtold, 2021. "Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1137-1165, December.
- Fosgerau, Mogens & Bierlaire, Michel, 2007.
"A practical test for the choice of mixing distribution in discrete choice models,"
Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
- Mogens Fosgerau & Michel Bierlaire, 2005. "A practical test for the choice of mixing distribution in a discrete choice model," Econometrics 0512002, University Library of Munich, Germany.
- Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," MPRA Paper 42276, University Library of Munich, Germany.
- JoonHwan Cho & Yao Luo & Ruli Xiao, 2022. "Deconvolution from Two Order Statistics," Working Papers tecipa-739, University of Toronto, Department of Economics.
- Sander Cranenburgh & Marco Kouwenhoven, 2021. "An artificial neural network based method to uncover the value-of-travel-time distribution," Transportation, Springer, vol. 48(5), pages 2545-2583, October.
- Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
- Li, Baibing, 2011. "The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 461-473, March.
- Bansal, Prateek & Daziano, Ricardo A & Guerra, Erick, 2018. "Minorization-Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 17-40.
- Bierens, Herman J. & Song, Hosin, 2012. "Semi-nonparametric estimation of independently and identically repeated first-price auctions via an integrated simulated moments method," Journal of Econometrics, Elsevier, vol. 168(1), pages 108-119.
- Small, Kenneth A., 2012. "Valuation of travel time," Economics of Transportation, Elsevier, vol. 1(1), pages 2-14.
- Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
More about this item
Keywords
Mixture distributions; Mixed logit; Simulation; Maximum simulated likelihood;All these keywords.
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:120:y:2013:i:2:p:206-210. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.