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个体化多层张量学习及其影像分析应用AI 解读

This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction models assuming a homogeneous feature structure. We develop an innovative multilayer tensor learning method to incorporate heterogeneity to a higher-order tensor…

通过原型分析进行非负矩阵分解AI 解读

Hamid Javadia* & Andrea Montanarib*a Department of Electrical and Computer Engineering, Rice University, Houston, TX; b Department of Electrical Engineering and Statistics, Stanford University, Stanford, CAColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Hamid Javadi hrhakim@rice.edu Department of…

面向大规模多输入计算机实验的多分辨率函数型方差分析AI 解读

The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input computer experiments that have become typical. We propose a multiresolution functional ANOVA (MRFA) model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input…

高维层级动态时空模型的集合卡尔曼方法AI 解读

We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for…

双重截断数据的回归分析AI 解读

Doubly truncated data are found in astronomy, econometrics and survival analysis literature. They arise when each observation is confined to an interval, i.e., only those which fall within their respective intervals are observed along with the intervals. Unlike the one-sided truncation that can be handled by counting process-based approach, doubly truncated data are much more difficult to handle. In their analysis of an astronomical data set, Efron and Petrosian (1999)…

高维矩阵值时间序列的约束因子模型AI 解读

High-dimensional matrix-variate time series data are becoming widely available in many scientific fields, such as economics, biology, and meteorology. To achieve significant dimension reduction while preserving the intrinsic matrix structure and temporal dynamics in such data, Wang, Liu, and Chen proposed a matrix factor model, that is, shown to be able to provide effective analysis. In this article, we establish a general framework for incorporating domain and prior…

随机设计下稀疏惩罚回归的置信区间AI 解读

With the abundance of large data, sparse penalized regression techniques are commonly used in data analysis due to the advantage of simultaneous variable selection and estimation. A number of convex as well as non-convex penalties have been proposed in the literature to achieve sparse estimates. Despite intense work in this area, how to perform valid inference for sparse penalized regression with a general penalty remains to be an active research problem. In this paper, by…

贝叶斯推断的几何变分方法AI 解读

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher–Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere S∞ in L2, and the Fisher–Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the…

利用条件粒子滤波器耦合进行平滑AI 解读

In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique…

右删失事件时间数据的 Copula 连接加性模型AI 解读

This article proposes an approach to estimate and make inference on the parameters of copula link-based survival models. The methodology allows for the margins to be specified using flexible parametric formulations for time-to-event data, the baseline survival functions to be modeled using monotonic splines, and each parameter of the assumed joint survival distribution to depend on an additive predictor incorporating several types of covariate effects. All the model’s…

离散空间局部 MCMC 的信息化提议分布AI 解读

There is a lack of methodological results to design efficient Markov chain Monte Carlo ( MCMC) algorithms for statistical models with discrete-valued high-dimensional parameters. Motivated by this consideration, we propose a simple framework for the design of informed MCMC proposals (i.e., Metropolis–Hastings proposal distributions that appropriately incorporate local information about the target) which is naturally applicable to discrete spaces. Using Peskun-type…

广义地理加性模型的估计与推断AI 解读

In many application areas, data are collected on a count or binary response with spatial covariate information. In this article, we introduce a new class of generalized geoadditive models (GGAMs) for spatial data distributed over complex domains. Through a link function, the proposed GGAM assumes that the mean of the discrete response variable depends on additive univariate functions of explanatory variables and a bivariate function to adjust for the spatial effect. We…

超高维异质分类数据的类别自适应变量筛选AI 解读

The populations of interest in modern studies are very often heterogeneous. The population heterogeneity, the qualitative nature of the outcome variable and the high dimensionality of the predictors pose significant challenge in statistical analysis. In this article, we introduce a category-adaptive screening procedure with high-dimensional heterogeneous data, which is to detect category-specific important covariates. The proposal is a model-free approach without any…

用 V-learning 估计移动健康中的动态处理策略AI 解读

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as…

离散结果 Copula 回归模型的非参数估计AI 解读

Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and biology. When the interplay between outcomes is significant, quantifying dependencies among interrelated variables is of great importance. Due to their ability to accommodate dependence flexibly, copulas are being applied increasingly. Yet, the application of copulas on discrete data is still in its infancy; one of the biggest barriers is the nonuniqueness of copulas, calling…

交叉验证、风险估计与模型选择：对Rosset与Tibshirani论文的评论AI 解读

How best to estimate the accuracy of a predictive rule has been a longstanding question in statistics. Approaches to this task range from simple methods like Mallow’s Cp to algorithmic techniques l...

《从固定 X 到随机 X 回归：偏差方差分解、协方差惩罚与预测误差估计》讨论AI 解读

Rosset and Tibshirani are to be congratulated for addressing an important yet under-studied topic of modern statistics, so-called Random-X prediction in regression analysis. In statistics, Random-X...

《随机对象理论：概率、随机过程与推断》AI 解读

The task of writing a novel textbook on general probability theory seems virtually impossible nowadays. In Theory of Stochastic Objects, the author Athanasios Micheas aims to overcome this challeng...

组学与成像大数据：整合分析与因果推断AI 解读

Despite our expanding knowledge, we are still very ignorant about many parts of the function and functioning of the human brain and the genes. The challenges and ignorance are equally large for sta...

《多元核平滑及其应用》书评AI 解读

Multivariate Kernel Smoothing and Its Applications, by J.E. Chacón and T. Duong, provides a comprehensive and up-to-date introduction of multivariate density estimation. The book is well-written an...

分布型数据的分位数函数对标量回归分析AI 解读

Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution…

离散结果的分位数函数与分位数效应函数的一般推断AI 解读

Quantile and quantile effect functions are important tools for descriptive and causal analyses due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This paper offers a simple, practical construction of simultaneous confidence bands for quantile and quantile effect functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for…

患者预警系统中缺失数据下的预测与推断AI 解读

We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-intensive care unit patients using ∼100 variables (vitals, lab results, assessments, etc.). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the…

惩罚与约束优化：高维网站广告中的应用AI 解读

Firms are increasingly transitioning advertising budgets to Internet display campaigns, but this transition poses new challenges. These campaigns use numerous potential metrics for success (e.g., reach or click rate), and because each website represents a separate advertising opportunity, this is also an inherently high-dimensional problem. Further, advertisers often have constraints they wish to place on their campaign, such as targeting specific sub-populations or…