每日简报

Econstellar：开源人工智能辅助计算金融计量研究引擎AI 解读

Turning a promising economic idea into a credible empirical finding is, in practice, an expensive undertaking: it demands a great deal of specialised computation, and the results are seldom released in a form that others can check or build upon. Econstellar is our response. It is an open, publicly served research engine that runs publication-grade financial econometrics from an ordinary web browser and explains what the results mean, so that a reader does not merely read a…

多维高斯过程的模型选择准则：径向速度应用AI 解读

Multidimensional Gaussian Process (multi-GP) regression is widely used to disentangle stellar and planetary signals in radial velocities (RVs) by jointly modelling ancillary activity indicators. However, identifying the combination of indicators that best constrains the stellar signal in the RVs is non-trivial, as classical model comparison methods are not directly applicable when multi-GPs involve different time series combinations. In this work, we present an information…

单变量函数型数据的双原型分析：宏观金融时间序列应用AI 解读

We introduce biarchetype analysis for the first time in the context of univariate functional data. This unsupervised methodology extends archetype analysis by simultaneously identifying archetypal structures across both the cases (countries, in our application) and the temporal argument. Both cases and time points are expressed as mixtures of biarchetypes, yielding a concise and highly interpretable representation of complex functional observations. Although biarchetype…

多消费者市场中个体需求的面板数据估计AI 解读

The purpose of this paper is to consider whether and how panel data can be used to estimate individual demand, as opposed to market-level demand, while accounting for simultaneity resulting from prices being determined in markets. We consider linear demand models and random coefficient demand models, together with linear supply models. We find that the bias of individual demand estimates obtained using familiar panel data methods, like differencing, disappears as the number…

基于非凸对偶全变差正则化的指数族张量补全AI 解读

With the emergence of various tensor data, tensor completion from partial measurements has attracted widespread attention in data science and signal processing. Total Variation (TV) has been widely used as an effective regularization technique for tensor completion; however, theoretical studies on TV regularization in this context remain limited. In this work, we present a rigorous theoretical analysis of TV regularization for tensor completion. Specifically, we consider…

可靠性的长期价值是什么？AI 解读

We describe Chronos LTV, a system to measure the long-term impact of delays and other service defects on key business metrics. We use Markov decision processes to model customer interactions over time, and formalize our target estimand as the marginal policy effect with respect to moving the average delay rate. Given this setup, we show that we can identify long-term effects under a sequential unconfoundedness assumption where delays are as good as random given observed…

超高维线性混合模型中固定效应的稳健可扩展确信筛选AI 解读

In modern applications of linear mixed models, the number of candidate fixed-effects covariates can grow exponentially with the sample size, while dependence induced by random effects and possible data contamination pose substantial challenges for existing variable screening methods. We propose a robust and computationally efficient sure screening procedure for identifying relevant fixed-effects covariates in ultrahigh-dimensional linear mixed models with known random…

无需梯度的快速混合马尔可夫链AI 解读

Most approaches for accelerating Markov chain mixing either rely on incorporating expensive geometric information in the proposals, or reduce the per-step cost of sampling via surrogate densities. We propose a localisation principle that allows a surrogate-based Metropolis-Hastings proposal to exploit gradient-level geometric information of the target density, without evaluating either the target gradient or the surrogate gradient. The construction relies on regularisation…

含测量误差的分位数回归AI 解读

We devise a novel estimator for a general quantile regression model with normal measurement errors in the covariates. The method is applicable to both linear and nonlinear quantile regressions and does not impose the quantile requirement on multiple quantile levels simultaneously. We circumvent the difficulties caused by discontinuity in quantile regression through kernel smoothing, and overcome the nonlinearity inherent in quantile regression via considering extension to…

不完整矩阵回归AI 解读

Matrix completion seeks to recover a low-rank matrix from a sparse and noisy subset of its entries. In many applications, such as recommendation systems and urban mobility, the observed matrix is accompanied by auxiliary covariates on its rows and columns and exhibits dependence across them. We propose Incomplete Matrix Regression (IMR), a distribution-free penalized regression framework that integrates such information into matrix completion. The target matrix is modeled as…

复杂抽样设计下多分格相关的稳健最小散度估计AI 解读

Standard maximum likelihood estimation of polychoric correlations is highly sensitive to contamination in survey data, including response errors, interviewer effects, and careless responding, yet assigns equal weight to all observations regardless of data quality. We develop robust estimators for polychoric correlation under complex survey designs based on two minimum divergence criteria -- Hellinger distance (HD) and negative exponential disparity (NED) -- incorporating…

基于谱分解的核密度估计：数据驱动窗与叠加方法AI 解读

Kernel density estimation depends largely on one choice, the smoothing bandwidth. We treat bandwidth selection and density estimation in the characteristic-function domain, where the cyclic group-averaged covariance of the binned data has the squared empirical characteristic function as its spectrum: the true characteristic function sits over a sampling-noise floor of $1/n$, and the bandwidth is the spectral cutoff where the two meet. Several methods follow. An automatic…

