Latest in stat.th

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Maximum Likelihood Estimation of Toric Fano VarietiesMay 17 2019We study the maximum likelihood estimation problem for several classes of toric Fano models. We start by exploring the maximum likelihood degree for all 2-dimensional Gorenstein toric Fano varieties. We show that the ML degree is equal to the degree of ... More
Pair Matching: When bandits meet stochastic block modelMay 17 2019The pair-matching problem appears in many applications where one wants to discover good matches between pairs of individuals. Formally, the set of individuals is represented by the nodes of a graph where the edges, unobserved at first, represent the good ... More
Analytic Basis Expansions for Functional SnippetsMay 16 2019Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean ... More
NANUQ: A method for inferring species networks from gene trees under the coalescent modelMay 16 2019Species networks generalize the notion of species trees to allow for hybridization or other lateral gene transfer. Under the Network Multispecies Coalescent Model, individual gene trees arising from a network can have any topology, but arise with frequencies ... More
Simplicial splines for representation of density functionsMay 16 2019In the context of functional data analysis, probability density functions as non-negative functions are characterized by specific properties of scale invariance and relative scale which enable to represent them with the unit integral constraint without ... More
Stochastic precedence and minima among dependent variables. A study based on the multivariate conditional hazard ratesMay 16 2019The notion of stochastic precedence between two random variables emerges as a relevant concept in several fields of applied probability. When one consider a vector of random variables $X_1,...,X_n$, this notion has a preeminent role in the analysis of ... More
Exact relaxation dynamics and quantum information scrambling in multiply quenched harmonic chainsMay 16 2019The quantum dynamics of isolated systems under quench condition exhibits a variety of interesting features depending on the integrable/chaotic nature of system. We study the exact dynamics of trivially integrable system of harmonic chains under a multiple ... More
When random initializations help: a study of variational inference for community detectionMay 16 2019Variational approximation has been widely used in large-scale Bayesian inference recently, the simplest kind of which involves imposing a mean field assumption to approximate complicated latent structures. Despite the computational scalability of mean ... More
Adaptive estimation in the linear random coefficients model when regressors have limited variationMay 16 2019We consider a linear model where the coefficients-intercept and slopes-are random and independent from regressors which support is a proper subset. When the density has finite weighted L 2 norm, for well chosen weights, the joint density of the random ... More
Moment-based Estimation of Mixtures of Regression ModelsMay 15 2019Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, ... More
Compound Dirichlet ProcessesMay 15 2019The compound Poisson process and the Dirichlet process are the pillar structures of Renewal theory and Bayesian nonparametric theory, respectively. Both processes have many useful extensions to fulfill the practitioners needs to model the particularities ... More
Transfer Entropy in Continuous TimeMay 15 2019Transfer entropy (TE) was introduced by Schreiber in 2000 as a measurement of the predictive capacity of one stochastic process with respect to another. Originally stated for discrete time processes, we expand the theory of TE to stochastic processes ... More
Fractal structure in Yang-Mills fields and non extensivityMay 15 2019Scaling properties of Yang-Mills fields are used to show that fractal structures are expected to be present in system described by those theories. We show that the fractal structure leads to recurrence formulas that allow the determination of non perturbative ... More
Quantum chaos challenges many-body localizationMay 15 2019Characterizing states of matter through the lens of their ergodic properties is a fascinating new direction of research. In the quantum realm, the many-body localization (MBL) was proposed to be the paradigmatic nonergodic phenomenon, which extends the ... More
Which principal components are most sensitive to distributional changes?May 15 2019PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensitivity ... More
Revisiting High Dimensional Bayesian Model Selection for Gaussian RegressionMay 15 2019Model selection for regression problems with an increasing number of covariates continues to be an important problem both theoretically and in applications. Model selection consistency and mean structure reconstruction depend on the interplay between ... More
A New Confidence Interval for the Mean of a Bounded Random VariableMay 15 2019We present a new method for constructing a confidence interval for the mean of a bounded random variable from samples of the random variable. We conjecture that the confidence interval has guaranteed coverage, i.e., that it contains the mean with high ... More
