Spurious Correlation Due to Scaling

Scaling is common in empirical accounting research. It is often done to mitigate heteroscedasticity or the influence of firm size on parameter estimates. However, Barth and Clinch conclude that common diagnostic tools are ineffective in detecting various scale effects. Using analytic results and Monte Carlo simulations, we show that common forms of scaling, when misapplied, induce substantial spurious correlation via biased parameter estimates. Researchers, when uncertain about the exact functional form of scale effect, are typically better off dealing with both heteroscedasticity and the influence of larger firms using techniques other than scaling.

Does Latent Tuberculosis Lead to a Spurious Correlation between BCG and COVID-19 Mortality?

Background: Many factors have been suggested to confound coronavirus disease 2019 (COVID-19) studies, and BCG studies have been criticized for not adjusting for many confounders. We conducted this study to analyze the presumed effectiveness of the Bacillus Calmette–Guérin (BCG) vaccine in decreasing the COVID-19 mortality rate, and to answer the question of whether this is confounded by latent tuberculosis (LTB) prevalence.Materials and methods: We chose sixty-nine malaria-free countries with different BCG vaccination policies. TB prevalence was considered as a proxy for LTB. The BCG, TB prevalence, and COVID-19 mortality data are publically available. Contingency coefficients (C.C.) and a ROC analysis were used to assess the relationship between TB prevalence and BCG status, and identify cutoff points in each BCG group category. A stem–leaf plot was also used to explore the data’s apparent behavior concerning COVID-19 in relation to the BCG groups.Results: TB prevalence was significantly associated with BCG status. The BCG vaccination status apparently had a relationship with BCG status.Conclusions: TB is suggested to have a confounding effect on BCG results, leading to a spurious correlation between BCG and COVID-19 mortality.

Inverse Potts model improves accuracy of phylogenetic profiling

Motivation: Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity evaluation measures in phylogenetic profiling showed high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. Existing studies have focused on the evolutionary bias, but the spurious correlation bias has not been analyzed. Results: To eliminate the spurious correlation bias, we applied an evaluation measure based on the inverse Potts model (IPM) to phylogenetic profiling. We also proposed an evaluation measure to remove both the evolutionary and spurious correlation biases using the IPM. In an empirical dataset analysis, we demonstrated that these IPM-based evaluation measures improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several evaluation measures, including the IPM-based evaluation measures, had superior performance to a single evaluation measure.

ADDRESSING THE CROSSTALK ISSUE IN IMAGING USING SEISMIC MULTIPLE WAVEFIELDS

Surface-related multiple wavefields constitute redundant information in conventional migration and can often be difficult to attenuate. However, when used for migration, multiple wavefields can improve subsurface illumination. Unfortunately, the process of imaging using multiples involves the management of crosstalk, which largely restricts its application. Crosstalk causes phantom images formed by spurious correlation of unrelated events in a migration process. These events can be unrelated orders of multiples in the source and receiver wavefields; they can also be one event associated with a reflector in the source wavefield and another event generated by a different reflector in the receiver wavefield. In this paper, we first examine crosstalk by explicitly investigating its generation mechanisms in a migration process and classifying it into different categories based on causality. Following this analysis, crosstalk can be predicted in a migration process and subtracted in the image domain; however, this method is usually difficult to apply due to the complexity of wavefield separation and adaptive subtraction. Furthermore, we present different algorithms to attenuate the crosstalk, including a deconvolution imaging condition, a least-squares migration (LSM) method, and an advanced algorithm combining LSM with a deconvolution imaging condition. We illustrate these different strategies on synthetic examples. A deconvolution imaging condition can attenuate some crosstalk, but it is less effective at suppressing strong coherent crosstalk events. However, the LSM method can fundamentally address the crosstalk issue, and this approach is further optimized when combined with a deconvolution imaging condition.

Women Leaders and Pandemic Performance: A Spurious Correlation

The connection between women leaders and superior pandemic performance is likely spurious. This narrative overlooks that women currently govern precisely the kinds of countries that should mount effective pandemic responses: wealthy democracies with high state capacity. This article maps where women currently serve as presidents and prime ministers. The article then uses data from the Varieties of Democracy Project and the Organisation for Economic Co-operation and Development to show that many women-led countries score high on state capacity and that high-capacity states have low coronavirus mortality regardless of whether they are led by women or by men. Arguments emphasizing women chief executives’ superior pandemic performance, while offered in good faith, are misleading.

Spurious Correlation? A review of the relationship between Vitamin D and Covid-19 infection and mortality

The study reviews the evidence presented in a recent study linking vitamin D levels and Covid-19 infection and mortality. It was argued that correlation alone may not be useful in establishing a relationship between vitamin D levels and Covid-19 infections and mortality. Appropriate controls need to be included for improved understanding of the relationship. We proposed life expectancy as a potential control. Including this control in a regression model, we find that vitamin D levels are not a statistically significant predictor of Covid-19 infections or mortality. Life expectancy, on the other hand, was found to be statistically significant predictor of infections and mortality at country level.