Name:
Date and place of birth:
Address:
Petros Dellaportas
2-2-1963, Athens
Department of Statistics
Athens University of Economics and Business
76 Patission STr.
10434 Athens
Greece
Tel: +30-210-8203567
Fax +30-210-8203565
E-mail: petros@aueb.gr www: http://stat-athens.aueb.gr/~ptd/
1985: Degree in Mathematics, University of Athens, Greece.
1986:
1990:
Msc in Statistics, University of Sheffield, UK.
PhD in Statistics, University of Plymouth (formerly Plymouth Polytechnic), UK
Supervisor: Dr. David Wright, External examiner: Prof. Adrian F M Smith
25/2/1987 - 30/9/1989: Research Assistant, Department of Mathematics, University of
Plymouth (formerly Plymouth polytechnic), UK
1/10/1989 - 31/3/1991: Post-doctoral Research Assistant, Department of Mathematics,
University of Nottigham, UK
1/10/1992- 1/2/1998: Lecturer, Department of Statistics, Athens University of Economics and
Business, Greece.
1/2/1998- 20/12/2000: Assistant Professor, Department of Statistics, Athens University of
Economics and Business, Greece.
20/12/2000 – Today: Associate Professor, Department of Statistics, Athens University of
Economics and Business, Greece
1/10/200230/9/2003: Visitor, Department of Mathematics, Imperial College, London, UK.
15/4/200315/7/2003: Visitor, Joint research Centre of EU, Ispra, Italy.

Associate editor for the Journal of the Royal statistical Society -series D (1996-2000)
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
Associate editor for the Statistical modeling: An international Journal (2001- )
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Associate editor for the Statistics and Computing: An international Journal (2001- )
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Member of the European Regional Committee of the Bernoulli Society (2002-2006)
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Elected member of ISBA (International Society for Bayesian Analysis) board (1997-2000)
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Member of the editorial board responsible for the ISBA proceedings that will be produced every 4 years.
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Local Research coordinator for the research network “Statistical and computational methods for the analysis of spatial data” (EU project TMR “Training and mobility of researchers”).
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Fellow of the Royal Statistical Society.
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Member of the American Statistical Association.
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Journals
1.
Roberts G.O., Papaspiliopoulos O. and Dellaportas P., (2003) Bayesian inference for Non-
Gaussian Ornstein-Uhlenbeck Stochastic Volatility processes. Journal of Royal Statistical
Society - series B, 66, 369-393.
2.
Stephens D A, Crowder M. J. and Dellaportas P. (2003) Quantification of Automobile
Insurance Liability: A Bayesian Failure Time Approach. Insurance: Mathematics and
Economic, 34, 1-21.
3.
Spezia L., Paroli R. and Dellaportas P. (2004). Periodic Markov switching autoregressive models for Bayesian analysis and forecasting of air pollution. Statistical modeling, 4, 19-38
4.
Dellaportas P., Giudici P. and Roberts G. (2003). Bayesian inference for nondecomposable graphical Gaussian models. Sankhya, Series A , 65, 43-55.
5.
Vrontos I.D, Dellaportas P. and Politis D.M. (2003). A full-factor multivariate GARCH model. The Econometrics Journal , 6,2,312-334.
6.
Bottolo P., Consonni G., Dellaportas P., and Lijoi A. (2003). Bayesian Analysis of extreme values by mixture modelling. Extremes, 6, 25-47 .
7.
Vrontos I.D, Dellaportas P. and Politis D.M. (2003). Inference for some multivariate ARCH and GARCH models. Journal of forecasting , 22, 427-446.
8.
Linardakis M. and Dellaportas P. (2002). Bayesian extensions of the multinomial probit model. Journal of Royal Statistical Society - series C (Applied Statistics), 52,2, 185-200.
9.
Kateri M., Papaioannou T. and Dellaportas P. (2002) Bayesian analysis of correlated proportions. Sankhya Series B, 63,3,270-285.
10.
Ntzoufras I., Dellaportas P. and Forster J.J. (2002). Bayesian variable and link determination for generalised linear models. Journal of statistical planning and inference , 111,165-180.
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11.
Ntzoufras I. and Dellaportas P. (2002). Bayesian Prediction of Outstanding Claims. North
American actuarial journal , 6,1,113-136.
12.
