Bibliography

AS72

M. Abramowitz and I.A. Stegun, editors. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover Publications, 1972. URL: https://personal.math.ubc.ca/~cbm/aands/intro.htm.

Bil95

P. Billingsley. Probability and Measure. John Wiley & Sons, 1995.

Bis06

C. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2006. URL: https://www.microsoft.com/en-us/research/people/cmbishop/.

BHK20

A. Blum, J. Hopcroft, and R. Kannan. Foundations of Data Science. Cambridge University Press, 2020. URL: https://www.cs.cornell.edu/jeh/book.pdf.

Bul03

P.S. Bullen. Handbook of Means and Their Inequalities. Springer Science+Business Media, Dordrecht, 2003.

CH91

J.M. Chambers and T. Hastie. Statistical Models in S. Wadsworth & Brooks/Cole, 1991.

CSN09

A. Clauset, C.R. Shalizi, and M.E.J. Newman. Power-law distributions in empirical data. SIAM Review, 51(4):661–703, 2009. doi:10.1137/070710111.

DJ03

T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003.

Dat03

C.J. Date. An Introduction to Database Systems. Pearson, 2003.

DFO20

M.P. Deisenroth, A.A. Faisal, and C.S. Ong. Mathematics for Machine Learning. Cambridge University Press, 2020. URL: https://mml-book.com/.

DKLM05

F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, and L.E. Meester. A Modern Introduction to Probability and Statistics: Understanding Why and How. Springer, 2005.

DGL96

L. Devroye, L. Györfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, 1996. doi:10.1007/978-1-4612-0711-5.

DD14

M.M. Deza and E. Deza. Encyclopedia of Distances. Springer, 2014.

FEHP10

C. Forbes, M. Evans, N. Hastings, and B. Peacock. Statistical Distributions. Wiley, 2010.

FD81

D. Freedman and P. Diaconis. On the histogram as a density estimator: L₂ theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 57:453–476, 1981.

Fri06

J.E.F. Friedl. Mastering Regular Expressions. O'Reilly, 2006.

Gag15a

M. Gagolewski. Data Fusion: Theory, Methods, and Applications. Institute of Computer Science, Polish Academy of Sciences, 2015. ISBN 978-83-63159-20-7.

Gag15b

M. Gagolewski. Spread measures and their relation to aggregation functions. European Journal of Operational Research, 241(2):469–477, 2015. doi:10.1016/j.ejor.2014.08.034.

Gag22

M. Gagolewski. stringi: Fast and portable character string processing in R. Journal of Statistical Software, 2022. in press. URL: https://stringi.gagolewski.com.

GBC16

M. Gagolewski, M. Bartoszuk, and A. Cena. Przetwarzanie i analiza danych w języku Python (Data Processing and Analysis in Python). PWN, 2016. ISBN 978-83-01-18940-2. in Polish.

Gen03

J.E. Gentle. Random Number Generation and Monte Carlo Methods. Springer-Verlag, 2003.

Gen09

J.E. Gentle. Computational Statistics. Springer-Verlag, 2009.

Gen17

J.E. Gentle. Matrix Algebra: Theory, Computations and Applications in Statistics. Springer, 2017.

Gen20

J.E. Gentle. Theory of Statistics. book draft, 2020. URL: https://mason.gmu.edu/~jgentle/books/MathStat.pdf.

Gol91

D. Goldberg. What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys, 21(1):5–48, 1991. URL: https://perso.ens-lyon.fr/jean-michel.muller/goldberg.pdf.

GMMP09

M. Grabisch, J.-L. Marichal, R. Mesiar, and E. Pap. Aggregation Functions. Cambridge University Press, 2009.

Gum39

E.J. Gumbel. La probabilité des hypothèses. Comptes Rendus de l'Académie des Sciences Paris, 209:645–647, 1939.

H+20

C.R. Harris and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.

HTF17

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2017. URL: https://hastie.su.domains/ElemStatLearn/.

Hig02

N.J. Higham. Accuracy and Stability of Numerical Algorithms. SIAM, Philadelphia, PA, 2002. URL: https://dx.doi.org/10.1137/1.9780898718027.

HU79

J.E. Hopcroft and J.D. Ullman. Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, 1979.

Hun07

J.D. Hunter. Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3):90–95, 2007.

HA21

R.J. Hyndman and G. Athanasopoulos. Forecasting: Principles and Practice. OTexts, 2021. URL: https://otexts.com/fpp3/.

