SELECTED PUBLICATIONS:

Papers:

  • M. Kubkowski, J. Mielniczuk (2017) ,Projections of a general binary model on a logistic regression, Linear Algebra and its Applications, to appear pdf
  • M. Kubkowski, J. Mielniczuk (2017) Active sets of predictors for misspecified logistic regression, Statistics, 51, 1023-1045 pdf
  • J. Mielniczuk, M. Rdzanowski (2017) Use of Information measures and their approximations to detect predictive gene-gene interactions, Entropy, 19, 1-23 pdf
  • P. Teisseyre, R. Klopotek, J. Mielniczuk (2016) Random Subspace Method for high-dimensional regression with the R package regRSM, Computational Statistics, 1-30 pdf
  • J. Mielniczuk, P. Teisseyre (2015) What do we choose when we err? Model selection and testing for misspecified logistic regression revisited. In: Challanges in Computational Statistics and Data Mining (S. Matwin, J. Mielniczuk, eds.), Springer, 271-296 pdf
  • J. Mielniczuk, H. Szymanowski (2015) Normalized and standard Dantzig estimators: two approaches, Electronic Journal of Statistics, 9, 1335-1356 pdf
  • Pokarowski, P. J. Mielniczuk (2015) Combined l_1 and greedy l_0 least squares for linear model selection, Journal of Machine Learning Research, 16, 961-992 pdf
  • J. Mielniczuk, H. Szymanowski (2015) Selection Consistency of Generalized Information Criterion for Sparse Logistic Model, in Stochatic Models, Statistics and Their Applications, Wrocław, Poland, 2015, Springer, 111-119 pdf
  • J. Mielniczuk, P. Teisseyre (2014) Using random subspace method for prediction and importance assessement in regression, Computational Statistics and Data Analysis,71,725-742 pdf
  • J. Mielniczuk, M. Wojtys (2014). P-value model selection criteria for exponential families of increasing dimension model selection, Metrika, 77,257-284 pdf
  • J. Mielniczuk (2013) WKŁAD POLAKÓW W ROZWÓJ STATYSTYKI MATEMATYCZNEJ I STOSOWANEJ PO DRUGIEJ WOJNIE ŚWIATOWEJ, LX (in Polish) pdf
  • J. Mielniczuk, P. Teisseyre (2012) Selection of regression and autoregression models with initial ordering of variables, Communications in Statistics, Theory and Methods, 41, 4484-4502 pdf
  • P. Borkowski, J. Mielniczuk (2012) Performance of variance function estimators for autoregressive time series of order one:asymptotic normality and numerical study, Control & Cybernetics, 42,415-441 pdf
  • J. Mielniczuk, P. Teisseyre (2011) Model selection in logistic regression using p-values and greedy search, LNCS 7053, Springer,128-141 pdf
  • T. Ledwina, J. Mielniczuk (2010) Variance function estimation via model selection. Applicationes Mathematicae, 37,387-411 pdf
  • W.B. Wu, J. Mielniczuk (2010) A new look at measuring dependence, in : Dependence in Probability and Statistics, P. Doukhan et al. (eds.), Lecture Notes in Statistics no. 200, 123-142 pdf
  • J. Mielniczuk, M. Wojtys (2010). Estimation of Fisher information using model selection, Metrika, 72,163-187 pdf
  • P. Borkowski, J. Mielniczuk (2009) Post model-selection estimators of variance function for non-linear autoregression,Journal of Time Series Analysis,31, 50-63 pdf
  • J. Mielniczuk, Z. Zhou, W.B. Wu (2009). On nonparametric prediction of linear processes, Journal of Time Series Analysis,30,163-187 pdf
  • A. Bryk, J. Mielniczuk, (2008). Using randomization to improve performance regression estimates under dependence, Acta Scientiarum Mathematicarum (Szeged), 73, 817--838 pdf
  • J. Mielniczuk, P. Wojdyllo, (2007) Decorrelation of wavelet coefficients for long-range dependent processes, IEEE Information Theory , 53, 1879-1883 pdf
  • J. Mielniczuk, P. Wojdyllo, (2007) Estimation of the Hurst exponent revisited, Computational Statistics & Data Analysis , 51, 4510-4525 pdf
  • A. Bryk, J. Mielniczuk, (2007) Randomized fixed design regression under long-range dependent errors , Communications in Statistics, Theory and Methods 37 ,520--531, pdf
