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动物运动的基函数模型AI 解读

Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-based data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal–environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal…

聚类观察研究中的最优多层匹配：智利教育券制度下私立学校效果案例AI 解读

A distinctive feature of a clustered observational study is its multilevel or nested data structure arising from the assignment of treatment, in a nonrandom manner, to groups or clusters of units or individuals. Examples are ubiquitous in the health and social sciences including patients in hospitals, employees in firms, and students in schools. What is the optimal matching strategy in a clustered observational study? At first thought, one might start by matching clusters of…

以主分层连接潜在类别生存模型：早期肾癌患者的异质性生存亚组AI 解读

Rates of kidney cancer have been increasing, with small incidental tumors experiencing the fastest growth rates. Much of the increase could be due to increased use of CT scans, MRIs, and ultrasounds for unrelated conditions. Many tumors might never have been detected or become symptomatic in the past. This suggests that many patients might benefit from less aggressive therapy, such as active surveillance by which tumors are surgically removed only if they become sufficiently…

带标志点约束的平面曲线弹性形状分析AI 解读

Various approaches to statistical shape analysis exist in current literature. They mainly differ in the representations, metrics, and/or methods for alignment of shapes. One such approach is based on landmarks, that is, mathematically or structurally meaningful points, which ignores the remaining outline information. Elastic shape analysis, a more recent approach, attempts to fix this by using a special functional representation of the parametrically defined outline to…

检验为克服计算不可解性而引入假设的新贝叶斯检验AI 解读

We present a new test of hypothesis in which we seek the probability of the null conditioned on the data, where the null is a simplification undertaken to counter the intractability of the more complex model that the simpler null model is nested within. With the more complex model rendered intractable, the null model uses a simplifying assumption that capacitates the learning of an unknown parameter vector given the data. Bayes factors are shown to be known only up to a…

精准医疗中基于生物标志物的贝叶斯Ⅰ/Ⅱ期剂量探索AI 解读

Beibei Guoa* & Ying Yuanba Department of Experimental Statistics, Louisiana State University, Baton Rouge, LAb Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TXCONTACT Beibei Guo beibeiguo@lsu.edu Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803Color 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…

利用时空随机游走的极限分布建模空间协方差AI 解读

We present an approach for modeling areal spatial covariance in observed genetic allele data by considering the stationary (limiting) distribution of a spatio-temporal Markov random walk model for gene flow. This stationary distribution corresponds to an intrinsic simultaneous autoregressive (SAR) model for spatial correlation, and provides a principled approach to specifying areal spatial models when a spatio-temporal generating process can be assumed. We apply the approach…

不可忽略缺失下估计 HIV 流行率的联立方程方法AI 解读

Estimates of HIV prevalence are important for policy to establish the health status of a country’s population and to evaluate the effectiveness of population-based interventions and campaigns. However, participation rates in testing for surveillance conducted as part of household surveys, on which many of these estimates are based, can be low. HIV positive individuals may be less likely to participate because they fear disclosure, in which case estimates obtained using…

含协变量的贝叶斯层级多种群多状态 Jolly–Seber 模型AI 解读

Estimating abundance for multiple populations is of fundamental importance to many ecological monitoring programs. Equally important is quantifying the spatial distribution and characterizing the migratory behavior of target populations within the study domain. To achieve these goals, we propose a Bayesian hierarchical multi-population multistate Jolly–Seber model that incorporates covariates. The model is proposed using a state-space framework and has several distinct…

贝叶斯多变量函数型动态线性模型AI 解读

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data—functional, time dependent, and multivariate components—we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the…

用于亚组识别与样本量计算的变平面分析AI 解读

Ailin Fana, Rui Songa & Wenbin Lua*a Department of Statistics, North Carolina State University, Raleigh, NCCONTACT Wenbin Lu lu@stat.ncsu.edu Department of Statistics, North Carolina State University, Raleigh, NC 27695ABSTRACTWe propose a systematic method for testing and identifying a subgroup with an enhanced treatment effect. We adopts a change-plane technique to first test the existence of a subgroup, and then identify the subgroup if the null hypothesis on nonexistence…

