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分布预测评分规则对缩放和测量误差的敏感性AI 解读

Summary This paper examines the impact of data rescaling and measurement error on scoring rules for distribution forecast. First, I show that all commonly used scoring rules for distribution forecasts are robust to rescaling the data. Second, the forecast ranking based on the continuous ranked probability score is less sensitive to gross measurement error than the ranking based on the log score. The theoretical results are complemented by a simulation study aligned with…

全球经济疲弱程度的实时测度AI 解读

Summary We propose an empirical framework to measure the real‐time weakness of the global economy. This framework relies on nonlinear factor models to identify recessionary and expansionary episodes, fitted to several macroeconomic variables, for the largest advanced and emerging economies. The country‐specific inferences are then combined to construct both a Global Weakness Index and a Global Intensity Index. As new economic data become available from different regions,…

由分位数回归构造密度预测：宏观金融动态中的多峰性AI 解读

Summary Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two‐step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the “data speak.” Simulation evidence and an application revisiting GDP growth uncertainties in the United States…

利用多元收益识别和预测牛熊市场AI 解读

Summary Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes that all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross‐section of state‐specific means and variances to differ over bull and…

通过平均场变分近似进行贝叶斯模型选择AI 解读

Abstract This article considers Bayesian model selection via mean-field (MF) variational approximation. Towards this goal, we study the non-asymptotic properties of MF inference that allows latent variables and model misspecification. Concretely, we show a Bernstein–von Mises (BvM) theorem for the variational distribution from MF under possible model misspecification, which implies the distributional convergence of MF variational approximation to a normal distribution…

Rajendra Bhansali 对《因果模型的参数化与模拟》的讨论意见AI 解读

Contribution: My own introduction to the subject area of this paper is through the notion of Granger causality, a topic which has attracted much attention in time series analysis and related disciplines, including signal processing. The authors have proposed a new approach to causal models. However, this involves working with full density functions. It seems to me that, from a data analysis point of view, the authors’ approach might be a bit too general, and it might be…

Heather Battey对《因果模型的参数化与模拟》的讨论评论AI 解读

My brief comment relates to the inferential discussion of Section 5. The sample-space factorization of the joint probability from the marginal structural model induces a parameter-space factorization of the likelihood function, with the implication that θZX is orthogonal to (⁠θY|X*,ϕYZ|X*⁠) in the sense of, e.g. Cox and Reid (1987), and leading to the simplified asymptotic covariance matrix of Theorem 5.1. The parameter cut discussed at the beginning of Section 5 also…

Richard Guo 对《因果模型的参数化与模拟》的讨论：两种分解与密度比AI 解读

I congratulate Evans and Didelez warmly on this innovative and inspiring paper. Here, I offer my interpretation of the approach in terms of two factorizations and a density ratio. Any distribution over Z,X,Y can be factorized in two ways: Factorization C−1 is the usual one; it is factorization A−1 that is the focus of the two authors. More concretely, suppose ϕZY∣X(z,y∣x) is a conditional copula density, i.e. for every value of x, (z,y)↦ϕZY∣X(z,y∣x) is a density function…

马丁努森对《因果模型的参数化与模拟》的讨论AI 解读

I congratulate the authors on an interesting and useful contribution to the literature on causal inference. The authors propose a nice way of generating data so that a given causal structure of interest is preserved. This is a handy tool to have when for instance one needs to generate data to investigate small sample performance of a new inference procedure. I find it less attractive to use the suggested procedure for estimation purposes as is also alluded to in the paper…

A. Philip Dawid对《因果模型的参数化与模拟》的讨论意见AI 解读

When discussing causal inference in sequential problems, the authors only consider interventions that set their target variables to pre-specified fixed values. A more practically relevant aim, as considered by Dawid and Didelez (2010), is to assess the effect of a pre-specified dynamic treatment strategy, which details how each decision is to depend (possibly even stochastically) on previously observed variables. Can the methods of this paper be applied to this case?

从极端数据估计指向根节点的有向树AI 解读

Abstract We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-linear Bayesian network, which has been designed for modelling causality in extremes. The algorithm estimates bivariate scores and returns a root-directed spanning tree. It performs extremely well on benchmark data and on new…

通过迭代投影奇异值分解进行半参数张量因子分析AI 解读

Abstract This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend tensor factor models by incorporating auxiliary covariates in the loading matrices. We propose an algorithm of iteratively projected singular value decomposition (IP-SVD) for the semi-parametric estimation. It iteratively…

多元函数型数据的梯度同步性及脑连接应用AI 解读

Abstract Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain…

Abstract This paper presents a conformal inference method for out-of-distribution testing that leverages side information from labelled outliers, which are commonly underutilized or even discarded by conventional conformal p-values. This solution is practical and blends inductive and transductive inference strategies to adaptively weight conformal p-values, while also automatically leveraging the most powerful model from a collection of one-class and binary classifiers.…

预算约束下组合治疗的最优个体化治疗规则AI 解读

Abstract The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt 1 of 2 or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this paper, we propose a novel double encoder model (DEM) to…

Abstract The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of…

同时方向推断AI 解读

Abstract We consider the problem of inference on the signs of n>1 parameters. We aim to provide 1−α post hoc confidence bounds on the number of positive and negative (or non-positive) parameters, with a simultaneous guarantee, for all subsets of parameters. We suggest to start by using the data to select the direction of the hypothesis test for each parameter; then, adjust the p-values of the one-sided hypotheses for the selection, and use the adjusted p-values for…

HulC：利用凸包构造置信区域AI 解读

Abstract We develop and analyse the HulC, an intuitive and general method for constructing confidence sets using the convex hull of estimates constructed from subsets of the data. Unlike classical methods which are based on estimating the (limiting) distribution of an estimator, the HulC is often simpler to use and effectively bypasses this step. In comparison to the bootstrap, the HulC requires fewer regularity conditions and succeeds in many examples where the bootstrap…

因果模型的参数化与模拟AI 解读

Abstract Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to…

解读 K 线：即时波动率的 OK 估计量AI 解读

Abstract We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based…

摘要暂缺

健康差异研究中的统计方法AI 解读

In Statistical Methods in Health Disparity Research, author J. Sunil Rao invokes the U.S. Department of Health and Human Services’ Healthy People 2020 definition of health disparity, which is “a pa...

联合气候重建不确定性量化的联合可交换过程模型AI 解读

Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. These boundary conditions are typically fixed using available reconstructions in climate modeling studies; however, in reality they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be…

电子健康记录中的树引导稀有特征选择与逻辑聚合AI 解读

Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer…