Papers

Nonparametric Bayes:

general | DP and related | longitudinal data | gene expression | other app's

wavelet based models

neural networks

Optimal design:

general | clinical | sequential

Dynamic Models:

state space models | applications

Case control studies

Ordinal data models

Other:

Nonparametric Bayes

• General Nonpar Bayes

  1. Mueller, P., and Quintana, F. (2004). ``Nonparametric Bayesian Data Analysis,'' pdf
  2. Freitas Lopes, H., Mueller, P., and Rosner, G. (2003). ``Meta-Analysis for Longitudinal Data Models using Multivariate Mixture Priors.'' Biometrics, 59, 66-75. (ps, tech report)
  3. Barnes, T.G., Jefferys, W.H., Berger, J.O., Mueller, P. Orr, K., and Rodriquez, R. (2003). ``A Bayesian Analysis of the Cepheid Distance Scale.'' Astrophysics Journal, to appear. pdf tech report
  4. Mueller, P., Rosner, G, Inoue, L., and Dewhirst, M.W. (2001). ``A Bayesian Model for Detecting Changes in Nonlinear Profiles.'' Journal of the American Statistical Association, 96,1215-1222. (ps, tech report)

• Dirichlet process and related models

  1. De Iorio, M., Mueller, P., Rosner, G., and Maceachern, S. (2002). ``An ANOVA Model for Dependent Random Measures,'' (pdf)
  2. DeIorio, M., Mueller, P., Rosner, G. L., and MacEachern, S. N., (2002). ``{ANOVA DDP} models: A review,'' in D. D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds), Nonlinear Estimation and Classification, Springer-Verlag, p. 467. (pdf)
  3. Mueller, P., Rosner, G., De Iorio, M., and MacEachern, S. (2002). ``A Nonparametric Bayesian Model for Inference in Related Studies.'' (pdf)
  4. Quintana, F., and Mueller, P. (2003). ``Nonparametric Bayesian Assessment of the Order of Dependence for Binary Sequences.'' Journal of Computational and Graphical Statistics, to appear. (pdf, tech report)
  5. MacEachern, S. and Mueller, P. (2000). ``Efficient MCMC Schemes for Robust Model Extensions using Encompassing Dirichelt Process Mixture Models,'' in Robust Bayesian Analysis, F. Ruggeri and D. Rios Insua (eds.), Springer-Verlag. (ps, tech report)
  6. MacEachern, S.N. and Mueller, P. (1998). ``Estimating Mixture of Dirichlet Process Models,'' Journal of Computational and Graphical Statistics, 7, 223--239. (ps, tech report)
  7. Mueller, P., Erkanli, A., and West, M. (1996). ``Bayesian curve fitting using multivariate normal mixtures,'' Biometrika, 83, 67-79. (ps, tech report)
  8. West, M., Mueller, P., and Escobar, M.D. (1994). ``Hierarchical priors and mixture models, with application in regression and density estimation,'' in Aspects of Uncertainty: A tribute to D. V. Lindley, A.F.M. Smith and P. Freeman, (eds.), pp. 363-386, Wiley, New York. (ps, tech report)

• Semiparametric Longitudinal Data Models

  1. Mueller, P., Quintana, F. and Rosner, G. (1999). ``Hierarchical Meta-Analysis over Related Non-parametric Bayesian Models.'' (ps)
  2. Mueller, P. and Rosner, G. (1997). ``A Bayesian population model with hierarchical mixture priors applied to blood count data,'' Journal of the American Statistical Association , 92, 1279-1292. (ps, tech report)
  3. Mueller, P. and Rosner, G. (1998), ``Semi-parametric PK/PD Models,'' in Practical Nonparametric and Semiparametric Bayesian Statistics, Dey, D., Mueller, P. and Sinha, D. (eds.), pp. 323-338, Springer-Verlag, New York, (ps, tech report)
  4. Rosner, G. and Mueller, P. (1997). ``Bayesian population pharmacokintecs and pharmacodynamic analyses using mixture models'', Journal of Pharmacokinetics and Biopharmaceutics, 25 (2), 209-233. (ps, tech report)
  5. Rosner, G. and Mueller, P. (1994). ``Pharmacodynamic Analysis of Hematologic Profiles,'' Journal of Pharmacokinetics and Biopharmaceutics , 22, 499-524. (ps, tech report)
  6. Rosner, G. and Mueller, P. (1995). ``Modeling multiple pharmacodynamic endpoints,'' in Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Volume 2, (D.Z. D'Argenio, ed.), pp. 45-60. Plenum Press, New York. (ps, tech report)

