IDEAS home Printed from https://ideas.repec.org/r/bla/jorssb/v59y1997i4p731-792.html
   My bibliography  Save this item

On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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.
  2. Athanasios Christou Micheas, 2014. "Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2596-2615, December.
  3. J. B. Shah & M. N. Patel, 2009. "Bayesian estimation of parameters of mixed geometric failure models from Type I group censored sample," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 495-506.
  4. Sylvia Kaufmann & Sylvia Frühwirth‐Schnatter, 2002. "Bayesian analysis of switching ARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 425-458, July.
  5. Roberto Zelli & Maria Grazia Pittau, 2006. "Empirical evidence of income dynamics across EU regions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 605-628.
  6. Alston, C.L. & Mengersen, K.L. & Robert, C.P. & Thompson, J.M. & Littlefield, P.J. & Perry, D. & Ball, A.J., 2007. "Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4282-4296, May.
  7. Gagnon, Philippe & Bédard, Mylène & Desgagné, Alain, 2019. "Weak convergence and optimal tuning of the reversible jump algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 32-51.
  8. Billio, M. & Monfort, A. & Robert, C. P., 1999. "Bayesian estimation of switching ARMA models," Journal of Econometrics, Elsevier, vol. 93(2), pages 229-255, December.
  9. Saverio Ranciati & Giuliano Galimberti & Gabriele Soffritti, 2019. "Bayesian variable selection in linear regression models with non-normal errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 323-358, June.
  10. Rosychuk, Rhonda J. & Shofiqul Islam, 2009. "Parameter estimation in a model for misclassified Markov data -- a Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3805-3816, September.
  11. Ho, Remus K.W. & Hu, Inchi, 2008. "Flexible modelling of random effects in linear mixed models--A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1347-1361, January.
  12. Jamie L. Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Dijk, 2024. "Flexible Negative Binomial Mixtures for Credible Mode Inference in Heterogeneous Count Data from Finance, Economics and Bioinformatics," Tinbergen Institute Discussion Papers 24-056/III, Tinbergen Institute.
  13. Robert, Christian P., 2004. "Bayesian computational methods," Papers 2004,18, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
  14. Francisco Richter & Bart Haegeman & Rampal S. Etienne & Ernst C. Wit, 2020. "Introducing a general class of species diversification models for phylogenetic trees," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 261-274, August.
  15. Papastamoulis, Panagiotis & Iliopoulos, George, 2009. "Reversible Jump MCMC in mixtures of normal distributions with the same component means," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 900-911, February.
  16. Markus Thamerus, 2003. "Fitting a Mixture Distribution to a Variable Subject to Heteroscedastie Measurement Errors," Computational Statistics, Springer, vol. 18(1), pages 1-17, March.
  17. Rodríguez, Carlos E. & Núñez-Antonio, Gabriel & Escarela, Gabriel, 2020. "A Bayesian mixture model for clustering circular data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  18. Fisher, Mark & Jensen, Mark J., 2022. "Bayesian nonparametric learning of how skill is distributed across the mutual fund industry," Journal of Econometrics, Elsevier, vol. 230(1), pages 131-153.
  19. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
  20. N. K. Unnikrishnan, 2004. "Bayesian Subset Model Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 671-690, September.
  21. Pierre Alquier & Karim Lounici, 2010. "Pac-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights," Working Papers 2010-40, Center for Research in Economics and Statistics.
  22. Kenneth A. Bollen & Surajit Ray & Jane Zavisca & Jeffrey J. Harden, 2012. "A Comparison of Bayes Factor Approximation Methods Including Two New Methods," Sociological Methods & Research, , vol. 41(2), pages 294-324, May.
  23. Lee, Jung Wun & Chung, Hwan & Jeon, Saebom, 2021. "Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
  24. Robert, Christian P. & Mengersen, Kerrie L., 1999. "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 29(3), pages 325-343, January.
  25. Lee, Sik-Yum & Song, Xin-Yuan, 2008. "On Bayesian estimation and model comparison of an integrated structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4814-4827, June.