Dellaportas P. and Karlis D. (2001). A simulation approach to empirical Bayes analysis.
International Statistical Review , 69, 1, 63-79.
13.
Dellaportas P., Smith A.F.M. and Stavropoulos P. (2001). Bayesian analysis of mortality data. Journal of Royal Statistical Society - series A , 164-2,275-292.
14.
Denison D.G.T., Dellaportas P., and Mallick B.K.(2001). Wind speed prediction in a complex terrain. Envirometrics, 12, 6, 499-515.
15.
Dellaportas P., Forster J.J., Ntzoufras I. (2002). On Bayesian Model and Variable Selection
Using MCMC. Statistics and Computing. 12, 27-36 .
16.
Vrontos I.D., Giakoumatos S.G., Dellaportas P., and Politis D.N.(2000). An application of three bivariate time varying volatility models. Applied stochastic models in business and industry , 17, 121-133.
17.
Ntzoufras I., P.Dellaportas and J.J.Forster (2000) Stochastic Search Variable Selection for
Hierarchical Log-linear Models. Journal of statistical computation and simulation, 68,23-
38.
18.
Vrontos I., Dellaportas P. and Politis D. (2000) Full Bayesian inference for GARCH and
EGARCH models. Journal of Business and Economics Statistics, 18, 187-198.
19.
Giakoumatos S.G., Vrontos I.D., Dellaportas P., and Politis D.N.(1999). An MCMC
Convergence Diagnostic using Subsampling. Journal of Computational and Graphical
Statistics , 8, 431-451.
20.
Zaphiropoulos Y., Dellaportas P., Morfiadakis E. and Glinou G. (1999) Prediction of wind speed and direction in potential site. Wind Engineering, 23,167-175.
21.
Dellaportas P and Forster J J (1999) Markov chain Monte Carlo Model Determination for
Hierarchical and Graphical Log-linear models. Biometrika, 86, 615-633.
22.
Mouzakis, F.; Morfiadakis, E.; Dellaportas, P. (1999) Fatigue loading parameter identification of a wind turbine operating in complex terrain . Journal of Wind Engineering and Industrial Aerodynamics , 82, 69 - 88.
23.
Dellaportas P. (1998). Bayesian classification of neolithic tools . Applied Statistics-Journal of the Royal Statistical Society series C , 47, 279-297.
24.
Brooks S., Dellaportas P. and Roberts G. (1997). An approach to diagnosing total variation convergence of MCMC algorithms. Journal of Computational and Graphical Statistics , 6,
251 -265.
25.
Cools R. and Dellaportas P. (1996). The role of embedded integration rules in Bayesian
Statistics. Statistics and Computing, 6, 245-250 ..
26.
Dellaportas P and Smith AFM (1996). Conflict between prior and current data-response.
Applied statistics-Journal of the Royal statistical society series C , 45, 2, 249-251.
27.
Dellaportas P. and Stephens D.A. (1995). Bayesian analysis of error-in-variables regression models. Biometrics, 51, 1085-1095.
28.
Dellaportas P. (1995). Random variate transformations on Gibbs sampler: issues of efficiency and convergence. Statistics and Computing, 5, 133-140.
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29.
Dellaportas P. and Smith A.F.M. (1993). Bayesian inference for generalised linear and proportional hazards models via Gibbs Sampler. Applied Statistics-Journal of the Royal
Statistical Society series C, 42, 443-459.
30.
Dellaportas P. and Wright D.E. (1991). Positive embedded integration in Bayesian analysis.
Statistics and Computin g, 1, 1-12.
31.
Dellaportas P. and Wright D.E. (1991). Numerical prediction for the two parameter Weibull distribution. The Statistician, 40, 365-372.

Contributions in books
1.
Dellaportas P. and Roberts G.O. (2003) An introduction to MCMC. In: Spatial Statistics and Computational methods , J. Muller (editor), 1-42. Springer-Verlag, NY.
2.
Dellaportas P, Forster J J and Ntzoufras I (2000) Bayesian model and variable selection for generalised linear models. Bayesian Generalised Linear models: A Bayesian perspective
(D.K.Dey, S. Ghosh and B. Mallick, eds). New York: Marcel Dekker.
3.
