统计研究的范围很广，从直接的主题合作到纯数学理论。 IMS 的最新期刊《应用统计年鉴》针对的是该范围内应用一半的论文。每季以印刷和电子形式出版，我们的目标是为应用统计的所有领域提供及时和统一的论坛。

The Annals of Applied Statistics is a journal that covers a wide range of statistical research, from collaborative studies on applied topics to pure mathematical theory. Published quarterly in both print and electronic formats, our goal is to provide a timely and cohesive forum for all areas of applied statistics.

《Annals of Applied Statistics》是一个涵盖广泛统计研究领域的期刊，既包括应用主题的合作研究，也涉及纯数学理论。该期刊每季度以印刷和电子形式出版，旨在为应用统计的各个领域提供一个及时且统一的学术交流平台。

Annals of Applied Statistics

Miaomiao Yu

Zhongfeng Jiang

Jiaxuan Li

Yong Zhou

The Annals of Applied Statistics

Incorporating auxiliary information for improved statistical inference and its extensions to distributed algorithms with an application to personal credit

结合辅助信息改进统计推断及其在分布式算法中的扩展及其在个人信用中的应用

The dramatic growth of big datasets presents a new challenge to data storage and analysis. Data reduction, or subsampling, that extracts useful information from datasets is a crucial step in big-data analysis. We propose an orthogonal subsampling (OSS) approach for big data with a focus on linear regression models. The approach is inspired by the fact that an orthogonal array of two levels provides the best experimental design for linear regression models in the sense that it minimizes the average variance of the estimated parameters and provides the best predictions. The merits of OSS are three-fold: (i) it is easy to implement and fast; (ii) it is suitable for distributed parallel computing and ensures the subsamples selected in different batches have no common data points, and (iii) it outperforms existing methods in minimizing the mean squared errors of the estimated parameters and maximizing the efficiencies of the selected subsamples. Theoretical results and extensive numerical results show that the OSS approach is superior to existing subsampling approaches. It is also more robust to the presence of interactions among covariates, and, when they do exist, OSS provides more precise estimates of the interaction effects than existing methods. The advantages of OSS are also illustrated through analysis of real data.

Lin Wang

Jake Elmstedt

Weng Kee Wong

Hongquan Xu

Orthogonal subsampling for big data linear regression

大数据线性回归的正交子采样

Functional MRI (fMRI) has become the most common method for investigating the human brain. However, fMRI data present some complications for statistical analysis and modeling. One recently developed approach to these data focuses on estimation of computational encoding models that describe how stimuli are transformed into brain activity measured in individual voxels. Here we aim at building encoding models for fMRI signals recorded in the primary visual cortex of the human brain. We use residual analyses to reveal systematic nonlinearity across voxels not taken into account by previous models. We then show how a sparse nonparametric method [J. Roy. Statist. Soc. Ser. B 71 (2009b) 1009–1030] can be used together with correlation screening to estimate nonlinear encoding models effectively. Our approach produces encoding models that predict about 25% more accurately than models estimated using other methods [Nature 452 (2008a) 352–355]. The estimated nonlinearity impacts the inferred properties of individual voxels, and it has a plausible biological interpretation. One benefit of quantitative encoding models is that estimated models can be used to decode brain activity, in order to identify which specific image was seen by an observer. Encoding models estimated by our approach also improve such image identification by about 12% when the correct image is one of 11,500 possible images.

Vincent Q. Vu

Pradeep Ravikumar

Thomas Naselaris

Kendrick N. Kay

Jack L. Gallant

Bin Yu

Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models

用稀疏非参数模型编码和解码V1对自然图像的fMRI响应

We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimate these forces from large-scale data, and we analyze a large dataset from a major chain grocery store. We are interested in answering counterfactual queries about changes in prices. We found that SHOPPER provides accurate predictions even under price interventions, and that it helps identify complementary and substitutable pairs of products.

Francisco J. R. Ruiz

Susan Athey

David M. Blei

Ann. Appl. Stat.

SHOPPER: A probabilistic model of consumer choice with substitutes and complements

SHOPPER：一种具有替代品和互补品的消费者选择的概率模型

When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers must investigate when the treatments do and do not work. Indeed, the estimation of treatment effect heterogeneity plays an essential role in (1) selecting the most effective treatment from a large number of available treatments, (2) ascertaining subpopulations for which a treatment is effective or harmful, (3) designing individualized optimal treatment regimes, (4) testing for the existence or lack of heterogeneous treatment effects, and (5) generalizing causal effect estimates obtained from an experimental sample to a target population. In this paper, we formulate the estimation of heterogeneous treatment effects as a variable selection problem. We propose a method that adapts the Support Vector Machine classifier by placing separate sparsity constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. The proposed method is motivated by and applied to two well-known randomized evaluation studies in the social sciences. Our method selects the most effective voter mobilization strategies from a large number of alternative strategies, and it also identifies the characteristics of workers who greatly benefit from (or are negatively affected by) a job training program. In our simulation studies, we find that the proposed method often outperforms some commonly used alternatives.

Kosuke Imai

Marc Ratkovic

Estimating treatment effect heterogeneity in randomized program evaluation

估计随机项目评估中的处理效应异质性

Can Xie

Xuelin Huang

Ruosha Li

Alexander Tsodikov

Kapil Bhalla

Individual dynamic prediction for cure and survival based on longitudinal biomarkers

基于纵向生物标志物的个体动态预测用于治愈和生存

Charlotte Z. Mann

Ben B. Hansen

Lauren Gaydosh

Early effects of 2014 U.S. Medicaid expansions on mortality: Design-based inference for impacts on small subgroups despite small-cell suppression

2014年美国医疗补助扩展对死亡率的早期影响：尽管存在小单元格抑制，但基于设计的推断对小亚组的影响

Lu Yang

Peng Shi

Shimeng Huang

A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims

带终止事件的标记点过程的Copula模型：保险索赔动态预测中的应用

Nathan B. Wikle

Corwin M. Zigler

Causal health impacts of power plant emission controls under modeled and uncertain physical process interference

火力发电厂排放控制对健康影响的因果关系：基于模型与不确定物理过程干扰的研究

Abbreviation	Ann. Appl. Stat.
Journal Impact	1.21
Quartiles(Global)	STATISTICS & PROBABILITY(Q2)

ISSN	1932-6157, 1941-7330
h-index	84

Publication Information	Publisher: Institute of Mathematical Statistics，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2008，Proportion of original research papers: 100.00%，Self Citation Rate:7.70%， Gold OA Rate: 3.11%
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 Applied Statistics

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Incorporating auxiliary information for improved statistical inference and its extensions to distributed algorithms with an application to personal credit

Orthogonal subsampling for big data linear regression

Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models

SHOPPER: A probabilistic model of consumer choice with substitutes and complements

Estimating treatment effect heterogeneity in randomized program evaluation

Incorporating auxiliary information for improved statistical inference and its extensions to distributed algorithms with an application to personal credit

Individual dynamic prediction for cure and survival based on longitudinal biomarkers

Early effects of 2014 U.S. Medicaid expansions on mortality: Design-based inference for impacts on small subgroups despite small-cell suppression

A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims

Causal health impacts of power plant emission controls under modeled and uncertain physical process interference