正则化最优传输的有限样本界AI 解读

We study the sample complexity of regularized optimal transport for general convex regularizations including the Kullback--Leibler divergence and $L^p$ penalties. Our main results are non-asymptotic bias and variance bounds for the empirical cost, with explicit dependence on the regularization parameter and on the intrinsic dimension of the marginals. Our approach simultaneously improves, unifies, and extends existing finite-sample bounds. In particular, we improve the state…

从大语言模型提示中获得部分识别AI 解读

Large language models are increasingly used as binary classifiers when the true label is latent. We study partial identification of the prevalence $θ= P(X^* = 1)$ from panels of LLM reports whose errors may be arbitrarily dependent given the truth. The design of replication determines the observable, and hence the identifying content: repeated prompts to one model yield a count, several named models a response vector, and both a response matrix. Cast as a two-component…

基于马蹄先验的多重检验AI 解读

We study multiple testing under continuous global--local shrinkage priors, with a focus on the horseshoe prior in high-dimensional sparse settings. While such priors provide adaptive shrinkage and computational scalability, they do not induce exact zeros and hence do not directly yield posterior inclusion probabilities, making principled false discovery control nontrivial. We propose posterior--based decision rules for signal detection that are applicable across a broad…

比 Fast-LTS 更快：基于 DC 规划的稳健回归与异常值检测AI 解读

When datasets contain outliers, robust regression is a well-established alternative to Ordinary Least Squares. A commonly employed robust estimator is Least Trimmed Squares (LTS), which computes the regression coefficients from a subset of observations. Determining the exact solution corresponds to a combinatorial problem with prohibitive computational costs, even for instances of moderate dimension. Thus, the most prevalent approach in practice remains a heuristic known as…

基于诱导平滑的删失断崖自适应岭秩回归AI 解读

Broken adaptive ridge (BAR) penalty approximates $L_0$-regularization through iterative reweighting of L2 penalties. This penalty enjoys both the oracle property and the grouping effect for highly correlated covariates, making it particularly attractive for penalized regression with complex dependence among predictors. In this paper, we develop a BAR-penalized linear rank regression method for the semiparametric accelerated failure time model with right-censored data.…

带图马蹄先验的多元变系数BART模型AI 解读

Modern multivariate regression problems involve several related outcomes whose regression effects are not only nonlinear, heterogeneous, and outcome-specific, but also where the residual dependence among outcomes is scientifically meaningful. Existing multivariate Bayesian tree-based methods typically address only part of this problem: some impose substantial sharing of tree architecture across outcomes, which is overly restrictive when responses depend on distinct…

用学校人口特征与教师特征建模教育表现AI 解读

High-dimensional educational datasets often exhibit sparsity, grouped predictors, and locally correlated covariates, limiting the effectiveness of conventional regression methods. We propose an Adaptive Weighted Group Fused LASSO estimator that jointly performs adaptive variable selection, group regularization, and coefficient fusion within a unified penalized regression framework. An efficient ADMM algorithm is developed, and asymptotic properties, including consistency,…

高维约束回归的稀疏凸化AI 解读

We study high-dimensional linear regression under a general symmetric convex constraint. Rather than imposing a specific sparsity-inducing penalty, we start from an arbitrary sign-symmetric and permutation-invariant convex body $K\subseteq \mathbb R^p$ and construct the sparse convexification hierarchy \[ K^{(s)} = \operatorname{conv}\{v\in K:\|v\|_0\le s\}. \] We propose a penalized least-squares estimator that searches over this hierarchy and adapts to the best sparse…

高维有界离散图模型的正则化广义得分匹配估计AI 解读

Graphical models for multivariate count data are widely used to characterize conditional dependence structures. For count variables with unbounded support, however, ensuring a finite normalizing constant typically imposes restrictive constraints on interaction parameters. We propose bounded discrete graphical models for multivariate discrete responses with finite support, which remove such constraints by construction while retaining interpretable dependence on the observed…

若干广义变分推断问题的闭式解AI 解读

The Donsker--Varadhan formula characterizes the ordinary Bayesian posterior as the solution of an unrestricted $\mathsf{KL}$-regularized variational problem. Generalized variational inference replaces this regularizer by other divergences, but the resulting measure-valued optimization problem is often studied only after restriction to a parametric variational family. This paper studies the unrestricted measure-level problem. Given a measurable space $(\mathcal{Z},\mathfrak{Z})$…

高维数据的椭圆正则化Hotelling检验AI 解读

We consider one-sample testing of a high-dimensional location parameter under elliptically symmetric distributions with heavy tails and pervasive cross-sectional dependence. We propose an elliptical regularized Hotelling test with Cauchy combination (ERHT--CC), based on the sample spatial median and the spatial-sign covariance matrix centered at that median. We derive its null asymptotic normality, consistent estimators of the centering and variance, and an explicit local…

XMSE 感知的自适应经验贝叶斯估计AI 解读

Empirical Bayes (EB) estimators can match the first-order asymptotic risk of maximum likelihood (ML) while behaving very differently at second order: recent excess mean squared error (XMSE) analysis shows that kernel-based EB estimation may be worse than ML when the kernel is poorly aligned with the true parameter. This paper turns that diagnostic into a design principle. We propose an XMSE-aware mixed estimator that interpolates between ML and EB shrinkage. Its fixed-weight…