BIBLIOGRAPHY. Azzalini, A., Bowman, A., and Hardle, W. (1986), On the Use of Nonparametric Regression for Model Checking, Biometrika, 76, 1, 2-12.

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1 BIBLIOGRAPHY Anderson, D. and Aitken, M. (1985), Variance Components Models with Binary Response: Interviewer Variability, Journal of the Royal Statistical Society B, 47, Aitken, M., Anderson, D., Hinde, J. (1981a), Statistical Modeling of Data on Teaching Styles, Journal of the Royal Statistical Society A, 144, 4, Aitken, M., Anderson, D., Hinde, J. (1981b), Statistical Modeling in School Effectiveness Studies (with Discussion), Journal of the Royal Statistical Society A, 149, Austin, P., Tu, J., and Alter, D. (2003), Comparing Hierarchical Modeling with Traditional Logisitic Regression Analysis Among Patients Hospitalized with Acute Myocardial Infarction: Should We be Analyzing Cardiovascular Outcomes Data Differently?, American Heart Journal, 145, 1, Azzalini, A., Bowman, A., and Hardle, W. (1986), On the Use of Nonparametric Regression for Model Checking, Biometrika, 76, 1, Belsley, D., Kuk, E., Welsch, R. (1980), Regression Diagnostics, John Wiley and Sons, New York. Breslow, N.E. and Clayton, D.G. (1993), Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, 88, 421, Browne, W. and Draper, D. (2002), A Comparison of Bayesian and Likelihood Methods for Fitting Multilevel Models, submitted. Bryk, A. and Raudenbush, S. (1992), Hierarchical Linear Models, Sage Publications, Newbury Park., Raudenbush, S., and Congdon, R. (1996), HLM Hierarchical Linear and Nonlinear Modeling with the HLM/2L and HLM/3L Programs, Scientific Software International, Chicago. Burden, R. and Faires, J. (1989), Numerical Analysis, PWS-KENT Publishing Company, Boston. Carroll, R., Wang, S., Simpson, D., Stromberg, A. and Ruppert, D. (1998), The Sandwich (Robust Covariance Matrix) Estimator, unpublished paper. Choi, J. and McHugh, R. (1989), A Reduction Factor in Goodness-of-fit and Independence Tests for Clustered and Weighted Observations, Biometrics,

2 Copas, J. (1989), Unweighted Sum of Squares Test for Proportions, Applied Statistics, 38, 1, Dempster, A., Laird, N., and Rubin, D. (1977), Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society B, 39, 1-8. Dwyer, T., Blizzard, L., Morley, R., and Ponsonby, A. (1999), Within Pair Association Between Birth Weight and Blood Pressure at Age 8 in Twins from a Cohort Study, British Medical Journal, 319, 7221, Evans, M., Hastings, N., and Peacock, B. (2000), Statistical Distributions, Third Edition, John Wiley and Sons, New York. Evans, S. (1998), Goodness-of-Fit in Two Models for Clustered Binary Data, Ph.D. Dissertation, University of Massachusetts Amherst, Ann Arbor: University Microfilms International. Fotiu, R. (1989), A Comparison of the EM and Data Augmentation Algorithms on Simulated Small Sample Hierarchical Data from Research on Education, Ph.D. Dissertation, University of Massachusetts Amherst, East Lansing: University Microfilms International. Fowlkes, E. (1987), Some Diagnostics for Binary Logistic Regression via Smoothing Methods, Biometrika, 74, Ghosh, M. (1992), Constrained Bayes Estimation with Applications, Journal of the American Statistical Association, 87, Gilks, W., Richardson, S., and Spiegelhalter, D. (1996), Markov Chain Monte Carlo in Practice, Chapman and Hall, London. Goldstein, H. (1986), Multilevel Mixed Linear Model Analysis Using Iterative Generalized Least-squares, Biometrika, 73, 1, (1989), Restricted Unbiased Iterative Generalized Least-squares Estimation, Biometrika, 76, 3, (1991), Nonlinear Multilevel Models, with an Application to Discrete Response Data, Biometrika, 78, 1, and Rasbash, J. (1992), Efficient Computational Procedures for the Estimation of Parameters in Multilevel Models Based on Iterative Generalized Least-squares, Computational Statistics & Data Analysis, 13, (1995), Multilevel Statistical Models, Second Edition, Arnold, London.

