arXiv subjects

Dario Gasbarra

Publications and source records attributed to Dario Gasbarra.

16 recordsLinked to original sources

arXiv2026

Parameter Estimation for the Mixed Fractional Merton Jump Diffusion Model with EM Algorithm

This paper proposes an Expectation--Maximization algorithm with Metropolis--Hastings sampling for parameter estimation in a Mixed Fractional Merton Jump Diffusion model. The model combines fractional Brownian motion to capture long-range dependence with a compound Poisson jump process to describe abrupt movements in financial returns. The latent jump process is inferred during the E-step using Markov Chain Monte Carlo sampling, while the M-step updates the model parameters by maximizing the expected complete-data likelihood. The consistency and asymptotic normality of the proposed estimator are established under suitable regularity conditions. The methodology is applied to the Helsinki Stock Index (OMXH25). The proposed estimation framework provides a reliable and computationally efficient approach for modeling financial time series exhibiting both long-memory dependence and jump behavior.

stat.ME↗

arXiv2026

Is control of type I error rate needed in Bayesian clinical trial designs?

Practical employment of Bayesian trial designs is still rare. Even if accepted in principle, the regulators have commonly required that such designs be calibrated according to an upper bound for the frequentist type I error rate. This represents an internally inconsistent hybrid methodology, where important advantages from following the Bayesian principles are lost. In particular, all preplanned interim looks have an inflating multiplicity effect on type I error rate. To present an alternative approach, we consider the prototype case of a 2-arm superiority trial with dichotomous outcomes. The design is adaptive, using error control based on sequentially updated posterior probabilities, to conclude efficacy of the experimental treatment or futility of the trial. As gatekeepers for a proposed design, the regulators have the main responsibility in determining the parameters of the control of false positives, whereas the trial sponsors and investigators will have a natural role in specifying the criteria for stopping the trial due to futility. It is suggested that the traditional frequentist operating characteristics in the design, type I and type II error rates, be replaced, respectively, by Bayesian criteria called False Discovery Probability (FDP) and False Futility Probability (FFP), both terms corresponding directly to their probability interpretations. Importantly, the sequential error control during the data analysis based on posterior probabilities will satisfy these numerical criteria automatically, without need of preliminary computations before the trial is started. The method contains the option of applying a decision rule for terminating the trial early if the predicted costs from continuing would exceed the corresponding gains.

stat.ME↗

arXiv2026

Discrete-time, discrete-state multistate Markov models from the perspective of algebraic statistics

We study discrete-time, discrete-state multistate Markov models from the perspective of algebraic statistics. These models are widely studied in event history analysis, and are characterized by the state space, the initial distribution and the transition probabilities. A finite path under the multistate Markov model is a particular set of states occupied at finite time instances $\{1, \dots, n\}$. The main goal of this paper is to establish a bridge between event history analysis and algebraic statistics. The joint probabilities of finite paths in these models have a natural monomial parametrization in terms of the initial distribution and the transition probabilities. We study the polynomial relations among joint path probabilities. When the statistical constraints on the parameters are disregarded, nonhomogeneous multistate Markov models of arbitrary order can be viewed as slices of decomposable hierarchical models. This yields a complete description of their vanishing ideals as toric ideals generated by explicit families of binomials. Moreover, the variety of this vanishing ideal equals the nonhomogeneous multistate Markov model on the probability simplex. In contrast, homogeneous multistate Markov models exhibit different algebraic behavior, as time homogeneity imposes additional polynomial relations, leading to vanishing ideals that are strictly larger than in the nonhomogeneous case. We also derive families of binomial relations that vanish on homogeneous multistate Markov models. We investigate maximum likelihood estimation from statistical and algebraic perspectives. For nonhomogeneous models, classical and algebraic formulas agree; in the homogeneous case, the algebraic approach is more complex. Lastly, we provide data applications where we demonstrate the statistical theory to obtain the maximum likelihood estimates of the parameters under specific multistate Markov models.

math.ST↗

arXiv2025

Estimating Transition Rates in Two-State Non-Homogeneous Markov Jump Processes with Intermittent Observations: A Pseudo-Marginal McMC Approach via Honest Times