Robust change point tests by bounded transformationsMay 15 2019Classical moment based change point tests like the cusum test are very powerful in case of Gaussian time series with one change point but behave poorly under heavy tailed distributions and corrupted data. A new class of robust change point tests based ... More
Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurementsMay 15 2019We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least-squares objective, with a penalty given by the ... More
Information criteria for non-normalized modelsMay 15 2019Many statistical models are given in the form of non-normalized densities with an intractable normalization constant. Since maximum likelihood estimation is computationally intensive for these models, several estimation methods have been developed which ... More
Measuring Bayesian Robustness Using Rényi's Divergence and Relationship with Prior-Data ConflictMay 15 2019This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two different classes of priors in the neighbourhood of the elicited prior are considered. The first one is the well-known $\epsilon$-contaminated class, while ... More
Minimax rates of estimation for smooth optimal transport mapsMay 14 2019Brenier's theorem is a cornerstone of optimal transport that guarantees the existence of an optimal transport map $T$ between two probability distributions $P$ and $Q$ over $\mathbb{R}^d$ under certain regularity conditions. The main goal of this work ... More
Approximation of Optimal Transport problems with marginal moments constraintsMay 14 2019Optimal Transport (OT) problems arise in a wide range of applications, from physics to economics. Getting numerical approximate solution of these problems is a challenging issue of practical importance. In this work, we investigate the relaxation of the ... More
Sample Efficient Toeplitz Covariance EstimationMay 14 2019May 15 2019We study the sample complexity of estimating the covariance matrix $T$ of a distribution $\mathcal{D}$ over $d$-dimensional vectors, under the assumption that $T$ is Toeplitz. This assumption arises in many signal processing problems, where the covariance ... More
Sample Efficient Toeplitz Covariance EstimationMay 14 2019We study the query complexity of estimating the covariance matrix $T$ of a distribution $\mathcal{D}$ over $d$-dimensional vectors, under the assumption that $T$ is Toeplitz. This assumption arises in many signal processing problems, where the covariance ... More
Perturbative post-quench overlaps in Quantum Field TheoryMay 14 2019In analytic descriptions of quantum quenches, the overlaps between the initial pre-quench state and the eigenstates of the time evolving Hamiltonian are crucial ingredients. We construct perturbative expansions of these overlaps in quantum field theories ... More
Multivariate Ranks and Quantiles using Optimal Transportation and Applications to Goodness-of-fit TestingMay 14 2019In this paper we study multivariate ranks and quantiles, defined using the theory of optimal transportation, and build on the work of Chernozhukov et al. (2017) and del Barrio et al. (2018). We study the characterization and properties of these multivariate ... More
Modeling failures times with dependent renewal type models via exchangeabilityMay 13 2019Failure times of a machinery cannot always be assumed independent and identically distributed, e.g. if after reparations the machinery is not restored to a same-as-new condition. Framed within the renewal processes approach, a generalization that considers ... More
Moment Identifiability of Homoscedastic Gaussian MixturesMay 13 2019We consider the problem of identifying a mixture of Gaussian distributions with same unknown covariance matrix by their sequence of moments up to certain order. Our approach rests on studying the moment varieties obtained by taking special secants to ... More
Exact high-dimensional asymptotics for support vector machineMay 13 2019Support vector machine (SVM) is one of the most widely used classification methods. In this paper, we consider soft margin support vector machine used on data points with independent features, where the sample size $n$ and the feature dimension $p$ grows ... More
Partially Specified Space Time Autoregressive Model with Artificial Neural NetworkMay 13 2019The space time autoregressive model has been widely applied in science, in areas such as economics, public finance, political science, agricultural economics, environmental studies and transportation analyses. The classical space time autoregressive model ... More
Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributionsMay 13 2019We propose the notion of sub-Weibull distributions, which are characterised by tails lighter than (or equally light as) the right tail of a Weibull distribution. This novel class generalises the sub-Gaussian and sub-Exponential to potentially heavier-tailed ... More
Is Volatility Rough ?May 13 2019Rough volatility models are continuous time stochastic volatility models where the volatility process is driven by a fractional Brownian motion with the Hurst parameter less than half, and have attracted much attention since a seminal paper titled "Volatility ... More