HF96

R.J. Hyndman and Y. Fan. Sample quantiles in statistical packages. American Statistician, 50(4):361–365, 1996. doi:10.2307/2684934.

Kle51

S.C. Kleene. Representation of events in nerve nets and finite automata. Technical Report RM-704, The RAND Corporation, Santa Monica, CA, 1951. URL: https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf.

Knu97

D.E. Knuth. The Art of Computer Programming II: Seminumerical Algorithms. Addison-Wesley, 1997.

Kuc22

A.M. Kuchling. Regular Expression HOWTO. 2022. URL: https://docs.python.org/3/howto/regex.html.

Lee11

J. Lee. A First Course in Combinatorial Optimisation. Cambridge University Press, 2011.

LR02

R.J.A. Little and D.B. Rubin. Statistical Analysis with Missing Data. John Wiley & Sons, 2002.

Llo2)

S.P. Lloyd. Least squares quantization in PCM. IEEE Transactions on Information Theory, 28:128–137, 1957 (1982). Originally a 1957 Bell Telephone Laboratories Research Report; republished in 1982. doi:10.1109/TIT.1982.1056489.

McK17

W. McKinney. Python for Data Analysis. O'Reilly, 2017.

MKK16

M. Modarres, M.P. Kaminskiy, and V. Krivtsov. Reliability Engineering and Risk Analysis: A Practical Guide. CRC Press, 2016.

New05

M.E.J. Newman. Power laws, Pareto distributions and Zipf's law. Contemporary Physics, pages 323–351, 2005. doi:10.1080/00107510500052444.

O+21

T. Oetiker and others. The Not So Short Introduction to LaTeX 2ε. 2021. URL: https://tobi.oetiker.ch/lshort/lshort.pdf.

O+22

F.W.J. Olver and others. NIST Digital Library of Mathematical Functions. 2022. URL: https://dlmf.nist.gov/.

OFK17

J.K. Ord, R. Fildes, and N. Kourentzes. Principles of Business Forecasting. Wessex Press, 2017.

PVG+11

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.

PTVF07

W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. Numerical Recipes. The Art of Scientific Computing. Cambridge University Press, 2007.

PFBG19

R. Pérez-Fernández, B. De Baets, and M. Gagolewski. A taxonomy of monotonicity properties for the aggregation of multidimensional data. Information Fusion, 52:322–334, 2019. doi:10.1016/j.inffus.2019.05.006.

RS59

M. Rabin and D. Scott. Finite automata and their decision problems. IBM Journal of Research and Development, 3:114–125, 1959.

RT70

D.M. Ritchie and K.L. Thompson. QED text editor. Technical Report 70107-002, Bell Telephone Laboratories, Inc., 1970. URL: https://wayback.archive-it.org/all/20150203071645/http://cm.bell-labs.com/cm/cs/who/dmr/qedman.pdf.

RC04

C.P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer-Verlag, 2004.

RRT99

P.J. Rousseeuw, I. Ruts, and J.W. Tukey. The bagplot: A bivariate boxplot. The American Statistician, 53(4):382–387, 1999. doi:10.2307/2686061.

Rub76

D.B. Rubin. Inference and missing data. Biometrika, 63(3):581–590, 1976.

Smi02

S.W. Smith. The Scientist and Engineer's Guide to Digital Signal Processing. Newnes, 2002. URL: https://www.dspguide.com/.

Ste96

K. Steiglitz. A Digital Signal Processing Primer: With Applications to Digital Audio and Computer Music. Pearson, 1996.

Tij03

H.C. Tijms. A First Course in Stochastic Models. Wiley, 2003.

vB18

S. van Buuren. Flexible Imputation of Missing Data. CRC Press, 2018. URL: https://stefvanbuuren.name/fimd/.

vdLdJ18

M. van der Loo and E. de Jonge. Statistical Data Cleaning with Applications in R. John Wiley & Sons, 2018.

V+20

P. Virtanen and others. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17:261–272, 2020. doi:10.1038/s41592-019-0686-2.

Was21

M.L. Waskom. seaborn: Statistical data visualization. Journal of Open Source Software, 6(60):3021, 2021. doi:10.21105/joss.03021.

Wic11

H. Wickham. The split-apply-combine strategy for data analysis. Journal of Statistical Software, 40(1):1–29, 2011. doi:10.18637/jss.v040.i01.

Wic14

H. Wickham. Tidy data. Journal of Statistical Software, 59(10):1–23, 2014. doi:10.18637/jss.v059.i10.

Xie15

Y. Xie. Dynamic Documents with R and knitr. Chapman and Hall/CRC, 2015.