arXiv subjects

Li Ma

Publications and source records attributed to Li Ma.

At least 163 records · Page 9Linked to original sources

arXiv2017

Fisher exact scanning for dependency

We introduce a method---called Fisher exact scanning (FES)---for testing and identifying variable dependency that generalizes Fisher's exact test on $2\times 2$ contingency tables to $R\times C$ contingency tables and continuous sample spaces. FES proceeds through scanning over the sample space using windows in the form of $2\times 2$ tables of various sizes, and on each window completing a Fisher's exact test. Based on a factorization of Fisher's multivariate hypergeometric (MHG) likelihood into the product of the univariate hypergeometric likelihoods, we show that there exists a coarse-to-fine, sequential generative representation for the MHG model in the form of a Bayesian network, which in turn implies the mutual independence (up to deviation due to discreteness) among the Fisher's exact tests completed under FES. This allows an exact characterization of the joint null distribution of the $p$-values and gives rise to an effective inference recipe through simple multiple testing procedures such as Šidák and Bonferroni corrections, eliminating the need for resampling. In addition, FES can characterize dependency through reporting significant windows after multiple testing control. The computational complexity of FES is approximately linear in the sample size, which along with the avoidance of resampling makes it ideal for analyzing massive data sets. We use extensive numerical studies to illustrate the work of FES and compare it to several state-of-the-art methods for testing dependency in both statistical and computational performance. Finally, we apply FES to analyzing a microbiome data set and further investigate its relationship with other popular dependency metrics in that context.

stat.ME↗

arXiv2017

Mixture modeling on related samples by $ψ$-stick breaking and kernel perturbation

There has been great interest recently in applying nonparametric kernel mixtures in a hierarchical manner to model multiple related data samples jointly. In such settings several data features are commonly present: (i) the related samples often share some, if not all, of the mixture components but with differing weights, (ii) only some, not all, of the mixture components vary across the samples, and (iii) often the shared mixture components across samples are not aligned perfectly in terms of their location and spread, but rather display small misalignments either due to systematic cross-sample difference or more often due to uncontrolled, extraneous causes. Properly incorporating these features in mixture modeling will enhance the efficiency of inference, whereas ignoring them not only reduces efficiency but can jeopardize the validity of the inference due to issues such as confounding. We introduce two techniques for incorporating these features in modeling related data samples using kernel mixtures. The first technique, called $ψ$-stick breaking, is a joint generative process for the mixing weights through the breaking of both a stick shared by all the samples for the components that do not vary in size across samples and an idiosyncratic stick for each sample for those components that do vary in size. The second technique is to imbue random perturbation into the kernels, thereby accounting for cross-sample misalignment. These techniques can be used either separately or together in both parametric and nonparametric kernel mixtures. We derive efficient Bayesian inference recipes based on MCMC sampling for models featuring these techniques, and illustrate their work through both simulated data and a real flow cytometry data set in prediction/estimation, cross-sample calibration, and testing multi-sample differences.

stat.ME↗

arXiv2017

Lambda constant and Ground states of Perelman's W-functional

In this paper, we consider the generalized lambda constant and the existence of ground states of the generalized Perelman's W-functional from a variational formulation. One result is concerned with the estimation of the generalized $λ$ constant. The other results are about the existence of ground states of generalized $F$-functional and W-functional both on a complete non-compact Riemannian manifold $(M,g)$ with positive injectivity radius and with Ricci curvature bounded from below. Our main results are Theorems 2,3 and 7. For the existence of the ground states we use Lions' concentration-compactness method.

math.DG↗

arXiv2017

A phylogenetic scan test on Dirichlet-tree multinomial model for microbiome data

In this paper we introduce the phylogenetic scan test (PhyloScan) for investigating cross-group differences in microbiome compositions using the Dirichlet-tree multinomial (DTM) model. DTM models the microbiome data through a cascade of independent local DMs on the internal nodes of the phylogenetic tree. Each of the local DMs captures the count distributions of a certain number of operational taxonomic units at a given resolution. Since distributional differences tend to occur in clusters along evolutionary lineages, we design a scan statistic over the phylogenetic tree to allow nodes to borrow signal strength from their parents and children. We also derive a formula to bound the tail probability of the scan statistic, and verify its accuracy through simulations. The PhyloScan procedure is applied to the American Gut dataset to identify taxa associated with diet habits. Empirical studies performed on this dataset show that PhyloScan achieves higher testing power in most cases.

stat.AP↗

arXiv2017

Recursive partitioning and multi-scale modeling on conditional densities

We introduce a nonparametric prior on the conditional distribution of a (univariate or multivariate) response given a set of predictors. The prior is constructed in the form of a two-stage generative procedure, which in the first stage recursively partitions the predictor space, and then in the second stage generates the conditional distribution by a multi-scale nonparametric density model on each predictor partition block generated in the first stage. This design allows adaptive smoothing on both the predictor space and the response space, and it results in the full posterior conjugacy of the model, allowing exact Bayesian inference to be completed analytically through a forward-backward recursive algorithm without the need of MCMC, and thus enjoying high computational efficiency (scaling linearly with the sample size). We show that this prior enjoys desirable theoretical properties such as full $L_1$ support and posterior consistency. We illustrate how to apply the model to a variety of inference problems such as conditional density estimation as well as hypothesis testing and model selection in a manner similar to applying a parametric conjugate prior, while attaining full nonparametricity. Also provided is a comparison to two other state-of-the-art Bayesian nonparametric models for conditional densities in both model fit and computational time. A real data example from flow cytometry containing 455,472 observations is given to illustrate the substantial computational efficiency of our method and its application to multivariate problems.

