Book chapters and other reviewed publications

• Bloomfield, P., Brillinger, D. R., Nychka, D. W. and Stolarski, R. (1988). ``Appendix 1 Statistical Approaches to Ozone Trend Detection.'' Present State of Knowledge of the Upper Atmosphere 1988: An Assessment Report NASA Reference Publication 1208, ed. Watson, R. T., NASA.

• Bloomfield, P., Brillinger, D. R., Nychka, D. W. and Stolarski, R. (1988). ``Appendix 1 Statistical Issues.'' Report of the NASA-WMO Ozone Trend Panel, ed. Watson, R. T., NASA.

• Nychka, D. (1994). Discussion to ``Epidemics: Models and Data.'' Journal of the Royal Statistical Society Series B.

• Handcock, M., Nychka, D. and Meier, K. (1994). Discussion to ``Kriging and Splines: An Empirical Comparison of Their Predictive Performance.'' Journal of the American Statistical Association, 89, 401-403.

• Nychka, D. and Cummins, D. (1996). Comment on: Eilers, P. and Marx, B. ``Flexible Smoothing with B-splines and Penalties.'' Statistical Science, 11, 104-105.

• Bailey, B., Ellner S. and Nychka D. (1997). ``Asymptotics and Applications of Local Lyapunov Exponents.'' Proceedings for the Fields/CRM Workshop: Nonlinear Dynamics and Time Series: Building a Bridge Between the Natural and Statistical Sciences, American Mathematical Society, 115-133.

• Hu, F., Hall, A. R. and Nychka, D. (2000). ``A Nonparametric Approach to Stochastic Discount Factor Estimation.'' Applying Kernel and Nonparametric Estimation to Economic Topics, eds. Fomby, T. and Hill, R.C., JAI Press Inc., Stamford Connecticut, 155-178.

• Nychka, D. (2000). ``Spatial Process Estimates as Smoothers.'' Smoothing and Regression. Approaches, Computation and Application, ed. Schimek, M. G., Wiley, New York, 393-424.

• Nychka, D. and Tebaldi, C. (2002), Comment on `Calculation of Average, Uncertainty Range and Reliability of Regional Climate Changes from AOGCM Simulations via the ``Reliability Ensemble Averaging'' (REA) method' Journal of Climate, 16, 883-884.

• Tebaldi, C. and Nychka, D. (2004) Invited discussion to ``Calibrated probabilistic mesoscale weather field forecasting: the geostatistical output perturbation (GOP) method''. Journal of the American Statistical Association. 99 583-585.

• Gilleland, E., D. Nychka and U. Schneider (2006). ``Spatial models for the distribution of extremes.'' In Applications of Computational Statistics in the Environmental Sciences: Hierarchical Bayes and MCMC Methods ed. J.S. Clark and A. Gelfand, Oxford University Press.