Paper proposes using truncated normal distribution for RRC model, improving detection of minority classes.
problem Improving weak classifiers in RRC models.
method Proposes using truncated normal distribution and soft confusion matrix for RRC model.
result Truncated-normal-based SCM algorithm outperforms beta distribution in discovering minority classes.
Gradually Truncated Log-normal distribution - Size distribution of firms Abstract Many natural and economical phenomena are described through power law or log- normal distributions. In these cases, probability decreases very slowly with step size compared to normal distribution. Thus it is essential to cut-off these di…
Paper defines new risk measures for elliptical distributions.
problem Risk measurement for elliptical distributions.
method DTM, DTS, DTK definitions and formula derivation for specific distributions.
result Explicit formulas for DTE, DTV, DTS, and DTK for various distributions.
Algorithm estimates multivariate normal parameters from hidden truncated samples.
problem Estimating parameters of multivariate normal distribution from hidden truncated samples.
method Efficient algorithm using polynomial-time estimation with oracle access to the subset S.
result Estimation of mean and covariance matrix with arbitrary accuracy.
A new algorithm speeds up elliptical slice sampling for truncated multivariate normals.
problem Efficiently sampling from truncated multivariate normal distributions with linear constraints.
method Adapting elliptical slice sampling to linearly truncated multivariate normals, with an algorithm for ellipse-polytope intersection in O(m log m) time.
result The algorithm enhances numerical stability, speeds up running time, and is easy to parallelize.
The paper calculates moments and conditional risks for skewed elliptical distributions.
problem Estimating moments and tail conditional risks for skewed elliptical distributions.
method Derives explicit expressions for multivariate doubly truncated moments and conditional risks for generalized skew-elliptical distributions.
result Explicit formulas for multivariate doubly truncated moments and conditional risks are derived for various skewed elliptical distributions.
New method for unbiased sampling of doubly-intractable distributions.
problem Hard computation of normalizing constants for complex probability distributions.
method Adapting random series truncation and Markov chain coupling for unbiased estimation of 1/Z.
result Estimators with lower variance and higher positive estimates.
Paper introduces a new GGM variant for efficient inference and unsupervised learning.
problem Limited modeling abilities of traditional Gaussian graphical models.
method Introduces a novel variant of Gaussian graphical models with truncated normal distributions and bipartite structure.
result Efficient inference and unsupervised learning capabilities demonstrated.
The paper improves asset allocation using a skew-normal distribution in the Black-Litterman model.
problem Improving asset allocation under skewed return distributions.
method Using the Black-Litterman model with hidden truncation skew-normal distribution and Simaan's three-moment risk model.
result Optimal portfolios have less risk and higher skewness compared to classical BL model.
Paper extends LME models to allow sign constraints on coefficients with SDTN random effects.
problem Inference with sign constraints on random effects in LME models.
method Proposes SDTN distribution for random effects and develops likelihood-based approaches for estimation.
result Proposed constrained model improves real-world interpretations and achieves satisfactory performance.
Proposes a new method to estimate Bayesian neural network depth.
problem Estimating the depth of Bayesian neural networks.
method Uses a discrete truncated normal distribution to learn depth mean and variance, inferring posterior distributions by minimizing variational free energy.
result Improves test accuracy and reduces posterior depth variance on the spiral dataset.
Paper simplifies data carving inference with a parametric distribution.
problem Valid inference after selection with data carving.
method Developed a parametric distribution for data carving inference.
result Exact inference for data carving can be computed trivially.
Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
New JPEG algorithm corrects biases in endogenously truncated data.
problem Correcting biases in data with endogenous truncation.
method Semiparametric wavelet-based approach, avoiding matrix computations.
result Corrects both endogenous truncation and covariate shifts biases.
New Poincaré inequalities for interval distributions improve sensitivity analysis.
problem Improving accuracy of sensitivity indices for truncated distributions.
method Developed new Poincaré inequalities for intervals, computed exact constants, and applied to hydrological sensitivity.
result Exact Poincaré constants for various truncated distributions provide more accurate sensitivity bounds.