交叉分类抽样下的估计及儿童调查应用AI 解读

The cross-classified sampling design consists in drawing samples from a two-dimensional population, independently in each dimension. Such design is commonly used in consumer price index surveys and has been recently applied to draw a sample of babies in the French Longitudinal Survey on Childhood, by crossing a sample of maternity units and a sample of days. We propose to derive a general theory of estimation for this sampling design. We consider the Horvitz–Thompson…

复发事件与失效时间的联合尺度变换模型AI 解读

Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association…

存在终末事件时基于标志物过程的非参数获益风险评估AI 解读

Benefit-risk assessment is a crucial step in medical decision process. In many biomedical studies, both longitudinal marker measurements and time to a terminal event serve as important endpoints for benefit-risk assessment. The effect of an intervention or a treatment on the longitudinal marker process, however, can be in conflict with its effect on the time to the terminal event. Thus, questions arise on how to evaluate treatment effects based on the two endpoints, for the…

使用依赖 Bernstein 多项式的有界数据全非参数回归AI 解读

We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the Dirichlet–Bernstein prior for single density estimation, by using dependent stick-breaking processes. A general model class and two simplified versions are discussed in detail. Appealing theoretical properties such as continuity, association structure, marginal distribution, large support, and consistency of the posterior…

竞争性终止事件下重复标记过程的效用测量评估AI 解读

Yifei Suna* & Mei-Cheng Wangaa Department of Biostatistics, Johns Hopkins University, Baltimore, MDCONTACT Yifei Sun ysun26@jhu.edu Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205Color 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.ABSTRACTIn follow-up…

稀疏多维图模型的统一贝叶斯框架AI 解读

Yang Niab, Francesco C. Stingoac* & Veerabhadran Baladandayuthapaniaa Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXb Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TXc Dipartimento di Statistica, Informatica, Applicazioni “G.Parenti,” University of Florence, Florence, ItalyCONTACT Francesco C. Stingo fstingo@mdanderson.org Dipartimento di Statistica, Informatica, Applicazioni “G.Parenti,”…

以成对信息为索引的随机划分分布AI 解读

We propose a random partition distribution indexed by pairwise similarity information such that partitions compatible with the similarities are given more probability. The use of pairwise similarities, in the form of distances, is common in some clustering algorithms (e.g., hierarchical clustering), but we show how to use this type of information to define a prior partition distribution for flexible Bayesian modeling. A defining feature of the distribution is that it…

高维线性模型的同时推断AI 解读

This article proposes a bootstrap-assisted procedure to conduct simultaneous inference for high-dimensional sparse linear models based on the recent desparsifying Lasso estimator. Our procedure allows the dimension of the parameter vector of interest to be exponentially larger than sample size, and it automatically accounts for the dependence within the desparsifying Lasso estimator. Moreover, our simultaneous testing method can be naturally coupled with the margin screening…

面向分位数的交互式 Q 学习AI 解读

A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with…

基于序模式依赖的结构突变检验AI 解读

We propose new concepts to analyze and model the dependence structure between two time series. Our methods rely exclusively on the order structure of the data points. Hence, the methods are stable under monotone transformations of the time series and robust against small perturbations or measurement errors. Ordinal pattern dependence can be characterized by four parameters. We propose estimators for these parameters, and we calculate their asymptotic distributions.…

通过信号压缩进行函数对函数线性回归AI 解读

We consider functional linear regression models with a functional response and multiple functional predictors, with the goal of finding the best finite-dimensional approximation to the signal part of the response function. Defining the integrated squared correlation coefficient between a random variable and a random function, we propose to solve a penalized generalized functional eigenvalue problem, whose solutions satisfy that projections on the original predictors generate…

层级拉丁超立方抽样AI 解读

Latin hypercube sampling (LHS) is a robust, scalable Monte Carlo method that is used in many areas of science and engineering. We present a new algorithm for generating hierarchic Latin hypercube sets (HLHS) that are recursively divisible into LHS subsets. Based on this new construction, we introduce a hierarchical incremental LHS (HILHS) method that allows the user to employ LHS in a flexibly incremental setting. This overcomes a drawback of many LHS schemes that require…

双变量区间删失失效时间回归的筛半参数极大似然方法AI 解读

Interval-censored failure time data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate data and there exists only limited research on bivariate data, especially on regression analysis of bivariate interval-censored data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The…