List of References for KSU workshop 2012. Albert, J. H. (1998). "Computational methods using a Bayesian hierarchical generalized linear model." Journal of the American Statistical Association 83: 1037-1044. Albert, J. H. and S. Chib (1993). "BAYESIAN-ANALYSIS OF BINARY AND POLYCHOTOMOUS RESPONSE DATA." Journal of the American Statistical Association 88(422): 669-679. Allison, D. B., X. Q. Cui, et al. (2006). "Microarray data analysis: from disarray to consolidation and consensus." Nature Reviews Genetics 7(1): 55-65. Bello, N. M., J. P. Steibel, et al. (2012). "Inferring upon heterogeneous associations in dairy cattle performance using a bivariate hierarchical model." Journal of Agricultural, Biological, and Environmental Statistics 17(1): 142-161. Bello, N. M., J. P. Steibel, et al. (2010). "Hierarchial Bayesian modeling of random and residual variancecovariance matrices in mixed effects models." Biometrical Journal 52: 297-313. Besag, J. and D. Higdon (1999). "Bayesian analysis of agricultural field experiments." Journal of the Royal Statistical Society Series B-Statistical Methodology 61: 691-717. Browne, W. J. and D. Draper (2006). "A comparison of Bayesian and likelihood-based methods for fitting multilevel models." Bayesian Analysis 1(3): 473-513. Cardoso, F. F., G. J. M. Rosa, et al. (2007). "Accounting for outliers and heteroskedasticity in multibreed genetic evaluations of postweaning gain of Nelore-Hereford cattle." Journal of Animal Science 85(4): 909-918. Carlin, B. P. and T. A. Louis (2008). Bayesian Methods for Data Analysis. Boca Raton, FL, CRC Press. Casella, G. (1985). "AN INTRODUCTION TO EMPIRICAL BAYES DATA-ANALYSIS." American Statistician 39(2): 83-87. Chib, S. and E. Greenberg (1995). "Understanding the Metropolis-Hastings Algorithm." The American Statistician 49(4): 327-335. Cowles, M. K. (1996). "Accelerating Monte Carlo Markov Chain Convergence for Cumulative-Link Generalized Linear Models." Statistics and Computing 6: 101-111. Cowles, M. K. and B. P. Carlin (1996). "Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review." Journal of the American Statistical Association 91(434): 883-904. Cressie, N., C. A. Calder, et al. (2009). "Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling." Ecological Applications 19(3): 553-570. Efron, B. and D. V. Hinkley (1978). "Assessing the Accuracy of the Maximum Likelihood Estimator: Observed Versus Expected Fisher Information." Biometrika 65(3): 457-482. Gelman, A. (2006). "Prior distributions for variance parameters in hierarchical models." Bayesian Analysis 1(3): 515-533. Gelman, A., J. B. Carlin, et al. (2003). Bayesian Data Analysis. Boca Raton, FL., CRC Press. Geman, S. and D. Geman (1984). "STOCHASTIC RELAXATION, GIBBS DISTRIBUTIONS, AND THE BAYESIAN RESTORATION OF IMAGES." Ieee Transactions on Pattern Analysis and Machine Intelligence 6(6): 721-741. Geyer, C. J. (1992). "Practical Markov Chain Monte Carlo." Statistical Science 7(4): 473-483. Gianola, D. and J. L. Foulley (1983). "SIRE EVALUATION FOR ORDERED CATEGORICAL-DATA WITH A THRESHOLD-MODEL." Genetics Selection Evolution 15(2): 201-223. Gill, J. L. (1978). Design and Analysis of Experiments in the Animal and Medical Sciences. Ames, IA, The Iowa State University Press. Gotway, C. A. and W. W. Stroup (1997). "A Generalized Linear Model Approach to Spatial Data and Prediction." Journal of Agricultural, Biological, and Environmental Statistics 2: 157-187. Harville, D. A. (1974). "BAYESIAN INFERENCE FOR VARIANCE COMPONENTS USING ONLY ERROR CONTRASTS." Biometrika 61(2): 383-385. 