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Volumn 140, Issue 12, 2010, Pages 3638-3654

Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities

Author keywords

Bayesian inference; Markov chain Monte Carlo; Mixture of experts; Variable selection; Volatility modeling

Indexed keywords


EID: 77955580236     PISSN: 03783758     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jspi.2010.04.031     Document Type: Article
Times cited : (27)

References (37)
  • 1
    • 77955588445 scopus 로고
    • (Eds.),. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Table. Courier Dover Publications, New York.
    • Abramowitz, M., Stegun, I. (Eds.), 1972. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Table. Courier Dover Publications, New York.
    • (1972)
    • Abramowitz, M.1    Stegun, I.2
  • 2
    • 0011836910 scopus 로고    scopus 로고
    • Range-based estimation of stochastic volatility models
    • Alizadeh S., Brandt M., Diebold F. Range-based estimation of stochastic volatility models. Journal of Finance 2002, 57(3):1047-1091.
    • (2002) Journal of Finance , vol.57 , Issue.3 , pp. 1047-1091
    • Alizadeh, S.1    Brandt, M.2    Diebold, F.3
  • 3
    • 77955570905 scopus 로고    scopus 로고
    • Modeling volatility
    • Palgrave Macmillan, New York
    • Baillie R.T. Modeling volatility. Handbook of Econometrics 2006, vol. 1:737-764. Palgrave Macmillan, New York.
    • (2006) Handbook of Econometrics , vol.1 , pp. 737-764
    • Baillie, R.T.1
  • 4
    • 0035636851 scopus 로고    scopus 로고
    • Testing density forecasts, with applications to risk management
    • Berkowitz J. Testing density forecasts, with applications to risk management. Journal of Business & Economic Statistics 2001, 19(4):465-474.
    • (2001) Journal of Business & Economic Statistics , vol.19 , Issue.4 , pp. 465-474
    • Berkowitz, J.1
  • 5
    • 0000375581 scopus 로고
    • A conditionally heteroskedastic time series model for speculative prices and rates of return
    • Bollerslev T. A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics 1987, 69(3):542-547.
    • (1987) The Review of Economics and Statistics , vol.69 , Issue.3 , pp. 542-547
    • Bollerslev, T.1
  • 6
    • 2242491935 scopus 로고    scopus 로고
    • Computational and inferential difficulties with mixture posterior distributions
    • Celeux G., Hurn M., Robert C. Computational and inferential difficulties with mixture posterior distributions. Journal of the American Statistical Association 2000, 95(451):957-970.
    • (2000) Journal of the American Statistical Association , vol.95 , Issue.451 , pp. 957-970
    • Celeux, G.1    Hurn, M.2    Robert, C.3
  • 9
    • 0000051984 scopus 로고
    • Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
    • Engle R. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society 1982, 50(4):987-1007.
    • (1982) Econometrica: Journal of the Econometric Society , vol.50 , Issue.4 , pp. 987-1007
    • Engle, R.1
  • 12
    • 0042042167 scopus 로고    scopus 로고
    • Sampling from the posterior distribution in generalized linear mixed models
    • Gamerman D. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing 1997, 7(1):57-68.
    • (1997) Statistics and Computing , vol.7 , Issue.1 , pp. 57-68
    • Gamerman, D.1
  • 13
    • 0001667705 scopus 로고
    • Bayesian inference in econometric models using Monte Carlo integration
    • Geweke J. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 1989, 57(6):1317-1339.
    • (1989) Econometrica , vol.57 , Issue.6 , pp. 1317-1339
    • Geweke, J.1
  • 14
    • 33847395816 scopus 로고    scopus 로고
    • Interpretation and inference in mixture models: simple MCMC works
    • Geweke J. Interpretation and inference in mixture models: simple MCMC works. Computational Statistics and Data Analysis 2007, 51(7):3529-3550.
    • (2007) Computational Statistics and Data Analysis , vol.51 , Issue.7 , pp. 3529-3550
    • Geweke, J.1
  • 15
    • 33947366371 scopus 로고    scopus 로고
    • Smoothly mixing regressions
    • Geweke J., Keane M. Smoothly mixing regressions. Journal of Econometrics 2007, 138(1):252-290.
    • (2007) Journal of Econometrics , vol.138 , Issue.1 , pp. 252-290
    • Geweke, J.1    Keane, M.2
  • 16
    • 0007995197 scopus 로고
    • Estimation of impurity profiles in ion-implanted amorphous targets using joined half-Gaussian distributions
    • Gibbons J., Mylroie S. Estimation of impurity profiles in ion-implanted amorphous targets using joined half-Gaussian distributions. Applied Physics Letters 1973, 22(11):568.
    • (1973) Applied Physics Letters , vol.22 , Issue.11 , pp. 568
    • Gibbons, J.1    Mylroie, S.2
  • 17
    • 0001619086 scopus 로고
    • Autoregressive conditional density estimation
    • Hansen B. Autoregressive conditional density estimation. International Economic Review 1994, 35(3):705-730.
    • (1994) International Economic Review , vol.35 , Issue.3 , pp. 705-730
    • Hansen, B.1
  • 19
    • 22544479764 scopus 로고    scopus 로고
    • Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
    • Jasra A., Holmes C., Stephens D. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statistical Science 2005, 20(1):50-67.
    • (2005) Statistical Science , vol.20 , Issue.1 , pp. 50-67