Functional Correlations in the Pursuit of Performance Assessment of ClassifiersMay 12 2019In statistical classification, machine learning, social and other sciences, a number of measures of association have been developed and used for assessing and comparing individual classifiers, raters, and their groups. Among the measures, we find the ... More
ACF estimation via difference schemes for a semiparametric model with $m$-dependent errorsMay 11 2019In this manuscript, we discuss a class of difference-based estimators of the autocovariance structure in a semiparametric regression model where the signal is discontinuous and the errors are serially correlated. The signal in this model consists of a ... More
String confinement in 2-form lattice gauge theoryMay 11 2019We study the confinement between vortex strings in the dual lattice gauge theory of the abelian Higgs model. The dual lattice gauge theory is described by a 2-form gauge field. We calculate the string-antistring potential from the surface operator of ... More
Prediction and outlier detection: a distribution-free prediction set with a balanced objectiveMay 10 2019We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction set) that constructs a prediction ... More
Prediction and outlier detection: a distribution-free prediction set with a balanced objectiveMay 10 2019May 14 2019We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction set) that constructs a prediction ... More
Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and ConsistencyMay 10 2019The Bayesian formulation of inverse problems is attractive for three primary reasons: it provides a clear modelling framework; means for uncertainty quantification; and it allows for principled learning of hyperparameters. The posterior distribution may ... More
Robust high dimensional learning for Lipschitz and convex lossesMay 10 2019We establish risk bounds for Regularized Empirical Risk Minimizers (RERM) when the loss is Lipschitz and convex and the regularization function is a norm. We obtain these results in the i.i.d. setup under subgaussian assumptions on the design. In a second ... More
Why scoring functions cannot assess tail propertiesMay 10 2019Motivated by the growing interest in sound forecast evaluation techniques with an emphasis on distribution tails rather than average behaviour, we investigate a fundamental question arising in this context: Can statistical features of distribution tails ... More
Large scale in transit computation of quantiles for ensemble runsMay 10 2019The classical approach for quantiles computation requires availability of the full sample before ranking it. In uncertainty quantification of numerical simulation models, this approach is not suitable at exascale as large ensembles of simulation runs ... More
Illumination depthMay 10 2019The concept of illumination bodies studied in convex geometry is used to amend the halfspace depth for multivariate data. The proposed notion of illumination enables finer resolution of the sample points, naturally breaks ties in the associated depth-based ... More
On limit theorems for persistent Betti numbers from dependent dataMay 10 2019We study persistent Betti numbers and persistence diagrams obtained from a time series and random fields. It is well known that the persistent Betti function is an efficient descriptor of the topology of a point cloud. So far, convergence results for ... More
Optimal rates for F-score binary classificationMay 10 2019We study the minimax settings of binary classification with F-score under the $\beta$-smoothness assumptions on the regression function $\eta(x) = \mathbb{P}(Y = 1|X = x)$ for $x \in \mathbb{R}^d$. We propose a classification procedure which under the ... More
Extreme events evaluation using CRPS distributionsMay 10 2019Verification of ensemble forecasts for extreme events remains a challenging question. The general public as well as the media naturely pay particular attention on extreme events and conclude about the global predictive performance of ensembles, which ... More
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPsMay 09 2019This paper establishes that optimistic algorithms attain gap-dependent and non-asymptotic logarithmic regret for episodic MDPs. In contrast to prior work, our bounds do not suffer a dependence on diameter-like quantities or ergodicity, and smoothly interpolate ... More
On Semi-parametric Bernstein-von Mises Theorems for BARTMay 09 2019Few methods in Bayesian non-parametric statistics/ machine learning have received as much attention as Bayesian Additive Regression Trees (BART). While BART is now routinely performed for prediction tasks, its theoretical properties began to be understood ... More
Stein Point Markov Chain Monte CarloMay 09 2019An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimising ... More
Conformal prediction for exponential families and generalized linear modelsMay 09 2019Conformal prediction methods construct prediction regions for iid data that are valid in finite samples. Distribution-free conformal prediction methods have been proposed for regression. Generalized linear models (GLMs) are a widely used class of regression ... More
