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#robust

2 posts2 participants0 posts today
Public
Dr Mircea Zloteanu 🌼🐝

#statstab #334 Workflow Techniques for the Robust Use of Bayes Factors

Thoughts: "We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis"

#bayesfactors #bayesian #r #robust

arxiv.org/abs/2103.08744

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arXiv.orgWorkflow Techniques for the Robust Use of Bayes FactorsInferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions. Moreover it's not clear how straightforwardly this approach can be implemented in practice, and in particular how sensitive it is to the details of the computational implementation. Here, we investigate these questions for Bayes factor analyses in the cognitive sciences. We explain the statistics underlying Bayes factors as a tool for Bayesian inferences and discuss that utility functions are needed for principled decisions on hypotheses. Next, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors. Importantly, it is unknown whether Bayes factor estimates based on bridge sampling are unbiased for complex analyses. We are the first to use simulation-based calibration as a tool to test the accuracy of Bayes factor estimates. Moreover, we study how stable Bayes factors are against different MCMC draws. We moreover study how Bayes factors depend on variation in the data. We also look at variability of decisions based on Bayes factors and how to optimize decisions using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis, and we illustrate this workflow using an example from the cognitive sciences. We hope that this study will provide a workflow to test the strengths and limitations of Bayes factors as a way to quantify evidence in support of scientific hypotheses. Reproducible code is available from https://osf.io/y354c/.
Public
AstroMancer5G (she/her)

If there's someone around you who is #suffering especially from #isolation and doesn't get to be with other #people in #community much, take the #initiative to #ReachOut and #invite them to engage in a #social #activity with you. Try to #identify and #eliminate the #barriers keeping them isolated, if they want to be more engaged. Part of having a strong community is having #robust, #systemic ways to #adapt in order to fold people in.

Replied in thread
Public *
Kevin Russell

@vor @Linknation @pschanen

This is horseshit.

This is FUD, presented as if by a "friend"
Like anonymous "climate activists" telling us to concentrate on carbon capture.

The second largest instllation demographic for Linux is "grandparents" running something their kids told them was windows but is actually a simple ui on Linux,--

--- SO THEY DONT HAVE TO FIX WINDOWS™FOR THEM EVERY WEEKEND.

Grandma installing printers. From reddit https://www.reddit.com/r/linuxmasterrace/comments/8am3ci/update_to_linux_for_grandma_texas_edition_i_woke/
#linux#easy#better
Public
Dr Mircea Zloteanu 🌼🐝

#statstab #260 Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data

Thoughts: "A_w and d_r were generally robust to these violations"

#robust #effectsize #ttest #2groups #metaanalysis #assumptions #ttest #cohend

link.springer.com/article/10.3

SpringerLinkEffect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data - Behavior Research MethodsIn psychological science, the “new statistics” refer to the new statistical practices that focus on effect size (ES) evaluation instead of conventional null-hypothesis significance testing (Cumming, Psychological Science, 25, 7–29, 2014). In a two-independent-samples scenario, Cohen’s (1988) standardized mean difference (d) is the most popular ES, but its accuracy relies on two assumptions: normality and homogeneity of variances. Five other ESs—the unscaled robust d (d r * ; Hogarty & Kromrey, 2001), scaled robust d (d r ; Algina, Keselman, & Penfield, Psychological Methods, 10, 317–328, 2005), point-biserial correlation (r pb ; McGrath & Meyer, Psychological Methods, 11, 386–401, 2006), common-language ES (CL; Cliff, Psychological Bulletin, 114, 494–509, 1993), and nonparametric estimator for CL (A w ; Ruscio, Psychological Methods, 13, 19–30, 2008)—may be robust to violations of these assumptions, but no study has systematically evaluated their performance. Thus, in this simulation study the performance of these six ESs was examined across five factors: data distribution, sample, base rate, variance ratio, and sample size. The results showed that A w and d r were generally robust to these violations, and A w slightly outperformed d r . Implications for the use of A w and d r in real-world research are discussed.
#psychology#statistics#research
Public
ThuH

Incense Temple L2 🏛️ 💨
One of Thuh first reations from 2020

When burning an incense cone under its roof fragrant smoke will emerge from under the eaves.
Finished in a robust terrazzo polish.

Dimensions of this structure are:
Length: 8 7/8" / 22,6 cm
Width: 2 5/8” / 6,6 cm
Height: 4 1/4” / 10,8 cm
Weight: 2.2 lb / 1 kg

Listed at ->
etsy.com/listing/752995712/abs

#incense#temple#terrazzo
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