New dispersion indices based on inaccuracy and divergence introduced for information measures.
problem Measuring variability in uncertainty measures.
method Introducing new dispersion indices based on Kerridge inaccuracy and Kullback-Leibler divergence.
result Properties, bounds, and examples of new dispersion indices presented.
The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of s…
Method meta-classifies semantic segmentation predictions using dispersion measures.
problem Assessing the quality of semantic segmentation predictions.
method Aggregates dispersion measures (entropy) of predicted probabilities to derive metrics correlated with IoU.
result Metrics correlate well with IoU, providing reliable prediction quality ratings.
Proposes a new portfolio optimization method considering reward, dispersion, and asymmetry.
problem Capturing fat-tails and asymmetry in asset return distributions.
method Market model with tempered stable distribution; extended mean-variance optimization.
result Closed-form solutions for VaR and CVaR; efficient frontier extended to three dimensions.
New framework controls statistical dispersion for high-stakes applications.
problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.
Study shows how to reduce variational inference bias by concentrating likelihood ratio distribution.
problem Bias and variance issues in variational inference.
method Upper bound variational gap using dispersion measure of likelihood ratio, suggesting methods to reduce bias.
result Reducing bias in variational inference can be achieved by making likelihood ratio distribution more concentrated.
This paper evaluates forecast quality in electricity markets beyond traditional accuracy measures.
problem Traditional accuracy measures fail to reflect the economic value of electricity price forecasts.
method Investigates four quality dimensions: accuracy, dispersion, association, and extremum identification.
result Dispersion- and association-based measures better capture forecast economic value.
This paper introduces new risk measures for systemic risk analysis.
problem Analyzing systemic risk in financial systems.
method Introducing conditional distortion risk measures and their properties.
result Presented sufficient conditions for ordering risk measures.
The standard deviation and Gini mean difference order based on tail behavior.
problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference ∣X−X′∣. result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.
Paper proposes a new method for feature importance in model selection.
problem Feature importance in model selection.
method New tree-model explanation approach using Coefficient of Variation of feature weight.
result Novel feature explanation performs better than cross-validation in model selection.
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two feat…
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.
In this paper we propose a novel index to quantify and measure the flow of information on macro and micro scales. We discuss the implications of this index for knowledge management fields and also as intellectual capital that can thus be utilized by entrepreneurs. We explore different function and human oriented metric…
Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace. Following the blueprint of classical Linear Discriminant Analysis (LDA), WDA selects the projection matrix that maximizes the …
This work investigates uncertainty quantification for black-box large language models in natural language generation.
problem Lack of trustworthiness in responses generated by black-box large language models.
method Differentiated uncertainty vs confidence, proposed and compared several confidence/uncertainty measures, applied to selective NLG.
result A simple measure for semantic dispersion can predict the quality of LLM responses.
The study analyzes the differences between physical and risk-neutral correlation estimates for equity baskets.
problem Analyzing the differences between physical and risk-neutral correlation estimates for equity baskets.
method Assumed equicorrelation, reduced dimensionality, approximated ICS from implied volatilities, analyzed dynamics using dynamic semiparametric factor model.
result Proposed profitability improvement schemes based on implied correlation forecasts.
New heat dispersion laws established for smooth compact manifolds.
problem Understanding heat dispersion in smooth compact manifolds.
method Established new heat dispersion laws through Theorem 1.1 and explored them further with Propositions 3.1 and 3.2.
result New heat dispersion laws for smooth compact manifolds.
Paper predicts urban dispersal events using deep survival analysis on mobility data.
problem Predicting abnormal dispersal events in urban areas to mitigate congestion and safety risks.
method Formulated as a survival analysis problem, developed a two-stage deep learning framework (DILSA).
result DILSA predicts dispersal events with F1-score of 0.7 and average time error of 18 minutes.