Dellaportas P., Stephens D.A., Smith A.F.M. and Cuttman I. (1995). A comparative study of perinatal mortality using a two-component mixture model. In: Bayesian Biostatistics, pp 601-
616, Berry D.A. and Stangl D.K. (editors), New York: Marcel Dekker.
4.
Kokolakis G. and Dellaportas P. (1995). Hierarchical classification of binary data. In
Bayesian Statistics 5, pp 647-652, Bernardo J.M., Berger J.O., Dawid A.P. and Smith A.F.M.
(editors), London: Oxford University Press
5.
Stephens D.A. and Dellaportas P. (1992). Bayesian inference of generalised linear models with covariate measurement errors. In Bayesian Statistics 4, pp 813-820, Bernardo J.M.,
Berger J.O., Dawid A.P. and Smith A.F.M. (editors), London: Oxford University Press.
6.
Dellaportas P. and Wright D.E. (1992). A numerical integration strategy in Bayesian analysis. Bayesian Statistics 4, 660-666, Oxford University Press.
7.
Dellaportas P. (1999). Invited discussion on the paper by Merlise Clyde "Model averaging and model search". In Bayesian Statistics 6 , pp 172-175, Bernardo J.M., Berger J.O., Dawid
A.P. and Smith A.F.M. (editors), London: Oxford University Press.
8.
Dellaportas P. (1997). Invited discussion on the paper by Ed George "Empirical Bayes variable selection". In: Model Selection , Racugno W. (Editor), p.p. 101-103, Pitagora editrice, Italy.
9.
Dellaportas P and Smith AFM (1996). Conflict between prior and current data - response.
Applied statistics-Journal of the Royal statistical society series C , 45, 2, 249-251.

Contributions in conference proceedings
1.
Dellaportas Π. and Moundrea-Agrafioti A. (1999). An Application of Markov chaim Monte
Carlo methodologies to some common archaeological problems. In : Proceedings of 3 rd archaeometry symposium , Athens, Greece.
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2.
Ntzoufras I., Dellaportas P., and Forster J.J. (1999). Specification and interpretation of prior distributions for variable selection in linear models . Hercma '98: 4th Hellenic European
Conference on Computer Mathematics and its applications , E.A. Lipitakis (Ed), pp. 451-458.
3.
Vrontos I.D., Dellaportas P. and Politis D.N. (1999). Bayesian analysis of bivariate ARCH and GARCH models. Hercma '98: 4th Hellenic European Conference on Computer
Mathematics and its applications , E.A. Lipitakis (Ed), pp. 459-466.
4.
Zaphiropoulos Y., Dellaportas P. and Morfiadakis E. (1999). Hercma '98: 4th Hellenic
European Conference on Computer Mathematics and its applications , E.A. Lipitakis (Ed), pp. 471-478.
5.
Giakoumatos S.G., Dellaportas P. and Politis D.N. (1999). Auxiliary variables in timevarying volatility models. Hercma '98: 4th Hellenic European Conference on Computer
Mathematics and its applications , E.A. Lipitakis (Ed), pp. 479-486.
10.
Linardakis M. and Dellaportas P. (1999). How much does your travel time cost?: A Bayesian evaluation. Hercma '98: 4th Hellenic European Conference on Computer Mathematics and its applications , E.A. Lipitakis (Ed), pp. 604-611.
11.
Glinou G., Morfiadakis E., Zaphiropoulos Y. and Dellaportas P. (1999). A statistical approach to wind potential assessment using multivariate ARFIMA modelling . Procs of the
European Wind Energy Conference, France, pp 1138-1141, James & James (Science
Publishers) Ltd., London.
12.
Dellaportas P. (1996). Computational strategies for the implementation of the Bayesian paradigm. Hermis'96: 3rd Hellenic European Conference on Mathematics and Informatics.
13.
Ntzoufras I, Dellaportas P. and Forster J J (1996). A comparison of Markov chain Monte
Carlo methods for model choice in log-linear models. Hermis'96: 3rd Hellenic European
Conference on Mathematics and Informatics.

Citations in books
1.
O’Hagan A. And Forster J. (2004) Kendall’s advanced theory of statistics, volume 2b:
Bayesian inference . Arnold, UK.
2.
Gordon P. (2002) Bayesian statistical modeling . Wiley, NY.
3.
