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Validation of the post-hoc method to estimate snout-vent length in the order Caudata Abstract. Amphibians are the most endangered class of vertebrates, with a high rate of decline recorded since the 20th century. Even activities related to the study of these animals for informing conservation actions, for instance by handling them to collect biometric individual parameters, can have negative effects on the amphibians’ health. A post-hoc method that estimates snout-vent length from dorsal photographs has been developed to reduce handling time and stress to individuals, providing additional advantages in precision and repeatability of measurements taken. However, at present, this methodology has been tested only on approximately 1% of the known salamanders, thereby limiting its broad application. Here, we tested this method on a diverse sample of Caudata that includes 25 species across 5 families and characterized by different morphologies. The correlation between predicted SVL (estimated from dorsal photographs) and observed SVL (measured directly from ventral photographs) values was assessed using Linear Mixed Models. The results showed a significant correlation between observed and predicted SVL, with an average and constant discrepancy of about 1.6 mm. When considering the increase of SVL, there was a slight tendency to underestimate SVLe in newts, plethodontids, and proteids. Estimation errors slightly increased with the SVL. The error increased in larger newts, while decreased in larger plethodontids. Our study highlighted the reliability and applicability of adopting this methodology for data collection in all Caudata species.   Keywords. SVL, measure, post-hoc method, salamander, Urodela, photograph, dorsal.

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.

In 1970 Henry Scheffé proposed a more powerful version of his well known post hoc multiple comparison procedure, only to fail to recommend it by the paper's end. The point of the current paper is to bring this simple modification to a wider audience, complete with an original derivation, in hopes that the method will be embraced by researchers despite its creator's hesitations. Specifically, whereas Scheffé's original (1953) procedure advocates testing any exploratory post hoc contrast or comparison using a critical value assuming k - 1 between-group degrees of freedom, Scheffé's later modification (1970) will be demonstrated here showing that a more liberal critical value assuming k - 2 between-group degrees of freedom may be used if an omnibus null hypothesis across all means has been rejected.

In applications of covariance structure modeling, practitioners frequently engage in post hoc model modification when confronted with models exhibiting unacceptable fit. Little is currently known about the extent to which such procedures capitalize on sampling error. In the present study, the problem of chance model modifications under varying levels of sample size, model size, and severity of misspecification in CFA models was examined. Under low levels of misspecification, specification searches yielded consistent modifications when N ≥ 800, but under more severe misspecifications, modifications tended to be unstable unless N ≥ 1,200. Modifications were somewhat more stable for the larger model. The findings suggest that practitioners should exercise caution when interpreting modified models unless sample size is quite large.

Scheffé's test (Scheffé, 1953), which is commonly used to conduct post hoc contrasts among k group means, is unnecessarily conservative because it guards against an infinite number of potential post hoc contrasts when only a small set would ever be of interest to a researcher. This paper identifies a set of post hoc contrasts based on subsets of the treatment groups and simulates critical values from the appropriate multivariate F-distribution to be used in place of those associated with Scheffé's test. The proposed method and its critical values provide a uniformly more powerful post hoc procedure.

Power analysis is a key component for planning prospective studies such as clinical trials. However, some journals in biomedical and psychosocial sciences ask for power analysis for data already collected and analysed before accepting manuscripts for publication. In this report, post hoc power analysis for retrospective studies is examined and the informativeness of understanding the power for detecting significant effects of the results analysed, using the same data on which the power analysis is based, is scrutinised. Monte Carlo simulation is used to investigate the performance of posthoc power analysis.

The governor valve actuator (GVA), as the actuating mechanism of the steam turbine governing system, directly impacts production safety and economic efficiency. Its highly coupled nature leads to high-dimensional operational data, complex fault modes, and inherent opacity in diagnostic algorithms, posing significant challenges to the real-time performance, reliability, and generalizability of fault diagnosis and early warning tasks. To address these challenges in complex multi-sensor networks, this paper proposes a post-hoc and pseudo-post-hoc predictive maintenance (PPPM) framework leveraging advanced machine learning and SHapley Additive exPlanations, an XAI technology. The PPPM optimizes fault diagnosis and early warning models and provides interpretable attribution analysis to guide predictive maintenance workflows. Experimental results on the GVA fault testing platform prove the effectiveness of the proposed method. For the fault diagnosis and localization task, taking the random forest model as an example, PPPM achieves the optimization of 50% of the measurement points of the sensor network and the attribution analysis of fault localization, which improves the real-time, generality and reliability of the diagnosis model. For the warning task, PPPM carries out sensor network optimization and attribution analysis to improve the pseudo-supervised warning model through the pseudo-supervised learning method. Taking isolated forests as an example, the optimized model improves the W-F1 score by 5.997% and the AUC by 6.942%.

While traditional multiple testing procedures prohibit adaptive analysis choices made by users, Goeman and Solari (Statist. Sci. 26 (2011) 584–597) proposed a simultaneous inference framework that allows users such flexibility while preserving high-probability bounds on the false discovery proportion (FDP) of the chosen set. In this paper, we propose a new class of such simultaneous FDP bounds, tailored for nested sequences of rejection sets. While most existing simultaneous FDP bounds are based on closed testing using global null tests based on sorted p-values, we additionally consider the setting where side information can be leveraged to boost power, the variable selection setting where knockoff statistics can be used to order variables, and the online setting where decisions about rejections must be made as data arrives. Our finite-sample, closed form bounds are based on repurposing the FDP estimates from false discovery rate (FDR) controlling procedures designed for each of the above settings. These results establish a novel connection between the parallel literatures of simultaneous FDP bounds and FDR control methods, and use proof techniques employing martingales and filtrations that are new to both these literatures. We demonstrate the utility of our results by augmenting a recent knockoffs analysis of the UK Biobank dataset.

This paper presents a theory of contrasts designed for modified Freeman–Tukey (FT) statistics which are derived through square-root transformations of observed frequencies (proportions) in contingency tables. Some modifications of the original FT statistic are necessary to allow for ANOVA-like exact decompositions of the global goodness of fit (GOF) measures. The square-root transformations have an important effect of stabilizing (equalizing) variances. The theory is then used to derive Tukey’s post-hoc pairwise comparison tests for contingency tables. Tukey’s tests are more restrictive, but are more powerful, than Scheffè’s post-hoc tests developed earlier for the analysis of contingency tables. Throughout this paper, numerical examples are given to illustrate the theory. Modified FT statistics, like other similar statistics for contingency tables, are based on a large-sample rationale. Small Monte-Carlo studies are conducted to investigate asymptotic (and non-asymptotic) behaviors of the proposed statistics.