Siegel der Universität Heidelberg

Enno Mammen

Professor for Mathematical Statistics

 Enno Mammen

Heidelberg University
Institute for Mathematics
MΛTHEMΛTIKON
Im Neuenheimer Feld 205
69120 Heidelberg, Germany
Phone: +49 (0) 6221 54 14180
Fax: +49 (0) 6221 54 14101
E-mail: mammen@math.uni-heidelberg.de

Publications

Preprints

  1. E.R. Lee, S. Park, E. Mammen and B.U. Park (2023). Efficient Functional Lasso Kernel Smoothing for High-dimensional Additive Regression Working paper.
  2. V. Konakov and E. Mammen (2023). Local Limit Theorems and Strong Approximations for Robbins-Monro Procedures. Working paper.
  3. A. Isakson, E. Mammen, J. P. Nielsen and C. Proust-Lima (2021). Superefficient estimation of future conditional hazards based on marker information. Working paper.
  4. E. Mammen, R.A. Wilke, and K. Zapp (2021). Data driven estimation of group structures in individual fixed effects models. Working paper.
  5. C. Jentsch, E. Mammen, H. Müller, J. Rieger and C. Schötz (2021). Text Mining methods for measuring the coherence of party manifestos for the German federal elections from 1990 to 2021. DoCMA Working Paper #8, Sept. 2021, https://doi.org/10.17877/de290r-22363.
  6. M. Hiabu, E. Mammen and J. T. Meyer (2021). Local linear smoothing in additive models as data projection. Working paper.
  7. M. Hiabu, E. Mammen and J. T. Meyer (2021). Random Planted Forest: a directly interpretable tree ensemble. Working paper.
  8. A. Kreiß , E. Mammen, and W. Polonik (2021). Testing for a parametric baseline-intensity in dynamic interaction networks. Working paper.

Monographs

  1. E. Mammen (1992) When does bootstrap work: asymptotic results and simulations. Lecture Notes in Statistics 77, Springer Verlag, New York, Heidelberg.

Proceedings

  1. E. Mammen, C. Rothe and M. Schienle (2013) Generated Covariates in Nonparametric Estimation: A Short Review. In: Recent Developments in Modeling and Applications in Statistics. (P.E. Oliveira, M. da Graça Temido, C. Henriques and M. Vichi, edit.). Springer Studies in Theoretical and Applied Statistics. Springer, Berlin Heidelberg, 97 - 105.
  2. E. Mammen and K. Yu (2007) Additive Isotone Regression. In: Asymptotics: Particles, Processes and Inverse Problems: Festschrift for Piet Groeneboom (Eric A. Cator, Geurt Jongbloed, Cor Kraaikamp, Hendrik P. Lopuhaä, Jon A. Wellner, eds.), Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007, 179 - 195. IMS Lecture Notes, 179 - 195.
  3. Linton, O. B., Lin, X. and Carroll, R. J. and E. Mammen (2003) Accounting for correlation in marginal longitudinal nonparametric regression. Second Seattle Symposium on Biostatistics, editors D. Lin and P.J. Heagerty, Lecture Notes in Statistics 179, Springer, New York.
  4. O. Linton and E. Mammen (2003) Nonparametric smoothing methods for a class of nonstandard curve estimation problems. In: Recent advances and trends in nonparametric statistics (M.G. Akritas and D. N. Politis, eds.), Elsevier, Amsterdam.
  5. W. Härdle and E. Mammen (1991) Bootstrap methods in nonparametric regression. In: Proceedings of the NATO Advanced Study Institute on Nonparametric functional estimation and related topics, Spetses, Greece ( ed. by G. Roussas), p. 111 - 124.
  6. E. Mammen (1991) Nonparametric curve estimation and simple curve characteristics. In: Pro- ceedings of the NATO Advanced Study Institute on Nonparametric functional estimation and related topics, Spetses, Greece ( ed. by G. Roussas), p. 133 - 140.
  7. E. Mammen (1989) A nonparametric regression estimator based on simple assumptions on the shape of the regression function. In: Proceedings of the International Workshop on Theory and Practice in Data Analysis, Akademie der Wissenschaften der DDR, Berlin, p. 35 - 41.