Denison D.G.T., Holmes C.C., Mallick B.K. and Smith A.F.M. (2002). Bayesian methods for nonlinear classification and regression . Wiley, NY.
4.
Robert C and Casella G (1999 ). Monte Carlo statistical methods , Springer-Verlag, NY.
5.
Politis D N, Romano J P and Wolf M. (1999). Subsampling . Springer Verlag; USA.
6.
Gentle J. E. (1998 ). Random Number Generation and Monte Carlo Methods.
Springer
Verlag; USA.
7.
Gamerman D (1997). Markov chain Monte Carlo . Chapman and Hall, GB.
8.
Carlin B P and Louis T A (1996) Bayes and Empirical Bayes methods for data analysis .
Chapman and Hall, NY.
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9.
Gelman A, Carlin J B, Stern H S and Rubin D B (1995). Bayesian data analysis . Chapman and Hall, UK.
10.
Bernardo J M and Smith A F M (1994) Bayesian Theory . John Wiley, NY.

Citations in journals
1.
Tjelmeland H, Eidsvik J (2004). On the use of local optimizations within Metropolis-Hastings updates
J ROY STAT SOC B 66: 411-427.
2.
Tarantola C (2004). MCMC model determination for discrete graphical models. STAT MODEL 4 (1):
39-61.
3.
Jackson CH, Sharples LD (2004). Models for longitudinal data with censored changepoints. J ROY
STAT SOC C-APP 53: 149-162.
4.
Petris G, Tardella L (2003). A geometric approach to transdimensional Markov chain Monte Carlo
CAN J STAT 31 (4): 469-482.
5.
Dagne GA, Brown CH, Howe GW (2003). Bayesian hierarchical modeling of heterogeneity in multiple contingency tables: An application to behavioral observation data. J EDUC BEHAV STAT
28 (4): 339-352.
6.
Lopes HF, West M (2004). Bayesian model assessment in factor analysis. STAT SINICA 14 (1): 41-
67.
7.
Gianola D, Odegard J, Heringstad B, et al. (2004). Mixture model for inferring susceptibility to mastitis in dairy cattle: a procedure for likelihood-based inference. GENET SEL EVOL 36 (1): 3-27.
8.
Eskandari F, Meshkani MR (2003) Empirical Bayes analysis of log-linear models for contingency tables IRAN J SCI TECHNOL 27 (A2): 389-401.
9.
Green PE, Park T (2004). Bayesian methods for contingency tables using Gibbs sampling. STAT
PAP 45 (1): 33-50.
10.
Wong F, Carter CK, Kohn R (2003) Efficient estimation of covariance selection models.
BIOMETRIKA 90 (4): 809-830.
11.
Yeung YG, Stanley ER (2003). Proteomic approaches to the analysis of early events in colonystimulating factor-1 signal transduction. MOL CELL PROTEOMICS 2 (11): 1143-1155.
12.
Yi, NJ , Xu, SZ and Allison, DB (2003). Bayesian model choice and search strategies for mapping interacting quantitative trait loci. GENETICS, 165, 867-883.
13.
Nicolato, E and Venardos, E (2003). Option pricing in stochastic volatility models of the Ornstein-
Uhlenbeck type. MATHEMATICAL FINANCE, 13, 445-466.
14.
Corander, J (2003). Labelled graphical models. SCANDINAVIAN JOURNAL OF STATISTICS, 30,
493-508.
15.
Barnes, TG, Jefferys, WH, Berger, JO, Mueller, PJ, Orr, K and Rodriguez, R (2003). A Bayesian analysis of the Cepheid distance scale. ASTROPHYSICAL JOURNAL, 592, 539-554.
16.
Yang, ZL, See, SP and Xie, M (2003). Transformation approaches for the construction of Weibull prediction interval. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 43,357-368.
17.
Gustafson, P, Le, ND and Vallee, M (2003). A Bayesian approach to case-control studies with errors in covariables. BIOSTATISTICS, 3, 229-243.
18.
Dunson, DB (2003). Bayesian inference on order-constrained parameters in generalized linear models. BIOMETRICS, 59, 286-295.
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19.
Corander, J (2003). Bayesian graphical model determination using decision theory. JOURNAL OF
MULTIVARIATE ANALYSIS, 85, 253-266.
20.