  26. Carnicero, José Antonio, 2008. "A semi-parametric model for circular data based on mixtures of beta distributions," DES - Working Papers. Statistics and Econometrics. WS ws081305, Universidad Carlos III de Madrid. Departamento de Estadística.
  27. Vaidehi Dixit & Ryan Martin, 2022. "Estimating a Mixing Distribution on the Sphere Using Predictive Recursion," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 596-626, November.
  28. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
  29. Nalini Ravishanker & Dipak K. Dey, 2000. "Multivariate Survival Models with a Mixture of Positive Stable Frailties," Methodology and Computing in Applied Probability, Springer, vol. 2(3), pages 293-308, September.
  30. Davide Ravagli & Georgi N. Boshnakov, 2022. "Bayesian analysis of mixture autoregressive models covering the complete parameter space," Computational Statistics, Springer, vol. 37(3), pages 1399-1433, July.
  31. Shuang Zhang & Xingdong Feng, 2022. "Distributed identification of heterogeneous treatment effects," Computational Statistics, Springer, vol. 37(1), pages 57-89, March.
  32. McGrory, C.A. & Pettitt, A.N. & Faddy, M.J., 2009. "A fully Bayesian approach to inference for Coxian phase-type distributions with covariate dependent mean," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4311-4321, October.
  33. José Dias & Jeroen Vermunt, 2008. "A bootstrap-based aggregate classifier for model-based clustering," Computational Statistics, Springer, vol. 23(4), pages 643-659, October.
  34. Roy Costilla & Ivy Liu & Richard Arnold & Daniel Fernández, 2019. "Bayesian model-based clustering for longitudinal ordinal data," Computational Statistics, Springer, vol. 34(3), pages 1015-1038, September.
  35. Fuentes-García, Ruth & Mena, Ramsés H. & Walker, Stephen G., 2019. "Modal posterior clustering motivated by Hopfield’s network," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 92-100.
  36. Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2007. "Nonparametric Regression Density Estimation Using Smoothly Varying Normal Mixtures," Working Paper Series 211, Sveriges Riksbank (Central Bank of Sweden).
  37. Lo, Yungtai, 2011. "Bias from misspecification of the component variances in a normal mixture," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2739-2747, September.
  38. D.S. Poskitt & Jing Zhang, 2004. "Estimating Components in Finite Mixtures and Hidden Markov Models," Monash Econometrics and Business Statistics Working Papers 10/04, Monash University, Department of Econometrics and Business Statistics.
  39. Obereder, Andreas & Scherzer, Otmar & Kovac, Arne, 2007. "Bivariate density estimation using BV regularisation," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5622-5634, August.
  40. Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.
  41. Liu, Rui-Yin & Tao, Jian & Shi, Ning-Zhong & He, Xuming, 2011. "Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1498-1508, March.
  42. Hlouskova, Jaroslava & Sögner, Leopold, 2020. "GMM estimation of affine term structure models," Econometrics and Statistics, Elsevier, vol. 13(C), pages 2-15.
  43. Song, Xin-Yuan & Lee, Sik-Yum, 2002. "A Bayesian model selection method with applications," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 539-557, September.
  44. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
  45. Naderi, Mehrdad & Mirfarah, Elham & Wang, Wan-Lun & Lin, Tsung-I, 2023. "Robust mixture regression modeling based on the normal mean-variance mixture distributions," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
  46. Ryan Martin, 2021. "A Survey of Nonparametric Mixing Density Estimation via the Predictive Recursion Algorithm," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 97-121, May.
  47. Yasutomo Murasawa, 2020. "Measuring public inflation perceptions and expectations in the UK," Empirical Economics, Springer, vol. 59(1), pages 315-344, July.
  48. Nian-Sheng Tang & De-Wang Li & An-Min Tang, 2017. "Semiparametric Bayesian inference on generalized linear measurement error models," Statistical Papers, Springer, vol. 58(4), pages 1091-1113, December.
  49. Panagiotis Papastamoulis & George Iliopoulos, 2013. "On the Convergence Rate of Random Permutation Sampler and ECR Algorithm in Missing Data Models," Methodology and Computing in Applied Probability, Springer, vol. 15(2), pages 293-304, June.