Book chapters

  1. J. Meis and E. Mammen (2021) Uncoupled isotonic regression. In: Advances in Contemporary Statistics and Econometrics. Festschrift in Honor of Christine Thomas-Agnan, edited by A. Daouia and A. Ruiz-Gazen, Springer, 123 – 138.
  2. E. Mammen (2017) Structured Nonparametric Curve Estimation. In: Modern problems of stochastic analysis and statistics - selected contributions in honor of Valentin Konakov, edited by V. Panov, Springer, Berlin, Heidelberg, 335 - 348.
  3. E. Mammen, B. U. Park and M. Schienle (2014) Additive Models: Extensions and Related Models. In: Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics (editors Racine, Ullah), 176 - 214.
  4. J. Franke and J.-P. Kreiss, E. Mammen (2009) Nonparametric Modelling in Financial Time Series. In: Handbook of Financial Time Series, edit. by Torben G. Andersen, Richard A. Davis, Jens-Peter Kreiss and Thomas Mikosch, Springer, New York, 2009, pp. 927 - 952.
  5. T. Maiwald, S. Nandi, J. Timmer, E. Mammen (2008) Surrogate data - a qualitative and quantitative analysis. In: Mathematical methods in signal processing and digital image analysis. (R. Dahlhaus, J. Kurths, P. Maass, J. Timmer, edit.), Springer, New York, 41 - 74.
  6. E. Mammen and S. Nandi (2004) Bootstrap and resampling. In: Handbook of Computational Statistics. Editors: J. E. Gentle, W. Härdle, Y. Mori, Springer Berlin, p. 467 - 496.
  7. E. Mammen (2000) Resampling methods for curve estimation. In: Smoothing and Regression. Approaches, Computation and Application (M. G. Schimek, edit.) Wiley, New York.
  8. E. Mammen (1997) Introduction to "P. Hall (1988). Theoretical comparison of bootstrap confidence intervals. Ann. Statist., 16, 927 - 985". In: Breakthroughs in Statistics Vol. III, edit. by S. Kotz and N. L. Johnson, Springer, New York, Berlin, Heidelberg 483 - 488.

Discussion of papers

  1. E. Mammen (2013) Comment on "Generalized Jackknife Estimators of Weighted Average Derivatives" by M.D. Cattaneo, R.K. Crump and M. Jansson, J. Amer. Statist. Assoc., 108, 1260 - 1262 (2013).
  2. E. Mammen (2012) Discussion of the paper "Nonparametric (Smoothed) Likelihood and Integral Equations", by P. Groeneboom, J. Stat. Plan. Inf. (2012).
  3. C. Jentsch and E. Mammen (2011) Discussion on the paper "Bootstrap for dependent data: a review", by Jens-Peter Kreiss and Efstathios Paparoditis (2011). J. Korean Statist. Soc., 40, 391 - 392.
  4. E. Mammen (2007) Discussion of "J. Fan and J. Jiang (2007). Nonparametric inference with generalized likelihood ratio tests. Test, 16, 409 - 478".
  5. E. Mammen (2001) Discussion of "Davies, P. L. and A. Kovac (2001). Local extremes, runs, strings and multiresolution. Annals of Statistics, 29".
  6. E. Mammen (2000) Discussion of "G. Kerkyacharian and D. Picard (2000). Thresholding algorithms, maxisets and well concentrated bases. Test, 9".
  7. E. Mammen (1997) Discussion of "L. Devroye (1997). Universal smoothing factor selection in densityestimation:theoryandpractice. Test, 6, p. 223 - 282".
  8. E. Mammen and V. Spokoiny (1994) Discussion of "Donoho, D.L., I. M. Johnstone, G. Kerkyacharian and D. Picard (1994). Wavelet Shrinkage. Asymptopia? Journal of the Royal Statistical Society B, 57, 301 - 369".
  9. E. Mammen and J. S. Marron (1992) Discussion of "Hall, P. and I. M. Johnstone (1992). Empirical functionals and efficient smoothing parameters election. J.Roy. Statist. Soc. B, 54475 - 530".

Book Reviews

  1. E. Mammen (2001) Review of "Politis, D. N., Romano, J. P. and Wolf, M. (1999). Subsampling, Springer, New York". Metrika, 53, 2001, 95 - 96.
  2. E. Mammen (2001) Review of "Hart, J. D. (1997). Nonparametric Smoothing and Lack-of-Fit Tests, Springer, New York". DMV Jahresbericht, 103/1 2001, 21 - 22.
  3. E. Mammen (1997) Review of "Hall, P. (1992). The Booststrap and Edgeworth Expansion. Springer, New York". Metrika.
  4. E. Mammen (1992) Review of "Härdle, W. (1990). Applied Nonparametric Regression, Cambridge University Press". Metrika.

