On the assumptions behind metacognitive measurements: Implications for theory and practice

Theory of visual confidence has largely been grounded in the gaussian signal detection framework. This framework is so dominant that people could be rather ignorant of idiosyncratic consequences from this distributional assumption. By contrasting gaussian and logistic signal detection models, this paper systematically evaluates the consequences of auxiliary distributional assumptions in the measurement of metacognitive accuracy and its theoretical implications. We found that these models can lead to opposing conclusions regarding the efficiency of confidence rating relative to objective decision (whether meta-d’ is larger or smaller than d’) as well as the metacognitive efficiency along the internal evidence continuum (whether meta-d’ is larger or smaller for higher levels of confidence). These demonstrations may call for reconsideration of hitherto established theories of metacognition that are critically dependent on auxiliary modeling assumptions. We deem there is no instant solution for this matter as our quantitative model comparisons on a large dataset did not decide on a clear victor between gaussian and logistic metacognitive models. Yet, being aware of the hidden modeling assumptions and their systematic consequences would facilitate cumulative development of the science of metacognition.

Addressing endogeneity: some (mostly) harmless recommendations

Purpose This paper aims to focus on addressing endogeneity using instrument-free methods. The authors discuss some extensions to well-known techniques. Design/methodology/approach This paper discusses some attractive methods to address endogeneity without the need for instruments. The methods are labeled are “harmless” in the sense that instruments are not needed and the distributional assumptions are kept to a minimum or they are replaced by more flexible semi-parametric assumptions. Findings Using a hospitality application, the authors provide evidence about the effectiveness of these techniques and provide directions for their implementation. Research limitations/implications Finding valid instruments has always been a key challenge for researchers in the field. This paper discusses and introduces methods that free researchers from the need to find instruments. Originality/value The paper discusses techniques that are introduced from the first time in the tourism literature.

Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ)

Background Identification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer from loss of power and may also present difficulties in adjusting for potential covariates. Most extant approaches also fail in the presence of heterogeneous effects. The field needs new non-parametric approaches that are tailored to microbiome data, robust to distributional assumptions, and powerful under heterogeneous effects, while permitting adjustment for covariates. Methods As an alternative to existing approaches, we propose a zero-inflated quantile approach (ZINQ), which uses a two-part quantile regression model to accommodate the zero inflation in microbiome data. For a given taxon, ZINQ consists of a valid test in logistic regression to model the zero counts, followed by a series of quantile rank-score based tests on multiple quantiles of the non-zero part with adjustment for the zero inflation. As a regression and quantile-based approach, the method is non-parametric and robust to irregular distributions, while providing an allowance for covariate adjustment. Since no distributional assumptions are made, ZINQ can be applied to data that has been processed under any normalization strategy. Results Thorough simulations based on real data across a range of scenarios and application to real data sets show that ZINQ often has equivalent or higher power compared to existing tests even as it offers better control of false positives. Conclusions We present ZINQ, a quantile-based association test between microbiota and dichotomous or quantitative clinical variables, providing a powerful and robust alternative for the current microbiome differential abundance analysis.

Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China

Factor models provide a cornerstone for understanding financial asset pricing; however, research on China’s stock market risk premia is still limited. Motivated by this, this paper proposes a four-factor model for China’s stock market that includes a market factor, a size factor, a value factor, and a liquidity factor. We compare our four-factor model with a set of prominent factor models based on newly developed likelihood-ratio tests and Bayesian methods. Along with the comparison, we also find supporting evidence for the alternative t-distribution assumption for empirical asset pricing studies. Our results show the following: (1) distributional tests suggest that the returns of factors and stock return anomalies are fat-tailed and therefore are better captured by t-distributions than by normality; (2) under t-distribution assumptions, our four-factor model outperforms a set of prominent factor models in terms of explaining the factors in each other, pricing a comprehensive list of stock return anomalies, and Bayesian marginal likelihoods; (3) model comparison results vary across normality and t-distribution assumptions, which suggests that distributional assumptions matter for asset pricing studies. This paper contributes to the literature by proposing an effective asset pricing factor model and providing factor model comparison tests under non-normal distributional assumptions in the context of China.

A Simulation Study of Bayesian Estimator for Seemingly Unrelated Regression under Different Distributional Assumptions

This paper presents Bayesian analysis of Seemingly Unrelated Regression (SUR) model. An independent prior for parameters was used. The Bayesian method was compared with classical method of estimation to know the most efficient estimator under different distributional assumptions through a simulation study. In order to facilitate comparison among these estimators, Mean Squared Error (MSE) was considered as a criterion. Furthermore, based on the simulation, it was deduced that MSE of the Bayesian estimator is smaller than all the classical methods of estimation for SUR model while Normal distribution was considered as an ideal distribution in generation of disturbances in any simulation study.

A simplified approach to bias estimation for correlations

Objectives We introduce a simple and unified methodology to estimate the bias of Pearson correlation coefficients, partial correlation coefficients, and semi-partial correlation coefficients. Methods Our methodology features non-parametric bootstrapping and can accommodate small sample data without making any distributional assumptions. Results Two examples with R code are provided to illustrate the computation. Conclusions The computation strategy is easy to implement and remains the same, be it Pearson correlation or partial or semi-partial correlation.