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深度 Cox 模型的变量显著性检验AI 解读

Deep learning has become enormously popular in the analysis of complex data, including event time measurements with censoring. To date, deep survival methods have mainly focused on prediction. Such methods are scarcely used in matters of statistical inference such as hypothesis testing. Due to their black-box nature, deep-learned outcomes lack interpretability which limits their use for decision-making in biomedical applications. This article provides estimation and…

Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: (a) the curse of dimensionality when applied to the high-dimensional input data, and (b) unreliable comparison of…

ERP 脑机接口迁移学习中的贝叶斯信号匹配AI 解读

An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities to communicate by decoding electroencephalogram (EEG) signals. A P300-ERP embedded in EEG signals arises in response to a rare, but relevant event (target) among a series of irrelevant events (non-target). Different machine learning methods have constructed binary classifiers to detect target events, known as calibration. The existing calibration strategy…

大型加性潜变量模型的统计分位数学习AI 解读

Large and complex datasets, emerging from technological advancements in fields such as genomics and brain imaging, hold ample promise for gaining new scientific insights. Yet, their inherent nonlinearity and high dimensionality present considerable theoretical, methodological, and application challenges to the statistics and machine learning community. This article introduces Statistical Quantile Learning (SQL), a new nonparametric method for estimating large additive latent…

用于词嵌入的词级最大均值差异正则化AI 解读

The technique of word embedding is widely used in natural language processing (NLP) to represent words as numerical vectors in textual datasets. However, the estimation of word embedding may suffer from severe overfitting due to the huge variety of words. To address the issue, this article proposes a novel regularization framework that recognizes and accounts for the “word-level distribution discrepancy”–a common phenomenon in a range of NLP tasks where word distributions…

高维一般学习程序的拟合优度评估AI 解读

Black-box learners have demonstrated remarkable success across various fields due to their high predictive accuracy. However, the complexity of their learning procedures poses significant challenges in evaluating whether a given learner has achieved optimal performance on datasets with unknown data-generating mechanisms. We propose a general goodness-of-fit test for assessing different learning procedures involving high-dimensional predictors, encompassing methods from…

高维空间面板网络的统一推断AI 解读

We propose employing a high-dimensional generalized method of moments (GMM) estimator, regularized for dimension reduction and subsequently debiased to correct for shrinkage bias (referred to as a debiased-regularized estimator), for inference on large-scale spatial panel networks. In particular, the network structure, which incorporates a flexible sparse deviation that can be regarded either as a latent component or as a misspecification of a predetermined adjacency matrix,…

动态分位数的函数系数 VAR 模型及非参数金融网络构建AI 解读

In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish…

连续处理的双重去偏机器学习非参数推断AI 解读

We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial effects are asymptotically normal with nonparametric convergence rates. The first-step estimators for the nuisance conditional expectation function…

隐含波动率预测中的神经切线核：非线性函数自回归方法AI 解读

Forecasting implied volatility across different levels of moneyness and maturity is crucial yet challenging due to the high dimensionality of the Implied Volatility Surface (IVS) and the nonlinearity that characterizes its temporal dependence. We adopt a Nonlinear Functional Autoregressive (NFAR) framework to a sequence of IVS and employ neural networks that admit a Neural Tangent Kernel (NTK) parameterization to capture nonlinear interactions between surfaces. We illustrate…

二元关系数据的双重／去偏机器学习AI 解读

This article presents novel methods and theories for estimation and inference about parameters in statistical models using machine learning for nuisance parameter estimation when data are dyadic. We propose a dyadic cross-fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross-fitting method enables root- n consistent estimation and inference robustly against dyadic dependence. We…

多向聚类数据下因果函数的估计与推断AI 解读

We propose methods for estimation and uniform inference for a broad class of causal functions, such as conditional average treatment effects and continuous treatment effects, under multi-way clustering. The causal function is identified as the conditional expectation of a Neyman-orthogonal signal that depends on high-dimensional nuisance parameters. We introduce a two-step procedure: the first step uses machine learning to estimate the nuisance parameters, and the second…

