arXiv research

New method estimates velocity fields for minimizing $f$-divergences without overfitting.

The article presents a new non-parametric approach for forecasting mortality and fertility using Gaussian process regression.

Closed form formulas for swaption prices in HJM model are derived. These formulas are used for nonparametric fit of deterministic forward volatility. It is demonstrated that this formula and non-parametric fit works very well and can be used to identify arbitrage opportunities

Study shows double descent curve in high-dimensional linear regression with random projections.

Study compares non-parametric models for predicting medical insurance reimbursement delays.

A new framework improves kernel Stein discrepancy tests for validating distributions.

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …

New test assesses probabilistic model calibration without expensive approximations.

New GoF test improves change point detection in multivariate time series.

Tests for equivariance in non-parametric regression models.

JAXFit speeds up curve fitting on GPUs.

SurvMixClust clusters survival data and predicts individual survival curves.

Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in …

Develops a goodness-of-fit test for self-exciting processes.

Study evaluates policies in partially observable environments without full model specification.

In this paper, we propose the exponential Levy neural network (ELNN) for option pricing, which is a new non-parametric exponential Levy model using artificial neural networks (ANN). The ELNN fully integrates the ANNs with the exponential Levy model, a conventional pricing model. So, the ELNN can improve ANN-based model…

New algorithms improve spectral clustering for finite mixture models.

New method distinguishes cause from effect using causal velocity.

In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allo…

We present a non-parametric method to estimate the discount curve from market quotes based on the Moore-Penrose pseudoinverse. The discount curve reproduces the market quotes perfectly, has maximal smoothness, and is given in closed-form. The method is easy to implement and requires only basic linear algebra operations…

A flexible machine learning model infers the morphology of the Galactic Center Excess.

New mixture models for clustering and density estimation of unknown distributions.

Every curve can fit countless rhombuses.

We present an arbitrage-free non-parametric yield curve prediction model which takes the full (discretized) yield curve as state variable. We believe that absence of arbitrage is an important model feature in case of highly correlated data, as it is the case for interest rates. Furthermore, the model structure allows t…

New algorithm forecasts health indicators for better equipment lifespan prediction.

A new method constructs smooth, arbitrage-free option surfaces efficiently.

Overparameterized MLR fits hyper-curves, improving model robustness.

A novel Hawkes Process model captures order sizes in LOBs, improving fit quality and market impact studies.

New neural network models extreme value distributions with preserved shape constraints.

Study evaluates different mathematical models for three case studies using statistical fitting.

Develops a new method to model overlapping asymmetric datasets effectively.

The coefficient of determination, known as $R^2$, is commonly used as a goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fittin…

Estimates latent positions in 1D torus from noisy pairwise affinities.

We derive a variational model to fit a composite Bézier curve to a set of data points on a Riemannian manifold. The resulting curve is obtained in such a way that its mean squared acceleration is minimal in addition to remaining close the data points. We approximate the acceleration by discretizing the squared second o…

Bayesian approach improves uncertainty in deep learning models.

Improved MMD test for non-Euclidean data with spectral regularization.

The paper explains how to construct a credit spread curve from bond prices.

KBB algorithm reduces sample complexity for policy evaluation in general state spaces.

Traditionally in regression one minimizes the number of fitting parameters or uses smoothing/regularization to trade training (TE) and generalization error (GE). Driving TE to zero by increasing fitting degrees of freedom (dof) is expected to increase GE. However modern big-data approaches, including deep nets, seem to…

Estimates individualized treatment effects using shared RBF-net neurons.

Multivariate boosted trees improve forecasting and control by capturing correlated predictions.

Solves index problem for curved BGG sequences in parabolic geometry.

Using tax and census data, we demonstrate that the distribution of individual income in the USA is exponential. Our calculated Lorenz curve without fitting parameters and Gini coefficient 1/2 agree well with the data. From the individual income distribution, we derive the distribution function of income for families wi…

The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models …

Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic li…

Proposes a deep neural network for event intensity estimation.

Tropical geometry and weighted lattices improve curve and surface fitting.

Paper estimates optimal ROC curve arc length and AUC, improving classification performance.