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Frédéric Ouimet

Publications and source records attributed to Frédéric Ouimet.

At least 19 recordsLinked to original sources

Overlap distributions under the Gibbs measure of a Haar model of $\log|ζ|$ on and near the critical line

We study the Gibbs measure of $H(h) = \Re\sum_{p^{j} \leq X}U_{p}^{j}/(j p^{j(σ+ i h)})$, a random field modeling large values of the log-modulus of the Riemann zeta function on and near the critical line, where the $U_{p}$ are independent and uniformly distributed on the complex unit circle. Writing $t = \log \log T$, the cutoff $X$ and real part $σ$ may vary with $T$ subject to $T^{1/t^Φ} \leq X \leq T^{1/t^ϕ}$ and $0 \leq σ- 1/2 \leq C_σt^Φ/\log T$, for fixed $Φ\geq ϕ> 1/2$ and $C_σ > 0$. On $I_θ = [ - (\log T)^θ,(\log T)^θ]$, define the Gibbs measure $G_{β,σ,T}^{\mathrm{H}}(\mathrm{d}h) := e^{βH(h)}\mathrm{d}h/\int_{I_θ}e^{βH(h')}\mathrm{d}h'$. For every fixed $θ> - 1$ and inverse temperature $β> 0$, the limiting joint distributions of correlations (overlaps) among points sampled from this measure are determined after averaging over the Haar phases. At and below $β_{c}(θ) = 2\sqrt{1 + (θ\vee 0)}$, the limiting two-overlap distribution is concentrated at $q_{0} = |θ\wedge 0|$. Above this threshold, it assigns masses $β_{c}(θ)/β$ and $1 - β_{c}(θ)/β$ to $q_{0}$ and $1$, respectively, and the limiting joint overlap distribution is described by a Poisson-Dirichlet process of parameter $β_{c}(θ)/β$. Approximate Ghirlanda-Guerra identities are shown to hold uniformly for bounded deterministic functions of the sampled positions. This paper is the first in a series devoted to extending these results to $\log|ζ|$ near the critical line.

math.PR↗

Spectral Analysis of Gaussian Integral Operators Arising in BHEP Tests

The Baringhaus-Henze-Epps-Pulley (BHEP) tests for multivariate normality are affine-invariant goodness-of-fit tests based on a Gaussian-weighted $L^2$ distance between empirical and Gaussian characteristic functions. In 1990, Henze and Zirkler expressed the limiting null distribution through the eigenvalues of an integral operator on the standard Gaussian space. In 1997, Henze and Wagner obtained a simpler covariance kernel and raised the problem of calculating the eigenvalues of the resulting operator on a Gaussian-weighted space. Although subsequent work treated the univariate case and numerical approximations in a few low dimensions, the complete all-dimensional spectral problem remained open. This paper determines both complete spectra for every dimension $d \in \mathbb{N}$ and every smoothing parameter $β> 0$. The two operators are shown to have the forms $\mathcal{X}_{β,d}^*\mathcal{X}_{β,d}$ and $\mathcal{X}_{β,d}\mathcal{X}_{β,d}^*$ for the same Hilbert-Schmidt operator $\mathcal{X}_{β,d}$. Consequently, their nonzero eigenvalues agree, including multiplicities, while the null space of the Henze-Zirkler operator is identified exactly. The Gaussian integral operator in the Henze-Wagner decomposition is diagonalized by Mehler's formula, and rotational symmetry confines the finite-rank correction to the sectors associated with spherical harmonics of degrees $0$, $1$, and $2$. The degree-$1$ and degree-$2$ eigenvalues are characterized by scalar transcendental equations, and the radial eigenvalues by an explicit pole-safe Fredholm determinant. The paper establishes nonnegativity, multiplicities, eigenfunction reconstruction, completeness, the trace identity, and a complete characterization of all exceptional pole cases.

math.ST↗

Strict Total Positivity of a Gauss Hypergeometric Kernel, and the Sharp Threshold for the One-Dimensional $SL(2,\mathbb{R})$ Conformal Block

