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A nonparametric test for panel count data

面板计数数据的非参数检验

Panel count data arise when a recurrent event is under investigation and each study subject is observed only at discrete time points. In this situation, observed data include only the numbers of occurrences of the event of interest between observation time points and no information is available on subjects between their observation time points. We propose a nonparametric test for comparing the point processes characterising the recurrent event when only panel count data are…

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A dependence measure for multivariate and spatial extreme values: Properties and inference

多变量与空间极值的依赖度量:性质与推断

We present properties of a dependence measure that arises in the study of extreme values in multivariate and spatial problems. For multivariate problems the dependence measure characterises dependence at the bivariate level, for all pairs and all higher orders up to and including the dimension of the variable. Necessary and sufficient conditions are given for subsets of dependence measures to be self‐consistent, that is to guarantee the existence of a distribution with such…

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Likelihood inference in nearest-neighbour classification models

最近邻分类模型中的似然推断

Traditionally the neighbourhood size k in the k‐nearest‐neighbour algorithm is either fixed at the first nearest neighbour or is selected on the basis of a crossvalidation study. In this paper we present an alternative approach that develops the k‐nearest‐neighbour algorithm using likelihood‐based inference. Our method takes the form of a generalised linear regression on a set of k‐nearest‐neighbour autocovariates. By defining the k‐nearest‐neighbour algorithm in this way we…

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Confidence regions when the Fisher information is zero

Fisher 信息为零时的置信区域

We examine the asymptotic behaviour of confidence regions in identifiable one‐dimensional parametric models with smooth likelihood function and information equal to zero at a critical point of the parameter space. Confidence regions are based on inversion of the likelihood ratio test statistic and of some common forms of the score and Wald test statistics. For fixed parameter values other than the critical point, all these statistics have limiting x2(1) distributions, but…

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Marginal nonparametric kernel regression accounting for within-subject correlation

考虑个体内相关性的边际非参数核回归

There has been substantial recent interest in non‐ and semiparametric methods for longitudinal or clustered data with dependence within clusters. It has been shown rather inexplicably that, when standard kernel smoothing methods are used in a natural way, higher efficiency is obtained by assuming independence than by using the true correlation structure. It is shown here that this result is a natural consequence of how standard kernel methods incorporate the within‐subject…

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Estimation of a failure time distribution based on imperfect diagnostic tests

基于不完美诊断检验的失效时间分布估计

Sequentially‐administered diagnostic tests used to determine the occurrence of a silent event are sometimes subject to error, leading to false positive and false negative test results. In such cases, standard methods for interval censored data do not give valid estimates of the distribution of the time to the event. We present methods for estimating the distribution of the time to the event that account for multiple types of imperfect diagnostic test, as well as differing…

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Implementing matching priors for frequentist inference

利用匹配先验实现频率学派推断

Nuisance parameters do not pose any problems in Bayesian inference as marginalisation allows for study of the posterior distribution solely in terms of the parameter of interest. However, no general solution is available for removing nuisance parameters under the frequentist paradigm. In this paper, we merge the two approaches to construct a general procedure for frequentist elimination of nuisance parameters through the use of matching priors. In particular, we perform…

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Nonparametric estimation in nonlinear mixed effects models

非线性混合效应模型中的非参数估计

A nonparametric approach is developed herein to estimate parameters in nonlinear mixed effects models. Asymptotic properties of the nonparametric maximum likelihood estimators and associated computational algorithms are provided. Empirical Bayes estimators of functionals of the random effects are also developed. Applications to population pharmacokinetics are given.

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Outlier Detection and False Discovery Rates for Whole-Genome DNA Matching

全基因组 DNA 匹配中的异常检测与错误发现率

We define a statistic, called the matching statistic, for locating regions of the genome that exhibit excess similarity among cases when compared to controls. Such regions are reasonable candidates for harboring disease genes. We find the asymptotic distribution of the statistic while accounting for correlations among sampled individuals. We then use the Benjamini and Hochberg false discovery rate (FDR) method for multiple hypothesis testing to find regions of excess…

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Functional Inference in Frailty Measurement Error Models for Clustered Survival Data Using the SIMEX Approach

聚类生存数据中脆弱性测量误差模型的 SIMEX 函数型推断

We consider frailty models for clustered survival data in the presence of measurement errors in covariates. We first show that when the measurement error is accounted for in a full likelihood analysis but the distribution of the unobserved covariate is misspecified, the maximum likelihood estimators are asymptotically biased, especially for the variance component, whose bias can be substantial. We then discuss making inference using functional estimation via the SIMEX method…

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Asymptotic Variance and Convergence Rates of Nearly-Periodic Markov Chain Monte Carlo Algorithms