Leonte, D, Nott, DJ and Dunsmuir, ATM (2003). Smoothing and change point detection for gamma ray count data. MATHEMATICAL GEOLOGY, 35, 175-194.
21.
Lopes, HF, Muller, P and Rosner, GL (2003). Bayesian meta-analysis for longitudinal data models using multivariate mixture priors. BIOMETRICS, 59, 66-75.
22.
Wright, DE and Bray, I (2003). A mixture model for rounded data. JOURNAL OF THE ROYAL
STATISTICAL SOCIETY SERIES D-THE STATISTICIAN, 52, 3-13.
23.
Cook, WD and Zhu, J (2003). Output deterioration with input reduction in data envelopment
Analysis. IIE TRANSACTIONS, 35, 309-320.
24.
Brooks, SP, Giudici, P and Philippe, A (2003). Nonparametric convergence assessment for MCMC model selection. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 12, 1-22.
25.
AU Brooks, SP, Giudici, P, Roberts, GO (2003). Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions. JOURNAL OF THE ROYAL STATISTICAL SOCIETY
SERIES B-STATISTICAL METHODOLOGY, 65, 3-39.
26.
Forster, JJ and Gill, RC (2003) Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions - Discussion on the paper by Brooks, Giudici and Roberts, JOURNAL OF
THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 65, 39-55.
27.
Merrick, JRW, Soyer, R and Mazzuchi, TA (2003). A Bayesian semiparametric analysis of the reliability and maintenance of machine tools. TECHNOMETRICS, 45, 58-69.
28.
Giudici, P, Mezzetti, M and Muliere, P (2003). Mixtures of products of Dirichlet processes for variable selection in survival analysis. JOURNAL OF STATISTICAL PLANNING AND
INFERENCE, 111, 101-115.
29.
Kestens, E and Teugels, JL (2002). Challenges in modelling stochasticity in wind.
ENVIRONMETRICS, 13, 821-830.
30.
Karlis, D and Kostaki, A (2003). Bootstrap techniques for mortality models. BIOMETRICAL
JOURNAL, 44, 850-866.
31.
Samonas, M and Petrou, M (2002). A peak preserving algorithm for the removal of colored noise from signals, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 50, 2683-2694.
32.
Chopin N (2002). A sequential particle filter method for static models BIOMETRIKA 89 (3): 539-
551.
33.
Giudici P, Castelo R (2003). Improving Markov Chain Monte Carlo model search for data mining.
MACH LEARN 50 (1-2): 127-158.
34.
Meyer MC, Laud PW (2002). Predictive variable selection in generalized linear models. J AM STAT
ASSOC 97 (459): 859-871.
35.
Erto P, Giorgio M (2002). Assessing high reliability via Bayesian approach and accelerated tests.
RELIAB ENG SYST SAFE 76 (3): 301-310.
36.
Karlis D (2002). An EM type algorithm for maximum likelihood estimation of the normal-inverse
Gaussian distribution. STAT PROBABIL LETT 57 (1): 43-52.
37.
Zuur G, Garthwaite PH, Fryer RJ (2002). Practical use of MCMC methods: Lessons from a case study. BIOMETRICAL J 44 (4): 433-455.
38.
Glickman ME, Gagnon DR (2002). Modeling the effects of genetic factors on late-onset diseases in cohort studies. LIFETIME DATA ANAL 8 (3): 211-228.
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39.
Walther G (2002). Detecting the presence of mixing with multiscale maximum likelihood. J AM
STAT ASSOC 97 (458): 508-513.
40.
Viallefont V, Richardson S, Green PJ (2002). Bayesian analysis of poisson mixtures. J
NONPARAMETR STAT 14 (1-2): 181-202.
41.
Dunson DB (2001). Bayesian modeling of the level and duration of fertility in the menstrual cycle.
BIOMETRICS 57 (4): 1067-1073.
42.
Tod M, Aouimer A, Petitjean O (2002). Estimation of pharmacokinetic parameters by orthogonal regression: comparison of four algorithms.COMPUT METH PROG BIO 67 (1): 13-26.
43.
Walther G (2001). Multiscale maximum likelihood analysis of a semiparametric model, with applications. ANN STAT 29 (5): 1297-1319.
44.
Billheimer D, Guttorp P, Fagan WF (2001). Statistical interpretation of species composition. J AM
STAT ASSOC 96 (456): 1205-1214.
