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高维多项 Logit 模型AI 解读

Summary The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Because many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data‐driven two‐way parameter clustering over…

Rajendra Bhansali 对《因果模型的参数化与模拟》的讨论意见AI 解读

Contribution: My own introduction to the subject area of this paper is through the notion of Granger causality, a topic which has attracted much attention in time series analysis and related disciplines, including signal processing. The authors have proposed a new approach to causal models. However, this involves working with full density functions. It seems to me that, from a data analysis point of view, the authors’ approach might be a bit too general, and it might be…

Heather Battey对《因果模型的参数化与模拟》的讨论评论AI 解读

My brief comment relates to the inferential discussion of Section 5. The sample-space factorization of the joint probability from the marginal structural model induces a parameter-space factorization of the likelihood function, with the implication that θZX is orthogonal to (⁠θY|X*,ϕYZ|X*⁠) in the sense of, e.g. Cox and Reid (1987), and leading to the simplified asymptotic covariance matrix of Theorem 5.1. The parameter cut discussed at the beginning of Section 5 also…

Richard Guo 对《因果模型的参数化与模拟》的讨论：两种分解与密度比AI 解读

I congratulate Evans and Didelez warmly on this innovative and inspiring paper. Here, I offer my interpretation of the approach in terms of two factorizations and a density ratio. Any distribution over Z,X,Y can be factorized in two ways: Factorization C−1 is the usual one; it is factorization A−1 that is the focus of the two authors. More concretely, suppose ϕZY∣X(z,y∣x) is a conditional copula density, i.e. for every value of x, (z,y)↦ϕZY∣X(z,y∣x) is a density function…

马丁努森对《因果模型的参数化与模拟》的讨论AI 解读

I congratulate the authors on an interesting and useful contribution to the literature on causal inference. The authors propose a nice way of generating data so that a given causal structure of interest is preserved. This is a handy tool to have when for instance one needs to generate data to investigate small sample performance of a new inference procedure. I find it less attractive to use the suggested procedure for estimation purposes as is also alluded to in the paper…

A. Philip Dawid对《因果模型的参数化与模拟》的讨论意见AI 解读

When discussing causal inference in sequential problems, the authors only consider interventions that set their target variables to pre-specified fixed values. A more practically relevant aim, as considered by Dawid and Didelez (2010), is to assess the effect of a pre-specified dynamic treatment strategy, which details how each decision is to depend (possibly even stochastically) on previously observed variables. Can the methods of this paper be applied to this case?

提前裁员通知与总体就业损失AI 解读

Summary We collect data from Worker Adjustment and Retraining Notification (WARN) Act notices and establish their usefulness as an indicator of aggregate job loss. The number of workers affected by WARN notices (“WARN layoffs”) leads state‐level initial unemployment insurance claims and unemployment rate (UR) and private employment changes. WARN layoffs comove with aggregate layoffs from Mass Layoff Statistics and the Job Openings and Labor Turnover Survey but are timelier…

协整多项式回归：完全修正最小二乘法的稳健性AI 解读

Cointegrating polynomial regressions (CPRs) include deterministic variables, integrated variables, and their powers as explanatory variables. Based on a novel kernel-weighted limit result and a novel functional central limit theorem, this paper shows that the fully modified ordinary least squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) is robust to being used in CPRs. Being used in CPRs refers to a widespread empirical practice…

高斯链图模型的可识别性与一致估计AI 解读

The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this paper, we first establish a set of novel…

极值随机森林AI 解读

Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme value theory can be used to extrapolate beyond the range of the data, and several approaches exist that use linear regression, kernel methods or generalized additive models. Most of these methods break down if the predictor space has more than a few dimensions or if the regression function of…

具有非对称反事实效用的政策学习AI 解读

Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker’s utility function is more properly characterized by the joint set of potential outcomes…

从极端数据估计指向根节点的有向树AI 解读

Abstract We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-linear Bayesian network, which has been designed for modelling causality in extremes. The algorithm estimates bivariate scores and returns a root-directed spanning tree. It performs extremely well on benchmark data and on new…

多变量鞅差假设的广义谱检验AI 解读

This study proposes new generalized spectral tests for multivariate martingale difference hypotheses, specifically geared toward high-dimensionality scenarios where the dimension of the time series is comparable or even larger than the sample size in practice. We develop an asymptotic theory and a valid wild bootstrapping procedure for the new test statistics, in which the dimension of the time series is fixed. We demonstrate that a bias-reduced version of the test…

允许偏斜厚尾误差的统计识别结构 VARAI 解读

Abstract We introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized t ‐distributions, whose flexibility compared with commonly considered Student's t ‐distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non‐Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical application, the data…

识别材料基因的算子诱导结构变量选择AI 解读

In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set of elementary algebraic operators through compositions. Standard practice directly analyzes the high-dimensional candidate predictor space in a linear model; statistical analyses are then substantially hampered by the daunting challenge posed by the astronomically large number of…

混合样本检测的现状数据回归分析AI 解读

Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested…

交叉验证中渐近最优带宽的显式解AI 解读

Abstract We show that least-squares cross-validation methods share a common structure that has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student-t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative, thus leading to very fast computations, their integrated…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：政策评价局限AI 解读

We thank Sävje (2024) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Sävje recommended misspecified exposure effects as a way to avoid strong assumptions about interference when analysing the results of an experiment. In this discussion, we highlight a key limitation of Sävje’s recommendation. Exposure effects are not generally useful for evaluating social policies without the strong assumptions…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：推断所需的干扰限制AI 解读

We congratulate the author on an insightful paper that provides a discerning critique of the use of exposure mappings in studies with interference. The main observation is that exposure mappings serve two different functions; they define the estimand and impose structure on potential outcomes by restricting interference. The first function commonly leads researchers to select simple exposure mappings that summarize treatments assigned to units ‘near’ the ego. However, due to…

多元函数型时间序列的图主成分分析AI 解读

In this paper, we consider multivariate functional time series with a two-way dependence structure: a serial dependence across time points and a graphical interaction among the multiple functions within each time point. We develop the notion of dynamic weak separability, a more general condition than those assumed in literature, and use it to characterize the two-way structure in multivariate functional time series. Based on the proposed weak separability, we develop a…

稳健验证：分布漂移下的预测不确定性保障AI 解读

While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy—coming from robust statistics and optimization—is thus to build a model robust to distributional perturbations. In this article, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point…

多个分布之间的核差异度量AI 解读

Given M≥2 distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD)—a parameter that quantifies the difference between the M distributions. The population KMD, which takes values between 0 and 1, is 0 if and only if all the M distributions are the same, and 1 if and only if all the distributions are mutually singular. Moreover, KMD possesses many properties commonly associated with f-divergences…

重印：高维分位数回归的假设检验AI 解读

Quantile regression has been an important analytical tool in econometrics since it was proposed in 1970s. Many advantages make it still popular in the era of big data. This paper focuses on the testing problems of high-dimensional quantile regression, supplementing to robust methods in the literature of high-dimensional hypothesis testing. We first construct a new test statistic based on the quantile regression score function. The new test statistic avoids the inverse…

重印：高维线性中介模型推断及新冠疫情股票反应应用AI 解读

Mediation analysis draws increasing attention in many research areas such as economics, finance and social sciences. In this paper, we propose new statistical inference procedures for high dimensional mediation models, in which both the outcome model and the mediator model are linear with high dimensional mediators. Traditional procedures for mediation analysis cannot be used to make statistical inference for high dimensional linear mediation models due to…