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references.bib
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@ARTICLE{Bartos2023-gu,
title = "Fair coins tend to land on the same side they started: Evidence
from 350,757 flips",
author = "Barto{\v s}, Franti{\v s}ek and Sarafoglou, Alexandra and
Godmann, Henrik R and Sahrani, Amir and Leunk, David Klein and
Gui, Pierre Y and Voss, David and Ullah, Kaleem and Zoubek,
Malte J and Nippold, Franziska and Aust, Frederik and Vieira,
Felipe F and Islam, Chris-Gabriel and Zoubek, Anton J and
Shabani, Sara and Petter, Jonas and Roos, Ingeborg B and
Finnemann, Adam and Lob, Aaron B and Hoffstadt, Madlen F and
Nak, Jason and de Ron, Jill and Derks, Koen and Huth, Karoline
and Terpstra, Sjoerd and Bastelica, Thomas and Matetovici, Magda
and Ott, Vincent L and Zetea, Andreea S and Karnbach, Katharina
and Donzallaz, Michelle C and John, Arne and Moore, Roy M and
Assion, Franziska and van Bork, Riet and Leidinger, Theresa E
and Zhao, Xiaochang and Motaghi, Adrian Karami and Pan, Ting and
Armstrong, Hannah and Peng, Tianqi and Bialas, Mara and Pang,
Joyce Y-C and Fu, Bohan and Yang, Shujun and Lin, Xiaoyi and
Sleiffer, Dana and Bognar, Miklos and Aczel, Balazs and
Wagenmakers, Eric-Jan",
abstract = "Many people have flipped coins but few have stopped to ponder
the statistical and physical intricacies of the process. In a
preregistered study we collected 350,757 coin flips to test the
counterintuitive prediction from a physics model of human coin
tossing developed by Diaconis, Holmes, and Montgomery (D-H-M;
2007). The model asserts that when people flip an ordinary coin,
it tends to land on the same side it started -- D-H-M estimated
the probability of a same-side outcome to be about 51\%. Our
data lend strong support to this precise prediction: the coins
landed on the same side more often than not,
$\text\{Pr\}(\text\{same side\}) = 0.508$, 95\% credible
interval (CI) [$0.506$, $0.509$],
$\text\{BF\}_\{\text\{same-side bias\}\} = 2364$. Furthermore,
the data revealed considerable between-people variation in the
degree of this same-side bias. Our data also confirmed the
generic prediction that when people flip an ordinary coin --
with the initial side-up randomly determined -- it is equally
likely to land heads or tails: $\text\{Pr\}(\text\{heads\}) =
0.500$, 95\% CI [$0.498$, $0.502$],
$\text\{BF\}_\{\text\{heads-tails bias\}\} = 0.183$.
Furthermore, this lack of heads-tails bias does not appear to
vary across coins. Our data therefore provide strong evidence
that when some (but not all) people flip a fair coin, it tends
to land on the same side it started. Our data provide compelling
statistical support for D-H-M physics model of coin tossing.",
publisher = "arXiv",
year = 2023
}
@ARTICLE{Kleinberg2003-mw,
author = "Kleinberg, Jon",
abstract = "A fundamental problem in text data mining is to extract
meaningful structure from document streams that arrive
continuously over time. E-mail and news articles are two natural
examples of such streams, each characterized by topics that
appear, grow in intensity for a period of time, and then fade
away. The published literature in a particular research field
can be seen to exhibit similar phenomena over a much longer time
scale. Underlying much of the text mining work in this area is
the following intuitive premise---that the appearance of a topic
in a document stream is signaled by a ``burst of activity,''
with certain features rising sharply in frequency as the topic
emerges.The goal of the present work is to develop a formal
approach for modeling such ``bursts,'' in such a way that they
can be robustly and efficiently identified, and can provide an
organizational framework for analyzing the underlying content.
