Flexible dependence modeling of operational risk losses and its impact on total capital requirements

Operational risk data, when available, are usually scarce, heavy-tailed and possibly dependent. In this work, we introduce a model that captures such real-world characteristics and explicitly deals with heterogeneous pairwise and tail dependence of losses. By considering flexible families of copulas, we can easily move beyond modeling bivariate dependence among losses and estimate the total risk capital for the seven- and eight-dimensional distributions of event types and business lines. Using real-world data, we then evaluate the impact of realistic dependence modeling on estimating the total regulatory capital, which turns out to be up to 38% smaller than what the standard Basel approach would prescrib

Factor copula models for item response data

Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized multivariate normal distribution (or multidimensional normal ogive model). Details on maximum likelihood estimation of parameters for the factor copula model are given, as well as analysis of the behavior of the log-likelihood. Our general methodology is illustrated with several item response data sets, and it is shown that there is a substantial improvement on existing models both conceptually and in fit to data

A European Perspective on Auditory Processing Disorder-Current Knowledge and Future Research Focus

Current notions of “hearing impairment,” as reflected in clinical audiological practice, do not acknowledge the needs of individuals who have normal hearing pure tone sensitivity but who experience auditory processing difficulties in everyday life that are indexed by reduced performance in other more sophisticated audiometric tests such as speech audiometry in noise or complex non-speech sound perception. This disorder, defined as “Auditory Processing Disorder” (APD) or “Central Auditory Processing Disorder” is classified in the current tenth version of the International Classification of diseases as H93.25 and in the forthcoming beta eleventh version. APDs may have detrimental effects on the affected individual, with low esteem, anxiety, and depression, and symptoms may remain into adulthood. These disorders may interfere with learning per se and with communication, social, emotional, and academic-work aspects of life. The objective of the present paper is to define a baseline European APD consensus formulated by experienced clinicians and researchers in this specific field of human auditory science. A secondary aim is to identify issues that future research needs to address in order to further clarify the nature of APD and thus assist in optimum diagnosis and evidence-based management. This European consensus presents the main symptoms, conditions, and specific medical history elements that should lead to auditory processing evaluation. Consensus on definition of the disorder, optimum diagnostic pathway, and appropriate management are highlighted alongside a perspective on future research focus

Simultaneous equation penalized likelihood estimation of vehicle accident injury severity

A bivariate system of equations is developed to model ordinal polychotomous dependent variables within a simultaneous additive regression framework. The functional form of the covariate effects is assumed fairly flexible with appropriate smoothers used to account for non‐linearities and spatial variability in the data. Non‐Gaussian error dependence structures are dealt with by means of copulas whose association parameter is also specified in terms of a generic additive predictor. The framework is employed to study the effects of several risk factors on the levels of injury sustained by individuals in two‐vehicle accidents in France. The use of the methodology proposed is motivated by the presence of common unobservables that may affect the interrelationships between the parties involved in the same crash and by the possible heterogeneity in individuals’ characteristics and accident dynamics. Better calibrated estimates are obtained and misspecification reduced via an enhanced model specification

High-throughput imaging of ATG9A distribution as a diagnostic functional assay for adaptor protein complex 4-associated hereditary spastic paraplegia

Adaptor protein complex 4-associated hereditary spastic paraplegia is caused by biallelic loss-of-function variants in AP4B1, AP4M1, AP4E1 or AP4S1, which constitute the four subunits of this obligate complex. While the diagnosis of adaptor protein complex 4-associated hereditary spastic paraplegia relies on molecular testing, the interpretation of novel missense variants remains challenging. Here, we address this diagnostic gap by using patient-derived fibroblasts to establish a functional assay that measures the subcellular localization of ATG9A, a transmembrane protein that is sorted by adaptor protein complex 4. Using automated high-throughput microscopy, we determine the ratio of the ATG9A fluorescence in the trans-Golgi-network versus cytoplasm and ascertain that this metric meets standards for screening assays (Z'-factor robust >0.3, strictly standardized mean difference >3). The 'ATG9A ratio' is increased in fibroblasts of 18 well-characterized adaptor protein complex 4-associated hereditary spastic paraplegia patients [mean: 1.54 ± 0.13 versus 1.21 ± 0.05 (standard deviation) in controls] and receiver-operating characteristic analysis demonstrates robust diagnostic power (area under the curve: 0.85, 95% confidence interval: 0.849-0.852). Using fibroblasts from two individuals with atypical clinical features and novel biallelic missense variants of unknown significance in AP4B1, we show that our assay can reliably detect adaptor protein complex 4 function. Our findings establish the 'ATG9A ratio' as a diagnostic marker of adaptor protein complex 4-associated hereditary spastic paraplegia

High-throughput imaging of ATG9A distribution as a diagnostic functional assay for adaptor protein complex 4-associated hereditary spastic paraplegia

Adaptor protein complex 4-associated hereditary spastic paraplegia is caused by biallelic loss-of-function variants in AP4B1, AP4M1, AP4E1 or AP4S1, which constitute the four subunits of this obligate complex. While the diagnosis of adaptor protein complex 4-associated hereditary spastic paraplegia relies on molecular testing, the interpretation of novel missense variants remains challenging. Here, we address this diagnostic gap by using patient-derived fibroblasts to establish a functional assay that measures the subcellular localization of ATG9A, a transmembrane protein that is sorted by adaptor protein complex 4. Using automated high-throughput microscopy, we determine the ratio of the ATG9A fluorescence in the trans-Golgi-network versus cytoplasm and ascertain that this metric meets standards for screening assays (Z'-factor robust >0.3, strictly standardized mean difference >3). The `ATG9A ratio' is increased in fibroblasts of 18 well-characterized adaptor protein complex 4-associated hereditary spastic paraplegia patients [mean: 1.54 +/- 0.13 versus 1.21 +/- 0.05 (standard deviation) in controls] and receiver-operating characteristic analysis demonstrates robust diagnostic power (area under the curve: 0.85, 95% confidence interval: 0.849-0.852). Using fibroblasts from two individuals with atypical clinical features and novel biallelic missense variants of unknown significance in AP4B1, we show that our assay can reliably detect adaptor protein complex 4 function. Our findings establish the 'ATG9A ratio' as a diagnostic marker of adaptor protein complex 4-associated hereditary spastic paraplegia

Interaction between Attention and Bottom-Up Saliency Mediates the Representation of Foreground and Background in an Auditory Scene

Bottom-up (stimulus-driven) and top-down (attentional) processes interact when a complex acoustic scene is parsed. Both modulate the neural representation of the target in a manner strongly correlated with behavioral performance