In this paper, we completely resolve the two total positivity problems raised by Li [J. High Energy Phys., 2023(7):Paper No. 167, 44 pp., 2023], thereby determining the precise scope of this positivity structure in the one-dimensional conformal bootstrap. First, the Gauss hypergeometric kernel $\mathcal{F}(Δ,z) = {}_2F_1(Δ,Δ;2Δ;z)$ is proved to be strictly totally positive of order infinity for $Δ> 0$ and $z \in (0,1)$. Second, the sharp lower $Δ$-parameter threshold for the associated one-dimensional $SL(2,\mathbb{R})$ conformal block $G_Δ(z) = z^Δ\mathcal{F}(Δ,z)$ is shown to be $1/2$: the conformal-block kernel $G_Δ(z)$ is strictly totally positive of order infinity for $Δ\geq 1/2$. For every $τ\in (0,1/2)$, there exists a strictly negative odd-order minor of $G_Δ(z)$ such that all its $Δ$-values are in $(τ,1/2)$ and all its $z$-values can be chosen arbitrarily close to one. Consequently, no restriction $z > z_0$ with $z_0 < 1$ can restore total positivity of order infinity over all $Δ> 0$.

math.CA↗

Bernstein-smoothed estimation and bootstrap inference for the lower-tail Spearman's rho curve

This paper studies a Bernstein-smoothed plug-in estimator for the lower-tail Spearman's rho curve, a rank-based measure of local concordance defined through a normalized copula integral over the lower-left square $[0,p]^2$. The estimator applies the lower-tail Spearman functional to the empirical Bernstein copula and introduces a degree parameter that controls finite-sample regularization. The contribution is target-specific estimation and inference for the lower-tail Spearman's rho curve rather than a new general-purpose copula estimator. We establish uniform strong consistency on compact intervals away from zero whenever $m \to \infty$. Under a target-specific integrated Bernstein-bias condition and $\sqrt n/m \to 0$, we derive functional weak convergence with the same first-order Gaussian limit as the empirical copula-based estimator; this degree regime includes $m = \lfloor n^{2/3}\rfloor$. For inference, we justify a smoothed beta bootstrap based on sampling from the empirical beta copula and construct pointwise confidence intervals from the absolute centered bootstrap root. Monte Carlo experiments for the Farlie--Gumbel--Morgenstern, Gaussian, Clayton, and Frank copulas assess pointwise coverage over the threshold grid and show that Bernstein smoothing generally reduces integrated variance and often lowers mean integrated squared error under weak to moderate dependence. Additional common-sample experiments compare the proposed estimator with the empirical beta and empirical checkerboard Bernstein copula estimators in terms of pointwise error, integrated error, computation time, selected degrees, numerical stability, and pointwise coverage. A sensitivity analysis shows that $m = \lfloor n^{2/3}\rfloor$ is a simple theoretically admissible default that avoids the most severe oversmoothing. A descriptive application to the Loss--ALAE insurance claims data illustrates our method.

math.ST↗

Minimax properties of gamma kernel density estimators under $L^p$ loss and $β$-Hölder smoothness of the target

This paper considers the asymptotic behavior in $β$-Hölder spaces, and under $L^p$ loss, of the non-modified gamma kernel density estimator introduced by Chen (Annals of the Institute of Statistical Mathematics, 52, 471-480, 2000) for the analysis of nonnegative data, in the situation where the target may have a finite effective or true upper endpoint but the estimator itself is left untruncated and treats the support as $[0,\infty)$. The finite endpoint is an analytical device in the definition of the function class and the risk, not information supplied to the estimator. The functional classes are chosen so that the target density matches smoothly to zero at the upper endpoint, which isolates the behavior at the origin and avoids additional upper-endpoint leakage bias. The estimator attains the minimax rate for $(p,β)\in [1,3)\times(0,2]$ and part of $[3,4)\times(0,2]$, but fails for $p\geq 4$ or $β>2$, leaving a small region unresolved.

