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前沿 AI 评测公共档案的贝叶斯推断与决策审计AI 解读

Public AI evaluations are often read as terminal leaderboards, yet the underlying evidence is a selective time series shaped by reporting rules, benchmark revisions, and missingness. Repeated public archives for LiveBench and Open LLM Leaderboard v2 serve as the primary longitudinal record; LMArena provides a preference stress test; and GAIA and tau-bench contribute limited agentic pilots. Together, these archives instantiate a Bayesian inference problem: under a fixed…

时间序列的生成式预测分布AI 解读

We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution in an appropriate generative representation that is based on a folklore result from measure theoretic probability. This representation provides a direct simulation-based approximation to the predictive distribution, enabling straightforward computation of forecasts for the conditional mean and…

货币政策中的风险与不确定性AI 解读

Central banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 1960s. The two are complementary but separate: scenarios provide narratives without probabilities, while predictive distributions provide probabilities with limited economic interpretation. Treating baseline forecasts and scenarios as conditional predictive densities, and distributional forecasts as…

hyreg2：混合连续与二元数据的潜在类别估计 R 包AI 解读

The R package hyreg2 introduces a frequentist framework for estimating latent class models for mixed outcome types using a joint likelihood approach. The method combines continuous and dichotomous data under the assumption that both outcome types arise from a common underlying data-generating process. In the implemented model, continuous responses are assumed to follow a normal distribution, while dichotomous responses are modeled using a binomial distribution. Such models…

收敛网络干扰下的处理效应估计AI 解读

Under network interference, a unit's observed outcome depends on the treatment assignment of its neighboring units in an exposure graph. Existing design-based asymptotic theory typically considers local interference by restricting neighborhood sizes in the exposure graph. Such methods do not apply to dense exposure graphs, so prior work has often adopted a superpopulation approach instead, imposing regularity through random-graph models. In this paper, we introduce a notion…

基于轮廓 M 估计的高维回归去偏推断AI 解读

Debiased inference for high-dimensional regression models has received substantial recent attention to ensure regularized estimators have valid inference. Many existing methods focus on achieving Neyman orthogonality through explicitly constructing projections onto the space of nuisance parameters, which is infeasible when an explicit form of the projection is unavailable. We introduce a general debiasing framework, Debiased Profile $M$-Estimation (DPME), which applies to a…

成绩测量误差影响处理分配时的倾向得分调整AI 解读

U.S. state education agencies mark schools displaying achievement gaps between demographic subgroups as needing improvement. Some schools may have few students in these subgroups, such that average end-of-year test scores only noisily measure the average "true" score-the score one would expect if students took the test many times. This, in addition to the masking of small subgroup averages in publicly available assessment data, poses challenges for evaluating interventions…

TrIM：变换迭代蒙德里安森林用于梯度降维与高维回归AI 解读

We propose a computationally efficient algorithm for gradient-based linear dimension reduction and high-dimensional regression. The algorithm initially computes a Mondrian forest and uses this estimator to identify a relevant feature subspace of the inputs from an estimate of the expected gradient outer product (EGOP) of the regression function. In addition, we introduce an iterative approach known as Transformed Iterative Mondrian (TrIM) forest to improve the Mondrian…

基于半参数加速失效时间模型的贝叶斯联合建模AI 解读

Longitudinal clinical studies often collect repeated measurements of biomarkers or health-related quality of life together with a time-to-event outcome. These processes are intrinsically linked: longitudinal trajectories may predict event risk, while event occurrence, or its anticipation, can induce informative censoring of the longitudinal process. Joint models provide a principled framework for handling this dependence, but most existing formulations rely on proportional…

弱模型依赖的空间参数收缩与模型平均AI 解读

Economic decisions often depend on many noisy estimates of quantities such as neighborhood effects, school quality, and hospital performance. Shrinkage estimation can improve decisions by pooling information across related units, but geography, adjacency, and shared characteristics each define a different notion of relatedness, and each implies a different way of pooling. We treat the choice of relatedness as part of the estimation problem, using Stein's Unbiased Risk…

相关 VAR 时间序列的匹配AI 解读

We study the problem of matching correlated VAR time series databases, where a multivariate time series is observed along with a perturbed and permuted version, and the goal is to recover the unknown matching between them. To model this, we introduce a probabilistic framework in which two time series $(x_t)_{t\in[T]},(x^\#_t)_{t\in[T]}$ are jointly generated, such that $x^\#_t=x_{π^*(t)}+σ\tilde{x}_{π^*(t)}$, where $(x_t)_{t\in[T]},(\tilde{x}_t)_{t\in[T]}$ are independent…