Double-calibration estimators accounting for under-coverage and nonresponse in socio-economic surveysMay 09 2019Under-coverage and nonresponse problems are jointly present in most socio-economic surveys. The purpose of this paper is to propose a completely design-based estimation strategy that accounts for both problems without resorting to models but simply performing ... More
Non-Asymptotic Sequential Tests for Overlapping Hypotheses and application to near optimal arm identification in bandit modelsMay 09 2019In this paper, we study sequential testing problems with \emph{overlapping} hypotheses. We first focus on the simple problem of assessing if the mean $\mu$ of a Gaussian distribution is $\geq -\epsilon$ or $\leq \epsilon$; if $\mu\in(-\epsilon,\epsilon)$, ... More
Activating critical exponent spectra with a slow driveMay 09 2019We uncover an aspect of the Kibble--Zurek phenomenology, according to which the spectrum of critical exponents of a classical or quantum phase transition is revealed by driving the system slowly in directions parallel to the phase boundary. This result ... More
Regression from Dependent ObservationsMay 08 2019The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, ... More
Molecular dynamics simulation of entanglement spreading in generalized hydrodynamicsMay 08 2019The so-called flea gas is an elementary yet very powerful method that allows the simulation of the out-of-equilibrium dynamics after quantum quenches in integrable systems. Here we show that, after supplementing it with minimal information about the initial ... More
Bounding distributional errors via density ratiosMay 08 2019We present some new and explicit error bounds for the approximation of distributions. The approximation error is quantified by the maximal density ratio of the distribution $Q$ to be approximated and its proxy $P$. This non-symmetric measure is more informative ... More
Bounding distributional errors via density ratiosMay 08 2019May 14 2019We present some new and explicit error bounds for the approximation of distributions. The approximation error is quantified by the maximal density ratio of the distribution $Q$ to be approximated and its proxy $P$. This non-symmetric measure is more informative ... More
Transition from a Dirac spin liquid to an antiferromagnet: Monopoles in a QED3-Gross-Neveu theoryMay 07 2019We study the quantum phase transition from a Dirac spin liquid to an antiferromagnet driven by condensing monopoles with spin quantum numbers. We describe the transition in field theory by tuning a fermion interaction to condense a spin-Hall mass, which ... More
Sliced Latin hypercube designs with arbitrary run sizesMay 07 2019Latin hypercube designs achieve optimal univariate stratifications and are useful for computer experiments. Sliced Latin hypercube designs are Latin hypercube designs that can be partitioned into smaller Latin hypercube designs. In this work, we give, ... More
Ergodic branching diffusions with immigration: properties of invariant occupation measure, identification of particles under high-frequency observation, and estimation of the diffusion coefficient at nonparametric ratesMay 07 2019In branching diffusions with immigration (BDI), particles travel on independent diffusion paths in $\mathbb{R}^d$, branch at position-dependent rates and leave offspring -- randomly scattered around the parent's death position -- according to position-dependent ... More
Minimax Hausdorff estimation of density level setsMay 07 2019Given a random sample of points from some unknown density, we propose a data-driven method for estimating density level sets under the r-convexity assumption. This shape condition generalizes the convexity property. However, the main problem in practice ... More
Moderate deviations in a class of stable but nearly unstable processesMay 07 2019We consider a stable but nearly unstable autoregressive process of any order. The bridge between stability and instability is expressed by a time-varying companion matrix $A_{n}$ with spectral radius $\rho(A_{n}) < 1$ satisfying $\rho(A_{n}) \rightarrow ... More
Tail dependence and smoothnessMay 07 2019The risk of catastrophes is related to the possibility of occurring extreme values. Several statistical methodologies have been developed in order to evaluate the propensity of a process for the occurrence of high values and the permanence of these in ... More
On the assumption of independent right censoringMay 07 2019Various assumptions on a right-censoring mechanism to ensure consistency of the Kaplan--Meier and Aalen--Johansen estimators in a competing risks setting are studied. Specifically, eight different assumptions are seen to fall in two categories: a weaker ... More
One-class classification with application to forensic analysisMay 07 2019The analysis of broken glass is forensically important to reconstruct the events of a criminal act. In particular, the comparison between the glass fragments found on a suspect (recovered cases) and those collected on the crime scene (control cases) may ... More
Boundary Tensor Renormalization GroupMay 07 2019We develop the tensor renormalization group (TRG) algorithm for statistical systems with open boundaries, which allows us to investigate not only the bulk but also the boundary property, such as the surface magnetization. We demonstrate that the tensors ... More
