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线性回归与面板模型中的空间相关稳健推断AI 解读

We consider inference about a scalar coefficient in a linear regression with spatially correlated errors. Recent suggestions for more robust inference require stationarity of both regressors and dependent variables for their large sample validity. This rules out many empirically relevant applications, such as difference-in-difference designs. We develop a robustified version of the recently suggested SCPC method that addresses this challenge. We find that the method has good…

单位超球面上的空间分位数AI 解读

We propose a concept of quantiles for probability measures on the unit hypersphere Sd−1 of Rd. The innermost quantile is the Fréchet median, that is, the L1-analog of the Fréchet mean. The proposed quantiles μα,um are directional in nature: they are indexed by a scalar order α∈[0,1] and a unit vector u in the tangent space TmSd−1 to Sd−1 at m. To ensure computability in any dimension d, our quantiles are essentially obtained by considering the Euclidean (Chaudhuri (J. Amer.…

通过逐次投影为文档分配主题AI 解读

Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering documents from big unstructured corpora into topics is an important task in various fields, such as image analysis, e-commerce, social networks and population genetics. Since the number of topics is typically substantially smaller than the size of the corpus and of the dictionary, the methods of topic…

处理决策存在策略互动的处理效应模型AI 解读

This study considers treatment effect models in which others’ treatment decisions can affect both one’s own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one’s own treatment and that from the partner are identifiable on the…

马尔可夫转换 VAR 模型中的矩、冲击与溢出AI 解读

To investigate how economies, financial markets or institutions can deal with stress, we often analyze the effects of shocks conditional on being in a recession or a bear market. MSVAR models are perfectly suited for such analyses because they combine gradual movements with sudden regime switches. In this paper, we develop a comprehensive methodology to conduct these analyses. We derive first and second moments conditional only on the regime distribution and propose impulse…

空间数据的非参数预测AI 解读

We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorization of the spectral density function. We provide theoretical results showing that the predictor has desirable asymptotic properties. Finite sample performance is assessed in a Monte Carlo study that also compares our algorithm to a rival nonparametric method based on the infinite $AR$ representation of the dynamics of the data. Finally, we apply our methodology to predict…

重新审视簇范围推断：脑活动的量化与定位AI 解读

Abstract Cluster inference based on spatial extent thresholding is a popular analysis method multiple testing in spatial data, and is frequently used for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding regions with some activation, the method as currently defined does not allow any further quantification or localisation of signal. In this paper, we repair this gap. We show that cluster-extent…

Van Werde对《Varimax经典因子分析可进行统计推断》的讨论AI 解读

I would like to point out that the author’s results have potential applications in the theory of random graphs beyond those for block models which are explicitly remarked upon in the paper. In particular, the results could be used to resolve the nonidentifiability associated with random dot product graphs (Athreya et al., 2017, Remark 1). The authors are most likely already aware of this connection: they have previously worked on generalized random dot product graphs in Rohe…

Pargent、Goretzko 与 von Oertzen 对《经典因子分析结合 Varimax 可进行统计推断》的讨论AI 解读

As psychologists, we appreciate Rohe & Zeng’s (R&Z; Rohe & Zeng, 2022) new insights into ‘vintage’ principal component analysis with varimax rotation (PCA+VR). Theories of intelligence and personality, perhaps psychology’s contributions best known outside of our field, have been a direct product of PCA. PCA+VR is still widely used for developing and evaluating psychological tests and questionnaires, although the literature has fought against it in favour of more complex…

Bickel 等对《Varimax 经典因子分析实现统计推断》的讨论意见AI 解读

We compliment the authors for their unification of old and new latent variable models. Their motivation seems to have come from Thurstone’s Factor Analysis and the wish to explain the somewhat mysterious success of Kaiser’s Varimax rule in that context. However, their major applications and notation stem from overlapping network community detection and Latent Dirichlet Allocation (LDA) (Blei et al., 2003). Inference for these models can be unified in another way via a…

倪晓月对《Varimax 旋转的传统因子分析可进行统计推断》的讨论AI 解读

I want to congratulate Rohe and Zeng on solving a long-time puzzle (and fight between statisticians and practitioners) of ‘why/whether the varimax rotation works’. Their inspiring results have some implications in the literature of latent space models for social networks (Hoff, 2021; Hoff et al., 2002). One of the appealing features of the latent space model is that one can extract the latent factors and analyse the unobserved features through the latent positions. However,…

