Recent Work || Earlier Work || Other Manuscripts of Interest

Kingsolver, Joel, Heckman, Nancy, Zhang, Jonathan, Carter, Patrick, Knies, Jennifer, Stinchcombe, John R. and Meyer, Karin. (2015 web-early-version). Genetic variation, simplicity, and evolutionary constraints for function-valued traits. The American Naturalist.

De Souza, Camila and Heckman, N. (2014). Switching nonparametric regression models, Journal of Nonparametric Statistics 26: 617--637.

Lei, E, Yao, F, Heckman, N and Meyer, K. (2014). Functional data model for genetically related individuals with application to cow growth. Journal of Computational and Graphical Statistics.

Zhang, J, Heckman, N, Cubranic, D, Gaydos, T and Marron J.O. (2014). Prinsimp: examples and details of the R package that accompanies Gaydos et al 2013. R-Journal. For the R package itself, please see CRAN - prinsimp.

Bonner, S, Newlands, N and Heckman, N. (2014). Modeling regional effects of teleconnections on BC climate through functional data analysis. Environmental and Ecological Statistics, 21: 1-26.

Travis L. Gaydos, Nancy E. Heckman, Mark Kirkpatrick, J. R. Stinchcombe, Johanna Schmitt, Joel Kingsolver, and J. S. Marron (2013). Visualizing genetic constraints. Annals of Applied Statistics 7: 860-882.

Heckman, N. (2012). Reproducing Kernel Hilbert Spaces made easy. Statistics Surveys.

Stinchcombe, J.R., J. Beder, P.A. Carter, et al. (2012). Genetics and evolution of function-valued traits: understanding environmentally responsive phenotypes. Trends in Ecology and Evolution 27: 637-647.

Earlier Work:

Heckman, N., Lockhard, R. and Nielsen, J.D. (2009). Penalized regression, mixed effects models and appropriate modelling. pdf

Griswold, C., Gomulkiewicz, R, and Heckman, N. (2008). Hypothesis testing in comparative and experimental studies of function-valued traits. Evolution, Vol 62, 1229-42.

Ghement, I., Heckman, N. and Petkau A.J. (2007). Seasonal confounding and residual correlation in analyses of health effects of air pollution. Environmetrics, Vol 18, 375-394. A longer version appears as a 2006 UBC Statistics Technical Report Number 217 - available as pdf.

Ghement, I. and Heckman, N. (2006). Inference in Partially Linear Models with Correlated Error. UBC Statistics Technical Report Number 222. Available as pdf.

Gijbels, I. and Heckman, N. (2004). "Nonparametric testing for a monotone hazard function via normalized spacings", Journal of Nonparametric Statistics 16: 463-478. An earlier version was a Discussion Paper 0028, Institute of Statistics, Catholic University of Louvain, Louvain-la-Neuve, Belgium and Technical Report 195, Statistics Department, University of British Columbia. Available as postscript.

Li, X. and Heckman, N. (2003). "Local linear extrapolation", Journal of Nonparametric Statistics 15: 565-578. An earlier version is in postscript. This is from Li's thesis.

Heckman, N. (2003). "Functional data analysis in evolutionary biology", Recent Advances and Trends in Nonparametric Statistics, 49-60. M.G. Akritas and D.N. Politis (editors). Elsevier.

Hall, P. and Heckman, N. (2002). "Estimating and depicting the structure of a distribution of random functions", Biometrika 89: 145-158. A previous version was a UBC Statistics Department Technical Report 197 available on line as postscript, with separate figures.

Harezlak, J. and Heckman, N. (2001). "Crisp: a Tool in Bump Hunting", Journal of Computational and Graphical Statistics, Vol 10, 713-729. A former version was a UBC Statistics Department Technical Report 187, available online postscript.

Heckman, N. and Ramsay, J.O. (2000). "Penalized regression with model-based penalties", Canadian Journal of Statistics 28: 241-258. A former version of this appears in postscript.

Heckman, N. and Zamar, R. (2000). "Comparing the shapes of regression functions", Biometrika 87: 135-144. An earlier longer version appeared as UBC Statistic Department Technical Report 184, which is available as postscript.

Hall, P. and Heckman, N. (2000). "Testing for monotonicity of a regression mean without selecting a bandwidth", Annals of Statistics 28: 20-39. A slightly different version appears as a 1998 UBC Department of Statistics Technical Report 178, available as postscript , with separate figures .