Renormalization Scheme Dependence, RG Flow and Borel Summability in $φ^4$ Theories in $d<4$May 06 2019Renormalization group (RG) and resummation techniques have been used in $N$-component $\phi^4$ theories at fixed dimensions below four to determine the presence of non-trivial IR fixed points and to compute the associated critical properties. Since the ... More
Estimating Piecewise Monotone SignalsMay 06 2019We study the problem of estimating piecewise monotone vectors. This problem can be seen as a generalization of the isotonic regression that allows a small number of order-violating changepoints. We focus mainly on the performance of the nearly-isotonic ... More
Exact Largest Eigenvalue Distribution for Doubly Singular Beta EnsembleMay 06 2019In [1] beta type I and II doubly singular distributions were introduced and their densities and the joint densities of nonzero eigenvalues were derived. We found simple formula to compute largest root distribution for doubly singular beta ensemble in ... More
Exact Largest Eigenvalue Distribution for Doubly Singular Beta EnsembleMay 06 2019May 07 2019In [1] beta type I and II doubly singular distributions were introduced and their densities and the joint densities of nonzero eigenvalues were derived. We found simple formula to compute largest root distribution for doubly singular beta ensemble in ... More
Free Component Analysis: Theory, Algorithms & ApplicationsMay 05 2019We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures. Random matrices play ... More
Toward a relative q-entropyMay 05 2019We address the question and related controversy of the formulation of the $q$-entropy, and its relative entropy counterpart, for models described by continuous (non-discrete) sets of variables. We notice that an $L_p$ normalized functional proposed by ... More
Topological invariants of the Ryu-Takayanagi ($RT$) surface used to observe holographic superconductor phase transitionMay 05 2019We study the phase transitions in the metal/superconductor system using topological invariants of the Ryu-Takayanagi ($RT$) surface and the volume enclosed by the $RT$ surface in the Lifshitz black hole background. It is shown that these topological invariant ... More
Improved Classification Rates for Localized SVMsMay 04 2019One of the main characteristics of localized support vector machines that solve SVMs on many spatially defined small chunks is, besides the computational benefit compared to global SVMs, the freedom of choosing arbitrary kernel and regularization parameter ... More
De-biased graphical Lasso for high-frequency dataMay 04 2019This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the precision ... More
Projection Theorems, Estimating Equations, and Power-Law DistributionsMay 04 2019Projection theorems of divergence functionals reduce certain estimation problems on some specific families of probability distributions to a linear problem. Most of these divergences are also popular in the context of robust statistics. In this paper ... More
Test for homogeneity with unordered paired observationsMay 04 2019In some applications, an experimental unit is composed of two distinct but related subunits. The response from such a unit is $(X_{1}, X_{2})$ but we observe only $Y_1 = \min\{X_{1},X_{2}\}$ and $Y_2 = \max\{X_{1},X_{2}\}$, i.e., the subunit identities ... More
Thermal CFTs in momentum spaceMay 03 2019We study some aspects of conformal field theories at finite temperature in momentum space. We provide a formula for the Fourier transform of a thermal conformal block and study its analytic properties. In particular we show that the Fourier transform ... More
Quantum Singular Value DecomposerMay 03 2019We present a variational quantum circuit that produces the Singular Value Decomposition of a bipartite pure state. The proposed circuit, that we name Quantum Singular Value Decomposer or QSVD, is made of two unitaries respectively acting on each part ... More
TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor NetworksMay 03 2019TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow. We describe a tree tensor network (TTN) algorithm for approximating the ground state of either a periodic quantum spin chain (1D) or a lattice model on a ... More
TensorNetwork: A Library for Physics and Machine LearningMay 03 2019TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, ... More
Learning Some Popular Gaussian Graphical Models without Condition Number BoundsMay 03 2019Gaussian Graphical Models (GGMs) have wide-ranging applications in machine learning and the natural and social sciences. In most of the settings in which they are applied, the number of observed samples is much smaller than the dimension and they are ... More
Entanglement hamiltonians in 1D free lattice models after a global quantum quenchMay 03 2019We study the temporal evolution of the entanglement hamiltonian of an interval after a global quantum quench in free lattice models in one spatial dimension. In a harmonic chain we explore a quench of the frequency parameter. In a chain of free fermions ... More
A Uniform Bound of the Operator Norm of Random Element Matrices and Operator Norm Minimizing EstimationMay 03 2019In this paper, we derive a uniform stochastic bound of the operator norm (or equivalently, the largest singular value) of random matrices whose elements are indexed by parameters. As an application, we propose a new estimator that minimizes the operator ... More