Articles

  1. C. Butucea, E. Mammen, M. Ndaoud and A. B. Tsybakov (2023). Variable selection, monotone likelihood ratio and group sparsity.Annals of Statistics, 51, 312 - 333.
  2. Y.K. Lee, E. Mammen, B.U. Park (2023) Hilbertian additive regression with parametric help. Journal of Nonparametric Statistics, forthcoming.
  3. E. Mammen and M. Müller (2023) Nonparametric estimation of locally stationary Hawkes processes.Bernoulli, 29, 2062 - 2083.
  4. J.M. Jeon, Y.K. Lee, E. Mammen, B.U. Park (2022) Locally polynomial Hilbertian additive regression.Bernoulli, 28, 2034 - 2066.
  5. E. Mammen and S. Sperlich (2022) Backfitting tests in generalized structured models. Biometrika, 109(1), 137 - 152.
  6. M.L. Gámiz Pérez, E. Mammen, M.D. Martínez Miranda and J.P. Nielsen (2022) Missing Link Survival Analysis with applications to available pandemic data. Computational Statistics and Data Analysis,169, 107405.
  7. M. Hiabu, E. Mammen, M. D. Martínez Miranda and J. P. Nielsen (2021) Smooth backfitting of proportional hazards with multiplicative components. J. Amer. Statist. Assoc. 116, 1983 - 1993.
  8. M.L. Gámiz Pérez, E. Mammen, M.D. Martínez Miranda and J.P. Nielsen (2022) Missing Link Survival Analysis with applications to available pandemic data.Computational Statistics and Data Analysis, 169, 107405.
  9. E. Mammen, M. D. Martínez Miranda, J. P. Nielsen and M. Vogt (2021) Calendar effect and in-sample forecasting. Insurance: Mathematics and Economics 96, 31 - 52.
  10. C. Jentsch, E. R. Lee and E. Mammen (2021) Poisson reduced rank models with an application to political text data. Biometrika 108, 455 - €“468.
  11. G.J. van den Berg, L. Janys, E. Mammen and J. P. Nielsen (2021) A general semiparametric approach to inference with marker-dependent hazard rate models. The Journal of Econometrics, 221(1), 43 - 67.
  12. K. Gregory, E. Mammen and M. Wahl (2021) Optimal estimation of sparse high-dimensional additive models. Annals of Statistics, 49, 1514 - 1536.
  13. Y. K. Lee, E. Mammen, J. P. Nielsen and B. U. Park (2020) Nonparametric regression with parametric help. Electronic Journal of Statistics, 14, 3845 - 3868.
  14. C. Breunig, E. Mammen and A. Simoni (2020) Ill-posed Estimation in High-Dimensional Models with Instrumental Variables. Journal of Econometrics 219, 171 - 200.
  15. S.M.S. Lo, E. Mammen and R.A. Wilke (2020) A Nested Copula Duration Model for Competing Risks with Multiple Spells. Computational Statistics and Data Analysis 150, 106986.
  16. D. Antonczyk, B. Fitzenberger, E. Mammen and K. Yu (2020) A nonparametric approach to identify age, time, and cohort effects. Journal of Statistical Planning and Inference 204, 96 - 115.
  17. C. Jentsch, E. R. Lee and E. Mammen (2020) Time-dependent Poisson reduced rank models for political text data analysis. Computational Statistics and Data Analysis, 142, 106813.
  18. G. J. van den Berg, P. Bonev and E. Mammen (2020) Nonparametric instrumental variable methods for dynamic treatment evaluation. The Review of Economics and Statistics 102, 355 - 367.
  19. G. J. van den Berg, L. Janys, E. Mammen and J. P. Nielsen (2020) A general semiparametric approach to inference with marker-dependent hazard rate models. The Journal of Econometrics, 221 (1), 43 - 67.
  20. E. Mammen, J. P. Nielsen, M. Scholz, and S. Sperlich (2019) Conditional variance forecasts for long-term stock returns. Risks, 7 (4), 113.
  21. A. Kreiß, E. Mammen and W. Polonik (2019) Nonparametric inference for continuous-time event counting and link-based dynamic network models. Electronic Journal of Statistics, 13 (2), 2764 - 2829.