  50. Rosineide Fernando da Paz & Jorge Luis Bazán & Luis Aparecido Milan, 2017. "Bayesian estimation for a mixture of simplex distributions with an unknown number of components: HDI analysis in Brazil," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(9), pages 1630-1643, July.
  51. Domingo Benítez & Gustavo Montero & Eduardo Rodríguez & David Greiner & Albert Oliver & Luis González & Rafael Montenegro, 2020. "A Phenomenological Epidemic Model Based On the Spatio-Temporal Evolution of a Gaussian Probability Density Function," Mathematics, MDPI, vol. 8(11), pages 1-22, November.
  52. Walker, Stephen G., 2023. "Comparing weak and strong convergence of density functions," Statistics & Probability Letters, Elsevier, vol. 200(C).
  53. A. Pollice & M. Bilancia, 2000. "A hierarchical finite mixture model for Bayesian classification in the presence of auxiliary information," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 109-131.
  54. Florian Huber & Tam'as Krisztin & Michael Pfarrhofer, 2018. "A Bayesian panel VAR model to analyze the impact of climate change on high-income economies," Papers 1804.01554, arXiv.org, revised Feb 2021.
  55. Hong-Tu Zhu & Sik-Yum Lee, 2001. "A Bayesian analysis of finite mixtures in the LISREL model," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 133-152, March.
  56. Arun Gopalakrishnan & Eric T. Bradlow & Peter S. Fader, 2017. "A Cross-Cohort Changepoint Model for Customer-Base Analysis," Marketing Science, INFORMS, vol. 36(2), pages 195-213, March.
  57. Yuan Fang & Dimitris Karlis & Sanjeena Subedi, 2022. "Infinite Mixtures of Multivariate Normal-Inverse Gaussian Distributions for Clustering of Skewed Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 510-552, November.
  58. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
  59. Oscar M Rueda & Ramón Díaz-Uriarte, 2007. "Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-8, June.
  60. Im, Yunju & Tan, Aixin, 2021. "Bayesian subgroup analysis in regression using mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
  61. Sik-Yum Lee, 2006. "Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 541-564, September.
  62. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2015. "Model-Based Clustering for Conditionally Correlated Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 145-175, July.
  63. Kazuhiko Kakamu, 2022. "Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data," Papers 2210.05115, arXiv.org, revised Sep 2023.
  64. Gustavo Alexis Sabillón & Luiz Gabriel Fernandes Cotrim & Daiane Aparecida Zuanetti, 2023. "A data-driven reversible jump for estimating a finite mixture of regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 350-369, March.
  65. J. Vermaak & C. Andrieu & A. Doucet & S. J. Godsill, 2004. "Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 785-809, November.
  66. Luigi Spezia, 2019. "Modelling covariance matrices by the trigonometric separation strategy with application to hidden Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 399-422, June.
  67. Hilger, James & Hanemann, Michael, 2006. "Heterogeneous Preferences for Water Quality: A Finite Mixture Model of Beach Recreation in Southern California," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt0565c0b2, Department of Agricultural & Resource Economics, UC Berkeley.
  68. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
  69. Michael E. Sobel & Bengt Muthén, 2012. "Compliance Mixture Modelling with a Zero-Effect Complier Class and Missing Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1037-1045, December.
  70. S. Upadhyay & M. Peshwani, 2008. "Posterior analysis of lognormal regression models using the Gibbs sampler," Statistical Papers, Springer, vol. 49(1), pages 59-85, March.
  71. Athanasios C. Micheas & Jiaxun Chen, 2018. "sppmix: Poisson point process modeling using normal mixture models," Computational Statistics, Springer, vol. 33(4), pages 1767-1798, December.
  72. Bettina Grün & Gertraud Malsiner-Walli & Sylvia Frühwirth-Schnatter, 2022. "How many data clusters are in the Galaxy data set?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 325-349, June.
  73. Shota Gugushvili & Frank Meulen & Peter Spreij, 2018. "A non-parametric Bayesian approach to decompounding from high frequency data," Statistical Inference for Stochastic Processes, Springer, vol. 21(1), pages 53-79, April.