High dimensional VAR with low rank transitionMay 02 2019We propose a vector auto-regressive (VAR) model with a low-rank constraint on the transition matrix. This new model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. We ... More
Learning Algebraic Structures: Preliminary InvestigationsMay 02 2019We employ techniques of machine-learning, exemplified by support vector machines and neural classifiers, to initiate the study of whether AI can "learn" algebraic structures. Using finite groups and finite rings as a concrete playground, we find that ... More
Many-body chaos near a thermal phase transitionMay 02 2019We study many-body chaos in a (2+1)D relativistic scalar field theory at high temperatures in the classical statistical approximation, which captures the quantum critical regime and the thermal phase transition from an ordered to a disordered phase. We ... More
Consistent Inversion of Noisy Non-Abelian X-Ray TransformsMay 02 2019For $M$ a simple surface, the non-linear and non-convex statistical inverse problem of recovering a matrix field $\Phi: M \to \mathfrak{so}(n)$ from discrete, noisy measurements of the $SO(n)$-valued scattering data $C_\Phi$ of a solution of a matrix ... More
Thermodynamics and Many Body Chaos for generalized large q SYK modelsMay 02 2019This paper considers a type of generalized large q SYK models which include multi-body interactions between Majorana fermions. At the double scaling limit, we derive the effective action and find a universal thermodynamics relation. We also consider the ... More
Sparsity Double Robust Inference of Average Treatment EffectsMay 02 2019Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method ... More
On The S-Matrix of Ising Field Theory in Two DimensionsMay 02 2019We explore the analytic structure of the non-perturbative S-matrix in arguably the simplest family of massive non-integrable quantum field theories: the Ising field theory (IFT) in two dimensions, which may be viewed as the Ising CFT deformed by its two ... More
On The S-Matrix of Ising Field Theory in Two DimensionsMay 02 2019May 12 2019We explore the analytic structure of the non-perturbative S-matrix in arguably the simplest family of massive non-integrable quantum field theories: the Ising field theory (IFT) in two dimensions, which may be viewed as the Ising CFT deformed by its two ... More
Functional central limit theorems for conditional Poisson samplingMay 02 2019This paper provides refined versions of some known functional central limit theorems for conditional Poisson sampling which are more suitable for applications. The theorems presented in this paper are generalizations of some results that have recently ... More
Total positivity in structured binary distributionsMay 01 2019We study binary distributions that are multivariate totally positive of order 2 (MTP2). Binary distributions can be represented as an exponential family and we show that MTP2 exponential families are convex. Moreover, MTP2 quadratic exponential families, ... More
Stochastic ordering results in parallel and series systems with Gumble distributed random variablesMay 01 2019The stochastic comparisons of parallel and series system are worthy of study. In this paper, we present some stochastic comparisons of parallel and series systems having independent components from Gumble distribution with two parameters (one location ... More
On the excursion area of perturbed Gaussian fieldsMay 01 2019We investigate Lipschitz-Killing curvatures for excursion sets of random fields on $\mathbb R^2$ under small spatial-invariant random perturbations. An expansion formula for mean curvatures is derived when the magnitude of the perturbation vanishes, which ... More
Asymptotically optimal sequential FDR and pFDR control with (or without) prior information on the number of signalsMay 01 2019We investigate asymptotically optimal multiple testing procedures for streams of sequential data in the context of prior information on the number of false null hypotheses ("signals"). We show that the "gap" and "gap-intersection" procedures, recently ... More
First digit law from Laplace transformApr 30 2019The occurrence of digits 1 through 9 as the leftmost nonzero digit of numbers from real-world sources is distributed unevenly according to an empirical law, known as Benford's law or the first digit law. It remains obscure why a variety of data sets generated ... More
On the Kolkata index as a measure of income inequalityApr 30 2019We study the mathematical and economic structure of the Kolkata (k) index of income inequality. We show that the k-index always exists and is a unique fixed point of the complementary Lorenz function, where the Lorenz function itself gives the fraction ... More
Estimating Proportion of True Null Hypotheses based on Sum of p-values and application to microarraysApr 30 2019A new estimator of proportion of true null hypotheses based on sum of all p- values has been proposed in this work which removes the problem of choosing tuning parameters in the existent estimators. Normality of gene expression levels and common t-test ... More