每日简报

黎曼流形上的自回归过程AI 解读

This paper introduces a Riemannian autoregressive (R-AR) model of order one for manifold-valued time series. The model is specified through an autoregressive process in the tangent space at a reference point, which is mapped to the manifold via the exponential map. It is characterised by two parameters: a reference point $μ$, representing the intrinsic central tendency through the Fréchet mean, and an autoregressive parameter $φ$, governing the dependence structure and…

两阶段抽样下加权处理效应的有效估计AI 解读

Two-phase sampling offers a practical way to collect costly confounders only in a subsample while retaining inexpensive information for a larger cohort. In observational causal studies, however, phase-2 selection can distort estimation of population causal effects if the sampling mechanism is ignored, and available phase-1 information may also be exploited to improve efficiency. Yet efficiency theory for causal estimands under such designs remains limited, particularly…

函数型时间序列的近似因子模型AI 解读

We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information…

类别数据的稳健估计与推断AI 解读

Categorical data pose a distinctive robustness challenge: contingency-table cells need not have a meaningful magnitude, ordering, or metric, so departures must instead be assessed through discrepancies between observed cell frequencies and model-implied probabilities. We develop $C$-estimation, a unifying framework for robust estimation in structured categorical models. $C$-estimation limits the influence of large frequency discrepancies and can be applied to unconditional,…

仅含当前治疗的 Cox 模型在因果推断中的局限AI 解读

Cox models with time-varying treatments often include only the current treatment level. Translating such a model into causally meaningful intervention-specific survival probabilities relies on the Markov property: that the hazard is independent of treatment history conditional on current treatment. For the Markov property to not be population-specific, it needs to hold also conditional on any unmeasured prognostic heterogeneity (frailty). Using a discrete-time argument,…

高维张量时间序列的 CP 因子分解与双投影迭代AI 解读

We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed based on the serial dependence structure of the data. The asymptotic properties of the proposed estimator are established under a general setting as long as the factor loading vectors are…

组成数据的合成控制AI 解读

When applying synthetic control to compositional outcomes (budget, vote shares), researchers commonly minimize Euclidean distances between raw shares. I propose constructing synthetic controls in Aitchison geometry, using centered log-ratio coordinates to select donor weights and form counterfactuals. This approach respects proportional comparisons and is exactly invariant to common multiplicative changes in relative odds. Under a multinomial-choice model, its weights…