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paper.bib
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@article{pymc3:2016,
author = {Salvatier, John and Wiecki, Thomas and Fonnesbeck, Christopher},
year = {2016},
month = {01},
pages = {},
title = {Probabilistic programming in {P}ython using PyMC3},
doi = {10.7287/PEERJ.PREPRINTS.1686V1}
}
@article{probabilisticp:2018,
author = {Meent, Jan-Willem and Paige, Brooks and Yang, Hongseok and Wood, Frank},
year = {2018},
month = {09},
pages = {},
title = {An Introduction to Probabilistic Programming},
doi = {10.48550/arXiv.1809.10756}
}
@article{pyro:2018,
author = {Bingham, Eli and Chen, Jonathan and Jankowiak, Martin and Obermeyer, Fritz and Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and Horsfall, Paul and Goodman, Noah},
year = {2018},
month = {10},
pages = {},
title = {Pyro: Deep Universal Probabilistic Programming},
doi = {10.48550/arXiv.1810.09538}
}
@article{stan:2017,
author = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
year = {2017},
month = {01},
pages = {},
title = {Stan : A Probabilistic Programming Language},
volume = {76},
journal = {Journal of Statistical Software},
doi = {10.18637/jss.v076.i01}
}
@article{kokonendji:2015,
author = {Jørgensen, B. and Kokonendji, C. C.},
year = {2015},
title = {Discrete dispersion models and their {T}weedie asymptotics.},
journal = {AStA Advances in Statistical Analysis, 100(1)},
pages = {43-78},
doi = {10.48550/arXiv.1409.7482}
}
@article{bent:1987,
author = {Jørgensen, B.},
year = {1987},
title = {Exponential dispersion models.},
journal = {Journal of the Royal Statistical Society},
doi = {10.1111/j.2517-6161.1987.tb01685.x}
}
@article{bent:1997,
author = {Jørgensen, B.},
year = {1997},
title = {The theory of dispersion models.},
journal = {CRC Press},
doi = {10.1002/1097-0258(20000730)19:14<1952::AID-SIM474>3.0.CO;2-K}
}
@article{glm:1972,
author = {Nelder, J.A. and Wedderburn, R.W.M.},
year = {1972},
pages = {370-384},
title = {Generalized {L}inear {M}odels.},
isbn = {978-0-8176-4810-7},
doi = {10.2307/2344614}
}
@article{GLM:2004,
author = {Müller, Marlene},
year = {2004},
month = {02},
pages = {},
title = {Generalized {L}inear {M}odels},
isbn = {978-3-540-66207-5},
doi = {10.1007/978-3-642-21551-3_24}
}
@article{GAM:1986,
title = {Generalized {A}dditive {M}odels (with discussion)},
author = {Hastie, T. and R. Tibshirani },
journal = {Statistical Science 1},
pages = {297-318},
year = {1986},
doi = {10.1214/ss/1177013604}
}
@article{10.1214/1176345451,
author = {Stephen M. Stigler},
title = {{G}auss and the invention of {L}east {S}quares},
volume = {9},
journal = {The Annals of Statistics},
number = {3},
publisher = {Institute of Mathematical Statistics},
pages = {465 -- 474},
keywords = {geodesy, History of statistics, Legendre, metric system, nonlinear least squares, priority disputes},
year = {1981},
doi = {10.1214/aos/1176345451},
URL = {https://doi.org/10.1214/aos/1176345451}
}
@inbook{Gauss-Marc,
author = {Hallin, Marc},
year = {2014},
month = {09},
pages = {},
title = {Gauss-{M}arkov Theorem in {S}tatistics},
isbn = {9781118445112},
doi = {10.1002/9781118445112.stat07536}
}
@article{galton:1886,
author = {Galton, F},
year = {1886},
pages = {246-263},
title = {Regression towards mediocrity in hereditary stature.},
journal = {Journal of the Anthropological Institute of Great Britain and Ireland},
doi = {10.2307/2841583},
}
@article{gauss:2022,
ISSN = {00063444},
URL = {http://www.jstor.org/stable/2333849},
abstract = {The linear regression model owes so much to Gauss that we believe it should bear his name. Other authors who made substantial contributions are: Cauchy who introduced the idea of orthogonality; Chebyshev who applied it to polynomial models; Pizzetti who found the distribution of the sum of squares of the residuals on the Normal assumption; Karl Pearson who linked the model with the multivariate Normal thereby broadening the field of applications; and R. A. Fisher whose extension of orthogonality to qualitative comparisons laid the foundations of the modern theory of experimental design.},
author = {Hilary L. Seal},
journal = {Biometrika},
number = {1/2},
pages = {1--24},
publisher = {[Oxford University Press, Biometrika Trust]},
title = {Studies in the History of Probability and Statistics. XV: The Historical Development of the {G}auss Linear Model},
urldate = {2022-12-22},
volume = {54},
year = {1967},
doi = {10.2307/2333849}
}
@Article{linearregression:1982,
title = {The Minimum Sum of Absolute Errors Regression: A State of the Art Survey.},
author = {Narula, Subhash C., and John F. Wellington.},
year = {1982},
month = {},
journal = {Internationale de Statistique},
volume = {585},
number = {3},
pages = {317-26},
