arXiv subjects

Yifei Xiong

Publications and source records attributed to Yifei Xiong.

8 recordsLinked to original sources

arXiv2026

Statistical Properties of Nonparametric MLE under Laplace Noise

Local differential privacy (LDP) protects individuals in a dataset by perturbing each measurement before release. For real-valued data, a widely used mechanism is additive Laplace noise. We study the problem of estimating the distribution of the latent confidential data from the privatized observations via the nonparametric maximum likelihood estimator (NPMLE) under an i.i.d. sampling model. We first show that under the Laplace convolution model, the NPMLE admits a finite-dimensional reformulation in which the support is restricted to the observation set. This reduces the original infinite-dimensional optimization over all mixing distributions to an $n$-dimensional convex optimization over mixture weights, where $n$ is the sample size. We then study the statistical convergence of the NPMLE under the 1-Wasserstein distance, and explicitly connect its convergence rate with the privacy noise scale. Allowing the privacy noise level to change with the sample size, our analysis shows that the NPMLE remains consistent when the Laplace noise grows at a rate slower than $n^{3/16}$. Conversely, when the Laplace noise is of the order $\sqrt n$ or larger, no estimator can achieve uniformly consistent recovery of the latent distribution.

math.ST↗

arXiv2026

Mock Observations for the CSST Mission: Integral Field Spectrograph--GEHONG: A Package for Generating Ideal Datacubes

We developed a Python package GEHONG to mock the three-dimensional spectral data cube under the observation of an ideal telescope for the Integral Field Spectrograph of the Chinese Space Station Telescope (CSST-IFS). This package can generate one-dimensional spectra corresponding to local physical properties at specific positions according to a series of two-dimensional distributions of physical parameters of target sources. In this way, it can produce a spatially resolved spectral cube of the target source. Two-dimensional distributions of physical parameters, including surface brightness, stellar population, and line-of-sight velocity, can be modeled using the parametric model or based on real observational data and numerical simulation data. For the generation of one-dimensional spectra, we have considered four types of spectra, including the stellar continuum spectra, ionized gas emission lines, AGN spectra, and stellar spectra. That makes GEHONG able to mock various types of targets, including galaxies, AGNs, star clusters, and HII regions.

astro-ph.IM↗

arXiv2025

SOMA: A Novel Sampler for Bayesian Inference from Privatized Data

Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved confidential data given noisy privatized summaries, enabling principled uncertainty quantification. However, standard DAMCMC often suffers from slow mixing due to component-wise Metropolis-within-Gibbs updates. We propose the Single-Offer-Multiple-Attempts (SOMA) sampler. This novel algorithm improves acceptance rates by generating a single proposal and simultaneously evaluating its suitability to replace all components. By sharing proposals across components, SOMA rejects fewer proposal points. We prove lower bounds on SOMA's acceptance probability and establish convergence rates in the two-component case. Experiments on synthetic and real census data with linear regression and other models confirm SOMA's efficiency gains.

stat.ME↗

arXiv2025

Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods

There is a growing interest in studying sequential neural posterior estimation (SNPE) techniques due to their advantages for simulation-based models with intractable likelihoods. The methods aim to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. As an SNPE technique, the automatic posterior transformation (APT) method proposed by Greenberg et al. (2019) performs well and scales to high-dimensional data. However, the APT method requires computing the expectation of the logarithm of an intractable normalizing constant, i.e., a nested expectation. Although atomic proposals were used to render an analytical normalizing constant, it remains challenging to analyze the convergence of learning. In this paper, we reformulate APT as a nested estimation problem. Building on this, we construct several multilevel Monte Carlo (MLMC) estimators for the loss function and its gradients to accommodate different scenarios, including two unbiased estimators, and a biased estimator that trades a small bias for reduced variance and controlled runtime and memory usage. We also provide convergence results of stochastic gradient descent to quantify the interaction of the bias and variance of the gradient estimator. Numerical experiments for approximating complex posteriors with multimodality in moderate dimensions are provided to examine the effectiveness of the proposed methods.

stat.CO↗

arXiv2025

Extended Emission-line Region in a Poststarburst Galaxy Hosting Tidal Disruption Event AT2019qiz and Quasiperiodic Eruptions

