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Saralees Nadarajah

Publications and source records attributed to Saralees Nadarajah.

At least 19 recordsLinked to original sources
arXiv2026

Near-Threshold Dynamics of \c{hi}c1(3872) and T+cc(3875) States via Effective Range Expansion and Monte Carlo Uncertainty Propagation

We investigate the near-threshold structure of the exotic tetraquark candidates $χ_{c1}(3872)$ and $T_{cc}^+(3875)$ using the effective-range expansion (ERE) and resonance compositeness relations. From the experimental masses and widths, we extract scattering lengths, effective ranges, and molecular compositeness coefficients within a two-channel framework, with uncertainties propagated through $N=50000$ Monte Carlo samples. We find large negative scattering lengths, $a=-8.49^{+0.93}_{-1.05}$ fm for $χ_{c1}(3872)$ and $a=-14.57^{+1.70}_{-1.66}$ fm for $T_{cc}^+$, and dominant molecular components $X_2>0.95$. The results are robust against variations of compositeness, experimental correlations, and the loop-function subtraction constant. An explicit ERE+CDD-pole analysis finds no physically reasonable bare-state contribution consistent with the observed poles and a natural background. Our results provide strong evidence for predominantly molecular structures of both states and demonstrate the robustness of the ERE approach to near-threshold exotic hadrons.

hep-ph↗
arXiv2026

Statistical Inference of Scattering Parameters for Exotic Hadronic States in the $J/ψ\,p$ Spectrum

We present a global two-channel Flatté amplitude analysis of the hidden-charm pentaquark candidates observed by the LHCb collaboration in the J/ψp invariant-mass spectrum using the Run 1+2 dataset. We simultaneously fit the three pentaquark amplitudes to the full spectrum, including a polynomial background and complex coupling phases. The scattering length and effective range are extracted from the fitted amplitudes using the effective range expansion, with uncertainties determined through non-parametric bootstrap resampling. While real couplings yield scattering parameters compatible with a molecular interpretation, this conclusion becomes less robust when coupling phases are included. We further find that interference effects are important for the closely spaced Pc(4440)+ and Pc(4457)+ states, demonstrating the limitations of the incoherent-sum approximation.

hep-ph↗
arXiv2026

Explicit Expressions for Multidimensional Value-at-Risk under Archimedean Copulas

This paper studies multivariate Value-at-Risk (VaR) for financial portfolios with a focus on modeling dependence structures through Archimedean copulas. Using the generator representation of Archimedean copulas, we derive explicit analytical expressions for the marginal lower-tail multivariate VaR in arbitrary dimensions. Closed-form formulas are obtained for several commonly used copula families, including Clayton, Frank, Gumbel-Hougaard, Joe and Ali--Mikhail--Haq copulas, allowing a direct assessment of the impact of dependence on multivariate risk. These results complement existing approaches, which largely rely on numerical or simulation-based methods, by providing tractable alternatives for theoretical and applied risk analysis. Monte Carlo simulations are conducted to evaluate the finite-sample performance of the proposed VaR estimator and to illustrate the role of different dependence structures. The proposed analytical setting offers transparent tools for multivariate risk measurement and systemic risk assessment.

stat.ME↗
arXiv2024

On the fractional integrals and derivatives of Bateman's matrix polynomials

The object of this paper is to investigate the certain results involving Bateman's matrix polynomials for integral index. We obtain some properties, integral representation and recurrence relations for hypergeometric matrix function. We introduce some matrix differential equations of the three order, integral transform and fractional integral formulas for hypergeometric matrix function by using the beta and Laplace transforms formula, Erdelyi Kober type fractional integral operators

math.GM↗
arXiv2024

New Modified Gamma and Beta Functions

This note introduces a new range of modified gamma and beta $k$ functions. The authors present new modified gamma and beta $k$-functions, first and second summation relations, various functionals, Mellin transforms, and integral representations. Furthermore, mean, variance and the moment generating function of a generalized beta distribution are obtained.

math.GM↗
arXiv2024

On Pólya's random walk constants

A celebrated result in probability theory is that a simple symmetric random walk on the $d$-dimensional lattice $\mathbb{Z}^d$ is recurrent for $d=1,2$ and transient for $d\geq 3$. In this note, we derive a closed-form expression, in terms of the Lauricella function $F_C$, for the return probability for all $d\geq3$. Previously, a closed-form formula had only been available for $d=3$.

math.PR↗
arXiv2023

Uniform convergence rates of skew-normal extremes

Let $M_n=\max \left(X_1, X_2, \ldots, X_n \right)$ denote the partial maximum of an independent and identically distributed skew-normal random sequence. In this paper, the rate of uniform convergence of skew-normal extremes is derived. It is shown that with optimal normalizing constants the convergence rate of $\left(M_{n}-b_n\right)/a_n$ to its ultimate extreme value distribution is proportional to $1/\log n$.

math.PR↗
arXiv2020

An n-dimensional Rosenbrock Distribution for MCMC Testing

The Rosenbrock function is an ubiquitous benchmark problem for numerical optimisation, and variants have been proposed to test the performance of Markov Chain Monte Carlo algorithms. In this work we discuss the two-dimensional Rosenbrock density, its current $n$-dimensional extensions, and their advantages and limitations. We then propose a new extension to arbitrary dimensions called the Hybrid Rosenbrock distribution, which is composed of conditional normal kernels arranged in such a way that preserves the key features of the original kernel. Moreover, due to its structure, the Hybrid Rosenbrock distribution is analytically tractable and possesses several desirable properties, which make it an excellent test model for computational algorithms.

stat.CO↗
arXiv2018

alphastable: An R Package for Modelling Multivariate Stable and Mixture of Symmetric Stable Distributions