• Nonparametric Bayes for Gene Expression Data

  1. Do, K-A., Mueller, P., and Tang, F. (2002). ``A Bayesian Mixture Model for Differential Gene Expression.'' (pdf)

• Nonpar Bayes -- Other Applications

  1. Malec, D. and Mueller, P. (1999). ``A Bayesian Semi-Parametric Model for Small Area Estimation.'' (ps)

• Wavelet based models

  1. Mueller, P., and Vidakovic, B. (1999). ``Bayesian Inference with Wavelets: Density Estimation,'' Journal of Computational and Graphical Statistics, 7, 456-468. (ps, tech report)
  2. Mueller, P. and Vidakovic, B. (1999). ``MCMC Methods in Wavelet Shrinkage: Non-Equally Spaced Regression, Density and Spectral Density Estimation,'' in Bayesian Inference in Wavelet-Based Models (P. Mueller and B. Vidakovic, eds.), pp. 187--202, Springer-Verlag, New York. (ps, tech report)
  3. Vidakovic, B. and Mueller, P. (1999). ``An Introduction to Wavelets,'' in Bayesian Inference in Wavelet-Based Models (P. Mueller and B. Vidakovic, eds.), pp. 1--18, Springer-Verlag, New York. ps
  4. Vidakovic, B. and Mueller, P. (1995). Wavelet Shrinkage with Affine Bayes Rules with Applications (ps, tech report)

• Neural Network Models

  1. Mueller, P. and Rios Insua, D. (1998). ``Issues in Bayesian Analysis of Neural Network Models,'' Neural Computation , 10, 571--592. (ps, tech report)
  2. Rios Insua, D. and Mueller, P. (1998). ``Feedforward neural networks for nonparametric regression,'' in Practical Nonparametric and Semiparametric Bayesian Statistics, Dey, D., Mueller, P. and Sinha, D. (eds.), pp. 181-194, Springer-Verlag, New York. (ps, tech report)
  3. Mueller, P. and Rios Insua, D. (1996). ``Posterior simulation for feed forward neural network models,'' in COMPSTAT, Proceedings in Computational Statistics (A. Prat ed.), pp. 385-390, Physica-Verlag, Heidelberg. (ps, tech report)