The approach is based on modeling the stream using an
infinite-state automaton, in which bursts appear naturally as
state transitions; it can be viewed as drawing an analogy with
models from queueing theory for bursty network traffic. The
resulting algorithms are highly efficient, and yield a nested
representation of the set of bursts that imposes a hierarchical
structure on the overall stream. Experiments with e-mail and
research paper archives suggest that the resulting structures
have a natural meaning in terms of the content that gave rise to
them.",
journal = "Data Min. Knowl. Discov.",
publisher = "Springer Nature",
volume = 7,
number = 4,
pages = "373--397",
year = 2003
}
@article{
Capretto2022,
title={Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python},
volume={103},
url={https://www.jstatsoft.org/index.php/jss/article/view/v103i15},
doi={10.18637/jss.v103.i15},
number={15},
journal={Journal of Statistical Software},
author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A},
year={2022},
pages={1–29}
}
@misc{clark2019shrinkage,
author = {Clark, Michael},
title = {Michael Clark: Shrinkage in Mixed Effects Models},
url = {https://m-clark.github.io/posts/2019-05-14-shrinkage-in-mixed-models/},
year = {2019}
}
@article{Bailey2013,
title = {The Probability of Back-Test Over-Fitting},
ISSN = {1556-5068},
url = {http://dx.doi.org/10.2139/ssrn.2326253},
DOI = {10.2139/ssrn.2326253},
journal = {SSRN Electronic Journal},
publisher = {Elsevier BV},
author = {Bailey, David H. and Borwein, Jonathan M. and Lopez de Prado, Marcos and Zhu, Qiji Jim},
year = {2013}
}
@article{Hartshorne2015,
title = {When Does Cognitive Functioning Peak? The Asynchronous Rise and Fall of Different Cognitive Abilities Across the Life Span},
volume = {26},
ISSN = {1467-9280},
url = {http://dx.doi.org/10.1177/0956797614567339},
DOI = {10.1177/0956797614567339},
number = {4},
journal = {Psychological Science},
publisher = {SAGE Publications},
author = {Hartshorne, Joshua K. and Germine, Laura T.},
year = {2015},
month = mar,
pages = {433–443}
}
@misc{Hartshorne_2014,
title={Data},
url={osf.io/4xp3g},
publisher={OSF},
author={Hartshorne, Joshua K},
year={2014},
month={Dec}
}
@misc{Germine_Hartshorne_2016,
title={Hartshorne & Germine (2015) When does cognitive functioning peak?},
url={osf.io/f2saj},
publisher={OSF},
author={Germine, Laura and Hartshorne, Joshua K},
year={2016},
month={Sep}
}
@MISC {Kolassa2017,
TITLE = {B-Splines VS high order polynomials in regression},
AUTHOR = {Stephan Kolassa},
year={2017},
HOWPUBLISHED = {Cross Validated},
NOTE = {URL:https://stats.stackexchange.com/q/314878 (version: 2017-11-21)},
EPRINT = {https://stats.stackexchange.com/q/314878},
URL = {https://stats.stackexchange.com/q/314878}
}
@article{Vaci2019,
title = {Large data and Bayesian modeling—aging curves of NBA players},
volume = {51},
ISSN = {1554-3528},
url = {http://dx.doi.org/10.3758/s13428-018-1183-8},
DOI = {10.3758/s13428-018-1183-8},
number = {4},
journal = {Behavior Research Methods},
publisher = {Springer Science and Business Media LLC},
author = {Vaci, Nemanja and Cocić, Dijana and Gula, Bartosz and Bilalić, Merim},
year = {2019},
month = jan,
pages = {1544–1564}
}
@article{Lipovetsky2010,
title = {Double logistic curve in regression modeling},
volume = {37},
ISSN = {1360-0532},
url = {http://dx.doi.org/10.1080/02664760903093633},
DOI = {10.1080/02664760903093633},
number = {11},
journal = {Journal of Applied Statistics},
publisher = {Informa UK Limited},
author = {Lipovetsky, Stan},
year = {2010},
month = oct,
pages = {1785–1793}
}
@article{e5a1bb8f-41b7-35c6-95cd-8b366d3e99bc,
ISSN = {00221082, 15406261},
URL = {http://www.jstor.org/stable/2975974},
author = {Harry Markowitz},
journal = {The Journal of Finance},
number = {1},
pages = {77--91},
publisher = {[American Finance Association, Wiley]},
title = {Portfolio Selection},
urldate = {2024-07-13},
volume = {7},
year = {1952}
}
@article{diamond2016cvxpy,
author = {Steven Diamond and Stephen Boyd},
title = {{CVXPY}: {A} {P}ython-embedded modeling language for convex optimization},
journal = {Journal of Machine Learning Research},
year = {2016},
volume = {17},
number = {83},
pages = {1--5},
}