stat.ME↗

arXiv2016

Remarks on Bernstein theorems for translating solitons

In this paper, we study the properties of potential function of the translating soliton $M$ in $R^{n+1}$ and the volume growth of the intersection of Euclidean balls with $M$. We give a condition to obtain the Bernstein theorem for the translating solitons. We also give an outline of a simple proof of the Bernstein theorem due to Bombieri-De Giorgi-Miranda.

math.DG↗

arXiv2016

Convexity and the Dirichlet problem of translating mean curvature flows

In this work, we propose a new evolving geometric flow (called translating mean curvature flow) for the translating solitons of hypersurfaces in $R^{n+1}$. We study the basic properties, such as positivity preserving property, of the translating mean curvature flow. The Dirichlet problem for the graphical translating mean curvature flow is studied and the global existence of the flow and the convergence property are also considered.

math.DG↗

arXiv2016

Magnetic moments of the hidden-charm pentaquark states

The magnetic moment of a baryon state is an equally important dynamical observable as its mass, which encodes crucial information of its underlying structure. According to the different color-flavor structure, we have calculated the magnetic moments of the hidden-charm pentaquark states with $J^P={\frac{1}{2}}^{\pm}$, ${\frac{3}{2}}^{\pm}$, ${\frac{5}{2}}^{\pm}$ and ${\frac{7}{2}}^{+}$ in the molecular model, the diquark-triquark model and the diquark-diquark-antiquark model respectively. Although a good description for the pentaquark mass spectrum and decay patterns has been obtained in all the three models, different color-flavor structures lead to different magnetic moments, which can be used to pin down their inner structures and distinguish various models.

hep-ph↗

arXiv2016

Bernstein theorem for translating solitons of hypersurfaces

In this paper, we prove a monotonicity formula and some Bernstein type results for translating solitons of hypersurfaces in $\re^{n+1}$, giving some conditions under which a trantranslating soliton is a hyperplane. We also show a gap theorem for the translating soliton of hypersurfaces in $R^{n+k}$, namely, if the $L^n$ norm of the second fundamental form of the soliton is small enough, then it is a hyperplane.

math.DG↗

arXiv2016

Liouville theorems, Volume growth, and volume comparison for Ricci shrinkers

In this paper, we study volume growth, Liouville theorem and the local gradient estimate for $f$-harmonic functions, and volume comparison property of unit balls in complete noncompact gradient Ricci shrinkers. We also study integral properties of f-harmonic functions and harmonic functions on such manifolds.

math.DG↗

arXiv2016

Analysis of distributional variation through multi-scale Beta-Binomial modeling

Many statistical analyses involve the comparison of multiple data sets collected under different conditions in order to identify the difference in the underlying distributions. A common challenge in multi-sample comparison is the presence of various confounders, or extraneous causes other than the conditions of interest that also contribute to the difference across the distributions. They result in false findings, i.e., identified differences that are not replicable in follow-up investigations. We consider an ANOVA approach to addressing this issue in multi-sample comparison---by collecting replicate data sets under each condition, thereby allowing the identification of the interesting distributional variation from the extraneous ones. We introduce a multi-scale Bayesian hierarchical model for the analysis of distributional variation (ANDOVA) under this design, based on a collection of Beta-Binomial tests targeting variations of different scales at different locations across the sample space. Instead treating the tests independently, the model employs a graphical structure to introduce dependency among the individual tests thereby allowing borrowing of strength among them. We derive efficient inference recipe through a combination of numerical integration and message passing, and evaluate the ability of our method to effectively address ANDOVA through extensive simulation. We utilize our method to analyze a DNase-seq data set for identifying differences in transcriptional factor binding.

stat.ME↗

arXiv2016

Radial symmetry results for fractional Laplacian systems

In this paper, we generalize the direct method of moving planes for the fractional Laplacian to the system case. Considering a coupled nonlinear system with fractional Laplacian, we first establish a decay at infinity principle and a narrow region principle. Using these principles, we obtain two radial symmetry results for the decaying solutions of the fractional Laplacian systems. Our method can be applied to fractional Schrödinger systems and fractional Hénon systems.

math.AP↗

arXiv2016

Strong decay patterns of the hidden-charm pentaquark states $P_c(4380)$ and $P_c(4450)$

