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方差分量线性组合的修正大样本置信区间AI 解读

We consider the problem of setting a confidence interval or bound for a linear combination of variance components related to a multivariate normal distribution, which includes important applications such as comparing variance components and testing the bioequivalence between two drug products. The lack of an exact confidence interval for a general linear combination of variance components spurred the development of a modified large-sample (MLS) method that was shown to be…

当前状态数据半参数回归模型的筛最大似然估计AI 解读

In a randomized controlled clinical trial study where the response variable of interest is the time to occurrence of a certain event, it is often too expensive or even impossible to observe the exact time. However, the current status of the subject at a random time of inspection is much more natural, feasible, and practical in terms of cost-effectiveness. This article considers a semiparametric regression model that consists of parametric and nonparametric regression…

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自然资源的空间均衡抽样AI 解读

The spatial distribution of a natural resource is an important consideration in designing an efficient survey or monitoring program for the resource. Generally, sample sites that are spatially balanced, that is, more or less evenly dispersed over the extent of the resource, are more efficient than simple random sampling. We review a unified strategy for selecting spatially balanced probability samples of natural resources. The technique is based on creating a function that…

用主分层评估部分受控的纵向处理：针具交换项目应用AI 解读

We consider studies for evaluating the short-term effect of a treatment of interest on a time-to-event outcome. The studies we consider are only partially controlled in the following sense: (1) Subjects' exposure to the treatment of interest can vary over time, but this exposure is not directly controlled by the study; (2) subjects' follow-up time is not directly controlled by the study; and (3) the study directly controls another factor that can affect subjects' exposure to…

依赖随机测度的 ANOVA 模型AI 解读

We consider dependent nonparametric models for related random probability distributions. For example, the random distributions might be indexed by a categorical covariate indicating the treatment levels in a clinical trial and might represent random effects distributions under the respective treatment combinations. We propose a model that describes dependence across random distributions in an analysis of variance (ANOVA)-type fashion. We define a probability model in such a…

利用误测协变量重复测量的半参数失效时间回归AI 解读

This article proposes a general strategy for the regression analysis of univariate and multivariate failure time data when a subset of covariates cannot be measured precisely but replicate measurements of their surrogates are available. Multivariate failure time data include recurrent events and clustered survival data. The number of replicate measurements can vary from subject to subject and can even depend on the failure time. No parametric assumption is imposed on the…

基于加权伽马过程的乘法强度模型计算方法AI 解读

We develop computational procedures for a class of Bayesian nonparametric and semiparametric multiplicative intensity models incorporating kernel mixtures of spatial weighted gamma measures. A key feature of our approach is that explicit expressions for posterior distributions of these models share many common structural features with the posterior distributions of Bayesian hierarchical models using the Dirichlet process. Using this fact, along with an approximation for the…

二元现状数据的独立性检验AI 解读

This article develops a nonparametric procedure for testing marginal independence based on bivariate current status data. Asymptotic properties of the proposed tests are derived, and their finite-sample performance is studied via simulations. The method is applied to analyze data from a community-based study of cardiovascular epidemiology in Taiwan.

用于多维疾病分类流行病学研究的两阶段回归模型AI 解读

Polytomous logistic regression is commonly used to analyze epidemiological data with disease subtype information. In this approach effects of exposures on different disease subtypes are studied through separate exposure odds ratios comparing different case groups to the common control group. This article considers the situation where disease subtypes can be defined using multiple characteristics of a disease. For efficient analysis of such data, a two-stage modeling approach…

期望值变化的非平稳空间数据：样本均值方差的子抽样估计AI 解读

Subsampling and block resampling methods have been suggested in the literature to nonparametrically estimate the variance of statistics computed from spatial data. Usually stationary data are required. However, in empirical applications, the assumption of stationarity often must be rejected. This article proposes nonparametric methods to estimate the variance of (functions of) sample means based on nonstationary spatial data using subsampling. We assume that data are…