  74. repec:dau:papers:123456789/4648 is not listed on IDEAS
  75. Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
  76. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  77. Han, Ningren & Ram, Rajeev J., 2020. "Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  78. Lee, Kuo-Jung & Feldkircher, Martin & Chen, Yi-Chi, 2021. "Variable selection in finite mixture of regression models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  79. De Blasi, Pierpaolo & Martínez, Asael Fabian & Mena, Ramsés H. & Prünster, Igor, 2020. "On the inferential implications of decreasing weight structures in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
  80. Matthew W. Wheeler & Todd Blessinger & Kan Shao & Bruce C. Allen & Louis Olszyk & J. Allen Davis & Jeffrey S Gift, 2020. "Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1706-1722, September.
  81. Weber, Anett & Steiner, Winfried J., 2021. "Modeling price response from retail sales: An empirical comparison of models with different representations of heterogeneity," European Journal of Operational Research, Elsevier, vol. 294(3), pages 843-859.
  82. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
  83. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.
  84. Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 33-64, March.
  85. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.
  86. Edefonti Valeria & Parmigiani Giovanni, 2017. "Combinatorial Mixtures of Multiparameter Distributions: An Application to Bivariate Data," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-31, May.
  87. Ani Eloyan & Sujit Ghosh, 2011. "Smooth density estimation with moment constraints using mixture distributions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 513-531.
  88. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
  89. Murasawa, Yasutomo, 2017. "Measuring the Distributions of Public Inflation Perceptions and Expectations in the UK," MPRA Paper 76244, University Library of Munich, Germany.
  90. Lo, Yungtai, 2005. "Likelihood ratio tests of the number of components in a normal mixture with unequal variances," Statistics & Probability Letters, Elsevier, vol. 71(3), pages 225-235, March.
  91. N. T. Longford & Pierpaolo D'Urso, 2011. "Mixture models with an improper component," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2511-2521, January.
  92. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
  93. Kazem Nasserinejad & Joost van Rosmalen & Wim de Kort & Emmanuel Lesaffre, 2017. "Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
  94. Arana, Jorge E. & Leon, Carmelo J., 2005. "Flexible mixture distribution modeling of dichotomous choice contingent valuation with heterogenity," Journal of Environmental Economics and Management, Elsevier, vol. 50(1), pages 170-188, July.
  95. Ungolo, Francesco & Kleinow, Torsten & Macdonald, Angus S., 2020. "A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 68-84.
  96. Ang Shan & Fengkai Yang, 2021. "Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm," Mathematics, MDPI, vol. 9(6), pages 1-13, March.
  97. Marco Berrettini & Giuliano Galimberti & Saverio Ranciati, 2023. "Semiparametric finite mixture of regression models with Bayesian P-splines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 745-775, September.
  98. Murray Aitkin & Duy Vu & Brian Francis, 2015. "A new Bayesian approach for determining the number of components in a finite mixture," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 155-176, August.
  99. Arima, Serena & Basset, Alberto & Jona Lasinio, Giovanna & Pollice, Alessio & Rosati, Ilaria, 2013. "A hierarchical Bayesian model for the ecological status classification of lagoons," Ecological Modelling, Elsevier, vol. 263(C), pages 187-195.
  100. McGrory, C.A. & Titterington, D.M., 2007. "Variational approximations in Bayesian model selection for finite mixture distributions," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5352-5367, July.
  101. Liu, Hefei & Song, Xinyuan, 2021. "Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states," Econometrics and Statistics, Elsevier, vol. 18(C), pages 29-43.
  102. Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.
  103. Cathy W. S. Chen & Mike K. P. So & Ming-Tien Chen, 2005. "A Bayesian threshold nonlinearity test for financial time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 61-75.
  104. José T.A.S. Ferreira & Miguel A Juárez & MArk F.J. Steel, 2005. "Directional Log-spline Distributions," Econometrics 0511001, University Library of Munich, Germany.
  105. Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2021. "Bayes estimates of multimodal density features using DNA and Economic Data," Tinbergen Institute Discussion Papers 21-017/III, Tinbergen Institute.
