A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Transformer improves parameter estimation without needing closed-form solutions.
A method for converting NIW parameters for better estimation.
This paper presents the asymptotic behavior of a linear instrumental variables (IV) estimator that uses a ridge regression penalty. The regularization tuning parameter is selected empirically by splitting the observed data into training and test samples. Conditional on the tuning parameter, the training sample creates …
New Riemannian radial distributions help estimate parameters on symmetric spaces.
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimator…
Random variables of the generalized Pareto distribution, can be transformed to that of the Pareto distribution. Explicit expressions exist for the maximum likelihood estimators of the parameters of the Pareto distribution. The performance of the estimation of the shape parameter of generalized Pareto distributed using …
Bayesian method estimates LTLL distribution parameters for time-to-event data.
EPD method accurately captures parameter distributions from RCS data.
Paper fine-tunes a simulation-driven estimator to reduce out-of-distribution errors.
The paper fits a seven-parameter GTS distribution to financial data.
Many banks adopt the Loss Distribution Approach to quantify the operational risk capital charge under Basel II requirements. It is common practice to estimate the capital charge using the 0.999 quantile of the annual loss distribution, calculated using point estimators of the frequency and severity distribution paramet…
Paper improves parameter estimation of continuous distributions using preference feedback.
Integrates estimation and optimization for uncertain parameters.
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in thr…
Efficiently estimates GEV distribution parameters using neural networks.
A fast method for estimating radar amplitude density parameters.
The modelling of data on a spherical surface requires the consideration of directional probability distributions. To model asymmetrically distributed data on a three-dimensional sphere, Kent distributions are often used. The moment estimates of the parameters are typically used in modelling tasks involving Kent distrib…
Singularities of a statistical model are the elements of the model's parameter space which make the corresponding Fisher information matrix degenerate. These are the points for which estimation techniques such as the maximum likelihood estimator and standard Bayesian procedures do not admit the root- parametric rate…
We treat the problem of estimation of orientation parameters whose values are invariant to transformations from a spherical symmetry group. Previous work has shown that any such group-invariant distribution must satisfy a restricted finite mixture representation, which allows the orientation parameter to be estimated u…
Study decomposes uncertainty in HK-distribution parameter estimation for QUS.
Autoencoder estimates parameters of noisy, multi-component damped signals.
Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, medicine and biology. In these domains, the size of the population under study is often very large, however, the number of observations availa…
A Bayesian approach termed BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parame…
Optimizes resource allocation for distributed parameter estimation in sensor networks.
Develops a novel stochastic algorithm for diagonal estimation of large matrices.
Study optimizes sensor placement for accurate parameter estimation in complex systems.
A method for estimating parameters from entangled single-sample distributions, robust to high-noise data.
Optimal portfolio selection problems are determined by the (unknown) parameters of the data generating process. If an investor wants to realise the position suggested by the optimal portfolios, he/she needs to estimate the unknown parameters and to account for the parameter uncertainty in the decision process. Most oft…
This paper considers statistical estimation problems where the probability distribution of the observed random variable is invariant with respect to actions of a finite topological group. It is shown that any such distribution must satisfy a restricted finite mixture representation. When specialized to the case of dist…
Efficiently estimate Boolean product distribution parameters from truncated samples.
AdaCat improves density estimation and planning in autoregressive models.
Proposes a new measure to evaluate stability of statistical parameters under distributional shifts.
We solve the mean parametrization of von Mises-Fisher distribution.
In this paper we develop an Expectation Maximization(EM) algorithm to estimate the parameter of a Yule-Simon distribution. The Yule-Simon distribution exhibits the "rich get richer" effect whereby an 80-20 type of rule tends to dominate. These distributions are ubiquitous in industrial settings. The EM algorithm presen…
Federated learning on graphs tackles heterogeneity with efficient parameter estimation.
Paper bridges score estimation to parameter and density estimation in DDPMs.
The paper provides bounds on estimation error in a distributed online learning setting.
Adaptive estimation of alpha-Stable distribution and Hurst exponent for nonstationary time series.
ALFI improves likelihood-free inference for black-box generators.
RODE-Net learns ODEs from data with random parameters using neural networks and GANs.
We define and study the statistical models in exponential family form whose sufficient statistics are the degree distributions and the bi-degree distributions of undirected labelled simple graphs. Graphs that are constrained by the joint degree distributions are called -graphs in the computer science literature and…
In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general the …
Efficiently learns exponential family distributions with i.i.d. samples.
We consider the problem of estimating the parameters of a -dimensional rectified Gaussian distribution from i.i.d. samples. A rectified Gaussian distribution is defined by passing a standard Gaussian distribution through a one-layer ReLU neural network. We give a simple algorithm to estimate the parameters (i.e., th…
The thesis models financial returns using mixtures of generalized normal distributions.
In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural network architecture …
This paper solves the convergence problem for estimating MGGD parameters with a convex formulation.
We present a novel approach for learning an HMM whose outputs are distributed according to a parametric family. This is done by {\em decoupling} the learning task into two steps: first estimating the output parameters, and then estimating the hidden states transition probabilities. The first step is accomplished by fit…