连续处理的数据驱动政策学习AI 解读

This paper studies policy learning for continuous treatments from observational data. Continuous treatments present more significant challenges than discrete ones because population welfare may need nonparametric estimation, and policy space may be infinite-dimensional and may satisfy shape restrictions. We propose to approximate the policy space with a sequence of finite-dimensional spaces and, for any given policy, obtain the empirical welfare by applying the kernel…

存在样本构成变化的双重差分AI 解读

This paper studies Difference-in-Differences (DiD) setups with repeated cross-sectional data and potential compositional changes across time periods. We begin our analysis by deriving the efficient influence function and the semiparametric efficiency bound for the average treatment effect on the treated (ATT). We introduce nonparametric estimators that attain the semiparametric efficiency bound under mild rate conditions on the estimators of the nuisance functions,…

空间处理的因果推断AI 解读

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from…

数据丰富环境中非对称损失下的二元选择：理论与算法公平性应用AI 解读

We study the binary choice problem in a data‐rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data‐rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be…

灵活应对赢家诅咒的推断方法AI 解读

Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly, in machine learning, practitioners are often interested in the population performance of the model that performs best empirically. However, cherry-picking the best…

完备性是否必要？未识别线性模型中的估计AI 解读

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the nonsingularity of a high-dimensional matrix or operator and may fail for finite samples or even at the population level. Regularized estimators provide a…

机器学习同行评审中用于指数族估计的保序机制AI 解读

Abstract In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their papers by perceived quality. In this paper, we leverage these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the isotonic mechanism to exponential family distributions. This mechanism produces adjusted scores closely aligned with the original scores while strictly adhering…

高维线性回归中低维参数推断的正交投影方法AI 解读

We consider the estimation and statistical inference for low dimensional parameters for a regression model with covariates whose dimension increases with sample size. We suggest a computationally simple one stage orthogonal projection approach to estimate the low dimensional parameters under strict or approximate sparsity conditions. The orthogonal projection approach is simple to implement and the inference for the low dimensional parameters is straightforward to derive…

双重差分因果森林及其在挪威工资税负担归宿中的应用AI 解读

ABSTRACT This paper introduces the difference‐in‐difference causal forest (DiDCF) method, which extends the causal‐forest technique for estimating heterogeneous treatment effects to settings with dynamic treatment effects. Regular causal forests require independence between treatment assignment and the outcome variable (after conditioning out observables). In contrast, DiDCFs provide consistent estimates with a parallel trend assumption. DiDCFs can be used to create…

用于政策学习的半参数工具变量双重差分方法AI 解读

Summary Recently, there has been a surge in methodological development for the difference-in-differences approach to evaluate causal effects. Standard methods in the literature rely on the parallel-trend assumption to identify the average treatment effect on the treated. However, the parallel-trend assumption may be violated in the presence of unmeasured confounding, and the average treatment effect on the treated may not be useful in learning a treatment assignment policy…

删失生存数据的多重校准与普适适应性AI 解读

Summary Traditional statistical and machine learning methods typically assume that the training and test data follow the same distribution. However, this assumption is frequently violated in real-world applications, where the training data in the source domain may underrepresent specific subpopulations in the test data of the target domain. This article addresses target-independent learning under covariate shift, focusing on multicalibration for survival probability and…

利用统计推断增强 AI 生成生物医学图像的可信度AI 解读

Generative artificial intelligence (AI) has transformed the biomedical imaging field through image synthesis, addressing challenges of data availability, privacy, and diversity in biomedical research. This paper proposes a novel nonparametric method within the functional data framework to discern significant differences between the mean and covariance functions of original and synthetic biomedical imaging data, thereby enhancing the fidelity and utility of synthetic data.…