Heckman, N. (1997). "The theory and application of penalized least squares methods or reproducing kernel Hilbert spaces made easy." UBC Statistics Department Technical Report number 216. Available as pdf.

Heckman, N. and Rice, J. (1997). "Line transects of two-dimensional random fields: estimation and design." Canadian Journal of Statistics, 25: 481-501.

Ramsay, J.O. and Heckman, N. (1996). "Some theory for L-spline smoothing." (to appear in the proceedings for the Workshop on Spline Functions and the Theory of Wavelets, Centre de recherches mathematiques of the University of Montreal) Available as postscript. Also appears as UBC Statistics Dept Tech Report 165.

Heckman, N. and Li, B. (1996). "Nonparametric tests for bounds on the derivative of a regression function." Institute of Statistical Mathematics, 48.

Fan, J., Heckman, N. and Wand, M. (1995) "Local polynomial smoothing in generalized linear models." Journal of the American Statistical Association, 90: 141-150.

Price, T., Schluter, D. and Heckman, N. (1993). Sexual selection when the female directly benefits." Biological Journal of the Linnean Society, 48: 187-211.

Gu, C., Heckman, N. and Wahba, G. (1992). "A note on generalized cross validation with replicates." Statistics and Probability Letters, 14: 283-287.

Heckman, N. (1992). "Bump hunting in regression analysis." Statistics and Probability Letters, 14: 141-152.

Heckman, N. (1991). "Minimax Bayes estimation, penalized likelihood methods, and restricted minimax estimation. Nonparametric Functional Estimation and Related Topics, George Roussas, editor, Kluwer Academic Publishers.

Heckman, N. and Woodroofe, M. (1991). "Minimax Bayes estimation in nonparametric regression." Annals of Statistics, 19: 2003-2014.

Kirkpatrick, M. and Heckman, N. (1989). "A quantitiative genetic model for growth, shape, reaction norms and other infinite-dimensional characters." Journal of Mathematical Biology, 27: 429-450.

Heckman, N. (1988). "Minimax estimates in a semiparametric model." Journal of the American Statistical Association, 83:1090-1096.

Heckman, N. (1987). "Robust design in a two treatment comparison in the presence of a covariate." Journal of Statistical Planning and Inference, 16: 75-81.

Heckman, N. (1986). "Spline smoothing in a partly linear model." Journal of the Royal Statistical Society B, 48: 244-248.

Heckman, N. (1986). "Repeated significance tests with biased coin allocation schemes." Probability Theory and Related Fields, 73: 627-635.

Heckman, N. (1985). "A local limit theorem for a biased coin design for sequential tests." Annals of Statistics, 13: 785-788.

Heckman, N. (1985). "A sequential probability ratio test using a biased coin design." Annals of Statistics, 13: 789-794.

Other Manuscripts of Interest:

Harezlak, Jarek (1998). Bump Hunting Revisited. MSc Thesis, UBC Department of Statistics.

This thesis considers testing for the number of bumps in a regression function or its derivative. The proposed test, CriSP (critical smoothing parameter), is similar to that of Silverman (1981) and Bowman, Jones and Gijbels (1998). The thesis can be downloaded in postscript form, or via anonymous ftp: ftp://ftp.stat.ubc.ca in pub/nancy/jarek.

St-Aubin, Robert (1998). Smoothing Parameter Selection when Errors are Correlated and Application to Ozone Data . MSc Thesis, UBC Department of Statistics.

This thesis considers fitting data which is roughly periodic but observed with dependent measurement error. Smoothing splines are used, with smoothing parameter chosen by a form of cross-validation in which 2l+1 points are omitted when trying to predict the i-th data point. The technique is applied to the analysis of ozone data.

Sun, Huiying (1999). Log Hazard Regression MSc Thesis, UBC Department of Statistics.

This thesis considers estimation of the difference of the log hazards of bivariate survival times, with standard errors. Estimates are gotten from marginal estimates, which are the univariate log hazards B-spline estimates of Kooperberg, Stone and Truong (1995). One can use these estimates to test the hypotheses that the marginals follow an exponential or Weibull distribution or that the two failure times have the same distribution or have proportional hazards. A simulation study indicates good performance of our estimates and some of the testing procedures. The methods are applied to diabetic retinopathy data.