用半参数惩罚样条估计公司债利率期限结构AI 解读

This article provides a new methodology for estimating the term structure of corporate debt using a semiparametric penalized spline model. The method is applied to a case study of AT&T bonds. Typically, very few data are available on individual corporate bond prices, too little to find a nonparametric estimate of term structure from these bonds alone. This problem is solved by “borrowing strength” from Treasury bond data. More specifically, we combine a nonparametric model…

非比例风险下的贝叶斯生存分析AI 解读

Both pravastatin and aspirin are approved by the U.S. Food and Drug Administration (FDA) for secondary prevention of cardiovascular events. This article describes statistical analyses used for a successful submission to the FDA that contends that copackaging pravastatin and aspirin provides a health benefit. From the efficacy perspective this is taken to mean that the combination is more effective than either agent considered alone. We present three Bayesian hierarchical…

软组织肉瘤试验中基于多种毒性的剂量探索AI 解读

The scientific goal of a phase I oncology trial of a new chemotherapeutic agent is to find a dose with an acceptable level of toxicity. For ethical reasons, dose-finding is done adaptively, with doses chosen for successive cohorts of patients based on the data obtained from previous cohorts. Typically, patients are at risk for several qualitatively different toxicities, each occurring at several possible severity levels. In this article, we describe how we addressed the…

模型选择的方法与准则AI 解读

Model selection is an important part of any statistical analysis and, indeed, is central to the pursuit of science in general. Many authors have examined the question of model selection from both frequentist and Bayesian perspectives, and many tools for selecting the “best model” have been suggested in the literature. This paper considers the various proposals from a Bayesian decision–theoretic perspective.

模型化地统计中的不一致估计与渐近等价插值AI 解读

It is shown that in model-based geostatistics, not all parameters in the Matérn class can be estimated consistently if data are observed in an increasing density in a fixed domain, regardless of the estimation methods used. Nevertheless, one quantity can be estimated consistently by the maximum likelihood method, and this quantity is more important to spatial interpolation. The results are established by using the properties of equivalence and orthogonality of probability…

多元 Student t 模型中 van Dyk–Meng 算法的几何遍历性AI 解读

Let π denote the posterior distribution that results when a random sample of size n from a d-dimensional location-scale Student's t distribution (with ν degrees of freedom) is combined with the standard noninformative prior. van Dyk and Meng developed an efficient Markov chain Monte Carlo (MCMC) algorithm for sampling from π and provided considerable empirical evidence to suggest that their algorithm converges to stationarity much faster than the standard data augmentation…

模型设定错误的 IOS 检验AI 解读

A new test of model misspecification is proposed, based on the ratio of in-sample and out-of-sample likelihoods. The test is broadly applicable and, in simple problems, approximates well-known, intuitive methods. Using jackknife influence curve approximations, it is shown that the test statistic can be viewed asymptotically as a multiplicative contrast between two estimates of the information matrix, both of which are consistent under correct model specification. This…

通过最大化“脏”马尔可夫随机场后验进行平滑的统计分析AI 解读

We consider Bayesian nonparametric function estimation using a Markov random field prior based on the Laplace distribution. We describe efficient methods for finding the exact maximum a posteriori estimate, which handle constraints naturally and avoid the problems posed by nondifferentiability of the posterior distribution; the methods also make links to spline and wavelet smoothers and to a dual posterior distribution. Three automatic smoothing parameter selection…

时间序列背景下条件均值差异的检验AI 解读

In this article we study tests for equality of two regression curves when the inputs are driven by a time series. The basic process underlying the test statistics is the empirical process of the time series marked by the difference in the pertaining dependent variables. The main results hold under strict stationarity of the input variables, but no mixing condition or special modeling of the time series will be necessary. A simulation study is reported on, which illustrates…

非线性时间序列的蒙特卡洛平滑AI 解读

We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the…

基于似然的条件方差函数局部线性估计AI 解读

We consider estimation of mean and variance functions with kernel-weighted local polynomial fitting in a heteroscedastic nonparametric regression model. Our preferred estimators are based on a localized normal likelihood, using a standard local linear form for estimating the mean and a local log-linear form for estimating the variance. It is important to allow two bandwidths in this problem, separate ones for mean and variance estimation. We provide data-based methods for…