  22. Y. K. Lee, E. Mammen, J. P. Nielsen and B. U. Park (2019) Generalised additive dependency inflated models including an aggregated covariate. Electronic Journal of Statistics, 13 (1), 67 - 93.
  23. E. Mammen, I. Van Keilegom and K. Yu (2019) Expansions for moments of regression quantiles with applications to nonparametric testing. Bernoulli, 25 (2), 793 - 827.
  24. S.M. Bischofberger, M. Hiabu, E. Mammen and J.P. Nielsen (2019) A comparison of in-sample forecasting methods. Computational Statistics and Data Analysis, 137, 133 - 154.
  25. C. Breunig, E. Mammen and A. Simoni (2018) Nonparametric Estimation in case of Endogenous Selection. J. Econometrics, 202 (2), 268 - 285.
  26. Y. K. Lee, E. Mammen, J. P. Nielsen and B. U. Park (2018) In-sample forecasting: A brief review and new algorithms. ALEA, Lat. Am. J. Probab. Math. Stat., 15, 875 - 895 (Invited paper).
  27. Y. K. Lee, E. Mammen, J. P. Nielsen and B. U. Park (2017) Operational time and in-sample density forecasting. Ann. Statist., 45, 1312 - 1341.
  28. S. Dlugosz, E. Mammen and R. A. Wilke (2017) Generalised partially linear regression with misclassified data and an application to labour market transitions. Computational Statistics and Data Analysis, 110, 145 - 159.
  29. E. R. Lee and E. Mammen (2016) Sparse High Dimensional Varying Coefficient Models. Elect. J. Statistics, 10, 855 - 894.
  30. E. Mammen, C. Rothe and M. Schienle (2016) Semiparametric Estimation with Generated Covariates. Econometric Theory, 32, 1140 - 1177.
  31. C. Conrad and E. Mammen (2016) Asymptotics for parametric GARCH-in-mean models. J. of Econometrics, 194, 319 - 329.
  32. M. L. Gámiz Pérez, E. Mammen, M. D. Martínez Miranda, and J. P. Nielsen (2016) Double one-sided cross-validation of local linear hazards. Journal of the Royal Statistical Society Series B, 78, 755 - 779.
  33. M. Hiabu, E. Mammen, M.D. Martínez Miranda, and J.P. Nielsen (2016) In sample forecasting with local linear survival densities. Biometrika , 103, 843 - 859.
  34. B. U. Park, Y. K. Lee and E. R. Lee and E. Mammen (2015) Varying Coefficient Regression Models: A Review and New Developments. International Statistical Review, 83, 1, 36 - 64.
  35. M.R. Fengler, E. Mammen and M. Vogt (2015) Specification and structural break tests for additive models with applications to realized variance data. Journal of Econometrics, 188, 196 - 218.
  36. E. Mammen, M.D. Martínez Miranda, and J.P. Nielsen (2015) In-Sample Forecasting Applied to Reserving and Mesothelioma Mortality. Insurance: Mathematics and Economics, 61, 76 - 86.
  37. Y.K. Lee, E. Mammen, J.P. Nielsen and B.U. Park (2015) Asymptotics for In-Sample Density Forecasting. Ann. Statist., 43, 620 - 645.
  38. Y. K. Lee, E. Mammen and B. U. Park (2014) Backfitting and smooth backfitting in varying coefficient quantile regression. Econometrics J., 17, Issue 2, 20 - 38.
  39. V. Konakov, E. Mammen and J. Woerner (2014) Statistical convergence of Markov experiments to diffusion limits. Bernoulli, 20, 623 - 644.
  40. V. Dunker, J.-P. Florens, T. Hohage, J. Johannes and E.Mammen (2014) Iterative Estimation of Solutions to Noisy Nonlinear Operator Equations in Nonparametric Instrumental Regression. J. of Econometrics, 178, 444 - 455.
  41. E. Mammen, M. D. Martínez Miranda, J. P. Nielsen and S. Sperlich (2014) Further theoretical and practical insight to the do-validated bandwidth selector. J. Korean Statistical Society, 43, 355 - 365.
  42. E. Mammen and W. Polonik (2013) Confidence Regions for Level Sets. J. of Multivariate Analysis, 122, 202 - 214.
  43. Y. K. Lee, E. Mammen and B. U. Park (2012) Projection-Type Estimation for Varying Coefficient Regression Models, Bernoulli, 18, 177 - 205.