  106. Zdravko I. Botev & Dirk P. Kroese, 2011. "The Generalized Cross Entropy Method, with Applications to Probability Density Estimation," Methodology and Computing in Applied Probability, Springer, vol. 13(1), pages 1-27, March.
  107. Kuaile Feng & Jianzhong Zhou & Yi Liu & Chengwei Lu & Zhongzheng He, 2019. "Hydrological Uncertainty Processor (HUP) with Estimation of the Marginal Distribution by a Gaussian Mixture Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 2975-2990, July.
  108. Lee, Minseo & Sohn, Keemin, 2015. "Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 1-17.
  109. Aurore Lomet & Gérard Govaert & Yves Grandvalet, 2018. "Model selection for Gaussian latent block clustering with the integrated classification likelihood," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 489-508, September.
  110. Moya, Blake & Walker, Stephen G., 2024. "Full uncertainty analysis for Bayesian nonparametric mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
  111. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
  112. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.
  113. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 117-143, March.
  114. Paroli, Roberta & Spezia, Luigi, 2008. "Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2311-2330, January.
  115. Grazian, Clara & Villa, Cristiano & Liseo, Brunero, 2020. "On a loss-based prior for the number of components in mixture models," Statistics & Probability Letters, Elsevier, vol. 158(C).
  116. Motegi, Ryosuke & Seki, Yoichi, 2023. "SMLSOM: The shrinking maximum likelihood self-organizing map," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
  117. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
  118. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
  119. McVinish, R. & Mengersen, K., 2008. "Semiparametric Bayesian circular statistics," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4722-4730, June.
  120. Creal, Drew & Kim, Jaeho, 2024. "Bayesian estimation of cluster covariance matrices of unknown form," Journal of Econometrics, Elsevier, vol. 241(1).
  121. Sanjeena Subedi & Paul D. McNicholas, 2021. "A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 89-108, April.
  122. M. Concepcion Ausin & Michael P. Wiper & Rosa E. Lillo, 2009. "Bayesian estimation of finite time ruin probabilities," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(6), pages 787-805, November.
  123. Riko Kelter, 2022. "A New Bayesian Two-Sample t Test and Solution to the Behrens–Fisher Problem Based on Gaussian Mixture Modelling with Known Allocations," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 380-412, December.
  124. McGrory, C.A. & Pettitt, A.N. & Titterington, D.M. & Alston, C.L. & Kelly, M., 2016. "Transdimensional sequential Monte Carlo using variational Bayes — SMCVB," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 246-254.
  125. Dingjing Shi & Xin Tong, 2017. "The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation," SAGE Open, , vol. 7(3), pages 21582440177, August.
  126. Edward P. Campbell, 2004. "Bayesian selection of threshold autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 467-482, July.
  127. Komárek, Arnost, 2009. "A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3932-3947, October.
  128. Nicolas Chopin & Christian P. Robert, 2010. "Properties of nested sampling," Biometrika, Biometrika Trust, vol. 97(3), pages 741-755.
  129. Sik-Yum Lee & Xin-Yuan Song, 2003. "Model comparison of nonlinear structural equation models with fixed covariates," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 27-47, March.
  130. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
  131. Wu, C.C. & Lee, Jack C., 2007. "Estimation of a utility-based asset pricing model using normal mixture GARCH(1,1)," Economic Modelling, Elsevier, vol. 24(2), pages 329-349, March.
  132. Assaf, A. George & Tsionas, Mike & Oh, Haemoon, 2018. "The time has come: Toward Bayesian SEM estimation in tourism research," Tourism Management, Elsevier, vol. 64(C), pages 98-109.
  133. Nicolas Chopin & Tony Lelievre & Gabriel Stoltz, 2010. "Free Energy Methods for Efficient Exploration of Mixture Posterior Densities," Working Papers 2010-33, Center for Research in Economics and Statistics.
  134. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
  135. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
  136. You, Na & Dai, Hongsheng & Wang, Xueqin & Yu, Qingyun, 2024. "Sequential estimation for mixture of regression models for heterogeneous population," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
  137. Jia-Chiun Pan & Chih-Min Liu & Hai-Gwo Hwu & Guan-Hua Huang, 2015. "Allocation Variable-Based Probabilistic Algorithm to Deal with Label Switching Problem in Bayesian Mixture Models," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-23, October.
