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On the Cox Model With Time-Varying Regression Coefficients

具有时变回归系数的Cox模型

In the analysis of censored failure time observations, the standard Cox proportional hazards model assumes that the regression coefficients are time invariant. Often, these parameters vary over time, and the temporal covariate effects on the failure time are of great interest. In this article, following previous work of Cai and Sun, we propose a simple estimation procedure for the Cox model with time-varying coefficients based on a kernel-weighted partial likelihood…

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Nonparametric Identification and Estimation of a Censored Location-Scale Regression Model

删失位置—尺度回归模型的非参数识别与估计

In this article we consider identification and estimation of a censored nonparametric location scale-model. We first show that in the case where the location function is strictly less than the (fixed) censoring point for all values in the support of the explanatory variables, the location function is not identified anywhere. In contrast, when the location function is greater or equal to the censoring point with positive probability, the location function is identified on the…

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A Statistical Model for Signature Verification

签名验证的统计模型

A Bayesian model for off-line signature verification involving the representation of a signature through its curvature is developed. The prior model makes use of a spatial point process for specifying the knots in an approximation restricted to a buffer region close to a template curvature, along with an independent time-warping mechanism. In this way, prior shape information about the signature can be built into the analysis. The observation model is based on additive white…

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Multifold Predictive Validation in ARMAX Time Series Models

ARMAX 时间序列模型的多折预测验证

This article presents a new procedure for multifold predictive validation in time series. The procedure is based on the so-called “filtered residuals,” in-sample prediction errors evaluated in such a way that they are similar to out-of-sample ones. The filtered residuals are obtained from parameters estimated by eliminating from the estimation process the estimated innovations at the points to be predicted. Thus, instead of using the deletion of observations to validate the…

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Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing

多重假设检验的精确与近似逐步下降方法

Consider the problem of testing k hypotheses simultaneously. In this article we discuss finite- and large-sample theory of stepdown methods that provide control of the familywise error rate (FWE). To improve on the Bonferroni method or on Holm's stepdown method, Westfall and Young made effective use of resampling to construct stepdown methods that implicitly estimate the dependence structure of the test statistics. However, their methods depend on an assumption known as…

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Optimal Experimental Designs When Some Independent Variables Are Not Subject to Control

部分自变量不可控时的最优实验设计

This article considers the problem of constructing optimal designs for regression models when the design space is a product space and some of the variables are not under the control of the practitioner. A variable that is not under control can have known values before the experiment is performed or else unknown values before the experiment is realized. The first case is briefly discussed in the literature. The aim of this work is to provide equivalence theorems for the…

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Predicting Random Effects From Finite Population Clustered Samples With Response Error

响应误差下有限总体整群样本的随机效应预测

In many situations there is interest in parameters (e.g., mean) associated with the response distribution of individual clusters in a finite clustered population. We develop predictors of such parameters using a two-stage sampling probability model with response error. The probability model stems directly from finite population sampling without additional assumptions and thus is design-based. The predictors are closely related to best linear unbiased predictors (BLUP) that…

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Predicting the Conditional Probability of Discovering a New Class

发现新类别的条件概率预测

Consider a population comprising disjoint classes. An important problem arising from various fields is prediction of the random conditional probability of discovering a new class. The asymptotic normality of the discovery probability is established in a Poisson model, where the number of individuals from each class is a Poisson process with a class-specific rate. A new derivation is presented for the well-known Good–Toulmin predictor as a moment-based estimator for the…

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Local Global Neural Networks

局部—全局神经网络

We propose the local-global neural networks model within the context of time series models. This formulation encompasses some already existing nonlinear models and also admits the mixture of experts approach. We emphasize the linear expert case and extensively discuss the theoretical aspects of the model: stationarity conditions, existence, consistency and asymptotic normality of the parameter estimates, and model identifiability. The proposed model consists of a mixture of…

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Models and Confidence Intervals for True Values in Interlaboratory Trials

实验室间比对中真值的模型与置信区间

We consider the one-way random-effects model with unequal sample sizes and heterogeneous variances. Using the method of generalized confidence intervals, we develop a new confidence interval procedure for the mean. Additionally, we investigate two alternative models based on different sets of assumptions regarding between-group variability and derive generalized confidence interval procedures for the mean. These procedures are applicable to small samples. Statistical…

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Fully Exponential Laplace Approximations Using Asymptotic Modes