1 Harville, D. A. and R. W. Mee (1984). "A MIXED-MODEL PROCEDURE FOR ANALYZING ORDERED CATEGORICAL-DATA." Biometrics 40(2): 393-408. Johnson, V. E. and J. H. Albert (1999). Ordinal Data Modeling. New York, Springer. Kizilkaya, K. and R. J. Tempelman (2005). "A general approach to mixed effects modeling of residual variances in generalized linear mixed models." Genetics Selection Evolution 37(1): 31-56. Kuehl, R. O. (2000). Design of experiments: statistical principles of research design, 2nd ed. Pacific Grove, CA, Brooks/Cole. Lindley, D. V. and A. F. M. Smith (1972). "Bayes Estimates for the Linear Model." Journal of the Royal Statistical Society. Series B (Methodological) 34(1): 1-41. Louis, T. A. (1982). "FINDING THE OBSERVED INFORMATION MATRIX WHEN USING THE EM ALGORITHM." Journal of the Royal Statistical Society Series B-Methodological 44(2): 226-233. Meuwissen, T. H. E., B. J. Hayes, et al. (2001). "Prediction of total genetic value using genome-wide dense marker maps." Genetics 157(4): 1819-1829. Raftery, A. E. and S. M. Lewis (1992). How many iterations in the Gibbs sampler? . Bayesian Statistics 4. J. M. Bernardo, et al, Oxford University Press: 763-773. Rao, C. R. (1971). Linear Statistical Inference and its Applications, 2nd Ed. New York, NY, John Wiley and Sons. Robinson, G. K. (1991). "That BLUP is a Good Thing: The Estimation of Random Effects." Statistical Science 6(1): 15-32. Rosa, G. J. M., D. Gianola, et al. (2004). "Bayesian Longitudinal Data Analysis with Mixed Models and Thick-tailed Distributions using MCMC." Journal of Applied Statistics 31(7): 855 - 873. Searle, S. R., G. Casella, et al. (1992). Variance Components. New York, John Wiley and Sons. Sorensen, D. and D. Gianola (2002). Likelihood, Bayesian, and MCMC methods in quantitative genetics. New York, Springer-Verlag. Sorensen, D. A., S. Andersen, et al. (1995). "Bayesian inference in threshold models using Gibbs sampling." Genetics Selection Evolution 27(3): 1-21. Stiratelli, R., N. Laird, et al. (1984). "RANDOM-EFFECTS MODELS FOR SERIAL OBSERVATIONS WITH BINARY RESPONSE." Biometrics 40(4): 961-971. Stroup, W. W. and R. C. Littell (2002). Impact of variance component estimates on fixed effect inference in unbalanced linear mixed models. Proceedings of the 14th Annual Kansas State University Conference on Applied Statistics in Agriculture, Manhattan, KS. Tanner, M. A. (1996). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (3rd Edition). New York, Springer. Tempelman, R. J. (1998). "Generalized linear mixed models in dairy cattle breeding." Journal of Dairy Science 81(5): 1428-1444. Tempelman, R. J. and D. Gianola (1993). "MARGINAL MAXIMUM-LIKELIHOOD-ESTIMATION OF VARIANCE-COMPONENTS IN POISSON MIXED MODELS USING LAPLACIAN INTEGRATION." Genetics Selection Evolution 25(4): 305-319. Tempelman, R. J. and D. Gianola (1996). "A mixed effects model for overdispersed count data in animal breeding." Biometrics 52(1): 265-279. Tempelman, R. J. and G. J. M. Rosa (2004). Empirical Bayes approaches to mixed model inference in quantitative genetics. Genetic Analysis of Complex Traits Using SAS. A. M. Saxton. Cary, NC, SAS Institute Inc.: 149-176. Varona, L. and P. Hernandez (2006). "A multithreshold model for sensory analysis." Journal of Food Science 71: S333-S336. Wolfinger, R. (1993). "Laplace's approximation for nonlinear mixed models." Biometrika 80: 791-795. Wright, G. W. and R. M. Simon (2003). "A random variance model for detection of differential gene expression in small microarray experiments." Bioinformatics 19(18): 2448-2455. 2 Yang, W. and R. J. Tempelman (2012). "A Bayesian antedependence model for whole genome selection." Genetics 190: 1491-1501. Zimmerman, D. L. and V. A. Nunoz-Anton (2010). Antedependence Models for Longitudinal Data, Chapman and Hall/CRC. 3