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弱模型假设下的Cox回归AI 解读

Inference for the conditional association between an exposure and a time-to-event endpoint, given covariates, is routinely based on partial likelihood estimators for hazard ratios indexing Cox proportional hazards models. This approach is flexible and makes testing straightforward, but is nonetheless not entirely satisfactory. First, there is no good understanding of what it infers when the model is misspecified. Second, it is common to employ variable selection procedures…

高维动态主成分分析AI 解读

Principal component analysis is a versatile tool to reduce dimensionality which has wide applications in statistics and machine learning. It is particularly useful for modeling data in high-dimensional scenarios where the number of variables p is comparable to, or much larger than the sample size n. Despite an extensive literature on this topic, researchers have focused on modeling static principal eigenvectors, which are not suitable for stochastic processes that are…

混杂变量维度增长时利用神经网络推断一般处理效应AI 解读

Semiparametric efficient estimation of various multi-valued causal effects, including quantile treatment effects, is important in economic, biomedical, and other social sciences. Under the unconfoundedness condition, adjustment for confounders requires estimating the nuisance functions relating outcome or treatment to confounders nonparametrically. This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial…

神经网络资产定价中的显著性检验AI 解读

This study proposes a novel hypothesis test for evaluating the statistical significance of input variables in multi-layer perceptron (MLP) regression models. Theoretical foundations are established through consistency results and estimation rate analysis using the sieves method. To validate the test’s performance in complex and realistic settings, an extensive Monte Carlo simulation is conducted. Results of the simulation reveal that the test has a high power and low rate of…

用于文本分析的稳健机器学习算法AI 解读

We study the Latent Dirichlet Allocation model, a popular Bayesian algorithm for text analysis. We show that the model's parameters are not identified, which suggests that the choice of prior matters. We characterize the range of values that the posterior mean of a given functional of the model's parameters can attain in response to a change in the prior, and we suggest two algorithms that report this range. Both of our algorithms rely on obtaining multiple Nonnegative…

厚尾依赖数据的机器学习面板回归：理论与应用AI 解读

The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and…

利用提升式离散时间竞争风险模型分析老年患者术后死亡AI 解读

Elderly patients are at a high risk of suffering from postoperative death. Personalized strategies to improve their recovery after intervention are therefore urgently needed. A popular way to analyze postoperative mortality is to develop a prognostic model that incorporates risk factors measured at hospital admission, for example, comorbidities. When building such models, numerous issues must be addressed, including censoring and the presence of competing events (such as…

良性过拟合与含噪特征AI 解读

Modern machine learning models often exhibit the benign overfitting phenomenon, which has recently been characterized using the double descent curves. In addition to the classical U-shaped learning curve, the learning risk undergoes another descent as we increase the number of parameters beyond a certain threshold. In this article, we examine the conditions under which benign overfitting occurs in the random feature (RF) models, that is, in a two-layer neural network with…

Google 数据何时有助于 GDP 即时预测？预筛选与收缩方法AI 解读

Alternative datasets are widely used for macroeconomic nowcasting together with machine learning–based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this article proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and…

非参数条件局部独立性检验AI 解读

Conditional local independence is an asymmetric independence relation among continuous time stochastic processes. It describes whether the evolution of one process is directly influenced by another process given the histories of additional processes, and it is important for the description and learning of causal relations among processes. We develop a model-free framework for testing the hypothesis that a counting process is conditionally locally independent of another…

多个预测变量与生存结局最大关联的有效估计AI 解读

This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high dimensions. Machine learning tools are commonly used to provide predictions of survival outcomes, but the estimated effect of a selected predictor suffers…

用于前沿效率分析的贝叶斯人工神经网络AI 解读

Artificial neural networks have offered their share of econometric insights, given their power to model complex relationships. One area where they have not been readily deployed is the estimation of frontiers. The literature on frontier estimation has seen its share of research comparing and contrasting data envelopment analysis (DEA) and stochastic frontier analysis (SFA), the two workhorse estimators. These studies rely on both Monte Carlo experiments and actual data sets…

用机器学习识别共同基金经理的技能AI 解读

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, before and after fees. The outperformance persists for more than three years. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment. Our…

陈云霄与徐恭军对《采用Varimax的经典因子分析可进行统计推断》的讨论AI 解读

We congratulate Rohe and Zeng for this insightful paper that elegantly connects psychometric methods and statistical and machine learning applications. We would like to mention several lines of related research. First, the problem is closely related to the latent variable selection problems (e.g., Y. Chen et al., 2015; Xu & Shang, 2018), where regularised estimation procedures are proposed to learn sparse loading structures. In fact, the vsp procedure can be viewed as the…

二元结果概率预测的诚实校准评估AI 解读

Summary Probability predictions from binary regressions or machine learning methods ought to be calibrated: if an event is predicted to occur with probability $x$, it should materialize with approximately that frequency, which means that the so-called calibration curve $p(\cdot)$ should equal the identity, i.e., $p(x) = x$ for all $x$ in the unit interval. We propose honest calibration assessment based on novel confidence bands for the calibration curve, which are valid…

NPIV模型平均导数的有效估计：神经网络估计量的模拟比较AI 解读

Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional variables more effectively than linear sieves. We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two efficient procedures for estimation and inference on a weighted average derivative (WAD): an…

集合识别线性模型的去偏机器学习AI 解读

This paper provides estimation and inference methods for an identified set’s boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample splitting ideas, I construct a root-N consistent, uniformly asymptotically Gaussian estimator of the boundary and propose a multiplier bootstrap procedure to…

分布不变的差分隐私AI 解读

Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy…

比较潜变量模型的核Stein检验AI 解读

Abstract We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the marginal distribution of the observed variables is intractable. The proposed test generalizes the recently proposed kernel Stein discrepancy (KSD) tests (Liu et al., Proceedings of the 33rd international conference on machine learning (pp. 276–284); Chwialkowski et al., (2016), In…

稀疏 Kronecker 积分解：图像回归信号区域检测的一般框架AI 解读

Abstract This paper aims to present the first Frequentist framework on signal region detection in high-resolution and high-order image regression problems. Image data and scalar-on-image regression are intensively studied in recent years. However, most existing studies on such topics focussed on outcome prediction, while the research on region detection is rather limited, even though the latter is often more important. In this paper, we develop a general framework named…

结合结构学习、监督学习与生成对抗学习检验有向无环图AI 解读

In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be…

对抗机器学习：贝叶斯视角AI 解读

Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting Machine Learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems.This creates a new class of security vulnerabilities that ML systems may face, and a new desirable property called adversarial robustness essential to trust operations based on ML outputs.Most work in AML is built upon a…

基于贝叶斯决策树集成的自适应条件分布估计AI 解读

We present a Bayesian nonparametric model for conditional distribution estimation using Bayesian additive regression trees (BART). The generative model we use is based on rejection sampling from a base model. Like other BART models, our model is flexible, has a default prior specification, and is computationally convenient. To address the distinguished role of the response in our BART model, we introduce an approach to targeted smoothing of BART models which is of…

条件抽样的深度生成方法AI 解读

We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator, so that a random sample from the target conditional distribution can be obtained by transforming a sample drawn from a reference distribution. The conditional generator is estimated…