使用渐近众数的全指数拉普拉斯近似

Posterior means of positive functions can be expressed as the ratio of integrals which is called fully exponential form. When approximating the posterior means analytically, we usually use Laplace's method. Tierney and Kadane presented a second-order approximation by using the Laplace approximations in each of the numerator and denominator of the fully exponential form. However, Laplace's method requires the exact mode of the integrand. In this article we introduce the…

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On a Likelihood Approach for Monte Carlo Integration

蒙特卡洛积分的似然方法

The use of estimating equations has been a common approach for constructing Monte Carlo estimators. Recently, Kong et al. proposed a formulation of Monte Carlo integration as a statistical model, making explicit what information is ignored and what is retained about the baseline measure. From simulated data, the baseline measure is estimated by maximum likelihood, and then integrals of interest are estimated by substituting the estimated measure. For two different situations…

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Location–Scale Depth

位置—尺度深度

This article introduces a halfspace depth in the location–scale model that is along the lines of the general theory given by Mizera, based on the idea of Rousseeuw and Hubert, and is complemented by a new likelihood-based principle for designing criterial functions. The most tractable version of the proposed depth—the Student depth—turns out to be nothing but the bivariate halfspace depth interpreted in the Poincaré plane model of the Lobachevski geometry. This fact implies…

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Structural Mean Effects of Noncompliance

不依从情形下的结构均值效应

A randomized, placebo-controlled blood pressure (BP) reduction trial has recorded compliance measures by electronic monitoring. Causal questions regarding dose effects are of interest, but one often wants to avoid the assumption that compliance quantiles on both randomized arms are comparable. Structural mean models (SMMs) do avoid this assumption; however, they do not allow for interaction effects between variables observed on different randomized arms. Such an interaction…

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Smoothing Spline Nonlinear Nonparametric Regression Models

平滑样条非线性非参数回归模型

Almost all of the current nonparametric regression methods, such as smoothing splines, generalized additive models, and varyingcoefficients models, assume a linear relationship when nonparametric functions are regarded as parameters. In this article we propose a general class of smoothing spline nonlinear nonparametric models that allow nonparametric functions to act nonlinearly. They arise in many fields as either theoretical or empirical models. Our new estimation methods…

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A Model-Based Background Adjustment for Oligonucleotide Expression Arrays

寡核苷酸表达芯片的模型化背景校正

High-density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the…

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Improved Semiparametric Time Series Models of Air Pollution and Mortality

空气污染与死亡率的改进半参数时间序列模型

In 2002, methodological issues around time series analyses of air pollution and health attracted the attention of the scientific community, policy makers, the press, and the diverse stakeholders concerned with air pollution. As the U. S. Environmental Protection Agency (EPA) was finalizing its most recent review of epidemiologic evidence on particulate matter air pollution (PM), statisticians and epidemiologists found that the S–PLUS implementation of generalized additive…

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Combining Independent Regression Estimators From Multiple Surveys

合并多个调查的独立回归估计量

Efficient generalized regression (GREG) procedures are proposed for the combination of comparable information collected independently from multiple surveys of the same population. In particular, for combination through alignment of estimated totals of common target characteristics, an efficiency improvement of Zieschang's original composite GREG method is developed, involving a correction of the GREG estimation for different effective sample sizes in a multiple-sample…

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Analysis of Time-to-Event Data With Incomplete Event Adjudication

事件判定不完整时的事件发生时间数据分析

In many multicenter, randomized clinical trials, the primary outcome is the time to the first of a number of possible clinical events. An event classification committee may be convened to determine whether events that have been reported by investigators meet the predetermined criteria for primary endpoint events. When interim analyses are performed in such trials, the final classification for many reported events will not be known. Failure to account for the uncertain status…

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Bayesian Estimation of the Spectral Density of a Time Series

时间序列谱密度的贝叶斯估计

This article describes a Bayesian approach to estimating the spectral density of a stationary time series. A nonparametric prior on the spectral density is described through Bernstein polynomials. Because the actual likelihood is very complicated, a pseudoposterior distribution is obtained by updating the prior using the Whittle likelihood. A Markov chain Monte Carlo algorithm for sampling from this posterior distribution is described that is used for computing the posterior…

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Testing for Trend in the Presence of Autoregressive Error

自回归误差存在时的趋势检验

A popular model for assessing dependence on time is the time series model composed of a linear trend plus a zero mean autoregressive (AR) process. Although considerable effort has been devoted developing tests for linear trend in the presence of serial correlation, the testing procedures used in practice are less than satisfactory for portions of the parameter space for the AR coefficient. This is because the variance of the feasible generalized least squares (FGLS)…

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