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Found 10 papers, 0 papers with code
Signature Isolation Forest
Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data.
Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity
We argue that the choice of such methods is akin to a form of model risk and we study the risk sensitivity that arise from choices relating to the class of robust estimation adopted and the impact of the settings associated with such methods on key actuarial tasks such as premium calculation in cyber insurance.
The Nature of Losses from Cyber-Related Events: Risk Categories and Business Sectors
In this study we examine the nature of losses from cyber related events across different risk categories and business sectors.
Stochastic measure distortions induced by quantile processes for risk quantification and valuation
This requires the introduction of a copula function in the composite map for the construction of quantile processes, which presents another new element in the risk quantification and modelling framework based on probability measure distortions induced by quantile processes.
Cyber Risk Frequency, Severity and Insurance Viability
We address these questions through a combination of regression models based on the class of Generalised Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions.
Parsimonious Feature Extraction Methods: Extending Robust Probabilistic Projections with Generalized Skew-t
We propose a novel generalisation to the Student-t Probabilistic Principal Component methodology which: (1) accounts for an asymmetric distribution of the observation data; (2) is a framework for grouped and generalised multiple-degree-of-freedom structures, which provides a more flexible approach to modelling groups of marginal tail dependence in the observation data; and (3) separates the tail effect of the error terms and factors.
Cost-aware Feature Selection for IoT Device Classification
In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features.
Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks
Spatial regression of random fields based on potentially biased sensing information is proposed in this paper.
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations.
Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World
Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee.
