Generalized smooth finite mixtures
We propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights. The component densities can belong to any parametric family, with each model parameter being a deterministic function of covariates through a link function. Our MCMC methodology allows for Bayesian variable selection among the covariates in the mixture components and in the mixing weights. The model’s parameterization and variable selection prior are chosen to prevent overfitting. We use simulated and real data sets to illustrate the methodology.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Norets, Andriy & Pelenis, Justinas, 2014.
"Posterior Consistency In Conditional Density Estimation By Covariate Dependent Mixtures,"
Cambridge University Press, vol. 30(03), pages 606-646, June.
- Norets, Andriy & Pelenis, Justinas, 2011. "Posterior Consistency in Conditional Density Estimation by Covariate Dependent Mixtures," Economics Series 282, Institute for Advanced Studies.
- Joao A. Bastos & Joaquim J. S. Ramalho, 2010. "Nonparametric models of financial leverage decisions," CEMAPRE Working Papers 1005, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
- Esmeralda A. Ramalho & Joaquim J.S. Ramalho & José M.R. Murteira, 2011.
"Alternative Estimating And Testing Empirical Strategies For Fractional Regression Models,"
Journal of Economic Surveys,
Wiley Blackwell, vol. 25(1), pages 19-68, 02.
- Esmeralda A. Ramalho & Joaquim J.S. Ramalho & José M.R. Murteira, 2009. "Alternative estimating and testing empirical strategies for fractional regression models," CEFAGE-UE Working Papers 2009_08, University of Evora, CEFAGE-UE (Portugal).
- Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
- Sally A. Wood, 2002. "Bayesian mixture of splines for spatially adaptive nonparametric regression," Biometrika, Biometrika Trust, vol. 89(3), pages 513-528, August.
- Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014.
"Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios,"
Journal of Financial and Quantitative Analysis,
Cambridge University Press, vol. 49(04), pages 1071-1099, August.
- Giordani, Paolo & Jacobson, Tor & von Schedvin , Erik & Villani, Mattias, 2011. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Working Paper Series 256, Sveriges Riksbank (Central Bank of Sweden).
- Chung, Yeonseung & Dunson, David B., 2009. "Nonparametric Bayes Conditional Distribution Modeling With Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1646-1660.
- David B. Dunson & Natesh Pillai & Ju-Hyun Park, 2007. "Bayesian density regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 163-183.
- Joseph Hilbe, 1994. "Negative binomial regression," Stata Technical Bulletin, StataCorp LP, vol. 3(18).
- Raghuram G. Rajan & Luigi Zingales, 1994.
"What Do We Know About Capital Structure? Some Evidence from International Data,"
NBER Working Papers
4875, National Bureau of Economic Research, Inc.
- Rajan, Raghuram G & Zingales, Luigi, 1995. " What Do We Know about Capital Structure? Some Evidence from International Data," Journal of Finance, American Finance Association, vol. 50(5), pages 1421-60, December.
- Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2009. "Regression density estimation using smooth adaptive Gaussian mixtures," Journal of Econometrics, Elsevier, vol. 153(2), pages 155-173, December.
- John Geweke, 1999.
"Using simulation methods for bayesian econometric models: inference, development,and communication,"
Taylor & Francis Journals, vol. 18(1), pages 1-73.
- John F. Geweke, 1998. "Using simulation methods for Bayesian econometric models: inference, development, and communication," Staff Report 249, Federal Reserve Bank of Minneapolis.
- Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
- David J. Nott & Robert Kohn, 2005. "Adaptive sampling for Bayesian variable selection," Biometrika, Biometrika Trust, vol. 92(4), pages 747-763, December.
- Peter J. Green, 2001. "Modelling Heterogeneity With and Without the Dirichlet Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(2), pages 355-375.
- Cook, Douglas O. & Kieschnick, Robert & McCullough, B.D., 2008. "Regression analysis of proportions in finance with self selection," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 860-867, December.
- Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer, vol. 5(2), pages 249-282, September.
When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:171:y:2012:i:2:p:121-133. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.