doi = {10.1038/s41586-020-2649-2},
publisher = {JSTOR},
url = {https://doi.org/10.2307/1402501}
}
@Article{harris2020array,
title = {Array programming with {NumPy}},
author = {Harris, Harris et al.},
year = {2020},
month = sep,
journal = {Nature},
volume = {585},
number = {7825},
pages = {357--362},
doi = {10.1038/s41586-020-2649-2},
publisher = {Springer Science and Business Media {LLC}},
url = {https://doi.org/10.1038/s41586-020-2649-2}
}
@ARTICLE{2020SciPy-NMeth,
author = {Virtanen, Pauli et al.},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
@article{Bonat:2018,
author = {Bonat, Wagner},
year = {2018},
month = {04},
pages = {},
title = {Multiple Response Variables Regression Models in {R} : The mcglm Package},
volume = {84},
journal = {Journal of Statistical Software},
doi = {10.18637/jss.v084.i04}
}
@article{Seabold:2010,
author = {Seabold, Skipper and Perktold, Josef},
year = {2010},
month = {01},
pages = {},
title = {Statsmodels: {E}conometric and Statistical Modeling with {P}ython},
volume = {2010},
journal = {Proceedings of the 9th Python in Science Conference},
doi = {10.25080/Majora-92bf1922-011}
}
@article {solid:2021,
article_type = {journal},
title = {Impact of Design Principles and Patterns on Software Flexibility: An Experimental Evaluation Using Flexible Point (FXP)},
author = {Rana, Muhammad Ehsan and Khonica, Eddy},
volume = {17},
number = {7},
year = {2021},
month = {Jul},
pages = {624-638},
doi = {10.3844/jcssp.2021.624.638},
url = {https://thescipub.com/abstract/jcssp.2021.624.638},
abstract = {Software flexibility is a crucial factor in designing and developing software as it reflects its capability to adapt to changes. It is a topic that has been discussed for a very long time which indicates its importance in software development. However, it is not easy to produce a flexible software design. Design principles provide fundamental concepts in designing good software. On the other hand, design patterns are proven solution to recurring problem. When used correctly, design principles and patterns can be used to improve software flexibility. However, it is necessary to evaluate its impact on software flexibility. For this purpose, this research will conduct an experiment by developing a simple application using Object-Oriented Programming (OOP) based on a case study. Based on the requirements of the case study, two SOLID design principles are chosen such as Single Responsibility Principle (SRP) and Open Closed Principle (OCP) while Strategy and Decorator for the patterns from the GoF. Then, Flexible Points (FXP) is used to measure the differences in software flexibility by comparing the solutions developed before and after applying design principles and patterns. This is aimed to prove that the chosen design principles and patterns have positive impact on software flexibility. Lastly, the result analysis shows that the use of the chosen design principles and patterns indeed improve the flexibility of the software. Therefore, the authors highly recommend adopting design principles and patterns in software development.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}
@article{Verbeke:2014,
title = {The Analysis of {M}ultivariate {L}ongitudinal Data: A Review},
author = {Verbeke, G. and Fieuws, S. and Molenberghs, G. and Davidian, M.},
journal = {Statistical Methods in Medical Research},
volume = {23},
number = {1},
pages = {42--59},
year = {2014},
doi = {10.1177/0962280212445834}
}
@article{Masarotto:2012,
title = {Gaussian {C}opula marginal regression},
author = {Masarotto, G. and Varin, C.},
journal = {Electronic Journal of Statistics},
volume = {6},
pages = {1517-1549},
year = {2012},
doi = {10.1214/12-EJS721}
}
@article{Krupskii:2013,
title = {Factor {C}opula {M}odels for multivariate data},
author = {Krupskii, P. and Joe, H.},
journal = {Journal of Multivariate Analysis},
volume = {120},
number = {1},
pages = {85-101},
year = {2013},
doi = {10.1016/j.jmva.2013.05.001}
}
@article{Zeger:1988,
title = {Longitudinal data analysis using {G}eneralized {L}inear {M}odels},
author = {Liang, Kung-Yee and Zeger, S. L.},
journal = {Biometrika},
volume = {73},
number = {1},
pages = {13-22},
year = {1986},
doi = {10.1093/biomet/73.1.13}
}
@article{Bonat:2016,
title = {Multivariate {C}ovariance {G}eneralized {L}inear {M}odels},
author = {Bonat, Wagner H. and J{\o}rgensen, Bent},
journal = {Journal of the Royal Statistical Society C},
doi = {10.1111/rssc.12145},
volume = {65},
number = {5},
pages = {649--675},
year = {2016},
}
@article{Bonat:2016b,
title = {Modeling {M}ixed Outcomes in {A}dditive {G}enetic {M}odels},
author = {Bonat, W. H.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
year = {2016},
doi = {10.1515/ijb-2017-0001}
}