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book.bib
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@Book{xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {http://yihui.org/knitr/},
}
@Book{shop-class,
title = {Shop Class As Soulcraft: An Inquiry into the Value of Work},
author = {Crawford, Matthew B.},
year = {2009}
}
@misc{tribal-knowledge,
title={Tribal knowledge},
url={https://en.wikipedia.org/wiki/Tribal_knowledge},
journal={Wikipedia},
publisher={Wikimedia Foundation},
year={2020},
month={Feb}}
@Book{cognitive-ease,
title={Thinking, Fast and Slow},
author={Kahneman, Daniel},
year={2011}
}
@Manual{lubridate-help,
title = {Dates and times made easy with lubridate},
author = {Vitalie Spinu, Garrett Grolemund, Hadley Wickham},
organization = {RStudio},
year = {2016},
url = {https://lubridate.tidyverse.org/reference/lubridate-package.html}
}
@Book{rfordatasci,
title = {R for Data Science},
author = {Hadley Wickham, Garrett Grolemund},
year = {2017},
url = {https://r4ds.had.co.nz/index.html},
}
@Book{outliers,
title = {NIST/SEMATECH e-Handbook of Statistical Methods},
url = {https://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm}
}
@Book{nigrini-forensicanalytics-ch11,
publisher = {John Wiley & Sons, Ltd},
isbn = {9781118386798},
title = {Identifying Anomalies Using the Relative Size Factor Test},
booktitle = {Forensic Analytics},
chapter = {Eleven},
pages = {212-232},
doi = {10.1002/9781118386798.ch11},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118386798.ch11},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118386798.ch11},
year = {2012},
keywords = {anomalies, size factor test, RSF test, size factor test in access, size factor test in excel},
abstract = {Relative Size Factor Test}
}
@Book{nigrini-forensicanalytics-ch12,
publisher = {John Wiley & Sons, Ltd},
isbn = {9781118386798},
title = {Identifying Fraud Using Abnormal Duplications within Subsets},
booktitle = {Forensic Analytics},
chapter = {Twelve},
pages = {233-262},
doi = {10.1002/9781118386798.ch12},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118386798.ch12},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118386798.ch12},
year = {2012},
keywords = {abnormal duplications, number duplication test, same-same-same test in access, same-same-same test in excel, subsets test in excel},
abstract = {This chapter contains sections titled: The Same–Same–Same Test The Same–Same–Different Test The Subset Number Duplication Test Running the Same–Same–Same Test in Access Running the Same–Same–Different Test in Access Running the Subset Number Duplication Test in Access Running the Same–Same–Same Test in Excel Running the Same–Same–Different Test in Excel Running the Subset Number Duplication Test in Excel Summary}
}
@article{isolationforest,
author = {Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
title = {Isolation-Based Anomaly Detection},
year = {2012},
issue_date = {March 2012},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {6},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/2133360.2133363},
doi = {10.1145/2133360.2133363},
abstract = {Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods.As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {3},
numpages = {39},
keywords = {outlier detection, isolation forest, ensemble methods, isolation, binary tree, Anomaly detection, random tree ensemble}
}
@misc{quebec,
title={Wikipedia, Name of Quebec City},
url={https://en.wikipedia.org/wiki/Name_of_Quebec_City},
journal={Wikipedia},
publisher={Wikimedia Foundation},
year={2020},
month={Oct}}
@Book{aicpa2018guide,
author = {AICPA, },
title = {Guide to Data Analytics},
publisher = {John Wiley \& Sons, Incorporated},
year = {2018},
address = {Newark},
isbn = {978-1-94549-864-0}
}
@Book{r-pkgs,
title = {R Packages},
author = {Hadley Wickham, Jennifer Bryan},
year = {2020},
url = {https://r-pkgs.org},
}
@website{sec-princples-based,
url = {https://www.sec.gov/rules/interp/2007/33-8810fr.pdf},
release = {Release Nos. 33–8810; 34–55929; FR–77; File No. S7–24–06},
reference = {17 CFR Part 241},
title = {Commission Guidance Regarding Management’s Report on Internal Control Over Financial Reporting Under Section 13(a) or 15(d) of the Securities Exchange Act of 1934; Final Rule},
section = {Principles-based Nature of Guidance for Conducting the Evaluation},
year = {2007}
}
@website{pcaob-as2201,
url = {https://pcaobus.org/Standards/Auditing/Pages/AS2201.aspx},
title = {AS 2201},
subtitle = {An Audit of Internal Control Over Financial Reporting That Is Integrated with An Audit of Financial Statements},
year = {2007}
}
@website{pcaob-as2315,
url = {https://pcaobus.org/Standards/Auditing/Pages/AS2315.aspx},
title = {AS 2315},
subtitle = {Audit Sampling},
year = {2015}
}
@article{sampling-technical,
url = {http://cte.univ-setif.dz/coursenligne/cheriguiomar/Ressources/SamplingGuideTechnicalNotes.pdf},
author = {Trevor R. Stewart},
title = {Technical Notes on the AICPA Audit Guide Audit Sampling},
year = {2008}
}
@inbook{sampling-guide,
publisher = {John Wiley & Sons, Ltd},
isbn = {9781119448617},
title = {Appendix A: Attributes Statistical Sampling Tables},
booktitle = {Audit Guide: Audit Sampling},
pages = {129-135},
doi = {10.1002/9781119448617.app1},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119448617.app1},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119448617.app1},
year = {2017}
}