The family of stable distributions received extensive applications in many fields of studies since it incorporates both the skewness and heavy tails. In this paper, we introduce a package written in the R language called alphastable. The alphastable performs a variety of tasks including: 1- generating random numbers from univariate, truncated, and multivariate stable distributions. 2- computing the probability density function of univariate and multivariate elliptically contoured stable distributions, 3- computing the distribution function of univariate stable distributions, 4- estimating the parameters of univariate symmetric stable, univariate Cauchy, mixture of Cauchy, mixture of univariate symmetric stable, multivariate elliptically contoured stable, and multivariate strictly stable distributions. This package, as it will be shown, is very useful for modelling data in univariate and multivariate cases that arise in the fields of finance and economics.

stat.CO↗
arXiv2018

An extension of Azzalini's method

The aim of this paper is to extend Azzalini's method. This extension is done in two stages: consider two dependent and non-identically distributed random variables say $X_1$ and $X_2$; model the dependence between $X_1$ and $X_2$ by a copula. To illustrate the new method, we assume $X_1$ and $X_2$ are exponential random variables. This assumption leads to a new distribution called the Generalized Weighted Exponential Distribution (GWED), a generalization of Gupta and Kundu (2009)'s Weighted Exponential Distribution (WED). Some mathematical properties of the GWED are derived, and its parameters estimated by maximum likelihood. The GWED is applied to biochemical data sets showing its good performance compared to the WED.

math.ST↗
arXiv2017

An alternative approach for compatibility of two discrete conditional distributions

Conditional specification of distributions is a developing area with increasing applications. In the finite discrete case, a variety of compatible conditions can be derived. In this paper, we propose an alternative approach to study the compatibility of two conditional probability distributions under the finite discrete setup. A technique based on rank-based criterion is shown to be particularly convenient for identifying compatible distributions corresponding to complete conditional specification including the case with zeros.The proposed methods are illustrated with several examples.

math.ST↗
arXiv2017

On some further properties and application of Weibull-R family of distributions

In this paper, we provide some new results for the Weibull-R family of distributions (Alzaghal, Ghosh and Alzaatreh (2016)). We derive some new structural properties of the Weibull-R family of distributions. We provide various characterizations of the family via conditional moments, some functions of order statistics and via record values.

math.ST↗
arXiv2012

Rates of convergence of extremes from skew normal samples

For a skew normal random sequence, convergence rates of the distribution of its partial maximum to the Gumbel extreme value distribution are derived. The asymptotic expansion of the distribution of the normalized maximum is given under an optimal choice of norming constants. We find that the optimal convergence rate of the normalized maximum to the Gumbel extreme value distribution is proportional to $1/\log n$.

stat.ME↗
arXiv2009

The distribution of the maximum of a first order moving average: the continuous case

We give the distribution of $M_n$, the maximum of a sequence of $n$ observations from a moving average of order 1. Solutions are first given in terms of repeated integrals and then for the case where the underlying independent random variables have an absolutely continuous density. When the correlation is positive, $$ P(M_n %\max^n_{i=1} X_i \leq x) =\ \sum_{j=1}^\infty β_{jx} ν_{jx}^{n} \approx B_{x} ν_{1x}^{n} $$ where %$\{X_i\}$ is a moving average of order 1 with positive correlation, and $\{ν_{jx}\}$ are the eigenvalues (singular values) of a Fredholm kernel and $ν_{1x}$ is the eigenvalue of maximum magnitude. A similar result is given when the correlation is negative. The result is analogous to large deviations expansions for estimates, since the maximum need not be standardized to have a limit. % there are more terms, and $$P(M_n <x) \approx B'_{x}\ (1+ν_{1x})^n.$$ For the continuous case the integral equations for the left and right eigenfunctions are converted to first order linear differential equations. The eigenvalues satisfy an equation of the form $$\sum_{i=1}^\infty w_i(λ-θ_i)^{-1}=λ-θ_0$$ for certain known weights $\{w_i\}$ and eigenvalues $\{θ_i\}$ of a given matrix. This can be solved by truncating the sum to an increasing number of terms.

stat.ME↗
arXiv2009

The distribution of the maximum of a first order moving average: the discrete case

We give the distribution of $M_n$, the maximum of a sequence of $n$ observations from a moving average of order 1. Solutions are first given in terms of repeated integrals and then for the case where the underlying independent random variables are discrete. When the correlation is positive, $$ P(M_n \max^n_{i=1} X_i \leq x) = \sum_{j=1}^\infty β_{jx} ν_{jx}^{n} \approx B_{x} r{1x}^{n} $$ where $\{ν_{jx}\}$ are the eigenvalues of a certain matrix, $r_{1x}$ is the maximum magnitude of the eigenvalues, and $I$ depends on the number of possible values of the underlying random variables. The eigenvalues do not depend on $x$ only on its range.

stat.ME↗
arXiv2009

Expansions for Quantiles and Multivariate Moments of Extremes for Distributions of Pareto Type

Let $X_{nr}$ be the $r$th largest of a random sample of size $n$ from a distribution $F (x) = 1 - \sum_{i = 0}^\infty c_i x^{-α- i β}$ for $α> 0$ and $β> 0$. An inversion theorem is proved and used to derive an expansion for the quantile $F^{-1} (u)$ and powers of it. From this an expansion in powers of $(n^{-1}, n^{-β/α})$ is given for the multivariate moments of the extremes $\{X_{n, n - s_i}, 1 \leq i \leq k \}/n^{1/α}$ for fixed ${\bf s} = (s_1, ..., s_k)$, where $k \geq 1$. Examples include the Cauchy, Student $t$, $F$, second extreme distributions and stable laws of index $α< 1$.

stat.ME↗