3 , and Rasbash, J. (1996), Improved Approximations for Multilevel Models with Binary Responses, Journal of the Royal Statistical Society A, 159, 3, and Spiegelhalter, D. (1996), League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance, Journal of the Royal Statistical Society A, 159, 3, , Browne, W. and Rasbash, J. (2002), Multilevel Modeling of Medical Data, Statistics in Medicine, to appear. Guo, G. and Zhao, H. (2000), Multilevel Modeling for Binary Data, Annual Reviews of Sociology, 26, Hardle, W. (1990), Applied Nonparametric Regression, Cambridge University Press, Cambridge. Harville, D.A. (1977), Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems, Journal of the American Statistical Association, 72, Heath, A., Yang, M., and Goldstein, H. (1996), Multilevel Analysis of the Changing Relationship between Class and Party in Britain , Quality and Quantity, 30, Hosmer, D. and Lemeshow, S. (1980), A Goodness-of-Fit Test for the Multiple Logistic Regression Model, Communications in Statistics, A10, Hosmer, D., Lemeshow, S., and Klar, J. (1988), Goodness-of-Fit Testing for Multiple Logistic Regression Analysis when the Estimated Probabilities are Small, Biometrical Journal, 30, Hosmer, D., Hosmer, T., le Cessie, S., and Lemeshow, S. (1997), A Comparison of Goodness-of-Fit Tests for the Logistic Regression Model, Statistics in Medicine, 16, Hosmer, D. and Lemeshow, S. (2000), Applied Logistic Regression 2 nd Edition, John Wiley & Sons, Inc., New York. Hox, J. (1995), Applied Multilevel Analysis, TT-Publikaties, Amsterdam. Jacob, M. (2000), Extra-binomial Variation in Logistic Multilevel Models a Simulation, Multilevel Modelling Newsletter, 12, 1, 8-13.

4 Johnson, N., Kotz, S. and Kemp, A. (1992), Univariate Discrete Distributions, John Wiley and Sons, New York. Kleinbaum, D. (2002), Analysis of Correlated Data, Course Notes, 2002 Summer Program in Applied Statistical Methods, The Ohio State University. Kreft, I. and De Leeuw, J. (199*), Introducing Multilevel Modeling, Sage, London. Langford, I. and Lewis, T. (1995), Detecting Outliers in Multilevel Models: an Overview, Multilevel Modelling Newsletter, 7, 2, 6-7. Langford, I. and Lewis, T. (1998), Outliers in Multilevel Data, Journal of the Royal Statistical Society A, 161, 2, le Cessie, S., and van Houwelingen, J. (1991), A Goodness-of-Fit Test for Binary Regression Models, Based on Smoothing Methods, Biometrics, 47, Lemeshow, S. and Hosmer, D. (1982), The Use of Goodness-of-Fit Statistics in the Development of Logistic Regression Models, American Journal of Epidemiology, 115, Leyland, A., Ed. and Goldstein, H., Ed. (2001), Multilevel Modelling of Health Sciences, John Wiley & Sons, Inc., New York. Li, B. (1993), A Deviance Function for the Quasi-likelihood Method, Biometrika, 80, 4, Liang, K. and Zeger, S.L. (1986), Longitudinal Data Analysis Using Generalized Linear Models, Biometrika, 73, 1, Lindsey, J. and Lambert, P. (1998), On the Appropriateness of Marginal Models for Repeated Measurements in Clinical Trials, Statistics in Medicine, 17, Littell, R., Milliken, G., Stroup, W., and Wolfinger, R. (1996), SAS System for Mixed Models, SAS Institute, Cary, NC. Lohr, S. (1999), Sampling: Design and Analysis, Duxbury Press, Pacific Grove. Longford, N.T. (1993a), Random Coefficient Models, Clarendon Press, Oxford.. (1993b), VARCL: Software for Variance Component Analysis of Data with Nested Random Effects (Maximum Likelihood). Manual, ProGAMMA, Groningen.