We present a comprehensive analysis of the extended emission line region (EELR) in the host galaxy of the tidal disruption event (TDE) AT2019qiz, utilizing VLT/MUSE integral-field spectroscopy. The high spatial-resolution data reveal a bi-conical emission structure approximately $3.7~\mathrm{kpc}$ in scale within the galactic center, characterized by a prominent [OIII] line in the nucleus and significant [NII] line emission extending into the EELR. Spectral analysis of the EELR indicates line ratios consistent with Seyfert ionization in the center and LINER-type ionization in the outer diffuse region, suggesting ionization from galactic nuclear activity. The required ionizing luminosity, estimated from the H$\rmα$ and H$\rmβ$ luminosities based on the photoionization and recombination balance assumption, is $10^{41.8}$ $\mathrm{erg\,s^{-1}}$ for all spaxels classified as active galactic nucleus (AGN), and $10^{40.7}$ $\mathrm{erg\,s^{-1}}$ for spaxels in the central $0.9~\mathrm{kpc}$ Seyfert region. However, the current bolometric luminosity of the nucleus $L_{\text{bol}} \leq 10^{40.8}\,\mathrm{erg\,s^{-1}}$, estimated from quiescent-state soft X-ray observations, is insufficient to ionize the entire EELR, implying a recently faded AGN or a delayed response to historical activity. Stellar population analysis reveals a post-starburst characteristic in the EELR, and the gas kinematics show disturbances and non-circular components compared to the stellar kinematics. Notably, the recent detection of quasi-periodic eruptions (QPEs) in the X-ray light curve of AT2019qiz confirms the TDE-QPE association. Our findings provide direct evidence for an AGN-like EELR in the host galaxy of the nearest TDE with QPE detection, offering new insights into the complex interplay between TDEs, QPEs, AGN activity, and host galaxy evolution.

astro-ph.GA↗

arXiv2025

Simulation-based Bayesian Inference from Privacy Protected Data

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Under a formal definition of privacy protection, differentially private algorithms inject calibrated noise into the confidential data or during the data analysis process to produce privacy-protected datasets or queries. However, restricting access to only privatized data during statistical analysis makes it computationally challenging to make valid statistical inferences. In this work, we propose simulation-based inference methods from privacy-protected datasets. In addition to sequential Monte Carlo approximate Bayesian computation, we adopt neural conditional density estimators as a flexible family of distributions to approximate the posterior distribution of model parameters given the observed private query results. We illustrate our methods on discrete time-series data under an infectious disease model and with ordinary linear regression models. Illustrating the privacy-utility trade-off, our experiments and analysis demonstrate the necessity and feasibility of designing valid statistical inference procedures to correct for biases introduced by the privacy-protection mechanisms.

stat.ML↗

arXiv2025

An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation

Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods. Unlike approximate Bayesian computation, SNPE techniques learn the posterior from sequential simulation using neural network-based conditional density estimators by minimizing a specific loss function. The SNPE method proposed by Lueckmann et al. (2017) used a calibration kernel to boost the sample weights around the observed data, resulting in a concentrated loss function. However, the use of calibration kernels may increase the variances of both the empirical loss and its gradient, making the training inefficient. To improve the stability of SNPE, this paper proposes to use an adaptive calibration kernel and several variance reduction techniques. The proposed method greatly speeds up the process of training and provides a better approximation of the posterior than the original SNPE method and some existing competitors as confirmed by numerical experiments. We also managed to demonstrate the superiority of the proposed method for a high-dimensional model with a real-world dataset.

stat.ML↗

arXiv2022

Colossal Nernst power factor in topological semimetal NbSb$_2$

Today solid-state cooling technologies below liquid nitrogen boiling temperature (77 K), crucial to quantum information technology and probing quantum state of matter, are greatly limited due to the lack of good thermoelectric and/or thermomagnetic materials. Here, we report the discovery of colossal Nernst power factor of 3800$\times$10$^{-4}$ W m$^{-1}$ K$^{-2}$ under 5 T at 25 K and high Nernst figure-of-merit of 71$\times$10$^{-4}$ K$^{-1}$ under 5 T at 20 K in topological semimetal NbSb$_2$ single crystals. The observed high thermomagnetic performance is attributed to large Nernst thermopower and longitudinal electrical conductivity, and relatively low transverse thermal conductivity. The large and unsaturated Nernst thermopower is the result of the combination of highly desirable electronic structures of NbSb$_2$ having compensated high mobility electrons and holes near Fermi level and strong phonon-drag effect. This discovery opens an avenue for exploring material option for the solid-state heat pumping below liquid nitrogen temperature.

cond-mat.mtrl-sci↗