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Feng 与 Sun 对“网络潜在增长过程的根与社群推断”的讨论AI 解读

We congratulate Drs. Crane and Xu for their fascinating piece of work. In the multiple roots PAPER model, one assumption is that different trees share the same affine preferential attachment (APA) growth parameters (α,β)⁠. In practice, the multiple trees in a network may have varying growth mechanisms, and it may be of interest to model each tree to have its own growth parameters. We consider such a heterogeneous affine preferential attachment (HAPA) model. Definition 1…

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范旺、余怡与里纳尔多对网络潜在生长过程根与社区推断的讨论AI 解读

We congratulate Prof. Crane and Dr Xu for introducing a very interesting and appealing statistical model for Markovian network growth. The PAPER model enjoys several qualities. In particular, (i) it strikes a rare balance between analytic elegance and tractability, and expressive power; (ii) it allows for practicable algorithms for statistical inference that scale to networks of very large size, and (iii) it is very flexible and can be readily generalized to model a variety…

Andrej Srakar 对《网络潜在增长过程的根节点与社区推断》的讨论评论AI 解读

Paper presented by Harry Crane and Min Xu develops a rather novel area of network archaeology, which builds on findings of network science, probability theory and (in Crane and Xu's article) Bayesian statistics. My comments are directed to possible extensions for future work in this promising area of research. Firstly, in terms of network stochastic processes (authors use a combination of preferential attachment and Erdős–Rényi models) paper does little to explain…

Catalano等对《网络潜在生长过程的根与社群推断》的讨论AI 解读

We congratulate the authors for their methodological and theoretical contribution to the statistical literature on networks. A natural extension of the proposed PAPER model is included, with K communities growing simultaneously and where new nodes are either assigned to an existing community or elected as a new root. The employed assignment rule is of Pólya-urn type, which leads to a logarithmic growth of the number of communities (Korwar & Hollander, 1973) and is known to…

对 Crane 与 Xu《网络潜在生长过程的根与社区推断》的致谢与讨论AI 解读

We commend the authors on an illuminating article which significantly extends the state of the art in network archaeology. Notably, whereas most other literature in the area has provided methods for root inference which are theoretically grounded only in the case of random trees, the techniques in this paper have been designed to model nontree-structured networks, as well. This has important practical implications, as many real-world networks contain one or more cycles. The…

杨青与童昕对“网络潜在增长过程的根节点与社区推断”的讨论AI 解读

First, we congratulate Dr. Crane and Dr. Xu for this important contribution to statistical network analysis. To address an overlooked fact that most real networks are formed through a growth process, the authors introduce a useful PAPER model and its variations and skilfully construct computationally feasible confidence sets for the root node(s). In the data analysis, the authors also use their models as a new approach to community detection. On this note, our interest lies…

Wyse 等对《网络潜在增长过程的根节点与社区推断》的讨论AI 解读

We congratulate the authors on a thought provoking paper introducing innovative ideas for the statistical modelling of networks. The emphasis on identifying highly probable root nodes is both original and intriguing. In particular, using the root node credible set methods for node clustering within the PAPER(α,β,α0,θ) random K model (Section 6.3) is an interesting perspective. We wonder how these approaches might connect with latent space model-based node clustering methods…

江一聪与柯征对《网络潜在增长过程的根节点与社群推断》的讨论AI 解读

We congratulate the authors on an excellent paper! Crane and Xu (2021) proposed novel methods for finding ‘root nodes’ from a single snapshot of a dynamic network process, with several interesting real-data examples. We now consider a new application for finding ‘root papers’ in a citation network. The MADStat dataset (Ji et al., 2022; Ke et al., 2023) consists of the bibtex and citation information of over 83 K papers, which we use to construct paper citation networks.…

Sayan Banerjee 对《网络潜在生长过程的根与社区推断》的讨论AI 解读

I congratulate the authors on their elegant and computationally tractable approach to inference problems in network archaeology. This is one of the few papers which rigorously analyses inference for noisy temporal networks. The key idea is to compute the distribution of the possible arrival orders of vertices given an unlabelled network realization (online detection) and construct the confidence set for the root by sorting vertices according to their probability of arriving…

