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Nonparametric Empirical Bayes Growth Curve Analysis

非参数经验贝叶斯生长曲线分析

Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

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Optimal and Robust Strategies for Cluster Sampling

整群抽样的最优与稳健策略

This article extends Royall's (1992) results on optimal and robust sampling strategies to cluster sampling. It also gives the lower bound on the model-based variances of best linear unbiased predictors of finite population totals under certain classes of superpopulation models.

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A Stepwise Resampling Method of Multiple Hypothesis Testing

多重假设检验的逐步重抽样方法

This article introduces a method of multiple hypothesis testing that combines the idea of sequential multiple testing procedures with the structure of resampling methods. The method can be seen as an alternative to the analytic method of Dunnett and Tamhane, which requires a specific distributional form. Resampling incorporates the covariance structure of the data without the need for distributional assumptions. Recent work by Westfall and Young has shown that a step-down…

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A Score Test against One-Sided Alternatives

针对单侧备择的得分类检验

A score-type statistic, T s , is introduced for testing H: ψ = 0 against K: ψ ≥ 0 and more general one-sided hypotheses when nuisance parameters may be present; ψ is a vector parameter. The main advantages of T s , are that it requires estimation of the model only under the null hypothesis and that, it is asymptotically equivalent to the likelihood ratio statistic; these are precisely the reasons for the popularity of the score tests for testing against two-sided…

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Exact Maximum Likelihood Estimation of Stationary Vector ARMA Models

平稳向量 ARMA 模型的精确最大似然估计

The problems of evaluating and subsequently maximizing the exact likelihood function of vector autoregressive moving average (ARMA) models are considered separately. A new and efficient procedure for evaluating the exact likelihood function is presented. This method puts together a set of useful features that can only be found separately in currently available algorithms. A procedure for maximizing the exact likelihood function, which takes full advantage of the properties…

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Estimation and Testing for Unit Roots in a Partially Nonstationary Vector Autoregressive Moving Average Model

部分非平稳向量 ARMA 模型的估计与单位根检验

The Gaussian estimation of a partially nonstationary autoregressive model and the related issue of testing for cointegration using the likelihood ratio test procedure have been considered by others. In this article we extend the Gaussian estimation procedure to partially nonstationary autoregressive moving average models and derive the asymptotic properties of the full-rank and reduced-rank Gaussian estimators. Based on these results, the asymptotic distribution of the…

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Monte Carlo EM Estimation for Time Series Models Involving Counts

计数时间序列模型的蒙特卡洛 EM 估计

The observations in parameter-driven models for time series of counts are generated from latent unobservable processes that characterize the correlation structure. These models result in very complex likelihoods, and even the EM algorithm, which is usually well suited for problems of this type, involves high-dimensional integration. In this article we discuss a Monte Carlo EM (MCEM) algorithm that uses a Markov chain sampling technique in the calculation of the expectation…

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Inference and Predictions from Poisson Point Processes Incorporating Expert Knowledge

融入专家知识的泊松点过程推断与预测

We present a Bayesian approach for inference and predictions from nonhomogeneous Poisson point processes. The novel feature of our approach is the use of “expert knowledge” or “engineering information” on the mean value function of the process. We describe two scenarios from the field of reliability in which engineering information on the mean value function is available. The first scenario pertains to the prediction of software failures during the debugging phase. Here…

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D -Optimum Designs for Heteroscedastic Linear Models

异方差线性模型的 D 最优设计

The methods of optimum experimental design are applied to models in which the variance, as well as the mean, is a parametric function of explanatory variables. Extensions to standard optimality theory lead to designs when the parameters of both the mean and the variance functions, or the parameters of only one function, are of interest. The theory also applies whether the mean and variance are functions of the same variables or of different variables, although the…

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Tests for Variation over Groups in Survival Data

生存数据的组间变异检验

When survival data can be divided into natural groups, such as in a clinical trial with patients from a number of different institutions, the question arises whether effects are different for the different groups. For situations where one is concerned with a random pattern of variation over groups, this article proposes testing the null hypothesis of no variation using statistics based on the sums of martingale residuals over subjects within groups. These tests are closely…

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Consistent Variable Selection in Linear Models

线性模型中的一致变量选择

A method of estimating linear model dimension and variable selection is proposed. This new criterion, which generalizes the Cp criterion, the Akaike information criterion (AIC), the Bayes information criterion, and the phiv criterion and is consistent under certain conditions, is based on a new class of penalty functions and a procedure of sorting covariates based on t-statistics. In the course of introducing this method, we discuss the important role of the penalty function…

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Semiparametric Regression Functionals

半参数回归泛函

A regression method is developed for a general class of functionals. A semiparametric linear model is adopted, and the regression parameters are estimated by maximizing a profiled nonparametric or empirical likelihood based on a local estimate of the conditional distribution function. Simulated and real data examples are shown, including an application of quantile regression to censored survival data from a clinical trial for myeloma.