45.
Stroud JR, Muller P, Sanso B (2001). Dynamic models for spatiotemporal data. J ROY STAT SOC
B 63: 673-689.
46.
King R, Brooks SP (2001). Prior induction in log-linear models for general contingency table analysis.
ANN STAT 29 (3): 715-747.
47.
Han C, Carlin BP (2001). Markov chain Monte Carlo methods for computing Bayes factors: A comparative review. J AM STAT ASSOC 96 (455): 1122-1132.
48.
Jefferys, WH (2000). Statistics for twenty-first century astrometry (2000 Heinrich K. Eichhorn
Memorial Lecture). CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY, 78, 3-16.
49.
Mattos, NMC and Migon, HD (2001). A Bayesian analysis of reliability in accelerated life tests using Gibbs sampler. COMPUTATIONAL STATISTICS, 16, 299-312.
50.
King, R and Brooks, SP (2001). On the Bayesian analysis of population size. BIOMETRIKA, 88,
317-336.
51.
Roberts, GO and Sahu, SK (2001) Approximate predetermined convergence properties of the Gibbs
Sampler. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 10, 216-229.
52.
Godsill, SJ (2001). On the relationship between Markov chain Monte Carlo methods for model uncertainty. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 10, 230-248.
53.
Fenwick, JD (2001). Impact of dose-distribution uncertainties on rectal ntcp modeling II: Uncertainty implications. MEDICAL PHYSICS, 28, 570-581.
54.
Davison, AC (2001). Biometrika Centenary: Theory and general methodology. BIOMETRIKA, 88,
13-52.
55.
Denison, DGT and Holmes, CC (2001). Bayesian partitioning for estimating disease risk.
BIOMETRICS, 143-149.
56.
Andrieu, C, Djuric, PM and Doucet, A (2001). Model selection by MCMC computation. SIGNAL
PROCESSING, 81, 19-37.
57.
Thurigen, D, Spiegelman, D, Blettner, M, Heuer, C, Brenner, H (2000). Measurement error correction using validation data: a review of methods and their applicability in case-control studies.
STATISTICAL METHODS IN MEDICAL RESEARCH, 9, 447-474.
58.
Ashby, D and Smith, AFM (2000). Evidence-based medicine as Bayesian decision-making,
STATISTICS IN MEDICINE, 19, 3291-3305.
59.
Fill, JA, Machida, M, Murdoch, DJ, Rosenthal, JS (2000). Extension of Fill's perfect rejection sampling algorithm to general chains. RANDOM STRUCTURES & ALGORITHMS, 17, 290-316.
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60.
Wright, WA, Ramage, G, Cornford, D, Nabney, IT (2000). Neural network modelling with input uncertainty: Theory and application. JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR
SIGNAL IMAGE AND VIDEO TECHNOLOGY, 26, 169-188.
61.
Bartolozzi G; Bizzozero N; Ghiringhelli L (2000) Analysis of frying oils. Industrie alimen tary, 39,
691-694.
62.
Eilstein D; Hedelin G; Schaffer P (2000). Incidence of colorectal cancer in Bas-Rhin, trend and prediction in 2009. Bulletin du Cancer, 87, 595-599.
63.
Giudici, P, Green, PJ (2000) Decomposable graphical Gaussian model determination.
Biometrika, 86, 785-801.
64.
Scurrah KJ, Palmer LJ and Burton PR (2000) Variance components analysis for pedigreebased censored survival data using generalized linear mixed models (GLMMs) and Gibbs sampling in BUGS. GENETIC EPIDEMIOLOGY 2000, Vol 19, Iss 2, pp 127-148
65.
Carlin, BP, Hodges, JS (2000). Hierarchical proportional hazards regression models for highly stratified data . Biometrics , 55, 1162-1170.
66.
Damien P, Wakefield J and Walker S (1999). Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables . Journal of the royal statistical society series Β-statistical methodology , Vol.61, No.Pt2, pp.331-344.
67.
Banerjee, S, Gelfand, AE, Polasek, W (2000). Geostatistical modelling for spatial interaction data with application to postal service performance. J ournal of statistical planning and inference , 90, 87-105.
68.