math.ST↗

Results related to the Gaussian product inequality conjecture for mixed-sign exponents in arbitrary dimension

This paper studies Gaussian product inequalities (GPIs) for centered Gaussian random vectors when positive and negative exponents appear simultaneously. We prove a quantitative lower bound for arbitrary mixed-sign patterns, conditional on the validity of a lower-dimensional GPI, and an upper bound for products of negative powers with convex functions of the remaining Gaussian components. The latter yields an explicit upper bound for mixed-sign products whenever the total positive-side exponent is at least $1/2$.

math.PR↗

A two-sample test for symmetric positive definite matrix distributions using Wishart kernel density estimators

We develop a nonparametric two-sample test for distributions supported on the cone of symmetric positive definite matrices. The procedure relies on the Wishart kernel density estimator (KDE) introduced by Belzile et al. (2026), whose support-adaptive kernel alleviates boundary bias by remaining confined to the cone. Our test statistic is the rescaled integrated squared difference between two Wishart KDEs and can be expressed as a two-sample $V$-statistic via an explicit closed-form overlap of Wishart kernels, avoiding numerical integration. Under the null hypothesis of equal densities, we derive the asymptotic distribution in both the common shrinking-bandwidth and fixed-bandwidth regimes. The proposed method provides a kernel-based competitor to the empirical Laplace-transform two-sample test of Lukić (2024). Unlike the orthogonally invariant Hankel-transform test of Lukić and Milošević (2024), our statistic can detect alternatives that differ only through eigenvector structure, for instance, Wishart models with the same shape parameter and the same scale eigenvalues but different orientations. A comparative simulation study examines the empirical Type I error rates and power of the proposed and Laplace-transform tests.

math.ST↗

On a conjecture of Lamkin and Tkocz: Log-convexity of moments of Bernoulli sample means

Let $X_1,X_2,\ldots$ be independent $\mathrm{Bernoulli}(θ)$ random variables, and let $\bar X_n = n^{-1}(X_1 + \cdots + X_n)$. We prove that, for every real $p \geq 1$, the sequence $\{\mathsf{E}(\bar X_n^p)\}_{n \geq 1}$ is log-convex. This proves the Bernoulli case of a conjecture of Lamkin and Tkocz [Canad. Math. Bull., 65(2):271-278, 2022]. The proof conditions on the total number of successes among $2n$ trials and reduces the desired inequality to a convex-order comparison between two normalized quadratic functions of hypergeometric random variables. All the log-convexity inequalities are strict for $p > 1$ and $0 < θ< 1$.

math.PR↗

Stein's method for the matrix normal distribution

This work presents the first systematic development of Stein's method for matrix distributions. We establish the basic essential ingredients of Stein's method for matrix normal approximation: we derive an extended-generator-based Stein identity from a matrix Ornstein-Uhlenbeck diffusion with two-sided scales, provide an explicit semigroup representation for the solution of the Stein equation, and obtain regularity estimates for the solution. The new methodology is demonstrated in three examples: (i) smooth Wasserstein distance bounds to quantify the matrix central limit theorem (a didactic example), (ii) a Wasserstein distance bound for the matrix normal approximation of the centered matrix $T$ distribution, and (iii) a Stein's method-of-moments approach to estimating the row and column covariance factors of the matrix normal, yielding a flexible class of weighted flip-flop Stein estimators that generalize Dutilleul's classical flip-flop algorithm and naturally accommodate row/column importance weights, systematic missingness, and projection onto structured covariance families. The latter two examples are intrinsically matrix-valued and cannot be treated using naive vectorization.

