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人工对照的反事实分析：推断、高维与非平稳性AI 解读

Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of “untre ated” peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual…

增广合成控制法AI 解读

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings…

反事实与合成控制的精确稳健共形推断方法AI 解读

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably…

因果面板数据模型的矩阵补全方法AI 解读

In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We propose a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the "missing" elements of the control outcome matrix,…

利用互联网搜索数据与 PRISM 预测失业保险初次申请人数AI 解读

Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users’ Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method Penalized Regression with Inferred Seasonality Module (PRISM), which uses publicly available online search data from Google. PRISM is a semiparametric method, motivated…

刻画有向脑网络的贝叶斯状态空间方法AI 解读

The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use intracranial electroencephalography (iEEG) to record the patient’s intracranial brain activity in many small regions. iEEG data are high-dimensional…

利用半正交非负矩阵分解对分诊记录进行主题建模AI 解读

Emergency department (ED) crowding is a universal health issue that affects the efficiency of hospital management and patient care quality. ED crowding frequently occurs when a request for a ward-bed for a patient is delayed until a doctor makes an admission decision. In this case study, we build a classifier to predict the disposition of patients using manually typed nurse notes collected during triage as provided by the Alberta Medical Center. These predictions can…

超高维图像对标量回归中的稀疏学习与结构识别AI 解读

This article considers high-dimensional image-on-scalar regression, where the spatial heterogeneity of covariate effects on imaging responses is investigated via a flexible partially linear spatially varying coefficient model. To tackle the challenges of spatial smoothing over the imaging response’s complex domain consisting of regions of interest, we approximate the spatially varying coefficient functions via bivariate spline functions over triangulation. We first study…

模型选择中多来源分块缺失数据的整合AI 解读

For multisource data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this article, we propose a multiple block-wise imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups…

配对错配污染下的球面回归及自动知识转换应用AI 解读

Motivated by a series of applications in data integration, language translation, bioinformatics, and computer vision, we consider spherical regression with two sets of unit-length vectors when the data are corrupted by a small fraction of mismatch in the response-predictor pairs. We propose a three-step algorithm in which we initialize the parameters by solving an orthogonal Procrustes problem to estimate a translation matrix W ignoring the mismatch. We then estimate a…

关于 Kelly、Manela 与 Moreira《文本选择》的讨论AI 解读

The article addresses the important question of how to infer information from text data, which is a high-dimensional form of data. The article proposes to estimate separately the probability of sel...

文本选择AI 解读

Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high-dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than…

高维条件变量下加性可分模型的推断AI 解读

This article studies nonparametric series estimation and inference for the effect of a single variable of interest x on an outcome y in the presence of potentially high-dimensional conditioning variables z. The context is an additively separable model E[y|x,z]=g0(x)+h0(z). The model is high-dimensional in the sense that the series of approximating functions for h0(z) can have more terms than the sample size, thereby allowing z potentially to have very many measured…

延迟接受随机游走 Metropolis 算法的效率AI 解读

Delayed-acceptance Metropolis–Hastings and delayed-acceptance pseudo-marginal Metropolis–Hastings algorithms can be applied when it is computationally expensive to calculate the true posterior or an unbiased stochastic approximation thereof, but a computationally cheap deterministic approximation is available. An initial accept–reject stage uses the cheap approximation for computing the Metropolis–Hastings ratio; proposals which are accepted at this stage are subjected to a…

凸约束下选择后推断的整合方法AI 解读

Inference after model selection has been an active research topic in the past few years, with numerous works offering different approaches to addressing the perils of the reuse of data. In particular, major progress has been made recently on large and useful classes of problems by harnessing general theory of hypothesis testing in exponential families, but these methods have their limitations. Perhaps most immediate is the gap between theory and practice: implementing the…

高阶全变差正则化最小二乘的预测界AI 解读

We establish adaptive results for trend filtering: least squares estimation with a penalty on the total variation of (k−1)th order differences. Our approach is based on combining a general oracle inequality for the ℓ1-penalized least squares estimator with “interpolating vectors” to upper bound the “effective sparsity.” This allows one to show that the ℓ1-penalty on the kth order differences leads to an estimator that can adapt to the number of jumps in the (k−1)th order…

用最优决策树自适应估计多元分段多项式与有界变差函数AI 解读

Proposed by Donoho (Ann. Statist. 25 (1997) 1870–1911), Dyadic CART is a nonparametric regression method which computes a globally optimal dyadic decision tree and fits piecewise constant functions in two dimensions. In this article, we define and study Dyadic CART and a closely related estimator, namely Optimal Regression Tree (ORT), in the context of estimating piecewise smooth functions in general dimensions in the fixed design setup. More precisely, these optimal…

高斯过程回归的变量选择一致性AI 解读

Bayesian nonparametric regression under a rescaled Gaussian process prior offers smoothness-adaptive function estimation with near minimax-optimal error rates. Hierarchical extensions of this approach, equipped with stochastic variable selection, are known to also adapt to the unknown intrinsic dimension of a sparse true regression function. But it remains unclear if such extensions offer variable selection consistency, that is, if the true subset of important variables…

隐私的代价：差分隐私参数估计的最优收敛率AI 解读

Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the classical low-dimensional and modern high-dimensional settings. A primary focus is to establish minimax optimality for statistical estimation with the…

用于可扩展模型选择的近似拉普拉斯近似AI 解读

Abstract We propose the approximate Laplace approximation (ALA) to evaluate integrated likelihoods, a bottleneck in Bayesian model selection. The Laplace approximation (LA) is a popular tool that speeds up such computation and equips strong model selection properties. However, when the sample size is large or one considers many models the cost of the required optimizations becomes impractical. ALA reduces the cost to that of solving a least-squares problem for each model.…

高维模型中个体化处理效应的最优统计推断AI 解读

Abstract The ability to predict individualized treatment effects (ITEs) based on a given patient's profile is essential for personalized medicine. We propose a hypothesis testing approach to choosing between two potential treatments for a given individual in the framework of high-dimensional linear models. The methodological novelty lies in the construction of a debiased estimator of the ITE and establishment of its asymptotic normality uniformly for an arbitrary future…

《高维普通最小二乘投影变量筛选》的引理修正说明AI 解读

The conclusion in Lemma 5 in the supplementary material (SM) of Wang and Leng (2016), on which Theorems 1–3 rely, is incorrect. Here we provide an additional condition under which the conclusion of this lemma holds. We assume that the rows of the matrix X follow a Gaussian distribution with precision matrix Ω = Σ−1 that satisfies c 4 - 1 n - τ / 2 ≤ λ min ( Ω ) ≤ λ max ( Ω ) ≤ c 4 n τ / 2 . This implies the assumption on the condition number of Σ in Assumption A3. We drop…

期权隐含状态价格密度的筛估计AI 解读

This paper proposes a nonparametric estimator for the state price density implied by a single cross-section of European options with different strikes and the same maturity. The proposed estimator has two distinctive features. First, it extracts information from both call and put options, as opposed to only call options. Second, it does not require estimating any second-order derivative; instead, it solves a constrained and penalized linear regression. The asymptotic…

时变参数高维预测回归的 Boosting 估计AI 解读

High dimensional predictive regressions are useful in wide range of applications. However, the theory is mainly developed assuming that the model is stationary with time invariant parameters. This is at odds with the prevalent evidence for parameter instability in economic time series, but theories for parameter instability are mainly developed for models with a small number of covariates. In this paper, we present two L2 boosting algorithms for estimating high dimensional…