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Simultaneous Confidence Intervals and Sample Size Determination for Multinomial Proportions

多项分布比例的同时置信区间与样本量确定

Simultaneous confidence interval procedures for multinomial proportions are used in many areas of science. In this article two new simultaneous confidence interval procedures are introduced. Numerical results are presented to evaluate these procedures and compare their performance with established methods that have been used in statistical literature. From the results presented in this article, it is evident that the new procedures are more accurate than the established…

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The P-P Plot as a Method for Comparing Treatment Effects

使用 P-P 图比较处理效应

This article examines the use of the probability-probability plot (p-p plot) as a method for comparing treatment effects. To begin in the context of three examples the p-p plot is contrasted with the quantile-quantile plot (q-q plot), which is an alternative means of describing treatment effects. In these examples it is shown that p-p plots representing different experimental conditions or patient populations allow scale-invariant comparisons of treatment effects but q-q…

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The Behavior of the Stahel-Donoho Robust Multivariate Estimator

Stahel–Donoho 稳健多元估计量的表现

The Stahel-Donoho estimators (t, V) of multivariate location and scatter are defined as a weighted mean and a weighted covariance matrix with weights of the form w(r), where w is a weight function and r is a measure of “outlyingness,” obtained by considering all univariate projections of the data. It has a high breakdown point for all dimensions and order √n consistency. The asymptotic bias of V for point mass contamination for suitable weight functions is compared with that…

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REML Estimation of Covariance Matrices with Restricted Parameter Spaces

受限参数空间下协方差矩阵的 REML 估计

Restricted parameter spaces for covariance matrices, such as ∑ = σ2 I or ∑ = αI + βJ, are often used to simplify estimation. In addition, fixed upper and/or lower bounds may be needed to ensure that estimates satisfy a priori hypotheses. With multivariate variance components models, several covariance matrices need to be simultaneously estimated and, even with a reduced parameter space, estimation can be difficult. In earlier work we have discussed estimation for a…

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Robust Likelihoods

稳健似然

Strong theoretical arguments imply that for directly representing and interpreting statistical data as evidence, the proper vehicle is the likelihood function. These arguments have had limited impact on statistical practice. One reason for this is that likelihood functions are explicitly model dependent and thus appear to be inherently nonrobust. In this article we examine the concept of robustness as it relates to likelihood functions. We note five ways that likelihood…

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Filtering and Smoothing via Estimating Functions

基于估计函数的滤波与平滑

We consider the problem of filtering and smoothing in state-space models, which include nonlinear and non-Gaussian models. We do not make any distributional assumptions about the processes involved. Our approach to these problems is based on the theory of estimating functions. Filter and smoother are obtained as solutions of estimating equations that are optimal in appropriate classes. We illustrate our procedures by simulation studies of a model where the observational…

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Supplementing the Intent-to-Treat Analysis: Accounting for Covariates Observed Postrandomization in Clinical Trials

补充意向治疗分析:处理临床试验中随机化后观测的协变量

A problem that arises frequently in the analysis of clinical trials is accounting for covariates observed postrandomization. Two examples are patient compliance measured through pill counts and the dose of a concomitant medication. Typically these are measured concurrently with the primary endpoint at the regularly scheduled follow-ups during the course of the study. To account for these effects, the confounders are sometimes introduced as time-dependent covariates into the…

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Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag

ARMA 模型单位根检验中的数据驱动截断滞后选择

We analyze the choice of the truncation lag in the context of the Said-Dickey test for the presence of a unit root in a general autoregressive moving average model. It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account. In particular, we study data-dependent rules that are not constrained to satisfy the lower bound condition imposed by Said-Dickey. Akaike's…

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Multiple Bayes Factors for Testing Hypotheses

用于假设检验的多重贝叶斯因子

Partial and multiple Bayes factors are introduced to obtain pairwise comparisons of hypotheses in a statistical experiment with a partition on the parameter space. Robust Bayesian analyses are performed by introducing suitable classes of priors and by calculating lower and upper bounds of Bayes factors and posterior probabilities. Classes of intuitively meaningful priors are introduced, including unimodal densities without the constraint of symmetry for the case of precise…

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Properties of Hazard-Based Residuals and Implications in Model Diagnostics

基于风险率的残差性质及其对模型诊断的影响

Model diagnostic procedures in failure time models using hazard-based residuals rely on the assumption that the residual vector closely resembles a random sample from a unit exponential distribution when the model holds. This article formally investigates the validity of this critical assumption by deriving and examining the properties of parametrically, semiparametrically, and nonparametrically estimated residuals for complete and right-censored data. The joint distribution…

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