  138. John M. Abowd & William R. Bell & J. David Brown & Michael B. Hawes & Misty L. Heggeness & Andrew D. Keller & Vincent T. Mule Jr. & Joseph L. Schafer & Matthew Spence & Lawrence Warren & Moises Yi, 2020. "Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report," Working Papers 20-33, Center for Economic Studies, U.S. Census Bureau.
  139. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
  140. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
  141. Rosella Castellano & Luisa Scaccia, 2014. "Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 285-305, June.
  142. Hikaru Hasegawa & Kazuhiro Ueda, 2007. "Measuring chronic and transient components of poverty: a Bayesian approach," Empirical Economics, Springer, vol. 33(3), pages 469-490, November.
  143. Jonathan Jaeger & Philippe Lambert, 2014. "Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2709-2726, December.
  144. Huber, Florian, 2018. "Dealing with heterogeneity in panel VARs using sparse finite mixtures," Department of Economics Working Paper Series 262, WU Vienna University of Economics and Business.
  145. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
  146. Samb Rawane & Khadraoui Khader & Belleau Pascal & Deschênes Astrid & Lakhal-Chaieb Lajmi & Droit Arnaud, 2015. "Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(6), pages 517-532, December.
  147. Nicolas Chopin & Pierre Jacob, 2010. "Free Energy Sequential Monte Carlo Application to Mixture Modelling," Working Papers 2010-34, Center for Research in Economics and Statistics.
  148. repec:dau:papers:123456789/6114 is not listed on IDEAS
  149. G. Iliopoulos & M. Kateri & I. Ntzoufras, 2009. "Bayesian Model Comparison for the Order Restricted RC Association Model," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 561-587, December.
  150. Alston, C.L. & Mengersen, K.L., 2010. "Allowing for the effect of data binning in a Bayesian Normal mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 916-923, April.
  151. Jasra, Ajay & Doucet, Arnaud & Stephens, David A. & Holmes, Christopher C., 2008. "Interacting sequential Monte Carlo samplers for trans-dimensional simulation," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1765-1791, January.
  152. Ruth Fuentes–García & Ramsés Mena & Stephen Walker, 2010. "A Probability for Classification Based on the Dirichlet Process Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 389-403, November.
  153. Sylvia Richardson & Laurent Leblond & Isabelle Jaussent & Peter J. Green, 2002. "Mixture models in measurement error problems, with reference to epidemiological studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 549-566, October.
  154. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.
  155. Christian P. Robert, 2010. "Bayesian Computational Methods," Working Papers 2010-27, Center for Research in Economics and Statistics.
  156. Xiang Lu & Yaoxiang Li & Tanzy Love, 2021. "On Bayesian Analysis of Parsimonious Gaussian Mixture Models," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 576-593, October.
  157. Korkmaz, E. & Fok, D. & Kuik, R., 2014. "The Need for Market Segmentation in Buy-Till-You-Defect Models," ERIM Report Series Research in Management ERS-2014-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  158. Polymenis, A. & Titterington, D. M., 1998. "On the determination of the number of components in a mixture," Statistics & Probability Letters, Elsevier, vol. 38(4), pages 295-298, July.
  159. van Hasselt, Martijn, 2011. "Bayesian inference in a sample selection model," Journal of Econometrics, Elsevier, vol. 165(2), pages 221-232.
  160. Mary Meyer & Amber Hackstadt & Jennifer Hoeting, 2011. "Bayesian estimation and inference for generalised partial linear models using shape-restricted splines," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 867-884.
  161. Marco Bertoletti & Nial Friel & Riccardo Rastelli, 2015. "Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 177-199, August.
  162. Abhijoy Saha & Sebastian Kurtek, 2019. "Geometric Sensitivity Measures for Bayesian Nonparametric Density Estimation Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 104-143, February.
  163. Raymond J. Carroll & Kathryn Roeder & Larry Wasserman, 1999. "Flexible Parametric Measurement Error Models," Biometrics, The International Biometric Society, vol. 55(1), pages 44-54, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.