| Statystyka: dla studentów kierunków technicznych i przyrodniczych J Koronacki, J Mielniczuk Wydawnictwa Naukowo-Techniczne, 2001 | 291 | 2001 |
| Estimating the density of a copula function I Gijbels, J Mielniczuk Communications in Statistics-Theory and Methods 19 (2), 445-464, 1990 | 182 | 1990 |
| Estimation of Hurst exponent revisited J Mielniczuk, P Wojdyłło Computational statistics & data analysis 51 (9), 4510-4525, 2007 | 172 | 2007 |
| Kernel density estimation for linear processes WB Wu, J Mielniczuk The Annals of statistics 30 (5), 1441-1459, 2002 | 130 | 2002 |
| Nonparametric regression under long-range dependent normal errors S Csorgo, J Mielniczuk The Annals of Statistics, 1000-1014, 1995 | 115 | 1995 |
| Some asymptotic properties of kernel estimators of a density function in case of censored data J Mielniczuk The Annals of Statistics, 766-773, 1986 | 115 | 1986 |
| Density estimation under long-range dependence S Csorgo, J Mielniczuk The Annals of Statistics, 990-999, 1995 | 70 | 1995 |
| Random-design regression under long-range dependent errors S Csörgö, J Mielniczuk Bernoulli, 209-224, 1999 | 63 | 1999 |
| Estimating density ratio with application to discriminant analysis J Ćwik, J Mielniczuk Communications in Statistics-Theory and Methods 18 (8), 3057-3069, 1989 | 59 | 1989 |
| The empirical process of a short-range dependent stationary sequence under Gaussian subordination S Csörgó, J Mielniczuk Probability Theory and Related Fields 104, 15-25, 1996 | 55 | 1996 |
| Local data-driven bandwidth choice for density estimation J Mielniczuk, P Sarda, P Vieu Journal of Statistical Planning and Inference 23 (1), 53-69, 1989 | 51 | 1989 |
| Consistency of multilayer perceptron regression estimators J Mielniczuk, J Tyrcha Neural Networks 6 (7), 1019-1022, 1993 | 47 | 1993 |
| Data-dependent bandwidth choice for a grade density kernel estimate J Ćwik, J Mielniczuk Statistics & Probability Letters 16 (5), 397-405, 1993 | 41 | 1993 |
| Local linear regression estimation for time series with long-range dependence E Masry, J Mielniczuk Stochastic processes and their applications 82 (2), 173-193, 1999 | 35 | 1999 |
| Using random subspace method for prediction and variable importance assessment in linear regression J Mielniczuk, P Teisseyre Computational Statistics & Data Analysis 71, 725-742, 2014 | 33 | 2014 |
| Stopping rules for mutual information-based feature selection J Mielniczuk, P Teisseyre Neurocomputing 358, 255-274, 2019 | 31 | 2019 |
| On random-design model with dependent errors J Mielniczuk, WB Wu Statistica Sinica, 1105-1126, 2004 | 30 | 2004 |
| Distant long-range dependent sums and regression estimation S Csörgő, J Mielniczuk Stochastic Processes and their Applications 59 (1), 143-155, 1995 | 29 | 1995 |
| Different strategies of fitting logistic regression for positive and unlabelled data P Teisseyre, J Mielniczuk, M Łazęcka International Conference on Computational Science, 3-17, 2020 | 26 | 2020 |
| The smoothing dichotomy in random-design regression with long-memory errors based on moving averages S Csörgő, J Mielniczuk Statistica Sinica, 771-787, 2000 | 25 | 2000 |
Paweł TeisseyreInstitute of Computer ScienceZweryfikowany adres z ipipan.waw.pl
