M. Płatek, J. Mielniczuk (2023), Enhancing naive classifier positive unlabeled
data based on logistic regression approach, Proceedings of 18th Conference on Computer Science and Intelligence Systems,
pdf
K. Furmańczyk, J. Mielniczuk, W. Rejchel, P. Teisseyre (2023), Double logistic regression approach to biased positive-unlabeled
data, Proceedings of the European Conference on Artificial Intelligence ECAI’23,
pdf
M. Łazęcka, B. Kołodziejek, J. Mielniczuk, (2023) Analysis of Conditional Randomisation and
Permutation schemes with application to conditional independence testing, Test, to appear
pdf
A. Wawrzeńczyk, J. Mielniczuk (2023), One-class classification approach to variational learning from
biased positive unlabelled data,
Proceedings of the European Conference on Artificial Intelligence ECAI’23,
pdf
A. Wawrzeńczyk, J. Mielniczuk (2023), Outlier detection under false omission rate control,
Proceedings of the International Conference on Computational Science ICCS’23,
pdf
J. Mielniczuk (2022), Information-theoretic methods for variable selection - a review, Entropy, 24,1-25
pdf
M. Łazęcka, J. Mielniczuk (2022),'Squared-error based shrinkage estimators of discrete probabilities and their application to feature selection',
Statistical Papers
pdf
A. Wawrzeńczyk, J. Mielniczuk (2022),'Strategies for fitting logistic regression for positive and unlabeled data revisited',
Int.J. Appl. Math. Comp. Sci., 299-309
pdf
E. Pośpiech, P. Teisseyre, J. Mielniczuk, W. Branicki (2022),
'Predicting Physical Appearance from DNA Data—Towards Genomic Solutions', Genes
pdf
M. Łazęcka, J. Mielniczuk (2021),'Multiple testing of conditional independence using information theoretic-approach,
Proceedings of Modelling Decisions for Artificial Intelligence'2021,LNAI 12898,1-12
pdf
P. Pokarowski, W. Rejchel, A. Sołtys,
M. Frej, J. Mielniczuk (2021), Improving Lasso for selection and prediction, Scandinavian Journal of Statistics,
pdf
M. Łazęcka, J. Mielniczuk, P. Teisseyre (2021)
Estimating the class prior for positive and unlabelled data
via logistic regression, Advances in Data Analysis and Classification
pdf
J. Mielniczuk, P. Teisseyre (2021) Detection of conditional independence between several variables using multiiformation,
Proceedings of the International Conference on Computational Science ICCS’21,
pdf
M. Kubkowski, J. Mielniczuk, P. Teisseyre (2021) How to gain on power: novel conditional independence tests
based on short expansion of Conditional Mutual Information, Journal of Machine Learning Research, 22, 1-57
pdf
M. Kubkowski, J. Mielniczuk (2021) Asymptotic distributions of empirical interaction
information, Methodology and Computing in Applied Probability,23, 291–315
pdf
M. Łazęcka, J. Mielniczuk, Analysis of information-based nonparametric variable selection criteria (2020), Entropy, 22,
pdf
M. Kubkowski, M. Łazęcka, J. Mielniczuk, Distributions of a general reduced-order dependence measure and conditional
independence testing, Proceedings of the International Conference on Computational Science ICCS’20,
pdf
P. Teisseyre, Jan Mielniczuk, M. Łazęcka, Different strategies of fitting logistic regression for positive and unlabelled data (2020), Proceedings of the International Conference on Computational Science ICCS’20,
pdf
supplement
P. Teisseyre, Jan Mielniczuk, M. Dąbrowski, Testing the significance of interactions in genetic studies using Interaction Information and resampling technique (2020), Proceedings of the International Conference
on Computational Science ICCS’20,
pdf
supplement
M. Kubkowski, J. Mielniczuk (2020) Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors, Entropy, 22, 153-181
pdf
J. Mielniczuk, P. Teisseyre (2019) Stopping rules for mutual information-based feature selection, Neurocomputing, 358, 255-274
pdf
Pawluk, M. P. Teisseyre, J. Mielniczuk, (2018) Information-theretic feature selection using higher-order interactions, LOD 2018, Volterra, Italy, Conference Proceeedings
pdf
J. Mielniczuk, P. Teisseyre (2018) A Deeper Look at Two Concepts of Measuring Gene-Gene
Interactions: Logistic Regression and Interaction Information Revisited, Genetic Epidemiology,42, 187-200
pdf
M. Kubkowski, J. Mielniczuk (2018) Projections of a general binary model on a logistic regression, Linear Algebra and its Applications, 536, 152-173
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 pdf
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 pdf
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 pdf
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. pdf
Csorgo, S. and Mielniczuk, J. (1995). Density estimation under long-range
dependence. Annals of Statistics vol. 23, 990-999 pdf
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 .
pdf
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. pdf
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.
Recent preprints:
Furmańczyk, K., Mielniczuk, J. Rejchel, W. and Teisseyre, P. (2022),' Joint estimation of posterior probability and propensity score functions',
, arXiv
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