A possibly time-dependent transition intensity matrix or generator $(Q(t))$ characterizes the law of a Markov jump process (MP). For a time homogeneous MP, the transition probability matrix (TPM) can be expressed as a matrix exponential of $Q$. However, when dealing with a time non-homogeneous MP, there is often no simple analytical form of the TPM in terms of $Q(t)$, unless they all commute. This poses a challenge because when a continuous MP is observed intermittently, a TPM is required to build a likelihood. In this paper, we show that the estimation of the transition intensities of a two-state nonhomogeneous Markov model can be carried out by augmenting the intermittent observations with honest random times associated with two independent driving Poisson point processes, and that sampling the full path is not required. We propose a pseudo-marginal McMC algorithm to estimate the transition rates using the augmented data. Finally, we illustrate our approach by simulating a continuous MP and by using observed (intermittent) time grids extracted from real clinical visits data.

stat.ME↗

arXiv2025

Polynomial Stein operators: a noncommutative algebra perspective

In this paper, we make a novel connection between Stein's method and noncommutative algebra by viewing polynomial Stein operators (Stein operators with polynomial coefficients) as elements of the first Weyl algebra. Through this connection we study the algebraic structure of classes of polynomial Stein operators. In the case of the standard Gaussian distribution, we provide a complete description of the corresponding class of polynomial Stein operators by (i) identifying it as a vector space over $\mathbb{R}$ with an explicit given basis and (ii) by showing that this class is a principal right ideal of the first Weyl algebra generated by the classical Gaussian Stein operator $\partial -x$, with $\partial$ denoting the usual differential operator. We also study the characterising property of polynomial Stein operators for the standard Gaussian distribution, and give examples of general classes of polynomial Stein operators that are characterising, as well as classes that are not characterising unless additional distributional assumptions are made. By appealing to a standard property of Weyl algebras, we shown that the non-characterising property possessed by a wide class of polynomial Stein operators for the standard Gaussian distribution is a consequence of a general result that is perhaps surprising from a probabilistic perspective: the intersection between the class of polynomial Stein operators for any two target distributions with holonomic densities or holonomic characteristic functions is non-trivial.

math.PR↗

arXiv2022

An asymptotic approach to proving sufficiency of Stein characterisations

In extending Stein's method to new target distributions, the first step is to find a Stein operator that suitably characterises the target distribution. In this paper, we introduce a widely applicable technique for proving sufficiency of these Stein characterisations, which can be applied when the Stein operators are linear differential operators with polynomial coefficients. The approach involves performing an asymptotic analysis to prove that only one characteristic function satisfies a certain differential equation associated to the Stein characterisation. We use this approach to prove that all Stein operators with linear coefficients characterise their target distribution, and verify on a case-by-case basis that all polynomial Stein operators in the literature with coefficients of degree at most two are characterising. For $X$ denoting a standard Gaussian random variable and $H_p$ the $p$-th Hermite polynomial, we also prove, amongst other examples, that the Stein operators for $H_p(X)$, $p=3,4,\ldots,8$, with coefficients of minimal possible degree characterise their target distribution, and that the Stein operators for the products of $p=3,4,\ldots,8$ independent standard Gaussian random variables are characterising (in both settings the Stein operators for the cases $p=1,2$ are already known to be characterising). We leverage our Stein characterisations of $H_3(X)$ and $H_4(X)$ to derive characterisations of these target distributions in terms of iterated Gamma operators from Malliavin calculus, that are natural in the context of the Malliavin-Stein method.

math.PR↗

arXiv2022

On algebraic Stein operators for Gaussian polynomials

The first essential ingredient to build up Stein's method for a continuous target distribution is to identify a so-called \textit{Stein operator}, namely a linear differential operator with polynomial coefficients. In this paper, we introduce the notion of \textit{algebraic} Stein operators (see Definition \ref{def:algebraic-Stein-Operator}), and provide a novel algebraic method to find \emph{all} the algebraic Stein operators up to a given order and polynomial degree for a target random variable of the form $Y=h(X)$, where $X=(X_1,\dots, X_d)$ has i.i.d$.$ standard Gaussian components and $h\in \mathbb{K}[X]$ is a polynomial with coefficients in the ring $\mathbb{K}$. Our approach links the existence of an algebraic Stein operator with \textit{null controllability} of a certain linear discrete system. A \texttt{MATLAB} code checks the null controllability up to a given finite time $T$ (the order of the differential operator), and provides all \textit{null control} sequences (polynomial coefficients of the differential operator) up to a given maximum degree $m$. This is the first paper that connects Stein's method with computational algebra to find Stein operators for highly complex probability distributions, such as $H_{20}(X_1)$, where $H_p$ is the $p$-th Hermite polynomial. Some examples of Stein operators for $H_p(X_1)$, $p=3,4,5,6$, are gathered in the Appendix and many other examples are given in the Supplementary Information.