Gelfand, AE, Ghosh, SK, Christiansen, C, Soumerai, SB, McLaughlin (2000)
Proportional hazards models: a latent competing risk approach. J ournal of the royal statistical society series c-applied statistics , 49, 385-397.
69.
Brooks, SP, Giudici, P (2000) Markov chain Monte Carlo convergence assessment via twoway analysis of variance. J ournal of computational and graphical statistics , 9, 266-285.
70.
Ashby D; Smith AFM (2000) Evidence-based medicine as Bayesian decision-making.
Statistics in Medicine , 19, 3291-3305
71.
Fill JA; Machida M; Murdoch DJ; Rosenthal JS (200) Extension of Fill's perfect rejection sampling algorithm to general chains. Random structures and algorithms , 17, 290-316
72.
Wright WA; Ramage G; Cornford D; Nabney IT (2000). Neural network modelling with input uncertainty: Theory and application. Journal of vlsi signal processing systems for signal image and video technology 2000 , 26, 169-188.
73.
Bekker, A, Roux, JJJ, Mostert, PJ (2000). A generalization of the compound Rayleigh distribution: Using a Bayesian method on cancer survival times . Communications in statistics-theory and methods , 29, 1419- 1433.
74.
Tsionas, EG (2000) Posterior analysis, prediction and reliability in three- parameter
Weibull distributions. C ommunications in statistics-theory and methods , 29, 1435-1449.
75.
Scurrah, KJ, Palmer, LJ, Burton, PR (2000). Variance components analysis for pedigreebased censored survival data using generalized linear mixed models (GLMMs) and Gibbs sampling in BUGS. G enetic epidemiology , 19, 127-148.
76.
Ghosh, M, Ghosh, A, Chen, MH, Agresti, A (2000). Noninformative priors for oneparameter item response models . Jjournal of statistical planning and inference , 88, 99-115.
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77.
Gelman, A, Goegebeur, Y, Tuerlinckx, F,Van Mechelen, I (2000). Diagnostic checks for discrete data regression models using posterior predictive simulations . Journal of the royal statistical society series c-applied statistics , 49, 247-268.
78.
Biller, C (2000) Adaptive Bayesian regression splines in semiparametric generalized linear models .Jjournal of computational and graphical statistics , 9, 122-140.
79.
Tu, XM, Jia, G, Kowalski, J, Bacanu, SA (2000) Bayesian regression analysis of data with censored initiating and terminating times: Applications to AIDS. J ournal of statistical computation and simulation , 65, 1- 21.
80.
Clyde, M (2000). Bayesian model averaging: A tutorial – Comment. S tatistical science , 14,
401-417.
81.
Gustafson, P, Le, ND, Vallee, M (2000). Parametric Bayesian analysis of case-control data with imprecise exposure measurements. S tatistics & probability letters , 47, 357-363.
82.
Wallace, CS, Dowe, DL (2000) MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. S tatistics and computing , 10, 73-83.
83.
Ishwaran, H (2000). Applications of hybrid Monte Carlo to Bayesian generalized linear models: Quasicomplete separation and neural networks. J ournal of computational and graphical statistics , 8, 779-799.
84.
Bartolozzi, G, Bizzozero, N, Ghiringhelli, L (2000). Analysis of frying oils. I ndustrie alimentari , 39, 691-694.
85.
Eilstein, D, Hedelin, G, Schaffer, P(2000). Incidence of colorectal cancer in Bas-Rhin, trend and prediction in 2009 . Bulletin du cancer , 87, 595-599.
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
Post-doc (1998 - )
1.
Stefano Tonellato : Bayesian space-time modelling of wind velocity data
2.
Claudia Tarantola: Graphical modelling of ordered categorical data
3.
Luigi Spezia: Hidden Markov models
4.
Stefano Tonelatto: Extreme theory
5.
Neil Friel: Bayesian inference of dicretely observed diffusions

PhD
1.
Ntzoufras I.. Aspects of Bayesian model and variable selection, graduated 1999.
2.
Vrontos I. Bayesian analysis of various autoregressive conditional heteroscedastic models graduated 2001.
3.
Linardakis M. Applications of latent variables in multivariate models for discrete responses.
Submitted
4.
Giakoumatos S.
.Bayesian analysis of stochastic volatility models submitted.
5.
Kalogeropoulos K. Inference on multivariate Continuous-time models.
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