Optimal Design

• General Bayes Optimal Design

  1. Mueller, P., Parmigiani, G., Robert, C., and Rousseau, J. (2002). ``Optimal Sample Size for Multiple Testing: the Case of Gene Expression Microarrays.'' (ps)
  2. Bielza, C., Mueller, P., and Rios Insua, D. (1999). ``Monte Carlo Methods for Decision Analysis with Applications to Influence Diagrams,'' Management Science, 45 (7), 995-1007. (ps, tech report)
  3. Mueller, P. (1999). ``Simulation Based Optimal Design,'' in Bayesian Statistics 6, J.O. Berger, J.M. Bernardo, A.P. Dawid and A.F.M. Smith (eds.), pp. 459--474, Oxford University Press. (ps, tech report)
  4. Mueller, P., Sanso, B., and DeIorio, M. (2002). ``Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation.'' (ps)
  5. Mueller, P. and Parmigiani, G. (1995). ``Optimal design via curve fitting of Monte Carlo experiments,'' Journal of the American Statistical Association , 90, 1322-1330. (ps, tech report)
  6. Sanso, B. and Mueller, P. (1999). ``Redesigning a Network of Rainfall Stations,'' in Case Studies in Bayesian Statistics IV , (C. Gatsonis, R. E. Kass, B. Carlin, A. Carriquiry, A. Gelman, I. Verdinelli, and M. West, eds.), pp. #1, Springer-Verlag, New York, 383--394. (ps, tech report)
  7. Clyde, M., Mueller, P. and Parmigiani, G. (1996). ``Inference and Design Strategies for a Hierarchical Logistic Regression Model,'' in Bayesian Biostatistics, Berry D.A. and Stangl, D. (eds.), pp. 297-320, Marcel Deckker, New York. (ps)
  8. Mueller, P. and Parmigiani, G. (1996). ``Numerical evaluation of information theoretic measures,'' in Bayesian Statistics and Econometrics: Essays in Honor of A. Zellner, Berry D.A., Chaloner K.M., and Geweke J.F. (eds.), pp. 397-406, Wiley, New York. (ps, tech report)
  9. Clyde, M., Mueller, P. and Parmigiani, G. (1995). ``Optimal Design for Heart Defibrillators,'' in Case Studies in Bayesian Statistics, II, C. Gatsonis, J. Hodges, R. E. Kass, N. Singpurwalla (eds.), pp. 278-292, Springer-Verlag, New York. (ps, tech report)
  10. Mueller, P. and Palmer, J. L. (1998). ``Optimal Design in Longitudinal Data Models,'' in Applied Decision Analysis, F.J. Gir\'on and M.L. Mart\'{\i}nez (eds.), pp. 123-131, Kluwer, Boston. (ps, tech report)
  11. Rios Insua, D, Salewicz, A., Mueller, P. and Bielza, C. (1997). ``Bayesian methods in reservoir operations: the Zambezi river case,'' in The Practice of Bayesian Analysis, S. French, J. Smith, (eds.), pp. 107--130, Wiley, New York. (ps, tech report)
  12. Parmigiani, G. and Mueller, P. (1994). ``Simulation Approach to One-Stage and Sequential Optimal Design Problems,'' in MODA 4 - Advances in Model-Oriented Data Analysis, Kitsos, C.P. and Mueller, W.G. (eds.), pp. 37-48. Physica-Verlag, Heidelberg. (ps, tech report)
  13. Clyde, M., Mueller, P., and Parmigiani, G. (1995), ``Exploring Expected Utility Surfaces by Markov Chains,'' (ps, tech report)

• Clinical Trial Design

  1. Quintana, F., and Mueller, P. (2002). ``Optimal Sampling for Repeated Binary Measurements.'' (ps)
  2. Thall, P., Millikan, R., Mueller, P., and Lee, S-J. (2003). ``Dose-Finding with two agents in phase I oncology trials.'' Biometrics, 59 (3), to appear. pdf
  3. Stroud, J.R., Mueller, P. and Rosner G.L. (2001). ``Optimal Sampling Times in Population Pharmacokinetic Studies,'' Applied Statistics, 50, 345-359. (abstract and ps)
  4. Berry, D.A., Mueller, P. Grieve, A.P, Smith, M., Parke, T., Blazek, R., Mitchard, N., and Krams, M. (2000). ``Adaptive Bayesian Designs for Dose-Ranging Drug Trials,'' in Case Studies in Bayesian Statistics V , (C. Gatsonis, R. E. Kass, B. Carlin, A. Carriquiry, A. Gelman, I. Verdinelli, and M. West, eds.), Springer-Verlag, New York.
     (abstract and pdf)
  5. Palmer, J. and M{ue}ller, P. (1998). {Bayesian Optimal Design in Population Models of Hematologic Data.} {\it Statistics in Medicine 17, 1613-1622. (ps)
  6. Christen, J.A., Mueller, P., Wathen, K., and Wolf, J. (2003). ``A Bayesian Randomized Clinical Trial: A Decision Theoretic Sequential Design'' ps

• Sequential Design

  1. Mueller, P., Berry, D., Grieve, A., Smith, M., and Krams, M. (1999). ``Simulation-Based Sequential Bayesian Design.'' (abstract and ps)