5 Louis, T. (1984), Estimating a Population of Parameter Values Using Bayes and Empirical Bayes Methods, Journal of the American Statistical Association, 79, McCullagh, J.A. and Nelder, P. (1989), Generalized Linear Models, Chapman and Hall, New York. McCulloch, C. and Searle, S. (2001), Generalized, Linear and Mixed Models, John Wiley and Sons, New York. Osius, G. and Rojek, D. (1992), Normal Goodness-of-Fit Tests for Multinomial Models with Large Degrees of Freedom, Journal of the American Statistical Association, 87, Pan, W. (2002), Application of Conditional Moment Tests to Model Checking for Generalized Linear Models, Biostatistics, 3, 2, Pregibon, D. (1981), Logistic Regression Diagnostics, The Annals of Statistics, 9, 4, Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2002), Reliable Estimation of Generalized Linear Mixed Models Using Adaptive Quadrature, The Stata Journal, 2, 1, Rasbash, J., Browne, W., Goldstein, H., Yang, M., Plewis, I., Healy, M., Woodhouse, G., Draper, D., Langford, I., and Lewis, T. (2000a), A User s Guide to MLwiN, Institute of Education, London., Browne, W., Goldstein, H., Yang, M., Woodhouse, G. (2000b), The MLwiN Command Interface, Institute of Education, London. Raudenbush, S.W. (1994), Equivalence of Fisher Scoring to Iterative Generalized Leastsquares in the Normal Case with Application to Hierarchical Linear Models, unpublished. Roberts, G., Rao, J. and Kumar, S. (1987), Logistic Regression Analysis of Sample Survey Data, Biometrika, 74, 1, Rodriguez, G. and Goldman, N. (1995), An Assessment of Estimation Procedures for Multilevel Models with Binary Responses, Journal of the Royal Statistical Society A, 158, 1, SAS Institute Inc. (2000), SAS Guide for Personal Computers, Version 8.02, SAS Institute Inc., Cary, North Carolina. Searle, S. (1982), Matrix Algebra Useful for Statistics, John Wiley and Sons, New York.

6 Seber, G. (1977), Linear Regression Analysis, John Wiley and Sons, New York. Singer, J.D. (1998), Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models, Journal of Educational and Behavioral Statistics, 24, 4, Snijders, T.A.B. and Bosker, R.J. (1999), Multilevel Analysis, Sage, London. StataCorp. (2000), Stata Statistical Software: Release 6.0, Stata Corporation, College Station, Texas. Vonesh, E., Chinchilli, V. and Pu, K. (1994), Goodness-of-fit in Generalized Non- Linear Mixed-Effects Models, unpublished. Wand, M. and Jones, M. (1995), Kernel Smoothing, Chapman & Hall/CRC, Boca Raton. Wolfinger, R. and O Connell, M. (1993), Generalized Linear Mixed Models: A Pseudolikelihood Approach, Journal Statistical Computation and Simulation, 48, Wong, G.Y. and Mason, W.M. (1985), The Hierarchical Logistic Regression Model for Multilevel Analysis, Journal of the American Statistical Association, 80, 391, Wright, D.B. (1997), Extra-binomial Variation in Multilevel Logistic Models with Sparse Structures, Journal of Mathematical and Statistical Psychology, 50, Zeger, S. and Karim, M. (1991), Generalized Linear Models with Random Effects; a Gibbs Sampling Approach, Journal of the American Statistical Society, 86,

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