    • Jasra, A.1    Holmes, C.2    Stephens, D.3
  • 20
  • 21
    • 0033248628 scopus 로고    scopus 로고
    • Hierarchical mixtures-of-experts for exponential family regression models: approximation and maximum likelihood estimation
    • Jiang W., Tanner M. Hierarchical mixtures-of-experts for exponential family regression models: approximation and maximum likelihood estimation. Annals of Statistics 1999, 27(3):987-1011.
    • (1999) Annals of Statistics , vol.27 , Issue.3 , pp. 987-1011
    • Jiang, W.1    Tanner, M.2
  • 22
    • 0033161418 scopus 로고    scopus 로고
    • On the approximation rate of hierarchical mixtures-of-experts for generalized linear models
    • Jiang W., Tanner M. On the approximation rate of hierarchical mixtures-of-experts for generalized linear models. Neural Computation 1999, 11(5):1183-1198.
    • (1999) Neural Computation , vol.11 , Issue.5 , pp. 1183-1198
    • Jiang, W.1    Tanner, M.2
  • 23
    • 0344343072 scopus 로고
    • The three-parameter two-piece normal family of distributions and its fitting
    • John S. The three-parameter two-piece normal family of distributions and its fitting. Communications in Statistics-Theory and Methods 1982, 11(8):879-885.
    • (1982) Communications in Statistics-Theory and Methods , vol.11 , Issue.8 , pp. 879-885
    • John, S.1
  • 24
    • 0000262562 scopus 로고
    • Hierarchical mixtures of experts and the EM algorithm
    • Jordan M., Jacobs R. Hierarchical mixtures of experts and the EM algorithm. Neural Computation 1994, 6(2):181-214.
    • (1994) Neural Computation , vol.6 , Issue.2 , pp. 181-214
    • Jordan, M.1    Jacobs, R.2
  • 27
    • 0010081155 scopus 로고    scopus 로고
    • Nonparametric regression using linear combinations of basis functions
    • Kohn R., Smith M., Chan D. Nonparametric regression using linear combinations of basis functions. Statistics and Computing 2001, 11(4):313-322.
    • (2001) Statistics and Computing , vol.11 , Issue.4 , pp. 313-322
    • Kohn, R.1    Smith, M.2    Chan, D.3
  • 28
    • 27944431773 scopus 로고    scopus 로고
    • Adaptive sampling for Bayesian variable selection
    • Nott D., Kohn R. Adaptive sampling for Bayesian variable selection. Biometrika 2005, 92(4):747-763.
    • (2005) Biometrika , vol.92 , Issue.4 , pp. 747-763
    • Nott, D.1    Kohn, R.2
  • 29
    • 3042586796 scopus 로고    scopus 로고
    • Sampling schemes for Bayesian variable selection in generalized linear models
    • Nott D., Leonte D. Sampling schemes for Bayesian variable selection in generalized linear models. Journal of Computational and Graphical Statistics 2004, 13(2):362-382.
    • (2004) Journal of Computational and Graphical Statistics , vol.13 , Issue.2 , pp. 362-382
    • Nott, D.1    Leonte, D.2
  • 32
    • 0034374610 scopus 로고    scopus 로고
    • Bayesian analysis of mixture models with an unknown number of components-an alternative to reversible jump methods
    • Stephens M. Bayesian analysis of mixture models with an unknown number of components-an alternative to reversible jump methods. The Annals of Statistics 2000, 28(1):40-74.
    • (2000) The Annals of Statistics , vol.28 , Issue.1 , pp. 40-74
    • Stephens, M.1
  • 33
    • 77955562639 scopus 로고    scopus 로고
    • Nonparametric regression density estimation using smoothly varying normal mixtures. Sveriges Riksbank Working Paper Series, no. 211, Available at.
    • Villani, M., Kohn, R., Giordani, P., 2007. Nonparametric regression density estimation using smoothly varying normal mixtures. Sveriges Riksbank Working Paper Series, no. 211, Available at. http://www.riksbank.com.
    • (2007)
    • Villani, M.1    Kohn, R.2    Giordani, P.3
  • 34
    • 70349427041 scopus 로고    scopus 로고
    • Regression density estimation using smooth adaptive Gaussian mixtures
    • Villani M., Kohn R., Giordani P. Regression density estimation using smooth adaptive Gaussian mixtures. Journal of Econometrics 2009, 153(2):155-173.
    • (2009) Journal of Econometrics , vol.153 , Issue.2 , pp. 155-173
    • Villani, M.1    Kohn, R.2    Giordani, P.3
  • 35
    • 13844262338 scopus 로고    scopus 로고
    • Bayesian mixture of splines for spatially adaptive nonparametric regression
    • Wood S., Jiang W., Tanner M. Bayesian mixture of splines for spatially adaptive nonparametric regression. Biometrika 2002, 89(3):513-528.
    • (2002) Biometrika , vol.89 , Issue.3 , pp. 513-528
    • Wood, S.1    Jiang, W.2    Tanner, M.3
  • 36
    • 0031037819 scopus 로고    scopus 로고
    • Density estimation through convex combinations of densities: approximation and estimation bounds
    • Zeevi A., Meir R. Density estimation through convex combinations of densities: approximation and estimation bounds. Neural Networks 1997, 10(1):99-109.
    • (1997) Neural Networks , vol.10 , Issue.1 , pp. 99-109
    • Zeevi, A.1    Meir, R.2
  • 37
    • 0002817906 scopus 로고
    • On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In: Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti.
    • Zellner, A., 1986. On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In: Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, vol. 6, pp. 233-243.
    • (1986) , vol.6 , pp. 233-243
    • Zellner, A.1


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