math.PR↗

arXiv2021

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Clinical trials are an instrument for making informed decisions based on evidence from well-designed experiments. Here we consider adaptive designs mainly from the perspective of multi-arm Phase II clinical trials, in which one or more experimental treatments are compared to a control. Treatment allocation of individual trial participants is assumed to take place according to a fixed block randomization, albeit with an important twist: The performance of each treatment arm is assessed after every measured outcome, in terms of the posterior distribution of a corresponding model parameter. Different treatments arms are then compared to each other, according to pre-defined criteria and using the joint posterior as the basis for such assessment. If a treatment is found to be sufficiently clearly inferior to the currently best candidate, it can be closed off either temporarily or permanently from further participant accrual. The latter possibility provides a method for adaptive treatment selection, including early stopping of the trial. The main development in the paper is in terms of binary outcomes, but some extensions, notably for handling time-to-event data, are discussed as well. The presentation is to a large extent comparative and expository.

stat.ME↗

arXiv2019

Estimation of Viterbi path in Bayesian hidden Markov models

The article studies different methods for estimating the Viterbi path in the Bayesian framework. The Viterbi path is an estimate of the underlying state path in hidden Markov models (HMMs), which has a maximum posterior probability (MAP). For an HMM with given parameters, the Viterbi path can be easily found with the Viterbi algorithm. In the Bayesian framework the Viterbi algorithm is not applicable and several iterative methods can be used instead. We introduce a new EM-type algorithm for finding the MAP path and compare it with various other methods for finding the MAP path, including the variational Bayes approach and MCMC methods. Examples with simulated data are used to compare the performance of the methods. The main focus is on non-stochastic iterative methods and our results show that the best of those methods work as well or better than the best MCMC methods. Our results demonstrate that when the primary goal is segmentation, then it is more reasonable to perform segmentation directly by considering the transition and emission parameters as nuisance parameters.

stat.CO↗

arXiv2019

New moments criteria for convergence towards normal product/tetilla laws

In the framework of classical probability, we consider the normal product distribution $F_\infty \sim N_1 \times N_2$ where $N_1, N_2$ are two independent standard normal random variable, and in the setting of free probability, $F_\infty \sim \left( S_1 S_2 + S_2 S_1 \right)/\sqrt{2}$ known as {\it tetilla law} \cite{d-n}, where $S_1, S_2$ are freely independent normalized semicircular random variables. We provide novel characterization of $F_\infty$ within the second Wiener (Wigner) chaos. More precisely, we show that for any generic element $F$ in the second Wiener (Wigner) chaos with variance one the laws of $F$ and $F_\infty$ match if and only if $μ_4 (F)= 9 \, (\mbox{resp. }φ(F^4)=2.5)$, and $μ_{2r}(F)= ((2r-1)!!)^2 \, (\mbox{resp. }φ(F^{2r})=φ(F^{2r}_\infty ))$ for some $r \ge 3$, where $μ_r (F)$ stands for the $r$th moment of the random variable $F$, and $φ$ is the relevant tracial state. We use our moments characterization to study the non central limit theorems within the second Wiener (Wigner) chaos and the target random variable $F_\infty$. Our results generalize the findings in Nourdin \& Poly \cite{n-p-2w}, Azmoodeh, et. al \cite{a-p-p} in the classical probability, and of Deya \& Nourdin \cite{d-n} in the free probability setting.

math.PR↗

arXiv2018

On a new Sheffer class of polynomials related to normal product distribution

Consider a generic random element $F_\infty= \sum_{\text{finite}} λ_k (N^2_k -1)$ in the second Wiener chaos with a finite number of non-zero coefficients in the spectral representation where $(N_k)_{k \ge 1}$ is a sequence of i.i.d $\mathscr{N}(0,1)$. Using the recently discovered (see Arras et al. \cite{a-a-p-s-stein}) stein operator $\RR_\infty$ associated to $F_\infty$, we introduce a new class of polynomials $$\PP_\infty:= \{ P_n = \RR^n_\infty \textbf{1} \, : \, n \ge 1 \}.$$ We analysis in details the case where $F_\infty$ is distributed as the normal product distribution $N_1 \times N_2$, and relate the associated polynomials class to Rota's {\it Umbral calculus} by showing that it is a \textit{Sheffer family} and enjoys many interesting properties. Lastly, we study the connection between the polynomial class $\PP_\infty$ and the non-central probabilistic limit theorems within the second Wiener chaos.