Joshua Cape 对《采用 Varimax 的经典因子分析实现统计推断》的讨论AI 解读

I congratulate Professor Rohe and Dr Zeng on their illuminating paper. Their broad contributions will no doubt redouble contemporary research activity in multivariate analysis for years to come. Even the paper’s appendices are full of valuable gems, not to be overlooked by readers. Rohe and Zeng contextualize their findings vis-à-vis classical developments in psychometrics and statistics. This choice is apt, for the authors champion a newfound understanding of varimax factor…

大众交通对通勤、劳动力与住房市场的影响：福利与识别AI 解读

Abstract I study Los Angeles Metro Rail's effects using panel data on bilateral commuting flows, a quantitative spatial model, and historically motivated quasi-experimental research designs. The model separates transit's commuting effects from local productivity or amenity effects, and spatial shift-share instruments identify inelastic labor and housing supply. Metro Rail connections increase commuting by 16% but do not have large effects on local productivity or amenities.…

异质数据的协变量辅助贝叶斯图学习AI 解读

In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets under-utilized in determining the joint dependency structure. In this article, we consider a Bayesian approach to model undirected graphs underlying heterogeneous multivariate observations with additional assistance from covariates. Building…

仅用一阶滞后建模长记忆AI 解读

Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a…

离散情形下因果推断的自动化方法AI 解读

When causal quantities cannot be point identified, researchers often pursue partial identification to quantify the range of possible values. However, the peculiarities of applied research conditions can make this analytically intractable. We present a general and automated approach to causal inference in discrete settings. We show causal questions with discrete data reduce to polynomial programming problems, and we present an algorithm to automatically bound causal effects…

空间混杂的谱域调整AI 解读

Summary Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures…

群组效应异质性与干扰下的时变因果效应调节AI 解读

Summary The micro-randomized trial is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health intervention components that may be delivered at hundreds or thousands of decision points. Micro-randomized trials have motivated a new class of causal estimands, termed causal excursion effects, for which semiparametric inference can be conducted via a weighted, centred least-squares criterion (Boruvka et al., 2018). Causal excursion…

稀疏网络中社区数量的估计AI 解读

Among the nonparametric methods of estimating the number of communities (K) in a community detection problem, methods based on the spectrum of the Bethe Hessian matrices (Hζ with the scalar parameter ζ) have garnered much popularity for their simplicity, computational efficiency, and robustness to the sparsity of data. For certain heuristic choices of ζ, such methods have been shown to be consistent for networks with N nodes with a common expected degree of ω( log N). In…

单指标 Fréchet 回归AI 解读

Single index models provide an effective dimension reduction tool in regression, especially for high-dimensional data, by projecting a general multivariate predictor onto a direction vector. We propose a novel single-index model for regression models where metric space-valued random object responses are coupled with multivariate Euclidean predictors. The responses in this regression model include complex, non-Euclidean data, including covariance matrices, graph Laplacians of…

含群组效应的高效同伴效应估计AI 解读

We study linear peer effect models where peers interact in groups and individual’s outcomes are linear in the group mean outcome and characteristics. We allow for unobserved random group effects as well as observed fixed group effects. The specification is in part motivated by the moment conditions imposed in Graham (2008). We show that these moment conditions can be cast in terms of a linear random group effects model and that they lead to a class of GMM estimators with…

社会互动阈值回归AI 解读

This paper develops a threshold social interaction model which introduces group-specific endogenous effects as well as contextual effects into a conventional spatial Durbin model. We propose a two-step GMM estimator for the threshold and regression parameters, derive asymptotic theory, and provide bootstrap inference. Finally, we assess the performance of our methods using a Monte Carlo simulation and provide an empirical application on the role of peer effects on student…

空间动态面板中潜在群组结构的识别AI 解读

This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group…

存在溢出与不依从的实验中如何识别因果效应AI 解读

This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of spillovers–one person’s treatment may affect another’s outcome–and one-sided non-compliance—subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person’s own treatment changes her outcome, while indirect effects quantify how her peers’…