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变维模型的最优辅助先验与可逆跳跃提议AI 解读

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm…

潜变量非参数协整回归AI 解读

This article studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified through the use of a proxy regressor in place of the true regressor. Such models occur in linear and nonlinear regressions where the regressor suffers from measurement error or where…

时间序列模型假设检验的自助法导论：双自回归模型应用AI 解读

In this article, we discuss the bootstrap as a tool for statistical inference in econometric time series models. Importantly, in the context of testing, properties of the bootstrap under the null (size) as well as under the alternative (power) are discussed. Although properties under the alternative are crucial to ensure the consistency of bootstrap-based tests, it is often the case in the literature that only validity under the null is discussed. We provide new results on…

处理状态误分类下的工具变量分位数回归AI 解读

Abstract This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics , 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the…

利用高维协变量特异性治疗效应曲线估计最优个体化治疗规则AI 解读

With a large number of baseline covariates, we propose a new semiparametric modeling strategy for heterogeneous treatment effect estimation and individualized treatment selection, which are two major goals in personalized medicine. We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is…

无直接效应假设下最优策略的有效估计AI 解读

Lin Liua , Zach Shahnb, James M. Robinsc* & Andrea Rotnitzkyda Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC and SJTU-Yale Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China; b IBM Research, Yorktown Heights, NY; c Department of Biostatistics and Epidemiology, Harvard University, Boston, MA; d Department of Economics, Universidad Torcuato Di Tella and CONICET, Buenos Aires, ArgentinaSupplementary materials for…

随机梯度马尔可夫链蒙特卡洛AI 解读

Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that performing exact inference generally requires all of the data to be processed at each iteration of the algorithm. For large datasets, the computational cost of MCMC can be prohibitive, which has led to recent developments in scalable Monte…

无限维、零直接样本情形的多分辨率近似理论：过渡推断与个体化预测AI 解读

Transitional inference is an empiricism concept, rooted and practiced in clinical medicine since ancient Greece. Knowledge and experiences gained from treating one entity (e.g., a disease or a group of patients) are applied to treat a related but distinctively different one (e.g., a similar disease or a new patient). This notion of “transition to the similar” renders individualized treatments an operational meaning, yet its theoretical foundation defies the familiar…

BAGS：具有亚组特异生存比较的贝叶斯自适应群序贯试验设计AI 解读

Ruitao Lin, Peter F. Thall* & Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Peter F. Thall rex@mdanderson.org Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TA 77030.AbstractA Bayesian group sequential design is proposed that performs survival comparisons within patient…

学习何时开始治疗的动态政策AI 解读

Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an “advantage doubly robust” estimator for learning such dynamic treatment rules using observational data under the assumption…

针对多领域潜在结局的个体化治疗规则学习AI 解读

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning…

长期平均结果的离策略估计及其移动健康应用AI 解读

Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions,…

学习最优个体化治疗规则的改进双重稳健估计AI 解读

Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator (AIPWE) of the expected clinical outcome over a class of ITRs. The method enjoys two key…

内生性下最优治疗规则的半参数工具变量方法AI 解读

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable (IV) approach to learning optimal treatment regimes under endogeneity. Specifically,…

利用观察数据估计与验证比值型条件平均处理效应AI 解读

While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in alleviating this problem. One common approach in trials is to predict the outcome of interest with separate regression models in each treatment arm, and…

情境老虎机在线决策中的统计推断AI 解读

Online decision making problem requires us to make a sequence of decisions based on incremental information. Common solutions often need to learn a reward model of different actions given the contextual information and then maximize the long-term reward. It is meaningful to know if the posited model is reasonable and how the model performs in the asymptotic sense. We study this problem under the setup of the contextual bandit framework with a linear reward model. The…

稳健 Q 学习AI 解读

Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic…

最优给药策略的双重稳健估计AI 解读

The goal of precision medicine is to tailor treatment strategies on an individual patient level. Although several estimation techniques have been developed for determining optimal treatment rules, the majority of methods focus on the case of a dichotomous treatment, an example being the dynamic weighted ordinary least squares regression approach of Wallace and Moodie. We propose an extension to the aforementioned framework to allow for a continuous treatment with the…

极端降水的层级最大无限可分空间模型AI 解读

Understanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable process models for block maxima have a rigid dependence structure that does not capture this type of behavior. We propose a flexible Bayesian model from a…

三治疗组因果效应的三元匹配：创伤中心等级与死亡率比较AI 解读

Giovanni Nattinoa, Bo Lua*, Junxin Shib , Stanley Lemeshowa & Henry Xiangbca Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OHb Center for Pediatric Trauma Research, Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OHc College of Medicine, The Ohio State University, Columbus, OHSupplementary materials for this article are available online. Please go to…

存在未测量混杂时个体化治疗规则的选择与排序AI 解读

It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is not point-identified when there is unmeasured confounding, it still defines a partial order among the treatment rules under Rosenbaum’s sensitivity analysis model. We first consider how to compare two treatment rules with unmeasured confounding in the single-decision setting and then use this…

工具变量与局部投影工具变量模型中偏误和检验尺寸扭曲的置信区间AI 解读

In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be…

分位数回归的平滑方法AI 解读

We propose to smooth the objective function, rather than only the indicator on the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur–Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of…

多值处理与分解分析：WIA 项目应用AI 解读

This article provides a general estimation and inference framework to study how different levels of program participation affect participants’ outcomes. We decompose differences in the outcome distribution into (i) a structure effect, arising due to the conditional outcome distributions given covariates associated with different levels of participation; and (ii) a composition effect, arising due to differences in the distributions of observable characteristics. These…