math.ST↗

A proof of the strong Gaussian product inequality conjecture

Let $\boldsymbol{X} = (X_1,\ldots,X_n)$ be a centered Gaussian vector, not necessarily nondegenerate. It is proved that, for every $α_1,\ldots,α_n > 0$, \[ \mathsf{E}\left[\prod_{i=1}^n |X_i|^{α_i}\right] \geq \prod_{i=1}^n \mathsf{E}\left[|X_i|^{α_i}\right]. \] When all marginal variances are positive, equality holds if and only if the coordinates are independent. This settles Frenkel's 18-year-old Gaussian product inequality (GPI) conjecture and, in fact, its later strengthening to arbitrary positive exponents. Through Frenkel's hafnian formulation, this result also provides a short proof of the 28-year-old real linear polarization constant conjecture, which was settled this year as a consequence of the strong polarization inequality. The main result leads to the exact real linear polarization constant, a sharp weighted product inequality for real linear functionals, spherical moment bounds, hafnian inequalities for positive-semidefinite matrices together with complete characterizations of their equality cases, logarithmic variance and covariance inequalities, a Rényi total-correlation certificate, and unconditional versions of mixed-sign Gaussian moment bounds previously conditional on the positive-exponent GPI.

math.PR↗

Bounds for the median of the generalized hyperbolic and related distributions

We prove monotonicity properties for medians of gamma sums and differences of the form $Z_α = αX_1 + (2 - α)X_2$, where $X_1$ and $X_2$ are independent gamma random variables with common shape parameter. By combining these monotonicity properties with known bounds for the median of the gamma distribution, we establish sharp bounds for the median of the variance-gamma and McKay Type I distributions. Also, by exploiting the normal variance-mean mixture representation of the generalized hyperbolic distribution together with bounds for a ratio of modified Bessel functions of the second kind, we obtain sharp bounds for the median of the generalized hyperbolic distribution. We thus resolve all five conjectures of Gaunt and Merkle (2021). As a by-product of our analysis, we show that the variance-gamma distributions with positive asymmetry parameter and the McKay Type I distributions satisfy the ``mode-median-mean'' inequality for all admissible parameter values, and that the same is true of the generalized hyperbolic distribution with positive asymmetry parameter.

math.PR↗

Log-convexity and log-concavity of noncentral gamma sums and differences

We study log-convexity and log-concavity of densities obtained from sums and differences of two independent noncentral gamma random variables. We give a complete classification of one-sided log-convexity for noncentral gamma differences, a complete log-convexity classification for sums of two independent central gamma random variables, and sharp log-concavity criteria for central differences and for common-scale sums. As special cases, we deduce a log-convexity classification for the density of the product of two correlated normal random variables with arbitrary means and variances, and log-convexity and log-concavity classifications for the densities of the variance-gamma and McKay Type I distributions.

math.PR↗

On the Dirichlet-kernel Gasser--Müller estimator and its competitors for fixed design regression on the simplex

A Dirichlet-kernel Gasser-Müller (D-GM) estimator is introduced for fixed design regression on the simplex, extending the univariate analog due to Chen [Statist. Sinica, vol. 10(1) (2000), pp. 73-91]. Its pointwise bias and variance, asymptotic normality, and mean integrated squared error are investigated. Some simulation experiments are conducted to compare its small-sample performance with that of two recently proposed alternatives: the Dirichlet-kernel Nadaraya-Watson (D-NW) and local linear (D-LL) estimators. The simulation results reveal that the D-LL estimator is best among the D-LL, D-NW, and D-GM estimators and that the proposed D-GM estimator is worst. A real data analysis is also reported for the GEMAS dataset to analyze the relationship between soil composition and pH levels across various agricultural and grazing lands in Europe.

math.ST↗

On Wilks' problem: Exact recursive formulas via Stein's method for the joint moments of disjoint principal minors of Wishart random matrices

In a 1934 Annals of Mathematics paper, Samuel S. Wilks posed the problem of computing joint moments of disjoint principal minors of Wishart random matrices, describing the general case as ``extremely complicated.'' These moments arise in classical multivariate statistics, including multivariate regression, analysis of variance, generalized correlation coefficients, and likelihood ratio testing. We solve Wilks' problem for nonnegative integer exponents and any finite number of disjoint principal minors using Stein's method, specifically the Wishart Stein characterization recently developed by Bailly et al. (2026). The Wishart Stein identity yields a degree-lowering recursion that terminates after finitely many steps and evaluates the desired moment exactly. Numerical experiments validate our recursive formulas against direct Monte Carlo simulations from the Wishart distribution.