With the heavy quark symmetry and spin rearrangement scheme, we study the strong decay behavior of the hidden-charm pentaquark states with $J^P={\frac{3}{2}}^{\pm}, {\frac{5}{2}}^{\pm}$ assuming they are molecular candidates composed of ${\bar D}^{(\ast)}$ and $Σ_c^{(\ast)}$. We obtain several typical ratios of the partial decay widths of the hidden-charm pentaquarks. For the three S-wave $(\bar{D}Σ_c^*)$, $(\bar{D}^*Σ_c)$ and $(\bar{D}^*Σ_c^*)$ molecular pentaquarks with $J^P={{3}/{2}}^{-}$, we have obtained the ratio of their $J/ψN$ decay widths: $Γ\left[(\bar D{Σ^{\ast}_c})\right]:Γ\left[({\bar D}^{\ast}Σ_c)\right]:Γ\left[({\bar D}^{\ast}{Σ^{\ast}_c})\right]=2.7:1.0:5.4$, which may be useful to further test the possible molecular assignment of the $P_c$ states.

hep-ph↗

arXiv2016

Efficient functional ANOVA through wavelet-domain Markov groves

We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical model called the Markov grove (MG) is designed to jointly model the spike-and-slab statuses at all location-scale combinations, which incorporates the clustering of each factor effect in the wavelet-domain thereby allowing borrowing of strength across location and scale. The posterior of this NIG-MG model is analytically available through a pyramid algorithm of the same computational complexity as Mallat's pyramid algorithm for discrete wavelet transform, i.e., linear in both the number of observations and the number of locations. Posterior probabilities of factor contributions can also be computed through pyramid recursion, and exact samples from the posterior can be drawn without MCMC. We investigate the performance of our method through extensive simulation and show that it outperforms existing wavelet-domain fANOVA methods in a variety of common settings. We apply the method to analyzing the orthosis data.

stat.ME↗

arXiv2016

$p$-adic Gross-Zagier Formula for Heegner Points on Shimura Curves over Totally Real Fields

The main result of this text is a generalization of Perrin-Riou's p-adic Gross-Zagier formula to the case of Shimura curves over totally real fields. Let $F$ be a totally real field. Let $f$ be a Hilbert modular form over $F$ of parallel weight $2$, which is a new form and is ordinary at $p$. Let $E$ be a totally imaginary quadratic extension of $F$ of discriminant prime to $p$ and to the conductor of $f$. We may construct a $p$-adic $L$ function that interpolates special values of the complex $L$ functions associated to $f$, $E$ and finite order Hecke characters of $E$. The $p$-adic Gross-Zagier formula relates the central derivative of this $p$-adic $L$ function to the $p$-adic height of a Heegner divisor on a certain Shimura curve. The strategy of the proof is close to that of the original work of Perrin-Riou. In the analytic part, we construct the analytic kernel via adelic computations; in the geometric part, we decompose the geometric kernel into two parts: places outside $p$ and places dividing $p$. For places outside $p$, the $p$-adic heights are essentially intersection numbers and are computed in works of S. Zhang, and it turns out that this part is closely related to the analytic kernel. For places dividing $p$, we use the method in the work of J. Nekovář to show that the contribution of this part is zero.

math.NT↗

arXiv2016

Hamilton type estimates for heat equations on manifolds

In this paper, we study the gradient estimates of Li-Yau-Hamilton type for positive solutions to both drifting heat equation and the simple nonlinear heat equation problem $$ u_t-Δu=au\log u, \ \ u>0 $$ on the compact Riemannian manifold $(M,g)$ of dimension $n$ and with non-negative (Bakry-Emery)-Ricci curvature. Here $a\leq 0$ is a constant. The latter heat equation is a basic evolution equation which is the negative gradient heat flow to the functional of Log-Sobolev inequality on the Riemannian manifold. We derive various versions of gradient estimates which generalize Hamilton's gradient estimate. An question concerning the Hamilton type gradient estimate for the simple nonlinear heat equation is addressed.

math.DG↗

arXiv2015

Strong decay patterns of the hidden-charm tetraquarks

With the spin rearrangement, we have performed a comprehensive investigation of the decay patterns of the S-wave tetraquarks and P-wave tetraquarks where the P-wave excitation exists either between the diquark and anti-diquark pair or inside the diquark. Especially, we compare the decay patterns of $Y(4260)$ with different inner structures such as the conventional charmonium, the molecule, the P-wave tetraquark and the hybrid charmonium. We notice the $J/ψππ$ mode is suppressed in the heavy quark symmetry limit if $Y(4260)$ is a molecular state. Moreover the hybrid charmonium and hidden-charm tetraquark have very similar decay patterns. Both of them decay into the $J/ψππ$ and open charm modes easily. We also discuss the decay patterns of $X(3872)$, $Y(4360)$, and several charged states such as $Z_c(4020)$. The $h_cπ^{\pm}$ decay mode disfavors the tetraquark assumption of $Z_c(4020)$.

hep-ph↗

arXiv2015

Liouville theorem for warped ancient Ricci solutions

In this note, we answer affirmatively the question if a warped product of a compact manifold with a line as an ancient solution to the Ricci flow is trivial. We also consider the global behavior of the Type III warping product Ricci flow.

math.DG↗