Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Ottoboni, Kellie"'
Autor:
Ottoboni, Kellie, Poulos, Jason
Publikováno v:
Journal of Causal Inference, 8(1), 108-130 (2020)
Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment
Externí odkaz:
http://arxiv.org/abs/1901.02991
We present a method and software for ballot-polling risk-limiting audits (RLAs) based on Bernoulli sampling: ballots are included in the sample with probability $p$, independently. Bernoulli sampling has several advantages: (1) it does not require a
Externí odkaz:
http://arxiv.org/abs/1812.06361
Autor:
Stark, Philip B., Ottoboni, Kellie
The pseudo-random number generators (PRNGs), sampling algorithms, and algorithms for generating random integers in some common statistical packages and programming languages are unnecessarily inaccurate, by an amount that may matter for statistical i
Externí odkaz:
http://arxiv.org/abs/1810.10985
Autor:
Ottoboni, Kellie, Stark, Philip B.
R (Version 3.5.1 patched) has an issue with its random sampling functionality. R generates random integers between $1$ and $m$ by multiplying random floats by $m$, taking the floor, and adding $1$ to the result. Well-known quantization effects in thi
Externí odkaz:
http://arxiv.org/abs/1809.06520
Risk-limiting audits (RLAs) offer a statistical guarantee: if a full manual tally of the paper ballots would show that the reported election outcome is wrong, an RLA has a known minimum chance of leading to a full manual tally. RLAs generally rely on
Externí odkaz:
http://arxiv.org/abs/1809.04235
Colorado conducted risk-limiting tabulation audits (RLAs) across the state in 2017, including both ballot-level comparison audits and ballot-polling audits. Those audits only covered contests restricted to a single county; methods to efficiently audi
Externí odkaz:
http://arxiv.org/abs/1803.00698
Hypothesis tests based on linear models are widely accepted by organizations that regulate clinical trials. These tests are derived using strong assumptions about the data-generating process so that the resulting inference can be based on parametric
Externí odkaz:
http://arxiv.org/abs/1702.04851
Autor:
Ottoboni Kellie N., Poulos Jason V.
Publikováno v:
Journal of Causal Inference, Vol 8, Iss 1, Pp 108-130 (2020)
Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment
Externí odkaz:
https://doaj.org/article/c3eb18820f2b4a51b72e224f1db31dab
Autor:
Geiger, R. Stuart, Sholler, Dan, Culich, Aaron, Martinez, Ciera, Hoces de la Guardia, Fernando, Lanusse, Francois, Ottoboni, Kellie, Stuart, Marla, Vareth, Maryam, Varoquaux, Nelle, Stoudt, Sara, van der Walt, Stefan
What are the challenges and best practices for doing data-intensive research in teams, labs, and other groups? This paper reports from a discussion in which researchers from many different disciplines and departments shared their experiences on doing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fe1d6e955f2a939af4548690a16e0b1
Publikováno v:
Statistics in Biopharmaceutical Research. Oct-Dec2018, Vol. 10 Issue 4, p264-273. 10p.