Paper proposes a method to estimate truncated density models using Score Matching.
problem Estimating parameters of truncated probability densities.
method Score Matching with a novel weight function derived from Stein discrepancy.
result The proposed method minimizes a weighted Fisher divergence and corrects outlier-trimming bias.
Efficiently estimate Boolean product distribution parameters from truncated samples.
problem Estimating parameters of Boolean product distributions from truncated samples.
method Introducing fatness of truncation set, using membership queries, and adapting Stochastic Gradient Descent.
result Efficiently learn Boolean product distributions from truncated samples with small sample complexity.
The paper models stock returns using q-Gaussians and negative binomials.
problem Modeling stock return distributions and pricing options.
method Proposes a generalized jump-diffusion model and uses q-Gaussians and negative binomial distributions. result An explicit option pricing formula is derived.
We accelerate CNF by reducing ODE truncation errors with polynomial regularization.
problem High computation cost of CNF due to large truncation errors in solving ODEs.
method Add polynomial regularization to approximate ODE trajectories with polynomial functions.
result 42.3% to 71.3% reduction of NFE on density estimation, 19.3% to 32.1% on variational auto-encoder.
This article reviews and explains HMC-based methods for sampling constrained continuous distributions.
problem Sampling from continuous distributions with constraints.
method HMC and related methods for constrained sampling.
result HMC and related methods are more efficient for constrained sampling.
Paper proposes a new generative model for discrete distributions using flows on submanifolds.
problem Discretization issues and complex statistical dependencies in discrete data.
method Continuous normalizing flows on factorizing discrete measures, geodesic flow matching.
result Efficient training and broad applicability demonstrated through experiments.
We consider free and proper cotangent-lifted symmetries of Hamiltonian systems. For the special case of G = SO(3), we construct symplectic slice coordinates around an arbitrary point. We thus obtain a parametrisation of the phase space suitable for the study of dynamics near relative equilibria, in particular for the B…
A new Heckman selection model uses a bivariate contaminated normal distribution for more accurate data analysis.
problem Sample selection biases in econometric data analysis.
method Introduces a Heckman selection model using a bivariate contaminated normal distribution and presents an efficient ECM algorithm for parameter estimation.
result The proposed model outperforms normal and Student's t counterparts in real data analysis and simulation studies.
In (exploratory) factor analysis, the loading matrix is identified only up to orthogonal rotation. For identifiability, one thus often takes the loading matrix to be lower triangular with positive diagonal entries. In Bayesian inference, a standard practice is then to specify a prior under which the loadings are indepe…
The method approximates stationary distributions of Markov models by truncating irrelevant states.
problem Computing the stationary distribution of complex Markov models is computationally challenging.
method A state-space lumping scheme that aggregates states in a grid structure, iteratively refining the state-space.
result The method provides a well-justified finite-state projection tailored to the stationary behavior of Markov models.
Normalizing flows model atomic solids without needing ground-truth samples.
problem Modeling atomic solids without ground-truth samples.
method Normalizing flows to transform a base distribution into the target solid.
result Excellent agreement between model estimates and literature values for Helmholtz free energy.
Novel variational EM approach using truncated distributions.
problem Efficiently modeling latent variables and mixtures.
method Truncated posterior distributions as variational parameters.
result Efficient and concise variational lower bounds.
Examines WENDy-IRLS algorithm's noise robustness and efficiency in various differential equations.
problem Noise robustness and efficiency of WENDy-IRLS algorithm.
method Studied coverage and bias properties of WENDy-IRLS algorithm's estimators in various differential equations and noise distributions.
result WENDy-IRLS algorithm shows notable noise robustness and computational efficiency.
The problem of an arbitrary truncated Levy flight description using the method of cumulant approach has been solved. The set of cumulants of the truncated Levy distribution given the assumption of arbitrary truncation has been found. The influence of truncation shape on the truncated Levy flight properties in the Gauss…
Paper estimates spectral risk measures for insurance data with truncated and censored data.
problem Estimating spectral risk measures for insurance data with left truncation and right censoring.
method Proposes a non-parametric estimator using product limit estimator and establishes asymptotic normality.
result Proposed estimator outperforms existing methods for small k and small sample sizes.