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…

正依赖条件下有序结果因果效应的界限AI 解读

Defining and estimating causal effects for ordinal data is challenging. Standard average treatment effects are not appropriate for ordinal scales, and alternative estimands, such as the probabilities that the treatment outcome exceeds or does not worsen the control outcome, are generally not identifiable. Existing work provides sharp bounds for these quantities based only on marginal distributions. Motivated by a previous observation showing that bounds obtained under an…

新产品需求的估计AI 解读

This paper develops an approach for estimating demand for a new product. Taking willingness to pay (WTP) as primitive, it establishes a general and yet analytically simple demand function, and proposes an estimation procedure that consistently recovers the underlying demand function from the WTP data. Monte Carlo simulations find the estimation procedure works well in identifying the demand function. This approach complements existing methods of demand estimation, and can be…

二项逻辑混合模型中的信息差距与可行性感知推断AI 解读

This paper studies the information gap between mixture detection and label recovery in binomial logistic mixtures. Standard likelihood-based criteria such as the Bayesian information criterion (BIC) can detect the presence of two components, but this does not guarantee that the corresponding labels are recoverable. We show that this gap is intrinsic to binomial logistic mixtures with a fixed number of trials: observed-data evidence for mixture structure and per-observation…

离散选择与竞争反应：R 包 cash 的端到端模拟AI 解读

Although discrete choice (choice-based conjoint) analysis has become a widely used technique for the elicitation of consumer preferences and hence a foundation for product design, to the best of our knowledge, there exists neither free and open-source nor commercial software that covers the game-theoretic simulation of competitive reactions among firms based on discrete choice models to improve decision making beyond traditional product (line) optimization. The R package…

校正测量误差的变效应贝叶斯函数型加速失效时间模型AI 解读

Functional data collected as continuously observed trajectories arise naturally in many biomedical settings, and a key inferential goal is understanding how such functional covariates relate to time-to-event outcomes while allowing that relationship to vary across subgroups defined by scalar characteristics. Existing frequentist approaches to functional accelerated failure-time (AFT) models struggle to flexibly capture the joint, nonlinear influence of scalar covariates and…

Azadkia–Chatterjee 条件图相关系数的极限定理AI 解读

Inferring the strength of conditional dependence and testing conditional independence are fundamental problems in statistics. A recent breakthrough by Azadkia and Chatterjee introduced, for the first time, a conditional dependence measure that equals $0$ if and only if the variables under study are conditionally independent, and equals $1$ if and only if they are conditionally perfectly dependent. They further proposed a computationally efficient and strongly consistent…

具有潜在组结构的面板随机前沿模型AI 解读

Stochastic frontier models have attracted considerable attention due to the incorporation of an inefficiency term in addition to the conventional error term. In this paper, we propose a general estimation framework for panel stochastic frontier models that accommodates potential heterogeneity through latent group structures. The framework is tailored to the distinctive features of stochastic frontier models and is paired with a practical hybrid estimation procedure that…

利用“软”符号限制加速紧识别 SVAR 的后验抽样AI 解读

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on…

原型发现问题的决策理论AI 解读

In the archetype discovery problem a researcher wants to summarize N heterogeneous policy effects of interest that vary over a discrete set of covariates. The goal is to partition the set of covariates into K<N groups -- the archetype sets -- and to provide a summary of the policy effects for each group. We use decision theory to show that, under a weighted mean-squared-error criterion, a procedure analogous to the Sorted Group Average Treatment Effects (GATES) solves the…

多重连续暴露的因果充分降维及其环境混合物应用AI 解读

Estimating causal effects with multivariate continuous exposures is challenging because causal exposure-response surfaces can be high-dimensional, complicating estimation and interpretation of joint exposure effects. Such settings arise in environmental epidemiology, where interest centers on the health effects of chemical and pollutant mixtures. We develop causal sufficient dimension reduction (CSDR), a semiparametric framework for representing causal exposure-response…

预测式贝叶斯回归中的设计分布AI 解读

The predictive approach to Bayesian inference accesses the posterior distribution via a sequence of one-step-ahead predictives, enabling inference via predictive resampling without Markov chain Monte Carlo. In the random-design regression setting, an explicit specification of the predictive design distribution is required, yet the impact of this choice has received little formal attention. We study the role of this predictive design distribution in parametric martingale…

含同期效应与指数族噪声的可识别马尔可夫切换模型AI 解读

Temporal systems often exhibit non-stationary behaviour, such as seasonal climate variation or glucose fluctuations in patients with type-1 diabetes. One way to model non-stationarity is through discrete latent regimes, i.e., stationary segments of time. Such systems induce a Markov Switching Model (MSM), a class of Hidden Markov Models with autoregressive dependencies among latent regimes and observed variables. Identifying latent regimes is challenging in the presence of…