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持续性预测回归中的参数不稳定与结构变化检验AI 解读

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and…

VAR 模型中脉冲响应的联合贝叶斯推断AI 解读

We derive the Bayes estimator of vectors of structural VAR impulse responses under a range of alternative loss functions. We also discuss the construction of joint credible regions for vectors of impulse responses as the lowest posterior risk region under the same loss functions. We show that conventional impulse response estimators such as the posterior median response function or the posterior mean response function are not in general the Bayes estimator of the impulse…

收益预测回归的一致局部谱推断AI 解读

This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and…

大型网络扩散的分解分析AI 解读

Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by estimating a cross-sectionally aggregated correlation between neighbors over time from data. However, the estimated diffusion can be misleading if the diffusion is confounded by omitted covariates. This paper focuses on the measure of diffusion…

平均密度估计量：效率与自助法一致性AI 解读

This paper highlights a tension between semiparametric efficiency and bootstrap consistency in the context of a canonical semiparametric estimation problem, namely the problem of estimating the average density. It is shown that although simple plug-in estimators suffer from bias problems preventing them from achieving semiparametric efficiency under minimal smoothness conditions, the nonparametric bootstrap automatically corrects for this bias and that, as a result, these…

高维二元回归中可扩展且准确的变分贝叶斯方法AI 解读

Summary Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent literature, bypassing such a trade-off is still an open problem even in routine binary regression models, and there is limited theory on the quality of variational approximations in high-dimensional settings. To address this gap, we study the approximation accuracy of routinely used…

随机森林的平均准确率下降：不一致性与 Sobol-MDA 解决方案AI 解读

Summary Variable importance measures are the main tools used to analyse the black-box mechanisms of random forests. Although the mean decrease accuracy is widely accepted as the most efficient variable importance measure for random forests, little is known about its statistical properties. In fact, the definition of mean decrease accuracy varies across the main random forest software. In this article, our objective is to rigorously analyse the behaviour of the main mean…

有限通信下粒子滤波的效率AI 解读

Summary Sequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between all particles. The $$\alpha$$-sequential Monte Carlo method was proposed recently as a potential solution to this that limits communication between particles. This limited communication is controlled through a sequence of stochastic matrices known as $$\alpha$$ matrices. We study the…

任意协方差下高维两样本 Behrens–Fisher 问题的近似随机化检验AI 解读

Summary This paper is concerned with the problem of comparing the population means of two groups of independent observations. An approximate randomization test procedure based on the test statistic of Chen & Qin (2010) is proposed. The asymptotic behaviour of the test statistic, as well as the randomized statistic, is studied under weak conditions. In our theoretical framework, observations are not assumed to be identically distributed even within groups. No condition on the…

随机对象的 Fréchet 充分降维AI 解读

Summary We consider Fréchet sufficient dimension reduction with responses being complex random objects in a metric space and high-dimensional Euclidean predictors. We propose a novel approach, called the weighted inverse regression ensemble method, for linear Fréchet sufficient dimension reduction. The method is further generalized as a new operator defined on reproducing kernel Hilbert spaces for nonlinear Fréchet sufficient dimension reduction. We provide theoretical…

部分干扰下网络处理效应的有效半参数估计AI 解读

Summary Although many estimators for network treatment effects have been proposed, their optimality properties, in terms of semiparametric efficiency, have yet to be resolved. We present a simple yet flexible asymptotic framework for deriving the efficient influence function and the semiparametric efficiency lower bound for a family of network causal effects under partial interference. An important corollary of our results is that one existing estimator, that proposed by Liu…

干扰下的平均直接与间接因果效应AI 解读

Summary We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is analogous to the standard definition of the average direct effect and can be expressed without needing to compare outcomes across multiple randomized experiments. We show that the proposed indirect effect satisfies a decomposition theorem stating that in a Bernoulli trial, the sum of the…

多中介自然中介效应的分解、识别与多重稳健估计AI 解读

Summary Natural mediation effects are desirable estimands for studying causal mechanisms in a population, but complications arise in defining and estimating natural indirect effects through multiple mediators with an unspecified causal ordering. We propose a decomposition of the natural indirect effect of multiple mediators into individual components, termed exit indirect effects, and a remainder interaction term, and study the similarities to and differences from existing…

处理影像研究异质性的函数型混合因子回归模型AI 解读

Summary This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer’s disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data…

调整Benjamini–Hochberg方法以控制knockoff变量选择的错误发现率AI 解读

Summary We consider the knockoff-based multiple testing set-up of Barber & Candès (2015). for variable selection in multiple regression. The method of Benjamini & Hochberg (1995) and an adaptive version of it are adjusted to this set-up, transforming them to valid $p$-value-based, false discovery rate-controlling methods that do not rely on specifying the correlation structure of the explanatory variables. Simulations and real data applications show that the proposed methods…

高维典型相关分析的显著性检验AI 解读

Summary We consider the problem of testing for the presence of linear relationships between large sets of random variables based on a postselection inference approach to canonical correlation analysis. The challenge is to adjust for the selection of subsets of variables having linear combinations with maximal sample correlation. To this end, we construct a stabilized one-step estimator of the Euclidean norm of the canonical correlations maximized over subsets of variables of…

时空数据的因果推断：估计伊拉克空袭对叛乱暴力的影响AI 解读

Abstract Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular…

拒绝潜在因子模型结构解释的统计检验AI 解读

Abstract Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the underlying latent, rather than the indicators, that is causally…

函数均值的经验似然推断及其可穿戴设备数据应用AI 解读

Abstract This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to construct confidence bands and compare functional means. Notably, our…

含噪网络关联数据的线性回归与推断AI 解读

Abstract Linear regression on network-linked observations has been an essential tool in modelling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors. This paper proposes a regression model with non-parametric network effects. The model does not assume that the relational data or…

Hilbert 空间中的条件独立检验及函数型数据应用AI 解读

Abstract We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalises testing the significance of X in a regression model of scalar response Y on functional regressors X and Z. We show, however, that even in the idealised setting where additionally (X, Y, Z) has a Gaussian distribution, the power of any test cannot exceed its size. Further modelling…

通过自归一化进行时间序列分段AI 解读

Abstract We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully non-parametric, robust to temporal dependence and avoids the demanding consistent estimation of long-run variance. One salient and distinct feature of the proposed method is its versatility, where it allows change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the…

广义贝叶斯损失函数选择与非正规化模型的使用AI 解读

Abstract Statisticians often face the choice between using probability models or a paradigm defined by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into a proper probability model, there are many tools to decide which model or loss is more appropriate for the observed data, in the sense of explaining the data's nature. However, when the loss leads to an improper model, there are no principled ways to guide this choice. We address…

扫描统计量的校准：有限样本表现与渐近理论AI 解读

Abstract We consider the problem of detecting an elevated mean on an interval with unknown location and length in the univariate Gaussian sequence model. Recent results have shown that using scale-dependent critical values for the scan statistic allows to attain asymptotically optimal detection simultaneously for all signal lengths, thereby improving on the traditional scan, but this procedure has been criticised for losing too much power for short signals. We explain this…