| Year |
Citation |
Score |
| 2019 |
Fu E, Heckman N. Model-based curve registration via stochastic approximation EM algorithm Computational Statistics & Data Analysis. 131: 159-175. DOI: 10.1016/J.Csda.2018.06.010 |
0.355 |
|
| 2018 |
Gomulkiewicz R, Kingsolver JG, Carter PA, Heckman N. Variation and Evolution of Function-Valued Traits Annual Review of Ecology, Evolution, and Systematics. 49: 139-164. DOI: 10.1146/Annurev-Ecolsys-110316-022830 |
0.373 |
|
| 2017 |
Lenzi A, Souza CPEd, Dias R, Garcia NL, Heckman NE. Analysis of aggregated functional data from mixed populations with application to energy consumption Environmetrics. 28. DOI: 10.1002/Env.2414 |
0.34 |
|
| 2017 |
Souza CPED, Heckman NE, Xu F. Switching nonparametric regression models for multi‐curve data Canadian Journal of Statistics-Revue Canadienne De Statistique. 45: 442-460. DOI: 10.1002/Cjs.11331 |
0.411 |
|
| 2016 |
Lee W, Greenwood PE, Heckman N, Wefelmeyer W. Pre-averaged kernel estimators for the drift function of a diffusion process in the presence of microstructure noise Statistical Inference For Stochastic Processes. 20: 237-252. DOI: 10.1007/S11203-016-9141-5 |
0.381 |
|
| 2015 |
Kingsolver JG, Heckman N, Zhang J, Carter PA, Knies JL, Stinchcombe JR, Meyer K. Genetic variation, simplicity, and evolutionary constraints for function-valued traits. The American Naturalist. 185: E166-81. PMID 25996868 DOI: 10.1086/681083 |
0.313 |
|
| 2015 |
Lei E, Yao F, Heckman N, Meyer K. Functional Data Model for Genetically Related Individuals With Application to Cow Growth Journal of Computational and Graphical Statistics. 24: 756-770. DOI: 10.1080/10618600.2014.948180 |
0.339 |
|
| 2014 |
de Souza CPE, Heckman NE. Switching nonparametric regression models Journal of Nonparametric Statistics. 26: 617-637. DOI: 10.1080/10485252.2014.941364 |
0.371 |
|
| 2014 |
Bonner SJ, Newlands NK, Heckman NE. Modeling regional impacts of climate teleconnections using functional data analysis Environmental and Ecological Statistics. 21: 1-26. DOI: 10.1007/S10651-013-0241-8 |
0.307 |
|
| 2013 |
Heckman N, Lockhart R, Nielsen JD. Penalized regression, mixed effects models and appropriate modelling Electronic Journal of Statistics. 7: 1517-1552. DOI: 10.1214/13-Ejs809 |
0.396 |
|
| 2013 |
Gaydos TL, Heckman NE, Kirkpatrick M, Stinchcombe JR, Schmitt J, Kingsolver J, Marron JS. Visualizing genetic constraints Annals of Applied Statistics. 7: 860-882. DOI: 10.1214/12-Aoas603 |
0.303 |
|
| 2008 |
Griswold CK, Gomulkiewicz R, Heckman N. Hypothesis testing in comparative and experimental studies of function-valued traits. Evolution; International Journal of Organic Evolution. 62: 1229-42. PMID 18266991 DOI: 10.1111/J.1558-5646.2008.00340.X |
0.373 |
|
| 2004 |
Gijbels I, Heckman N. Nonparametric testing for a monotone hazard function via normalized spacings Journal of Nonparametric Statistics. 16: 463-477. DOI: 10.1080/10485250310001622668 |
0.319 |
|
| 2003 |
Li X, Heckman NE. Local linear extrapolation Journal of Nonparametric Statistics. 15: 565-578. DOI: 10.1080/10485250310001605432 |
0.348 |
|
| 2002 |
Hall P, Heckman NE. Estimating and depicting the structure of a distribution of random functions Biometrika. 89: 145-158. DOI: 10.1093/Biomet/89.1.145 |
0.322 |
|
| 2001 |
Harezlak J, Heckman NE. CriSP: A tool for bump hunting Journal of Computational and Graphical Statistics. 10: 713-729. DOI: 10.1198/106186001317243412 |
0.356 |
|
| 2000 |
Heckman NE, Ramsay JO. Penalized regression with model-based penalties Canadian Journal of Statistics. 28: 241-258. DOI: 10.2307/3315976 |
0.329 |
|
| 2000 |
Hall P, Heckman NE. Testing for monotonicity of a regression mean by calibrating for linear functions Annals of Statistics. 28: 20-39. DOI: 10.1214/Aos/1016120363 |
0.372 |
|
| 2000 |
Heckman NE, Zamar RH. Comparing the shapes of regression functions Biometrika. 87: 135-144. DOI: 10.1093/Biomet/87.1.135 |
0.385 |
|
| 1997 |
Ramsay JO, Heckman N, Silverman BW. Spline smoothing with model-based penalties Behavior Research Methods, Instruments, and Computers. 29: 99-106. DOI: 10.3758/Bf03200573 |
0.412 |
|
| 1997 |
Heckman N, Rice J. Line transects of two‐dimensional random fields: Estimation and design Canadian Journal of Statistics-Revue Canadienne De Statistique. 25: 481-501. DOI: 10.2307/3315343 |
0.317 |
|
| 1996 |
Heckman NE, Li B. Nonparametric tests for bounds on the derivative of a regression function Annals of the Institute of Statistical Mathematics. 48: 315-336. DOI: 10.1007/Bf00054793 |
0.33 |
|
| 1995 |
Fan J, Heckman NE, Wand MP. Local polynomial kernel regression for generalized linear models and quasi-likelihood functions Journal of the American Statistical Association. 90: 141-150. DOI: 10.1080/01621459.1995.10476496 |
0.411 |
|
| 1992 |
Heckman NE. Bump hunting in regression analysis Statistics and Probability Letters. 14: 141-152. DOI: 10.1016/0167-7152(92)90078-J |
0.347 |
|
| 1992 |
Gu C, Heckman N, Wahba G. A note on generalized cross-validation with replicates Statistics and Probability Letters. 14: 283-287. DOI: 10.1016/0167-7152(92)90058-D |
0.387 |
|
| 1991 |
Heckman NE, Woodroofe M. Minimax Bayes Estimation in Nonparametric Regression Annals of Statistics. 19: 2003-2014. DOI: 10.1214/Aos/1176348383 |
0.358 |
|
| 1989 |
Kirkpatrick MA, Heckman N. A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters Journal of Mathematical Biology. 27: 429-450. PMID 2769086 DOI: 10.1007/Bf00290638 |
0.338 |
|
| 1988 |
Heckman NE. Minimax estimates in a semiparametric model Journal of the American Statistical Association. 83: 1090-1096. DOI: 10.1080/01621459.1988.10478706 |
0.394 |
|
| 1987 |
Heckman NE. Robust design in a two treatment comparison in the presence of a covariate Journal of Statistical Planning and Inference. 16: 75-81. DOI: 10.1016/0378-3758(87)90057-7 |
0.395 |
|
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