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通过预测推断实现最优子抽样AI 解读

In the big data era, subsampling or sub-data selection techniques are often adopted to extract a fraction of informative individuals from the massive data. Existing subsampling algorithms focus mainly on obtaining a representative subset to achieve the best estimation accuracy under a given class of models. In this article, we consider a semi-supervised setting wherein a small or moderate sized “labeled” data is available in addition to a much larger sized “unlabeled” data.…

面向始终有效高维动态定价的在线正则化AI 解读

–Devising a dynamic pricing policy with always valid online statistical learning procedures is an important and as yet unresolved problem. Most existing dynamic pricing policies, which focus on the faithfulness of adopted customer choice models, exhibit a limited capability for adapting to the online uncertainty of learned statistical models during the pricing process. In this article, we propose a novel approach for designing a dynamic pricing policy based on regularized…

贝叶斯样条隐马尔可夫模型及其活动与睡眠分析应用AI 解读

B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states. A parsimonious knot configuration of the B-splines is identified by the use of a trans-dimensional Markov chain sampling algorithm, while model…

非参数与高维模型中的贝叶斯可忽略性是一种信息性先验AI 解读

In problems with large amounts of missing data one must model two distinct data generating processes: the outcome process, which generates the response, and the missing data mechanism, which determines the data we observe. Under the ignorability condition of Rubin, however, likelihood-based inference for the outcome process does not depend on the missing data mechanism so that only the former needs to be estimated; partially because of this simplification, ignorability is…

通过度量空间中的度量分布函数开展非参数统计推断AI 解读

The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties. This connection creates a paradigm for statistical inference. However, existing distribution functions are defined in Euclidean spaces and are no longer convenient to use in rapidly evolving data objects of complex nature. It is imperative to develop the…

Fréchet 回归的充分降维AI 解读

With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Petersen and Müller) provides a promising framework for regression analysis with metric space-valued responses. In this article, we introduce a flexible sufficient dimension reduction (SDR) method for Fréchet regression to achieve two purposes: to mitigate the curse of…

面板数据模型的高阶展开与统计推断AI 解读

In this paper, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical…

包络 Huber 回归AI 解读

Huber regression (HR) is a popular flexible alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from…

双边关系数据核密度估计的一致推断AI 解读

Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimax-optimal uniform convergence rate of the dyadic…

强干扰实验的新估计目标AI 解读

In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such “interference between units” violates traditional approaches for causal inference, so that additional assumptions are often imposed to model or limit the underlying social mechanism. For binary outcomes, we propose new estimands that can be estimated without such assumptions, allowing for interval estimates that assume only…

基于 Rayleigh 商的稀疏典型相关分析极小极大准贝叶斯估计AI 解读

Canonical correlation analysis (CCA) is a popular statistical technique for exploring relationships between datasets. In recent years, the estimation of sparse canonical vectors has emerged as an important but challenging variant of the CCA problem, with widespread applications. Unfortunately, existing rate-optimal estimators for sparse canonical vectors have high computational cost. We propose a quasi-Bayesian estimation procedure that not only achieves the minimax…

学习最优动态治疗方案时控制累积不良风险AI 解读

Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward…

单元格层面的最小协方差行列式估计量AI 解读

The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave differently from the majority of cases, raising suspicion that they might belong to a different population. On the other hand, cellwise outliers are individual cells in the data matrix. When a row contains one or more outlying cells, the other cells in the same row still contain useful information…

具有隐含结构的序列相关多变量数据 Copula 建模AI 解读

We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a…

存在隐蔽混杂的因果网络发现与推断AI 解读

This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental…

基于回归的遗传协方差稳健估计与推断AI 解读

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined as the underlying covariance of genetic effects and can be used to measure the shared genetic architecture. The data used to estimate such a genetic covariance can be from the same group or different groups of individuals, and the traits…

高维多变量二元响应的贝叶斯推断AI 解读

It has become increasingly common to collect high-dimensional binary response data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, multivariate probit (MVP) models are routinely used for inferences. However, algorithms for fitting such models face issues in scaling up to high dimensions due to the intractability of the likelihood, involving an integral over a multivariate normal distribution having no analytic form. Although a…

《误差可预测时的反事实结果插补》讨论：误设与敏感性分析AI 解读

Gonçalves and Ng propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of the errors is correctly…

从双重差分与滞后因变量视角理解 PUPAI 解读

I discuss the practical unbiased predictor (PUP; Gonçalves and Ng) from the viewpoint of the literature on identification in event studies. The PUP can be seen as the prediction based on a generalized estimand that encompasses both the difference-in-differences (DID) and the lagged dependent variable (LDV). This feature of the PUP allows for a doubly robust property that the identification is achieved when either the parallel trend assumption or the LDV assumption holds at…

误差可预测时的反事实结果插补AI 解读

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual…

状态空间模型的分块相关伪边际采样器AI 解读

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly.Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches.The improvement is achieved by the following innovations.First, a novel block version of PMMH that works with multiple particle filters is proposed.Second, the trimmed mean of the unbiased…

稳健最窄显著区间搜索：中位数多重变点推断AI 解读

We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localized regions in data sequences which each must contain a change-point in the median, at a prescribed global significance level.RNSP works by fitting the postulated constant model over many regions of the data using a new signmultiresolution sup-norm-type loss, and greedily identifying the shortest intervals on which the constancy is significantly violated.By working with the signs of…

分位数预测最优性检验AI 解读

Quantile forecasts made across multiple horizons have become an important output of many financial institutions, central banks and international organisations. This paper proposes misspecification tests for such quantile forecasts that assess optimality over a set of multiple forecast horizons and/or quantiles. The tests build on multiple Mincer-Zarnowitz quantile regressions cast in a moment equality framework. Our main test is for the null hypothesis of autocalibration, a…

人应当向机器谎报吗？Lasso 与广义线性结构稀疏估计的激励相容性AI 解读

We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users. The predictor is incentive-compatible if the user has no incentive to misreport her covariates. Focusing on the popular Lasso estimation technique, we borrow tools from high-dimensional statistics to characterize sufficient conditions that ensure that Lasso is incentive compatible in the asymptotic case. We…