  • J. Mielniczuk, P. Wojdyllo, (2005) Wavelets for time series data: review and new results, Control & Cybernetics , 34, 1-31 pdf
  • A. Bryk, J. Mielniczuk, (2005) Asymptotic properties of kernel density estimates for linear processes: application of projection method. Nonparametric Statistics , 14, 121-133 pdf
  • Mielniczuk, J. and Wu, W.B., (2004) On random-design model with dependent errors. Statistica Sinica , 14, 1105-1126 pdf
  • Rekawek, J., Miszczak-Knecht, M., Kawalec, W., Mielniczuk, J. (2003). Heart variability in healthy children. Folia Cardiologica , 10, 203-211
  • Wu, W.B., Mielniczuk, J. (2002). Kernel density estimation for linear proceses. Annals of Statistics , 30, 1441-1459 pdf
  • Mielniczuk, J. (2002). Some remarks on the almost sure Central Limit Theorem for dependent sequences. in Limit Theorems in Probability and Statistics II I. Berkes, E. Csaki, M. Csorgo, eds., 391-403, Bolyai Institute Publications, Budapeszt
  • Cwik, J. and Mielniczuk, J. (2001). On construction of confidence intervals for a mean of dependent data. Discussiones Mathematicae. Probability and Statistics. 21, 121-147.
  • Csorgo, S. and Mielniczuk, J. (2000). The smoothing dichotomy in random-design regression with long-memory errors based on moving averages . Statistica Sinica vol. 10, pp. 771-787
  • Cwik, J., Koronacki, J. and J. Mielniczuk Testing for a difference between conditional variance functions of nonlinear time series, Control & Cybernetics, vol. 29 , 33-50 (for abstract and introduction click here).
  • Mielniczuk, J. (2000). Some properties of random stationary sequences with bivariate densities having diagonal expansions and nonparametric estimators based on them Journal of Nonparametric Statistics vol. 12, 223-243 pdf
  • Masry, E. and Mielniczuk, J. (1999). Local linear regression estimation for time series with long-range dependence Stochastic Processes and Their Applications vol. 82 , 173-194 pdf
  • L. Gajek and Mielniczuk, J. (1999). Long- and short-range dependent sequences under exponential subordination. Statistics and Probability Letters vol. 43, 113-122 pdf
  • Csorgo, S. and Mielniczuk, J. (1999). Random-design regression under long-range dependent errors. Bernoulli vol. 5, 209-224
  • J. Mielniczuk (1997). Short-range and long-range dependence sums for infinite-order moving averages and regression estimation, Acta Scientiarum Mathematicarum (Szeged) vol. 67, 301-316
  • J. Mielniczuk (1997). On the asymptotic mean integrated squared error of a kernel density estimator for dependent data, Statistics & Probability Letters vol. 34, 53-58 pdf
  • Csorgo, S. and Mielniczuk, J. (1996). The empirical process of a short-range dependent stationary sequence under Gaussian subordination. Probability Theory and Related Fields vol. 104, 15-25
  • Csorgo, S. and Mielniczuk, J. (1995). Extreme values of derivatives of smoothed fractional Brownian motions. Probability and Mathematical Statistics vol. 16, 211-219
  • Hossjer, O. and Mielniczuk, J. (1995). Delta-method for long-range dependent observations. Journal of Nonparametric Statistics , vol. 5, 75-82.
  • Csorgo, S. and Mielniczuk, J. (1995). Close short-range dependent sums and regression estimation. Acta Scientiarum Mathematicarum (Szeged) vol. 60, 177-196.
  • Csorgo, S. and Mielniczuk, J. (1995). Distant long-range dependent sums with application to regression estimation. Stochastic Processes & their Applications vol. 59, 143-155 pdf
  • Csorgo, S. and Mielniczuk, J. (1995). Nonparametric regression under long-range dependent normal errors. Annals of Statistics vol. 23, 1000-1014.
  • Csorgo, S. and Mielniczuk, J. (1995). Density estimation under long-range dependence. Annals of Statistics vol. 23, 990-999
  • Gijbels, I. and Mielniczuk, J. (1995). Rates of uniform strong consistency for grade estimates of a Radon-Nikodym derivative. Statistica Sinica vol. 5, 261-278.
