Partial Coherence Estimation via Spectral Matrix Shrinkage under Quadratic Loss

Partial coherence is an important quantity derived from spectral or precision matrices and is used in seismology, meteorology, oceanography, neuroscience and elsewhere. If the number of complex degrees of freedom only slightly exceeds the dimension of the multivariate stationary time series, spectral matrices are poorly conditioned and shrinkage techniques suggest themselves. When true partial coherencies are quite large then for shrinkage estimators of the diagonal weighting kind it is shown empirically that the minimization of risk using quadratic loss (QL) leads to oracle partial coherence estimators superior to those derived by minimizing risk using Hilbert-Schmidt (HS) loss. When true partial coherencies are small the methods behave similarly. We derive two new QL estimators for spectral matrices, and new QL and HS estimators for precision matrices. In addition for the full estimation (non-oracle) case where certain trace expressions must also be estimated, we examine the behaviour of three different QL estimators, the precision matrix one seeming particularly robust and reliable. For the empirical study we carry out exact simulations derived from real EEG data for two individuals, one having large, and the other small, partial coherencies. This ensures our study covers cases of real-world relevance

Assessing the role of Ku70 vWA domain phosphorylation in the inhibition of Aurora B and activation of the DNA damage response

Ku is a key component of the Non-Homologous End Joining DNA repair pathway. Recently, a function for Ku in DNA damage response (DDR) signalling was identified through studies exploring a Ku70 S155D phosphomimetic mutant. We hypothesize that Ku70 S155D mimics phosphorylation of Ku70 in response to DNA damage, and that Ku S155 phosphorylation inhibits Aurora B and causes sustained DDR activation. In this study we show that the S155D mutant is competent for heterodimerization, and its expression does not induce DNA damage. Phosphorylated Ku70 associates with Aurora B by co-immunoprecipitation and this association was demonstrated in situ with Ku70 S155D. Additionally, we demonstrate that the Ku70 S155D vWA domain is sufficient to inhibit Aurora B in an in vitro kinase assay. Finally, Aurora B inhibitor treatment of Ku70 S155D cells does not increase the prevalence of a DDR marker gammaH2AX. This work suggests that Ku70 S155 phosphorylation leads to an inhibition of Aurora B and DDR activation

A frequency domain test for propriety of complex-valued vector time series

This paper proposes a frequency domain approach to test the hypothesis that a stationary complexvalued vector time series is proper, i.e., for testing whether the vector time series is uncorrelated with its complex conjugate. If the hypothesis is rejected, frequency bands causing the rejection will be identified and might usefully be related to known properties of the physical processes. The test needs the associated spectral matrix which can be estimated by multitaper methods using, say, K tapers. Standard asymptotic distributions for the test statistic are of no use since they would require K → ∞, but, as K increases so does resolution bandwidth which causes spectral blurring. In many analyses K is necessarily kept small, and hence our efforts are directed at practical and accurate methodology for hypothesis testing for small K. Our generalized likelihood ratio statistic combined with exact cumulant matching gives very accurate rejection percentages. We also prove that the statistic on which the test is based is comprised of canonical coherencies arising from our complex-valued vector time series. Frequency specific tests are combined using multiple hypothesis testing to give an overall test. Our methodology is demonstrated on ocean current data collected at different depths in the Labrador Sea. Overall this work extends results on propriety testing for complex-valued vectors to the complex-valued vector time series setting

Constructing brain connectivity group graphs from EEG time series

Graphical analysis of complex brain networks is a fundamental area of modern neuroscience. Functional connectivity is important since many neurological and psychiatric disorders, including schizophrenia, are described as ‘dys-connectivity’ syndromes. Using electroencephalogram time series collected on each of a group of 15 individuals with a common medical diagnosis of positive syndrome schizophrenia we seek to build a single, representative, brain functional connectivity group graph. Disparity/distance measures between spectral matrices are identified and used to define the normalized graph Laplacian enabling clustering of the spectral matrices for detecting ‘outlying’ individuals. Two such individuals are identified. For each remaining individual, we derive a test for each edge in the connectivity graph based on average estimated partial coherence over frequencies, and associated p-values are found. For each edge these are used in a multiple hypothesis test across individuals and the proportion rejecting the hypothesis of no edge is used to construct a connectivity group graph. This study provides a framework for integrating results on multiple individuals into a single overall connectivity structure

Comparing Three Theories of the Gender Gap in Information Technology Careers: The Role of Salience Differences

The information technology (IT) field faces a skills shortage. Only 17% of a projected 3.5 million computing job openings are expected to be filled by 2026 (National Association for Women & Information Technology, 2018). Yet the number of women pursuing IT careers continues to decrease—only 19% of IT bachelor’s degrees in 2016 were awarded to women compared to 57% of bachelor’s degrees overall. We compared three theories that could explain this gender gap in the pursuit of IT careers: expectancy-value theory, role congruity theory, and field-specific ability beliefs theory. We find that women and men are similar in their levels of important factors related to career interest, but that two of these factors—technical learning self-efficacy and agentic goals—have increased salience for women. This suggests that some of the gender gap in the IT field could be addressed by placing more focus on developing technical learning self-efficacy in both men and women. While this could help both women and men, it would likely have an outsized effect on the IT career pursuit of women

The Determinants of Women and Racial Minority High School Students’ Willingness to Pursue an IT Major

Women and racial minorities are underrepresented in IT careers. One reason for this is that women and racial minorities choose to major in IT subjects in college at a lower rate than overrepresented groups in IT careers do. Thus, it is important to better understand how high school students make decisions about whether to major in IT subjects in college. We report on a racially diverse, nationwide sample of college-bound high school seniors and their intentions to major in IT subjects in college. Using expectancy-value theory, we add the construct of outside opportunities (i.e., how many options one has for a major) with cumulative high school GPA as a proxy. We find that higher GPAs actually tend to increase the intention to major in IT for several underrepresented groups but decrease the intention to major in IT for some overrepresented groups. Policy implications include including IT training in high schools