Tianxi Li 对《网络潜在生长过程的根节点与社群推断》的讨论AI 解读

I want to congratulate Prof. Crane and Prof. Xu for their impressive work. The paper introduces two novel components: the PAPER model and the inference framework for the tree. Both of them bring many insights into handling complex network data and open the doors to new problems. I will discuss extension problems from both aspects. The PAPER model assumes preferential attachment trees with additional the Erdős-Rényi (ER) edges. One nice property of the model lies in its…

Ascolani、Lijoi 与 Prünster 对《网络潜在生长过程的根与社群推断》的讨论AI 解读

We congratulate the authors for their timely and insightful contribution, which introduces a novel Bayesian approach to inferring the latent structure (early history and community detection) given a current observation of a network with n nodes. The Bayesian component of the model is summarized through a uniform prior on the random relabelling associated to the observed network. The methodological innovation is remarkably complemented by strong theoretical (frequentist)…

GENIUS-MAWII：应对大量弱且无效工具变量的稳健孟德尔随机化AI 解读

Abstract Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence…

用不完全 U 统计量检验可能不规则模型中的大量约束AI 解读

Abstract We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order or even larger than the number of observed samples. Moreover, standard distributional approximations may be invalid due to irregularities in the null hypothesis. We propose a general testing methodology that aims to circumvent…

协变量漂移下预测集的双重稳健校准AI 解读

Abstract Conformal prediction has received tremendous attention in recent years and has offered new solutions to problems in missing data and causal inference; yet these advances have not leveraged modern semi-parametric efficiency theory for more efficient uncertainty quantification. We consider the problem of obtaining well-calibrated prediction regions that can data adaptively account for a shift in the distribution of covariates between training and test data. Under a…

高维多项分布检验及其文本分析应用AI 解读

Abstract Motivated by applications in text mining and discrete distribution inference, we test for equality of probability mass functions of K groups of high-dimensional multinomial distributions. Special cases of this problem include global testing for topic models, two-sample testing in authorship attribution, and closeness testing for discrete distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null hypothesis, is…

网络潜在生长过程的根节点与社区推断AI 解读

Abstract Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdős–Rényi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdős–Rényi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component…

固定效应与广义 Mundlak 估计量AI 解读

Abstract We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics—group-level averages of functions of treatments and covariates—are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression…

控制错误覆盖声明率的选择性保形推断AI 解读

SUMMARY Conformal inference is a popular tool for constructing prediction intervals. We consider here the scenario of post-selection/selective conformal inference, that is, prediction intervals are reported only for individuals selected from unlabelled test data. To account for multiplicity, we develop a general split conformal framework to construct selective prediction intervals with the false coverage-statement rate control. We first investigate the false coverage…

混合样本检测的现状数据回归分析AI 解读

Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested…

交叉验证中渐近最优带宽的显式解AI 解读

Abstract We show that least-squares cross-validation methods share a common structure that has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student-t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative, thus leading to very fast computations, their integrated…

协变量诱导的依赖左截断下的双重稳健估计AI 解读

Summary In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of the time to event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time,…

Chatterjee 秩相关的自助法失效问题AI 解读

Abstract While researchers commonly use the bootstrap to quantify the uncertainty of an estimator, it has been noticed that the standard bootstrap, in general, does not work for Chatterjee’s rank correlation. In this paper, we provide proof of this issue under an additional independence assumption, and complement our theory with simulation evidence for general settings. Chatterjee’s rank correlation thus falls into a category of statistics that are asymptotically normal, but…

面对相关或异方差响应时实验设计的极小极大稳健性札记AI 解读

Abstract We present a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. This is used to show that experimental designs that are optimal under an assumption of independent, homoscedastic responses can be minimax robust, in broad classes of alternate covariance structures. In particular, it can justify the common practice of disregarding possible dependence, or heteroscedasticity, at the design stage of…