  44. Y. K. Lee, E. Mammen and B. U. Park (2012) Flexible Generalized Varying Coefficient Regression Models. Ann. Statist., 40, 1906 - 1933.
  45. E. Mammen, C. Rothe and M. Schienle (2012) Nonparametric Regression with Nonparametrically Generated Covariates. Ann. Statist., 40, 1132 - 1170.
  46. K. Yu, B. U. Park and E. Mammen (2011) Semiparametric Regression: Efficiency Gains From Modeling the Nonparametric Part. Bernoulli, 17, 736 - 748.
  47. O. Linton, E. Mammen, J. P. Nielsen and I. van Keilegom (2011) Nonparametric Regression with Filtered Data. Bernoulli, 17, 60 - 87.
  48. E. Mammen, M. D. Martínez Miranda, J. P. Nielsen and S. Sperlich (2011) Do-validation with kernel density estimation. J. Amer. Stat. Assoc., 106(494): 651 - 660.
  49. E. Mammen, J.P. Nielsen and B. Fitzenberger (2011) Generalised linear time series regression. Biometrika, 98 (4): 1007 - 1014.
  50. J. Horowitz and E. Mammen (2011) Oracle-efficient estimation of an additive model with an unknown link function. Econometric Theory, 27, 582 - 608.
  51. S. Hoderlein, E. Mammen and K. Yu (2011) Nonparametric Models in Binary Choice Fixed Effects Panel Data Econometrics Journal, 14, 351 - 367.
  52. Y. K. Lee , E. Mammen and B. Park (2010) Bandwidth selection for kernel regression with correlated errors. Statistics, 44, 327 - 340.
  53. J. Klemelä and E. Mammen (2010) Empirical risk minimization in inverse problems. Ann. Statist., 38, 482 - 511.
  54. S. Hoderlein, J. Klemelä and E. Mammen (2010) Reconsidering the Random Coefficient Model. Econometric Theory, 26, 03, 804 - 837.
  55. Y. K. Lee, E. Mammen and B. U. Park (2010) Backfitting and smooth backfitting for additive quantile models. Ann. Statist., 38, 2857–2883; Correction: 2012, 40, 2356 - 2357.
  56. B. Park, E. Mammen, W. Härdle and S. Borak (2009) Time Series Modelling with Semiparametric Factor Dynamics. Journal Amer. Statist. Assoc., 104, 284 - 298.
  57. R. Carroll, A. Maity, E. Mammen and N. Chatterjee (2009) Powerful tests for genetic association using semiparametric models for gene-environment interactions. J. Royal Statist. Soc. Ser. B, 71 , 75 - 96.
  58. E. Mammen, B. Støve and D. Tjøstheim (2009) Nonparametric additive models for panels of time series. Econometric Theory, 25, 442 - 481.
  59. E. Mammen and K. Yu (2009) Nonparametric estimation of noisy integral equations of the second kind. (with discussion) J. Korean Statist. Soc., with discussion 38, 99 - 124.
  60. T. Maiwald, E. Mammen, S. Nandi, and J. Timmer (2009) Effect of Jump Discontinuity for Phase-Randomized Surrogate Data Testing. International Journal of Bifurcation and Chaos, 19, 403 - 408.
  61. V. Konakov and E. Mammen (2009) Small time Edgeworth-type expansions for weakly convergent nonhomogenous Markov chains. Prob. Theory and Rel. Fields, 143, 137 - 176.
  62. K. Yu, R. Carroll, A. Maity, and E. Mammen (2009) Nonparametric Additive Regression for Repeatedly Measured Data. Biometrika 96, 383 - 398.
  63. K. Yu, R. Carroll, A. Maity and E. Mammen (2009) Efficient Semiparametric Marginal Estimation for the Partially Linear Additive Model for Longitudinal/Clustered Data. Stat. Biosci., 1, 10 - 31.
  64. S. Hoderlein and E. Mammen (2009) Partial identification and nonparametric estimation of nonseparable, nonmonotonic functions. Econometrics Journal, 12, 1 - 25.
  65. K. Yu, B. U. Park, and E. Mammen (2008) Smooth backfitting in generalized additive models. Ann. Statist., 36, 228 - 260.
  66. O. Linton and E. Mammen (2008) Nonparametric Transformation to White Noise. J. of Econometrics, 142, 241 - 264.
  67. E. Mammen and S. Nandi (2008) Some Theoretical Properties of Phase Randomized Multivariate Surrogates. Statistics, 42, 195 - 205.