  • Cwik, J. and Mielniczuk, J. (1995). A nonparametric rank discrimination method. Computational Statistics & Data Analysis vol. 19, 59-74 .
  • Mielniczuk, J. and Tyrcha, J. (1993). Strong consistency of multilayer perceptron regression estimate. Neural Networks vol. 6, 1019-1022
  • Cwik, J. and Mielniczuk, J. (1993). Data-dependent bandwidth choice for a grade density kernel estimate. Statistics & Probability Letters vol. 16, 397-405.
  • Mielniczuk, J. (1992). Grade estimation of Kullback-Leibler information number. Probability and Mathematical Statistics vol. 13, 139-147.
  • Mielniczuk, J. (1991). Some asymptotic properties of nonparametric regression estimators in case of censored data. Statistics vol. 22, 85-93.
  • Cwik, J. and Mielniczuk, J. (1990). Some topics in estimation of Neyman-Pearson and performance curves. In Proceedings of the Conference on Stochastic Methods in Experimental Sciences COSMEX'89, September 1989, Szklarska Poreba, World Scientific, 114-129.
  • Gijbels, J. i Mielniczuk, J. (1990). Estimation of the density of a copula function. Communications in Statistics, Ser. A vol.19, 445-464.
  • Mielniczuk, J. (1990). Remark concerning data dependent bandwidth choice for density estimation. Statistics & Probability Letters 9, 27-33.
  • Cwik, J. i Mielniczuk, J. (1989). Estimation of density ratio with application to discriminant analysis. Communications in Statistics, Ser. A vol. 18, 3057-3069.
  • Mielniczuk, J., Sarda, P. and Vieu, P. (1989). Local data driven bandwidth choice for density estimation. Journal of Statistical Planning and Inference vol. 23, 53-69.
  • Bretagnolle, J. i Mielniczuk, J. (1988). On asymptotic minimaxity of the adaptive kernel estimate of a density function. Annales d'Institut Henri Poincare vol. 15, 143-153.
  • Csorgo, S. and Mielniczuk, J. (1988). Density estimation of the proportional hazards model. Statistics & Probability Letters vol. 6, 419-426.
  • Mielniczuk, J. (1987). A remark concerning strong uniform consistency of the conditional Kaplan-Meier estimator. Statistics & Probability Letters vol. 5, 333-337.
  • Mielniczuk, J. (1987). Asymptotic confidence bands for densities based on nearest neighbor estimators under censoring. Statistics & Probability Letters vol. 5, 125-128.
  • Mielniczuk, J. and Kowalczyk, T. (1987). A screening method for a nonparametric model. Statistics & Probability Letters vol. 5, 163-167.
  • Mielniczuk, J. (1986). Przyklady modelowania matematycznego w archeologii. W W. Hensel, G. Donato, S. Tabaczynski, wyd., {/it Teoria i praktyka badan archeologicznych.} Vol. I. Ossolineum, pp. 329-361. Italian translation in: Teoria e practica della ricerca archeologica. I. Premesse metodologische . Il Quadrante Edizioni, str. 325-351.
  • Mielniczuk, J. (1986). Some asymptotic properties of kernel estimators of a density function in case of censored data. , Annals of Statistics , vol. 14, 767-773.
  • Mielniczuk, J. (1985). Note on estimation of number of errors in case of repetitive quality control. Probability and Mathematical Statistics vol. 6, 131-137.
  • Mielniczuk, J. (1985). Properties of the kernel estimators and of the adapted Loftsgarden-Quesenberry estimator of a density function for censored data. Periodica Mathematica Hungarica} vol. 16, 69-81.

    Books:

  • J. Koronacki and J. Mielniczuk Statystyka dla studentow kierunkow technicznych i przyrodniczych (spis tresci oraz przedmowa; a textbook in Polish on Statistics for Engineering and the Sciences), WNT, Warsaw, 2009, 492 pp. (fourth edition, 1st edition was published in 2001)
  • J. Cwik and J. Mielniczuk Statystyczne systemy uczace sie - cwiczenia w oparciu o pakiet R ( spis tresci oraz przedmowa ), Oficyna Wydawnicza PW, Warszawa, 2009, 192 pp.
  • J. Mielniczuk Analysis of Time Series: Theory, Monograph Series ICS PAS
  • S. Matwin and J. Mielniczuk (editors) Challanges in Computational Statistics and Data Mining, Studies in Computational Intelligence 605, Springer 2016