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协变量存在测量误差时匹配病例对照研究的优势比回归估计AI 解读

This article studies estimators of the odds ratio and odds ratio regression parameters in finely matched case-control studies containing a binary exposure of primary interest and subject-specific covariates that are subject to measurement error. A retrospective logistic regression model for the binary exposure variable is used. The effect of measurement errors on the conditional maximum likelihood estimator is determined. Three alternatives are considered: bias-corrected,…

均匀分布尺度同质性检验：双侧与有序单侧备择AI 解读

Consider the model where Xij, i = 1, 2, …, k; j = 1, …, n i are independent uniform random variables with scale parameter θ i > 0. Test H 0: θl = ··· = θ k versus H 2: not H 0. Also consider the alternative H 1: θl ≤ ··· ≤ θ k . For H 0 versus H 2 and n i = n, we obtain a complete class of constant-size permutation invariant tests and show that each test in the class is unbiased. The likelihood ratio test and others are in this class. For H 0 versus H 1 we obtain a complete…

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不完整数据下有序备择假设的检验AI 解读

In testing the null hypothesis of no treatment effects in a randomized block experiment, a researcher may restrict attention to an ordered alternative and thereby increase the power of his test. Jonckheere and later Page proposed such test statistics based on the Kendall and Spearman correlation coefficients. Motivated by notions of distance between permutations, we generalize Jonckheere's and Page's tests to the situation in which one or more observations are missing from…

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贝叶斯图像分析中的新损失函数AI 解读

Unlike the development of more accurate prior distributions for use in Bayesian imaging, the design of more sensible estimators through loss functions has been neglected in the literature. We discuss the design of loss functions with a local structure that depend only on a binary misclassification vector. The proposed approach is similar to modeling with a Markov random field. The Bayes estimate is calculated in a two-step algorithm using Markov chain Monte Carlo and…

噪声图像中真实灰度级数量、取值与相对频率的估计AI 解读

In some applications information is presented as a two-dimensional image corrupted by random noise. Due to the precision of the equipment that forms the image, we can typically have a large number, v, of observed gray levels. But in many situations we know that the number of true gray levels, p, corresponding to, for example, the number of tissue types in a brain slice, is much less than v. In this article we propose a method based on the kernel density estimator for…

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放射治疗中的剂量与局部控制建模AI 解读

We discuss models for predicting local control (prevention of tumor recurrence) after therapeutic radiation in cancer patients. The probability of control is first formulated from theoretical precepts. Biophysical principles dictate that the three factors in therapy that most universally influence outcome are total dose, number of sessions in which the dose is administered, and total time under treatment. We show that these principles also suggest the scale, or link…

不确定性的诸方面：献给 D. V. LindleyAI 解读

Dennis Lindley: The First 70 Years The Operational Bayesian Approach Pivotal Inference Illustrated on the Darwin Maize Data Bayes Theory in Latent Variable Modelling Bayesian Sampling Schemes for Auditors Optimal Prediction with Hierarchical Models: Bayesian Clustering Inference for a Covariance Matrix The Role of Statistical Theory in Decision Aiding The Signature as a Covariate in Reliability and Biometry Residual Analysis and Outliers in Hierarchical Models The Island…

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多阶段选择与排序程序：二阶渐近理论AI 解读

Theory of sequential and multistage procedures selecting the best normal population selecting the best negative exponential population estimation of ordered parameters selecting the best component in a multivariate normal population estimation after selection and ranking additional topics notation and abbreviations.

回归图形分析导论AI 解读

This article, which is based on an Interface tutorial, presents an overview of regression graphics, along with an annotated bibliography.The intent is to discuss basic ideas and issues without delving into methodological or theoretical details, and to provide a guide to the literature.

We study the problem of how to estimate the parameters of a multivariate hypergeometric distribution when the sample size n is assumed to be an ancillary statistic. The minimax estimator for squared error loss is given. This estimator differs from the well-known minimax estimator for fixed n. Furthermore, that classical estimator is shown to be not even admissible when n is random.

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异方差误差下线性回归的同时置信带AI 解读

The Scheffé method may be used to construct simultaneous confidence bands for a regression surface for the whole predictor space. When the bands need only hold for a subset of that space, previous authors have described how the bands can be appropriately narrowed while still maintaining the desired level of confidence. Data with heteroscedastic errors occur often, and unless some transformation is feasible, there is no obvious way to construct bands using the current…

We investigate linear approximation (LA) confidence intervals for functions g(θ) of the parameters θ in a nonlinear regression model. These intervals are almost universally used and generally perform well, but at times they have poor coverage probabilities. A diagnostic plot and index are developed to detect these failures. We show how these diagnostics may be used to estimate coverage probabilities and these are used to calibrate the diagnostics. The performance of the…

近似线性检验的功效稳健化AI 解读

We present a general method of improving the power of linear and approximately linear tests when deviations from a translation family of distributions must be taken into account. This method involves the combination of a linear statistic measuring location and a quadratic statistic measuring change of shape of the underlying distribution. The tests (“funnel tests”) are constructed as certain Bayes tests. In general they gain a sizeable amount of power over the linear tests…

基于周期抽样的自相关置信区间AI 解读

Clinger and Van Ness have shown how to sample a stochastic process in a periodic manner such that the autocorrelation function for that process can be estimated at all lags. We derive approximate confidence intervals for these estimates, focusing on low-order autoregressive moving average processes. Our methods are illustrated with an example from plant disease epidemiology.

用于分位数同时置信区间的上下界分布AI 解读

We propose a new method for constructing parametric confidence intervals for quantiles of an unknown distribution. These confidence intervals are constructed so that the joint probability that all intervals simultaneously contain their respective percentiles is at least a preset value. Thus we may also use the set of either upper or lower confidence limits to define an upper (or lower) bound distribution function, which we call an upper (or lower) tolerance distribution…

监测嵌套随机效应模型参数的似然比方法AI 解读

In many practical situations the variance of a set of measurements can be attributed to several known sources of variability. For example, if several measurements of each item of a lot are taken, then one may need to deal not only with the within-item variability, but also with item-to-item within-lot and lot-to-lot components of variability. In such cases conventional control charts tend to produce an unacceptably high rate of false alarms and in general represent a rather…

线性回归中的序约束推断AI 解读

Regression analysis constitutes a large portion of the statistical repertoire in applications. In cases where such analysis is used for exploratory purposes with no previous knowledge of the structure, one would not wish to impose any constraints on the problem. But in many applications we are interested in curve fitting with a simple parametric model to describe the structure of a system with some prior knowledge about the structure. An important example of this occurs when…

参数线性约束下最大似然估计的受限 EM 算法AI 解读

The EM algorithm is one of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems. But when the parameters must satisfy a set of linear restrictions, the EM algorithm may be too complicated to apply directly. In this article we propose maximum likelihood estimation procedures under a set of linear restrictions for situations in which the EM algorithm could be used if there were no such restrictions on the parameters. We…

变量选择的影响：贝叶斯诊断视角AI 解读

Variable selection is ubiquitous in statistical practice. Forward selection, backwards elimination, or a combination thereof are among the most popular techniques. Statistical texts teach direct interpretation of coefficients after variable selection. For scientific purposes, the desired interpretation is unconditional on the variable selection. In this article an influence analysis of variable selection is performed from a Bayesian viewpoint. Variable selection is shown to…