math.PR↗

Stein's method for the symmetric matrix normal distribution with an application to the approximation of the Wishart law

In this paper, we extend Stein's method to the symmetric matrix normal distribution. In particular, we obtain a Stein characterization of the symmetric matrix normal distribution involving the extended generator of the symmetric matrix Ornstein-Uhlenbeck process, present a semigroup representation of the solution of the corresponding Stein equation, and establish regularity estimates for the solution. This framework of Stein's method for symmetric matrix normal approximation complements the recent theory of Stein's method for matrix normal approximation, and we make an explicit connection between these frameworks. We apply this theory to derive a Wasserstein distance bound for the symmetric matrix normal approximation of the Wishart distribution.

math.PR↗

Dirichlet kernel density estimation on the simplex with missing data

Nonparametric density estimation for compositional data supported on the simplex is examined under a missing at random mechanism. Rather than imputing missing values and estimating the density from a completed data set, we adopt a strategy based on inverse probability weighting. The proposed estimator uses an adaptive Dirichlet kernel, which ensures nonnegativity on the simplex and favorable behavior near the boundary. When the observation probabilities are unknown, they are estimated through a Nadaraya-Watson regression step. The large-sample properties of the estimator are derived, including pointwise bias and variance expansions, optimal smoothing rates, and asymptotic normality. A simulation study investigates its finite-sample performance under varying sample sizes and missing rates. Simulations show our method outperforms inverse-probability-weighted kernel density estimators based on additive and isometric log-ratio transformations of the data for certain target densities. The methodology is further illustrated through an application to leukocyte composition data from the National Health and Nutrition Examination Survey (NHANES), which allows for the identification of the modal immune profile in the sampled population.

stat.ME↗

Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices

A Wishart kernel density estimator (KDE) is introduced for density estimation in the cone of positive definite matrices. The estimator is boundary-aware and mitigates the boundary bias suffered by conventional KDEs, while remaining simple to implement. Its mean squared error, uniform strong consistency on expanding compact sets, and asymptotic normality are established under the Lebesgue measure and suitable mixing conditions. This work represents the first study of density estimation for dependent data on this space under any metric. For independent observations, an asymptotic upper bound on the mean absolute error is also derived. A simulation study compares the performance of the Wishart KDE with that of the log-Gaussian KDE, another boundary-aware estimator based on the matrix-variate lognormal distribution proposed by Schwartzman [Int. Stat. Rev., 2016, 84(3), 456--486], and with the naive Gaussian KDE on the ambient Euclidean space. When estimating the stationary marginal density of a Wishart autoregressive process for several autoregressive coefficient matrices and innovation covariance matrices, the Wishart KDE exhibits the best overall accuracy and stability. The practical utility of the Wishart KDE is illustrated by estimating the marginal density of a one-year time series of realized covariance matrices computed from 5-minute intra-day returns on Amazon Corp. shares and on the Standard & Poor's 500 exchange-traded fund. All code is publicly available via the R package ksm to facilitate implementation of the method and reproducibility of the findings.

stat.ME↗

Stein's method for the Wishart distribution

In this work, we develop Stein's method for the Wishart distribution on the cone of positive definite matrices. We establish the basic ingredients of a Wishart Stein framework: we derive an extended-generator-based Stein characterization from the Wishart diffusion process, identify the corresponding transition semigroup through the noncentral Wishart law, provide an explicit semigroup representation for the solution of the Stein equation, and obtain regularity estimates for the solution. The new methodology is demonstrated in four applications: (i) an order $n^{-1}$ bound, for smooth test functions, for the Wishart approximation of uncentered group-mean scatter matrices in MANOVA; (ii) a quantitative multivariate Satterthwaite approximation; (iii) local/integrated De Bruijn identities and logarithmic Sobolev inequalities for the Wishart measure; and (iv) Stein's method of moments for the shape and scale parameters, including structured scale estimation.

math.PR↗