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多目标先验选择：协调随机效应模型的贝叶斯与经典方法AI 解读

–The two-level normal hierarchical model has played an important role in statistical theory and applications. In this article, we first introduce a general adjusted maximum likelihood method for estimating the unknown variance component of the model and the associated empirical best linear unbiased predictor of the random effects. We then discuss a new idea for selecting prior for the hyperparameters. The prior, called a multi-goal prior, produces Bayesian solutions for…

利用干扰参数惩罚回归对高维数据开展目标推断AI 解读

Analysis of high dimensional data has received considerable and increasing attention in statistics. In practice, we may not be interested in every variable that is observed. Instead, often some of the variables are of particular interest, and the remaining variables are nuisance. To this end, we propose the nuisance penalized regression which does not penalize the parameters of interest. When the coherence between interest parameters and nuisance parameters is negligible, we…

空间索引函数的主成分分析AI 解读

We develop an expansion, similar in some respects to the Karhunen–Loève expansion, but which is more suitable for functional data indexed by spatial locations on a grid. Unlike the traditional Karhunen–Loève expansion, it takes into account the spatial dependence between the functions. By doing so, it provides a more efficient dimension reduction tool, both theoretically and in finite samples, for functional data with moderate spatial dependence. For such data, it also…

球面上仿射等变空间中位数的类似估计量AI 解读

Robust estimation of location for data on the unit sphere Sp−1 is an important problem in directional statistics even though the influence functions of the sample mean direction and other location estimators are bounded. A significant limitation of previous literature on this topic is that robust estimators and procedures have been developed under the assumption that the underlying population is rotationally symmetric. This assumption often does not hold with real data and…

尾部基尼泛函的极限与推断及尾部风险度量应用AI 解读

–Tail risk analysis focuses on the problem of risk measurement on the tail regions of financial variables. As one crucial task in tail risk analysis for risk management, the measurement of tail risk variability is less addressed in the literature. Neither the theoretical results nor inference methods are fully developed, which results in the difficulty of modeling implementation. Practitioners are then short of measurement methods to understand and evaluate tail risks, even…

基于 Metropolis–Hastings 的 Knockoff 采样AI 解读

Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This article introduces techniques for…

片段式及相关类型函数型数据的协方差估计AI 解读

Aurore Delaiglea , Peter Halla, Wei Huanga* & Alois Kneipb a ACEMS and School of Mathematics and Statistics, University of Melbourne , Parkville , VIC , Australia;; b Department of Economics and Hausdorff Center for Mathematics, Universität Bonn , Bonn , Germany † This work was initiated with Peter Hall during a visit of Kneip to the University Melbourne. Peter died on January 9, 2016.Color versions of one or more of the figures in the article can be found online at…

快速校准的加性分位数回归AI 解读

Matteo Fasioloa*, Simon N. Wooda , Margaux Zaffranb, Raphaël Nedellecc & Yannig Goudec a School of Mathematics, University of Bristol , Bristol , UK;; b ENSTA ParisTech , Paris , France;; c Électricité de France R&D , Paris , France Color 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 Matteo Fasiolo…

置换单调矩阵模型中的最优排列恢复AI 解读

Motivated by recent research on quantifying bacterial growth dynamics based on genome assemblies, we consider a permuted monotone matrix model Y=ΘΠ+Z , where the rows represent different samples, the columns represent contigs in genome assemblies and the elements represent log-read counts after preprocessing steps and Guanine-Cytosine (GC) adjustment. In this model, Θ is an unknown mean matrix with monotone entries for each row, Π is a permutation matrix that permutes the…

回归模型与多元生命表AI 解读

Semiparametric, multiplicative-form regression models are specified for marginal single and double failure hazard rates for the regression analysis of multivariate failure time data. Cox-type estimating functions are specified for single and double failure hazard ratio parameter estimation, and corresponding Aalen-Breslow estimators are specified for baseline hazard rates. Generalization to allow classification of failure times into a smaller set of failure types, with…

稀疏捕获数据的多系统估计：名单不重叠时的推断挑战AI 解读

Multiple systems estimation strategies have recently been applied to quantify hard-to-reach populations, particularly when estimating the number of victims of human trafficking and modern slavery. In such contexts, it is not uncommon to see sparse or even no overlap between some of the lists on which the estimates are based. These create difficulties in model fitting and selection, and we develop inference procedures to address these challenges. The approach is based on…

利用辅助信息与降维的条件期望非参数估计AI 解读

Nonparametric estimation of the conditional expectation E(Y|U) of an outcome Y given a covariate vector U is of primary importance in many statistical applications such as prediction and personalized medicine. In some problems, there is an additional auxiliary variable Z in the training dataset used to construct estimators, but Z is not available for future prediction or selecting patient treatment in personalized medicine. For example, in the training dataset longitudinal…