Dynamic Models

• Dynamic State Space Models

  1. Stroud, J., Mueller, P., and Polson, N. (2003). ``Nonlinear State-Space Models with State-Dependent Variance Functions.'' Journal of the American Statistical Association, 98, 377-386. (abstract)
  2. Calder, C., Lavine, M., Mueller, P., and Clark, J. (2002). ``Incorporating Multiple Sources of Stochasticity into Dynamic Population Models.'' Ecology, in press. (abstract)
  3. Stroud, J., Mueller, P., and Sanso, B. (2001). ``Dynamic Models For Spatio-Temporal Data.'' Journal of the Royal Statistical Society, Series B, 63, 673-689. (abstract)
  4. Mueller, P. and Pole, A. (1998). ``Monte Carlo posterior integration in GARCH models,'' Sankhya, Series B, 60, 127-144. (ps)
  5. Cargnoni, C., Mueller, P., and West, M. (1997). ``Bayesian Forecasting of Multinomial Time Series Through Conditionally Gaussian Dynamic Models,'' Journal of the American Statistical Association , 92, 640-647. (abstract)
  6. Mueller, P., West, M. and MacEachern, S. (1997). ``Bayesian Models for Non-Linear Auto-Regressions,'' Journal for Time Series Analysis, 18, 593--614. (abstract)
  7. Mueller, P. (1992). ``Posterior integration in dynamic models,'' Computing Science and Statistics 24, 318--324. (ps)
  8. Mueller, P. (1991). ``Monte Carlo integration in general dynamic models,'' Contemporary Mathematics{ 115}, 145-164.
  9. Polasek, W. and Mueller, P. (1994). ``Gibbs Sampling for ARCH models in finance,'' in MODA 4 - Advances in Model-Oriented Data Analysis, Kitsos, C.P. and Mueller, W.G. (eds.), pp. 251--260, Physica-Verlag, Heidelberg.

• Applications

  1. Polson, N., Stroud, J., and Mueller, P. (2001). ``Affine State Dependent Variance Models,'' .
  2. Polson, N., Stroud, J., and Mueller, P. (2003) `` Practical Filtering for Stochastic Volatility Models,'' in State Space and Unobserved Component Models (Harvey, A., Koopman, S.J., and Shephard, N. eds.), Cambridge University Press, to appear.

Case Control Studies

1. Mueller, P., Parmigiani, G., Schildkraut, J. and Tardella, L. (1999). ``A Bayesian Hierarchical Approach for Combining Case-control and Prospective Studies,'' Biometrics, 55, 258--266. (ps)
2. Parmigiani, G., Berry, D., Iversen, E., Mueller, P., Schildkraut, J. and Winer, E.P. (1999). ``Modeling Risk of breast cancer and decisions about genetic testing,'' in Case Studies in Bayesian Statistics IV , (C. Gatsonis, R. E. Kass, B. Carlin, A. Carriquiry, A. Gelman, I. Verdinelli, and M. West, eds.), pp. #1, Springer-Verlag, New York, 133--204. (abstract)
3. Mueller, P. and Roeder, K. (1997). ``A Bayesian Semiparametric Model for Case-Control Studies With Errors in Variables,'' Biometrika , 84, 523-537. (ps)

Ordinal and Categorical Data Models

1. Kottas, A., Mueller, P. and Quintana, F. (2003), ``Nonparametric Bayesian modeling for multivariate ordinal data.'' (ps)
2. Erkanli, A., Stangl, D.K., and Mueller, P. (1993). ``A Bayesian analysis of ordinal data using mixtures,'' ASA Proceedings of the Section on Bayesian Statistical Science, 51-56. (ps)

Other

1. Mueller, P. (2001). ``Markov Chain Monte Carlo Methods,'' in International Encyclopedia of the Social & Behavoiral Sciences. Pergamon, Oxford. N.J. Smelser and P.B. Baltes eds. pp. 9236-9240. (ps)
2. Liechty, J., Liechty, M., and Mueller, P. (2003). ``Bayesian Correlation Estimation,'' Biometrika, to appear. pdf
3. Damien, P. and Mueller, P. (1998). ``A Bayesian Bivariate Failure Time Regression Model'', Computational Statistics and Data Analysis, 28, 77-85. (ps)
4. Mueller, P. (1991). ``A generic approach to posterior integration and Bayesian sampling,'' Technical Report{ 91-09}, Statistics Department, Purdue University. (ps)