  68. Fengler, M., W. Härdle and E. Mammen (2007) A semiparametric factor model for implied volatility surface dynamics J. Financial Econometrics, 5, 189 - 218.
  69. J. Horowitz and E. Mammen (2007) Rate-optimal estimation for a general class of nonparametric regression models. Ann. Statist., 35, 2589 - 2619.
  70. S. Hoderlein and E. Mammen (2007) Identification of Marginal Effects in Nonseparable Models without Monotonicity. Econometrica, 75, 1513 - 1518.
  71. E. Mammen and J. P. Nielsen(2007) A general approach to the predictability issue in survival analysis with applications. Biometrika, 94, 873 - 892.
  72. J. Horowitz, J. Klemelä and E. Mammen (2006) Optimal estimation in additive regression models. Bernoulli, 12, 271 - 298.
  73. E. Mammen and B. U. Park (2006) A simple smooth backfitting method for additive models. Ann. Statist., 34, 2252 - 2271.
  74. V. Konakov and E. Mammen (2005) Edgeworth type expansions for transition densities of Markov chains converging to diffusions. Bernoulli, 11591 - 641.
  75. O. Linton and E. Mammen (2005) Estimating semiparametric ARCH(∞) models by kernel smoothing methods. Econometrica , 73, 771 - 836.
  76. E. Mammen and B. U. Park (2005) Bandwidth selection for smooth backfitting in additive models. Ann. Statist., 33, 1260 - 1294.
  77. W. Härdle, S. Huet, E. Mammen and S. Sperlich (2004) Bootstrap inference in semiparametric generalized additive models. Econometric Theory, 20, 265 - 300.
  78. E. Mammen and S. Nandi (2004) Change of the nature of a test when surrogate data are applied. Physical Review E, 70, 016121 (11 pages).
  79. J. Horowitz and E. Mammen (2004)Nonparametric estimation of an additive model with a link function. Ann. Statist., 32, 2412 - 2443.
  80. R. J. Carroll, O. Linton, E. Mammen and Z. Xiao (2003) More efficient kernel estimation in nonparametric regression with autocorrelated errors. Journal of the American Statistical Association, 98, 980 - 992.
  81. V. Konakov and E. Mammen (2002) Edgeworth type expansions for Euler schemes for stochastic differential equations. Monte Carlo Methods and Applications, 8, 271 - 286.
  82. J. Franke, J.-P. Kreiss and E.Mammen (2002) Bootstrap of kernel smoothing in nonlinear time series. Bernoulli, 8, 1 - 39.
  83. R. J. Carroll, W. Hãrdle and E. Mammen (2002) Estimation in an additive model when the parameters are linked parametrically. Econometric Theory, 18, 886 - 912.
  84. J. Franke, J.-P. Kreiss, E. Mammen and and M. H. Neumann (2002) Properties of the nonparametric autoregressive bootstrap. J. Time Series Analysis, 23, 555 - 586.
  85. E. Mammen, J. S. Marron, B. A. Turlach and M. P. Wand (2001) A general framework for constrained smoothing. Statistical Science, 16, 232 - 248.
  86. V. Konakov and E. Mammen (2001) Local approximations of Markov random walks by diffusions. Stochastic Processes and their Applications, 96, 73 - 98.
  87. O. Linton, E. Mammen, J. Nielsen and C. Tanggaard (2001) Estimating yield curves by kernel smoothing methods. Journal of Econometrics, 105/1, 185 - 223.
  88. W. Härdle, E. Mammen and I. Proença (2001) A bootstrap test for single index models. Statistics, 35, 427 - 452.
  89. E. Mammen and J. P. Nielsen (2001) Generalised structured models. Biometrika, 90, 551 - 566.
  90. V. Konakov and E. Mammen (2000) Local limit theorems for transition densities of Markov chains converging to diffusions. Probability Theory and rel. Fields, 117, 551 - 587.
  91. E. Mammen and C. Thomas-Agnan (1999) Smoothing splines and shape restrictions. Scand. J. Statist., 26, 239 - 252.