math.PR↗

arXiv2017

Eigenvalues of random matrices with isotropic Gaussian noise and the design of Diffusion Tensor Imaging experiments

Tensor-valued and matrix-valued measurements of different physical properties are increasingly available in material sciences and medical imaging applications. The eigenvalues and eigenvectors of such multivariate data provide novel and unique information, but at the cost of requiring a more complex statistical analysis. In this work we derive the distributions of eigenvalues and eigenvectors in the special but important case of $m \times m$ symmetric random matrices, $D$, observed with isotropic matrix-variate Gaussian noise. The properties of these distributions depend strongly on the symmetries of the mean tensor/matrix, $\bar D$. When $\bar D$ has repeated eigenvalues, the eigenvalues of $D$ are not asymptotically Gaussian, and repulsion is observed between the eigenvalues corresponding to the same $\bar D$ eigenspaces. We apply these results to diffusion tensor imaging (DTI), with $m=3$, addressing an important problem of detecting the symmetries of the diffusion tensor, and seeking an experimental design that could potentially yield an isotropic Gaussian distribution. In the 3-dimensional case, when the mean tensor is spherically symmetric and the noise is Gaussian and isotropic, the asymptotic distribution of the first three eigenvalue central moment statistics is simple and can be used to test for isotropy. In order to apply such tests, we use quadrature rules of order $t \ge 4$ with constant weights on the unit sphere to design a DTI-experiment with the property that isotropy of the underlying true tensor implies isotropy of the Fisher information. We also explain the potential implications of the methods using simulated DTI data with a Rician noise model.

stat.ME↗

arXiv2016

Convergence rate for the hedging error of a path-dependent example

We consider a Brownian functional $F=g\bigl(\int_0^T η(s) dW_s\bigr)$ with $g \in L_2(γ)$ and a singular deterministic $η$. We deduce the $L_2$-convergence rate for the approximation $F^{(n)} = E F + \int_0^T ϕ^{(n)}(s) dW_s$ for a class of piecewise constant predictable integrands $ϕ^{(n)}$ from the fractional smoothness of $g$ quantified by Besov spaces and the rate of singularity of $η$.

math.PR↗

arXiv2015

Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm

This paper presents a fast computational method, the Expectation Maximization algorithm, for Maximum Likelihood (ML) estimation in diffusion tensor imaging under the Rice noise model. We further extend the ML framework to the maximum a posterior (MAP) estimation and describe the numerical similarities of both ML and MAP estimators. This novel method is implemented and applied using both synthetic and real data in a wide range of b amplitudes. The comparison with other popular methods are made in accuracy, methodology and computation.

stat.CO↗

arXiv2014

Data augmentation in Rician noise model and Bayesian Diffusion Tensor Imaging

Mapping white matter tracts is an essential step towards understanding brain function. Diffusion Magnetic Resonance Imaging (dMRI) is the only noninvasive technique which can detect in vivo anisotropies in the 3-dimensional diffusion of water molecules, which correspond to nervous fibers in the living brain. In this process, spectral data from the displacement distribution of water molecules is collected by a magnetic resonance scanner. From the statistical point of view, inverting the Fourier transform from such sparse and noisy spectral measurements leads to a non-linear regression problem. Diffusion tensor imaging (DTI) is the simplest modeling approach postulating a Gaussian displacement distribution at each volume element (voxel). Typically the inference is based on a linearized log-normal regression model that can fit the spectral data at low frequencies. However such approximation fails to fit the high frequency measurements which contain information about the details of the displacement distribution but have a low signal to noise ratio. In this paper, we directly work with the Rice noise model and cover the full range of $b$-values. Using data augmentation to represent the likelihood, we reduce the non-linear regression problem to the framework of generalized linear models. Then we construct a Bayesian hierarchical model in order to perform simultaneously estimation and regularization of the tensor field. Finally the Bayesian paradigm is implemented by using Markov chain Monte Carlo.

stat.CO↗

arXiv2011

Initial Enlargement in a Markov chain market model

Enlargement of filtrations is a classical topic in the general theory of stochastic processes. This theory has been applied to stochastic finance in order to analyze models with insider information. In this paper we study initial enlargement in a Markov chain market model, introduced by R. Norberg. In the enlargened filtration several things can happen: some of the jumps times can be accessible or predictable, but in the orginal filtration all the jumps times are totally inaccessible. But even if the jumps times change to accessible or predictable, the insider does not necessarily have arbitrage possibilities.

q-fin.TR↗