MallowsPO enhances LLM fine-tuning with a dispersion index of human preferences.
problem Lack of diversity in human preferences in DPO.
method Developed a dispersion index based on Mallows' theory to characterize preference diversity.
result Demonstrated improved performance in various tasks using the dispersion index.
Geometric focusing affects dispersive estimates for Schrödinger and wave equations.
problem Long-time decay rate in dispersive estimates for Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones.
method Classifying the long-time decay rate in dispersive estimates for the Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones in terms of the intensity of geometric focusing.
result Each multiplicity of conjugate points within distance π on Y = ∂X0 leads to a |t|1/2-loss in the long-time decay order and a half-order shift in the regularity index in the dispersive estimate for the Schrödinger equation.
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
In the recent years, banks have sold structured products such as worst-of options, Everest and Himalayas, resulting in a short correlation exposure. They have hence become interested in offsetting part of this exposure, namely buying back correlation. Two ways have been proposed for such a strategy : either pure correl…
We establish several new stylised facts concerning the intra-day seasonalities of stock dynamics. Beyond the well known U-shaped pattern of the volatility, we find that the average correlation between stocks increases throughout the day, leading to a smaller relative dispersion between stocks. Somewhat paradoxically, t…
The study examines Hawkes processes and their long-term behavior.
problem Understanding the long-term behavior of Hawkes processes.
method Proving functional limit theorems under various conditions on the dispersion of child events.
result Functional limit theorems hold for Hawkes processes with different levels of child event dispersion.
The paper critiques existing uncertainty concepts and proposes a new decision-theoretic approach.
problem Incoherence in existing discussions of aleatoric and epistemic uncertainty.
method Decision-theoretic perspective that relates uncertainty, predictive performance, and statistical dispersion.
result Popular information-theoretic quantities can be poor estimators but still useful for guiding data acquisition.
Machine learning improves fiber nonlinearity detection.
problem Mitigating nonlinear effects in optical fiber channels.
method Parzen window classifier applied to detect nonlinear fiber channel.
result Performance improvement in dispersion managed and unmanaged systems.
Machine learning classifies surface wave dispersion curves from ambient noise.
problem Classifying surface wave dispersion curves from ambient noise.
method Convolutional neural network (U-net) with transfer learning and supervised learning.
result Machine classification nearly identical to human-picked phases.
Efficiently infers graph edges from genetic similarity data in landscape genetics.
problem Inferring unknown graph edges from genetic similarity data in a heterogeneous landscape.
method Developed an efficient first-order optimization method to solve the inverse landscape genetics problem.
result Our method provides fast and reliable convergence, significantly outperforming existing heuristics.
Bayesian model tackles spatial count data issues with flexible non-parametric techniques.
problem Challenges in traditional parametric models for spatial count data with unbalanced distributions and complex dependencies.
method Bayesian semi-parametric spatial dispersed count model combining non-parametric techniques and adapted count models.
result Demonstrates superior performance in managing dispersion and capturing intricate spatial patterns.
New method uses reinforcement learning to accurately estimate available network bandwidth.
problem Accurate and fast estimation of available bandwidth in networks with varying cross-traffic.
method Employed reinforcement learning, specifically the ε-greedy algorithm in a multi-armed bandit approach. result Proposed method identifies available bandwidth with high precision and converges under various challenging conditions.
This paper generalizes beta divergence beyond its classical form associated with power variance functions of Tweedie models. Generalized form is represented by a compact definite integral as a function of variance function of the exponential dispersion model. This compact integral form simplifies derivations of many pr…
The trimming scheme with a prefixed cutoff portion is known as a method of improving the robustness of statistical models such as multivariate Gaussian mixture models (MG- MMs) in small scale tests by alleviating the impacts of outliers. However, when this method is applied to real- world data, such as noisy speech pro…
Decomposes portfolio returns into drift and asset price distribution changes.
problem Understanding efficient markets through portfolio returns and asset price distributions.
method Continuous semimartingale price representations and accounting identity.
result Existence of an asset pricing factor emerges from an accounting identity across various economic and financial environments.
Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.
problem Ensembling neural networks struggles with dispersed, narrow peaks in likelihood surfaces.
method Uses Bayesian Quadrature to construct weighted ensembles of architectures.
result Empirically outperforms state-of-the-art baselines in test likelihood, accuracy, and expected calibration error.
We consider time-domain digital backpropagation with chromatic dispersion filters jointly optimized and quantized using machine-learning techniques. Compared to the baseline implementations, we show improved BER performance and >40% power dissipation reductions in 28-nm CMOS.
Study on billiard trajectories with fixed bounces.
problem Counting periodic trajectories with specific bounces.
method Analyzes two-dimensional dispersive billiard systems.
result Asymptotic growth of primitive periodic trajectories.
Paper transforms a complex equation into simpler forms for analysis.
problem Analyzing a fourth-order dispersive flow equation on Kähler manifolds.
method Developed the generalized Hasimoto transformation to simplify the equation.
result Explicit expressions derived for three examples of compact Kähler manifolds.
A new model relaxes constraints on exponential dispersion models.
problem Tight conditions on cumulant function limit the class of exponential dispersion models.
method Introduces K-LED model with Legendre cumulant function and Bregman divergence guidance.
result The model allows for easier computation of mean parameter and includes various distributions.
Probabilistic modeling is cyclical: we specify a model, infer its posterior, and evaluate its performance. Evaluation drives the cycle, as we revise our model based on how it performs. This requires a metric. Traditionally, predictive accuracy prevails. Yet, predictive accuracy does not tell the whole story. We propose…
This paper proposes the k-generalized distribution as a model for describing the distribution and dispersion of income within a population. Formulas for the shape, moments and standard tools for inequality measurement - such as the Lorenz curve and the Gini coefficient - are given. A method for parameter estimation is …
We present the Shortfall Deviation Risk (SDR), a risk measure that represents the expected loss that occurs with certain probability penalized by the dispersion of results that are worse than such an expectation. SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating t…
Reduced-order model improves LES for atmospheric pollutant dispersion.
problem Accurate near-field pollutant concentration tracking in urban areas.
method Combining POD and GPR for non-intrusive reduced-order modeling.
result Component-by-component optimization captures spatial scales in high-order modes.
New Gini indices capture more nuanced income inequality.
problem Measuring joint dispersion across multiple observations.
method Axiomatic approach to define and characterize n-th order Gini deviations.
result Higher-order Gini coefficients reveal more extreme income disparities.
Dropout improves regularization in flexible models for rare features.
problem Understanding theoretical properties of dropout in generalized linear models.
method Theoretical analysis and application to adaptive smoothing with B-splines.
result Dropout prefers rare features in mean and dispersion parameters.
Study dispersive estimates for Schrödinger and wave equations on a cone with specific metric.
problem Pointwise decay estimates for Schrödinger and wave equations on a product cone.
method Modified Hadamard parametrix on Y with ε>π to prove dispersive estimates. result Threshold of conjugate radius ε>π for pointwise dispersive estimates. We discuss a short-time existence theorem of solutions to the initial value problem for a third order dispersive flow for closed curves into a compact almost Hermitian manifold. Our equations geometrically generalize a physical model describing the motion of vortex filament. The classical energy method cannot work for …
Neural networks improve wave-equation simulation accuracy.
problem Inaccurate discretization of Laplacian in wave-equation simulation leads to numerical dispersion.
method Intersperse CNNs between low-fidelity timesteps to correct wavefield and limit numerical dispersion.
result Neural network augmentation reduces numerical dispersion artifacts in wave-equation simulation.
The purpose of this paper is to study the generalized Fong--Vasicek two-factor interest rate model with stochastic volatility. In this model the dispersion of the stochastic short rate (square of volatility) is assumed to be stochastic as well and it follows a non-negative process with volatility proportional to the sq…