  92. I. Gijbels, E. Mammen, B.U. Park and L. Simar (1999) On Estimation of Monotone and Concave Frontier Functions. J. Amer. Statist. Assoc., 94220 - 228.
  93. E. Mammen and A. B. Tsybakov (1999) Smooth discrimination analysis. Ann. Statist., 27, 1808 - 1829.
  94. E. Mammen, O. Linton and J. Nielsen (1999) The existence and asymptotic properties of a backfitting projection algorithm under weak conditions. Ann. Statist., 27, 1443 - 1490.
  95. I. Gijbels and E. Mammen (1998) On local adaptivity of kernel estimates with plug - in local bandwidth selectors. Scand. J. Statist., 25503 - 520.
  96. J. Fan, W. Härdle and E. Mammen (1998) Direct estimation of low dimensional components in additive models. Ann. Statist., 26, 943 - 971.
  97. W. Härdle, E. Mammen and M. Müller (1998) Testing parametric versus semiparametric modelling in generalized linear models. J. Amer. Statist. Assoc., 93, 1461 - 1474.
  98. E. Mammen and S. van de Geer (1997) Local adaptive regression splines. Ann. Statist., 25, 387 - 413.
  99. O. V. Lepskii , E. Mammen and V. G. Spokoiny (1997) Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors. Ann. Statist., 25, 929 - 947.
  100. E. Mammen and S. van de Geer (1997) Penalized quasi-likelihood estimation in partial linear models. Ann. Statist., 25, 1014 - 1035.
  101. E. Mammen and J. S. Marron (1997) Mass recentered kernel smoothers. Biometrika, 84, 765 - 778.
  102. E. Mammen and B.U. Park (1997) Optimal smoothing inadaptive location estimation. J. Stat. Plann. Inference, 58, 333 - 348, Add. 67 165.
  103. V. Konakov and E. Mammen (1997) The shape of kernel density estimates in higher dimensions. Mathematical Methods of Statisitcs, 6, 440 - 464.
  104. E. Mammen and B. U. Park (1996) Behaviour of kernel density estimates and bandwidth selectors for contaminated data sets. Statistics, 28, 89 - 104.
  105. E. Mammen (1996) Empirical processes of residuals for high - dimensional linear models. Ann. Statist., 24, 307 - 335.
  106. E. Mammen and S. van de Geer (1996) Estimation of functions with spatially inhomogeneous smoothness: an approach based on total variation penalties. ZAMM - Zeitschrift für Anandte Mathematik und Mechanik, 76, Suppl. 3, 139 - 142.
  107. E. Mammen (1995) On qualitative smoothness of kernel density estimates. Statistics, 26, 253 - 267.
  108. W. Ehm, E. Mammen and D. W. Müller (1995) Power robustification of approximately linear tests. Journal American Statist. Assoc., 90, 1025 - 1033.
  109. E. Mammen and A. B. Tsybakov (1995) Asymptotical minimax recovery of sets with smooth boundaries. Ann. Statist., 23, 502 - 524.
  110. N. I. Fisher, E. Mammen and J. S. Marron (1994) Testing for multimodality. Computational Statistics & Data Analysis, 18, 499 - 512.
  111. P. Hall and E. Mammen (1994) On general resampling algorithms and their performance in distribution estimation. Ann. Statist., 22, 2011 - 2031.
  112. W. Härdle and E. Mammen (1993) Comparing nonparametric versus parametric regression fits. Ann. Statist., 21, 1926 - 1947.
  113. E. Mammen (1993) Bootstrap and wild bootstrap for high - dimensional linear models. Ann. Statist., 21255 - 285.
  114. E. Mammen (1992) Higher - order accuracy of bootstrap for smooth functionals. Scand. J. Statist., 19, 255 - 270.
  115. E. Mammen, J. S. Marron and N. I. Fisher (1992) Some asymptotics for multimodality tests based on kernel density estimates. Probab. Th. Rel. Fields, 91, 115 - 132.
  116. E. Mammen (1992) Bootstrap, wild bootstrap, and asymptotic normality. Probab. Th. Rel. Fields, 93, 439 - 455.
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Last edited: 2022-01-19 by jw

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