Paper tackles overestimation bias in continuous control, improving performance by 25%.
problem Overestimation bias in off-policy learning.
method Truncated Quantile Critics (TQC) combines distributional representation, truncation, and ensembling of critics.
result TQC outperforms state-of-the-art methods by 25% on the Humanoid environment.
Efficiently learns exponential family distributions with i.i.d. samples.
problem Learning natural parameters of truncated exponential families efficiently.
method Proposes a novel loss function and computationally efficient estimator.
result Achieves optimal sample complexity and asymptotic normality.
Bayesian method estimates LTLL distribution parameters for time-to-event data.
problem Parameter estimation for time-to-event data with left truncation.
method Bayesian inference using Metropolis-Hastings algorithm.
result Bayesian estimation provides more stable and reliable parameter estimates.
Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the P…
New HMC framework for truncated distributions using sigmoid approximation.
problem Hamiltonian Monte Carlo struggles with truncated distributions due to discontinuities.
method Introduce sigmoid factor to approximate probability drop, smooth potential function.
result Comparable efficiency to other methods on various sampling tasks.
TGGM models nonlinear learning using truncated Gaussian variables.
problem Designing statistical models for nonlinear learning.
method Introducing TGGM, integrating latent nonnegative variables, using EM algorithm, breaking inference into TGGM problems.
result TGGM models induce non-Gaussian, nonlinear relations among variables.
To know the statistical distribution of a variable is an important problem in management of resources. Distributions of the power law type are observed in many real systems. However power law distributions have an infinite variance and thus can not be used as a standard distribution. Normally professionals in the area …
New method for constructing truncated vine copulas.
problem High-dimensional parameter space in vine copulas.
method Propose a new score and algorithm for constructing truncated vines.
result New algorithms exploit conditional independences.
For an arrangement with complement X and fundamental group G, we relate the truncated cohomology ring, H^{<=2}(X), to the second nilpotent quotient, G/G_3. We define invariants of G/G_3 by counting normal subgroups of a fixed prime index p, according to their abelianization. We show how to compute this distribution fro…
In the context of the Dragulescu-Yakovenko (2000) model, we show that empirical income distribution with truncated datasets, cannot be properly modeled by the one-parameter exponential distribution. However, a truncated version characterized by an exponential distribution with two parameters gives an accurate fit.
Paper compares VaR from aggregated and single loss distributions in credit risk.
problem Estimating VaR in credit risk portfolios with varying severities.
method Uses Monte Carlo simulation with Gamma and truncated exponential distributions.
result Truncated exponential distribution yields VaR closer to aggregated loss approach.
The paper tackles flexible modeling of constrained multimodal data.
problem Flexible modeling of multimodal data on constrained spaces.
method Algorithm based on rejection sampling and data augmentation.
result Improved flexibility and applicability of mixture models to constrained spaces.
The generalized correlation approach, which has been successfully used in statistical radio physics to describe non-Gaussian random processes, is proposed to describe stochastic financial processes. The generalized correlation approach has been used to describe a non-Gaussian random walk with independent, identically d…
We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the applic…
A new method for multi-objective Bayesian optimization using entropy search and variational lower bound maximization.
problem Efficiently optimizing multiple objectives in continuous domains.
method Approximates the Pareto-frontier using a mixture distribution and optimizes the balance through variational lower bound maximization.
result Demonstrated effectiveness especially with many objective functions.
Unified framework for mean testing under truncation bias.
problem High-dimensional mean testing under arbitrary truncation.
method Characterizes fundamental limits and develops a simple second-order test.
result Unified framework connects finite-moment, sub-Gaussian, and median-regular structural regimes.
Algorithm estimates Gaussian parameters under unknown truncation sets.
problem Estimating Gaussian parameters when samples are truncated to unknown sets.
method Efficient algorithm for arbitrary unknown truncation sets, using Gaussian surface area as complexity measure.
result Algorithm works for large families of sets including intersections of halfspaces and general convex sets.
We use daily data on bilateral interbank exposures and monthly bank balance sheets to study network characteristics of the Russian interbank market over Aug 1998 - Oct 2004. Specifically, we examine the distributions of (un)directed (un)weighted degree, nodal attributes (bank assets, capital and capital-to-assets ratio…