极值指数的趋势AI 解读

We consider extreme value analysis for independent but nonidentically distributed observations. In particular, the observations do not share the same extreme value index. Assuming continuously changing extreme value indices, we provide a nonparametric estimate for the functional extreme value index. Besides estimating the extreme value index locally, we also provide a global estimator for the trend and its joint asymptotic theory. The asymptotic theory for the global…

个体化多方向变量选择AI 解读

In this article, we propose a heterogeneous modeling framework which achieves individual-wise feature selection and heterogeneous covariates’ effects subgrouping simultaneously. In contrast to conventional model selection approaches, the new approach constructs a separation penalty with multidirectional shrinkages, which facilitates individualized modeling to distinguish strong signals from noisy ones and selects different relevant variables for different individuals.…

通过递归在线得分估计进行高维模型推断AI 解读

In this paper, we develop a new estimation and valid inference method for single or low-dimensional regression coefficients in high-dimensional generalized linear models. The number of the predictors is allowed to grow exponentially fast with respect to the sample size. The proposed estimator is computed by solving a score function. We recursively conduct model selection to reduce the dimensionality from high to a moderate scale and construct the score equation based on the…

函数型 Cox 模型的半参数推断AI 解读

This article studies penalized semiparametric maximum partial likelihood estimation and hypothesis testing for the functional Cox model in analyzing right-censored data with both functional and scalar predictors. Deriving the asymptotic joint distribution of finite-dimensional and infinite-dimensional estimators is a very challenging theoretical problem due to the complexity of semiparametric models. For the problem, we construct the Sobolev space equipped with a special…

依从者随机直接效应：识别与稳健估计AI 解读

Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this paper, we identify the instrumental variable-direct effect of the exposure on the outcome not through the mediator, using randomization of the instrument. We call this estimand the complier stochastic direct effect (CSDE). To our knowledge, such an estimand has not previously been considered or estimated. We propose and evaluate several estimators for the CSDE: a…

树上 Wasserstein 距离的最优估计及微生物组应用AI 解读

The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the…

面向个性化预测的可扩展协同排序AI 解读

Ben Daiab, Xiaotong Shena, Junhui Wangb & Annie Quc* a School of Statistics, University of Minnesota , Minneapolis , MN ; b School of Data Science, City University of Hong Kong , Kowloon Tong, Hong Kong ; c Department of Statistics, University of Illinois at Urbana Champaign , Champaign , IL Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Annie Qu anniequ@illinois.edu Department of Statistics, University of…

ALMOND：基于神经网络与 Langevin 扩散的自适应潜变量建模和优化AI 解读

Latent variable models cover a broad range of statistical and machine learning models, such as Bayesian models, linear mixed models, and Gaussian mixture models. Existing methods often suffer from two major challenges in practice: (a) a proper latent variable distribution is difficult to be specified; (b) making an exact likelihood inference is formidable due to the intractable computation. We propose a novel framework for the inference of latent variable models that…

《指数族嵌入及其在单细胞 RNA 测序发育轨迹中的应用》讨论AI 解读

Exponential-family singular value decomposition (eSVD) is a new approach for embedding multivariate data into a lower-dimensional space. It provides an elegant dimension reduction framework with flexibility to handle one-parameter exponential family distributions and proven consistency. This approach adds a valuable new tool to the toolbox of data analysts. Here we discuss a number of open problems and challenges that remain to be addressed in the future in order to unleash…

多项 Logistic 与计数回归的对数线性贝叶斯加性回归树AI 解读

We introduce Bayesian additive regression trees (BART) for log-linear models including multinomial logistic regression and count regression with zero-inflation and overdispersion. BART has been applied to nonparametric mean regression and binary classification problems in a range of settings. However, existing applications of BART have been mostly limited to models for Gaussian “data,” either observed or latent. This is primarily because efficient MCMC algorithms are…

人口特征与社交距离指标在美国COVID-19病例深度预测中的作用AI 解读

With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the United States and the rest of the world are still suffering from the effects of the virus, the importance of assigning growth membership to counties and understanding the determinants of the growth is increasingly evident. For the two communities (faster versus…

基于代入抽样合成数据的多元回归推断AI 解读

In this article, the authors derive the likelihood-based exact inference for singly and multiply imputed synthetic data in the context of a multivariate regression model. The synthetic data are generated via the Plug-in Sampling method, where the unknown parameters in the model are set equal to the observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Simulation studies are carried out in…