Bootstrapping with R to Determine Variances of Mixture Model Estimates in Predicting Confidence Intervals for Population Sizes

Ujeh, Chareena; Satitvipawee, Pratana; Sillabutra, Jutatip; Soontornpipit, Pichitpong; Kitidamrongsuk, Prasong; Viwatwongkasem, Chukiat

doi:10.1016/j.procs.2016.05.096

Cited by 1 publication

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“…The most common way to do so is by providing confidence intervals. We followed the common practice of using bootstrapping to obtain confidence intervals for mixture models (Ujeh et al, 2016). As bootstrapping is a resource-intensive process, we used 500 samples for the simulation studies.…”

Section: Curve Fittingmentioning

confidence: 99%

Z-curve 2.0: Estimating Replication Rates and Discovery Rates

Bartoš

Schimmack

2022

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Selection for statistical significance is a well-known factor that distorts the published literature and challenges the cumulative progress in science. Recent replication failures have fueled concerns that many published results are false-positives. Brunner and Schimmack (2020) developed z-curve, a method for estimating the expected replication rate (ERR) – the predicted success rate of exact replication studies based on the mean power after selection for significance. This article introduces an extension of this method, z-curve 2.0. The main extension is an estimate of the expected discovery rate (EDR) – the estimate of a proportion that the reported statistically significant results constitute from all conducted statistical tests. This information can be used to detect and quantify the amount of selection bias by comparing the EDR to the observed discovery rate (ODR; observed proportion of statistically significant results). In addition, we examined the performance of bootstrapped confidence intervals in simulation studies. Based on these results, we created robust confidence intervals with good coverage across a wide range of scenarios to provide information about the uncertainty in EDR and ERR estimates. We implemented the method in the zcurve R package (Bartoš & Schimmack, 2020).

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Section: Curve Fittingmentioning

confidence: 99%

Z-curve 2.0: Estimating Replication Rates and Discovery Rates

Bartoš

Schimmack

2022

View full text Add to dashboard Cite

show abstract

Bootstrapping with R to Determine Variances of Mixture Model Estimates in Predicting Confidence Intervals for Population Sizes

Cited by 1 publication

References 3 publications

Z-curve 2.0: Estimating Replication Rates and Discovery Rates

Z-curve 2.0: Estimating Replication Rates and Discovery Rates

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