《统计年鉴》旨在发表反映当代统计诸多方面的最高质量研究论文。主要强调重要性和独创性，而不是形式主义。该杂志旨在涵盖统计学的所有领域，尤其是数理统计学和应用与跨学科统计学。当然，许多最好的论文将涉及不止一个这些一般领域，因为统计学科深深植根于数学和实质性科学领域。

The Annals of Statistics aims to publish high-quality research papers that reflect various aspects of contemporary statistics. The journal emphasizes the importance and originality of the research rather than formalism. It covers all areas of statistics, particularly mathematical statistics and applied and interdisciplinary statistics. Many of the best papers will involve more than one of these general areas, as the field of statistics is deeply rooted in mathematics and substantive sciences.

《Annals of Statistics》旨在发表反映当代统计学各个方面的高质量研究论文。该期刊主要强调研究的重要性和独创性，而非形式主义。它涵盖统计学的所有领域，特别是数理统计和应用统计及跨学科统计。许多优秀的论文往往涉及多个领域，因为统计学与数学及其他实质性科学领域密切相关。

Annals of Statistics

A novel sequential change detection problem is proposed, in which the change should be not only detected but also accelerated. Specifically, it is assumed that the sequentially collected observations are responses to treatments selected in real time. The assigned treatments not only determine the pre-change and post-change distributions of the responses, but also influence when the change happens. The problem is to find a treatment assignment rule and a stopping rule that minimize the expected total number of observations subject to a user-specified bound on the false alarm probability. The optimal solution to this problem is obtained under a general Markovian change-point model. Moreover, an alternative procedure is proposed, whose applicability is not restricted to Markovian change-point models and whose design requires minimal computation. For a large class of change-point models, the proposed procedure is shown to achieve the optimal performance in an asymptotic sense. Finally, its performance is found in two simulation studies to be close to the optimal, uniformly with respect to the error probability.

Yanglei Song

Georgios Fellouris

The Annals of Statistics

Change acceleration and detection

变化加速与检测

A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here, we conduct an in-depth study of this trade-off for parameter estimation in the β-model (Ann. Appl. Probab. 21 (2011) 1400–1435) for edge differentially private network data released via jittering (J. R. Stat. Soc. Ser. C. Appl. Stat. 66 (2017) 481–500). Unlike most previous approaches based on maximum likelihood estimation for this network model, we proceed via the method of moments. This choice facilitates our exploration of a substantially broader range of privacy levels—corresponding to stricter privacy—than has been to date. Over this new range, we discover our proposed estimator for the parameters exhibits an interesting phase transition, with both its convergence rate and asymptotic variance following one of three different regimes of behavior depending on the level of privacy. Because identification of the operable regime is difficult, if not impossible in practice, we devise a novel adaptive bootstrap procedure to construct uniform inference across different phases. In fact, leveraging this bootstrap we are able to provide for simultaneous inference of all parameters in the β-model (i.e., equal to the number of nodes), which, to our best knowledge, is the first result of its kind. Numerical experiments confirm the competitive and reliable finite sample performance of the proposed inference methods, next to a comparable maximum likelihood method, as well as significant advantages in terms of computational speed and memory.

Jinyuan Chang

Qiao Hu

Eric D. Kolaczyk

Qiwei Yao

Fengting Yi

Edge differentially private estimation in the β-model via jittering and method of moments

通过抖动和矩法在β模型中进行边缘差分隐私估计

We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. First, we show that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively. We establish local and global convergence guarantees, under both local strong convexity and self-concordance, showing that our private estimators converge with high probability to a small neighborhood of the nonprivate M-estimators. Second, we tackle the problem of parametric inference by constructing differentially private estimators of the asymptotic variance of our private M-estimators. This naturally leads to approximate pivotal statistics for constructing confidence regions and conducting hypothesis testing. We demonstrate the effectiveness of a bias correction that leads to enhanced small-sample empirical performance in simulations. We illustrate the benefits of our methods in several numerical examples.

Marco Avella-Medina

Casey Bradshaw

Po-Ling Loh

Ann. Statist.

Differentially private inference via noisy optimization

通过噪声优化实现差分隐私推断

Inspired by sample splitting and the reusable holdout introduced in the field of differential privacy, we consider selective inference with a randomized response. We discuss two major advantages of using a randomized response for model selection. First, the selectively valid tests are more powerful after randomized selection. Second, it allows consistent estimation and weak convergence of selective inference procedures. Under independent sampling, we prove a selective (or privatized) central limit theorem that transfers procedures valid under asymptotic normality without selection to their corresponding selective counterparts. This allows selective inference in nonparametric settings. Finally, we propose a framework of inference after combining multiple randomized selection procedures. We focus on the classical asymptotic setting, leaving the interesting high-dimensional asymptotic questions for future work.

Xiaoying Tian

Jonathan Taylor

Selective inference with a randomized response

选择性推断与随机应答

We introduce a quantile-adaptive framework for nonlinear variable screening with high-dimensional heterogeneous data. This framework has two distinctive features: (1) it allows the set of active variables to vary across quantiles, thus making it more flexible to accommodate heterogeneity; (2) it is model-free and avoids the difficult task of specifying the form of a statistical model in a high dimensional space. Our nonlinear independence screening procedure employs spline approximations to model the marginal effects at a quantile level of interest. Under appropriate conditions on the quantile functions without requiring the existence of any moments, the new procedure is shown to enjoy the sure screening property in ultra-high dimensions. Furthermore, the quantile-adaptive framework can naturally handle censored data arising in survival analysis. We prove that the sure screening property remains valid when the response variable is subject to random right censoring. Numerical studies confirm the fine performance of the proposed method for various semiparametric models and its effectiveness to extract quantile-specific information from heteroscedastic data.

Xuming He

Lan Wang

Hyokyoung Grace Hong

Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data

分位数自适应的无模型变量筛选方法用于高维异质数据

Tobias Kley

Yuhan Philip Liu

Hongyuan Cao

Wei Biao Wu

Change-point analysis with irregular signals

不规则信号的变点分析

Geert Mesters

Piotr Zwiernik

Nonndependent components analysis

独立成分分析

Chen Cheng

Andrea Montanari

Dimension free ridge regression

无维数限制的岭回归

Jun Yang

Krzysztof Łatuszyński

Gareth O. Roberts

Stereographic Markov chain Monte Carlo

立体投影马尔可夫链蒙特卡洛

Sohom Bhattacharya

Jianqing Fan

Debarghya Mukherjee

Deep neural networks for nonparametric interaction models with diverging dimension

用于具有发散维度的非参数交互模型的深度神经网络

Abbreviation	Ann. Statist.
Journal Impact	3.22
Quartiles(Global)	STATISTICS & PROBABILITY(Q1)

ISSN	0090-5364, 2168-8966
h-index	192
Top Journals	Yes

Publication Information	Publisher: Institute of Mathematical Statistics，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1996，Proportion of original research papers: 100.00%，Self Citation Rate:9.40%， Gold OA Rate: 1.06%
Average review cycle	网友分享经验：较慢,6-12周
Average recruitment ratio	网友分享经验：较易

Annals of Statistics

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