近周期马尔可夫链蒙特卡洛算法的渐近方差与收敛速度

This article considers nearly-periodic Markov chains that may have excellent functional estimation properties but poor distributional convergence rate. It shows how simple modifications of the chain (involving using a random number of iterations) can greatly improve the distributional convergence of the chain. Various theoretical results about convergence rates of the modified chains are proven. A number of examples, including a transdimensional Markov chain Monte Carlo…

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Distribution of Rankings for Groups Exhibiting Heteroscedasticity and Correlation

存在异方差与相关性的群组排名分布

In one-way analysis of variance, a main interest is in differences among the groups that comprise the population. For a given parameter, such as a mean value, data yield parameter estimates for each group, as well as group rankings based on these statistics. Here ranking probabilities are studied under the assumption that parameter estimates are well approximated by a normal distribution, either in finite samples or asymptotically, with possible intergroup heteroscedasticity…

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A Reexamination of Diffusion Estimators With Applications to Financial Model Validation

扩散估计量的重新审视及其在金融模型检验中的应用

Time-homogeneous diffusion models have been widely used for describing the stochastic dynamics of the underlying economic variables. Recently, Stanton proposed drift and diffusion estimators based on a higher-order approximation scheme and kernel regression method. He claimed that “higher order approximations must outperform lower order approximations” and concluded nonlinearity in the instantaneous return function of short-term interest rates. To examine the impact of…

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Alternating Subspace-Spanning Resampling to Accelerate Markov Chain Monte Carlo Simulation

通过交替子空间张成重采样加速马尔可夫链蒙特卡洛模拟

This article provides a simple method to accelerate Markov chain Monte Carlo sampling algorithms, such as the data augmentation algorithm and the Gibbs sampler, via alternating subspace-spanning resampling (ASSR). The ASSR algorithm often shares the simplicity of its parent sampler but has dramatically improved efficiency. The methodology is illustrated with Bayesian estimation for analysis of censored data from fractionated experiments. The relationships between ASSR and…

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Robust Indirect Inference

稳健间接推断

In this article we develop robust indirect inference for a variety of models in a unified framework. We investigate the local robustness properties of indirect inference and derive the influence function of the indirect estimator, as well as the level and power influence functions of indirect tests. These tools are then used to design indirect inference procedures that are stable in the presence of small deviations from the assumed model. Although indirect inference was…

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The Impact of Prior Distributions for Uncontrolled Confounding and Response Bias

未控制混杂与应答偏差的先验分布对推断的影响

This article examines the potential for misleading inferences from conventional analyses and sensitivity analyses of observational data, and describes some proposed solutions based on specifying prior distributions for uncontrolled sources of bias. The issues are illustrated in a sensitivity analysis of confounding in a study of residential wire code and childhood leukemia and in a pooled analysis of 12 studies of magnetic-field measurements and childhood leukemia. Both…

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On Additive Conditional Quantiles With High-Dimensional Covariates

高维协变量下的加性条件分位数

We investigate the estimation of the conditional quantile when many covariates are involved. In particular, we model the conditional quantile of a response as a nonlinear additive function of relevant covariates. For this setup, we propose a nonparametric smoother to estimate the unknown functions. The estimator provides direct computation of the nonlinear functions. Because it does not require any iteration, the estimator allows fast and routine data analysis. On the…

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Estimating Vaccine Efficacy From Secondary Attack Rates

利用二代罹患率估计疫苗效力

Epidemiologists have used secondary attack rates (SARs) to estimate the protective effects of vaccination since the 1930s. SARs can also be used to estimate the effect of vaccination on reducing infectiousness in breakthrough cases. The conventional SAR approach has been to pool the denominators and numerators across transmission units, then to use a confidence interval for a simple relative risk. We demonstrate appropriate model-based methods to estimate vaccine efficacy…

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Empirical Limits for Time Series Econometric Models

时间序列计量模型的实证极限

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and…

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A Bias-Reduced Log-Periodogram Regression Estimator for the Long-Memory Parameter

长记忆参数的偏差缩减对数周期图回归估计量

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^dr, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The…

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Long‐range dependence in Spanish political opinion poll series

西班牙政治民意调查序列的长程依赖

Abstract This paper investigates the time series properties of partisanship for five political parties in Spain. It is found that pure fractional processes with a degree of integration, d , between 0.6 and 0.8 fit the time‐series behaviour of aggregate opinion polls for mainstream parties quite well, whereas values of d in the range of 0.3 to 0.6 are obtained for opinion polls related to smaller regional parties. Those results are in agreement with theories of political…

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Retesting Fair's (1978) model on infidelity

重新检验Fair关于婚外情的模型

Abstract Modern econometrics stresses the diagnostic testing of estimated models as an important part of the model‐building process. In a survey article published in this journal (Pagan and Vella 1989 ), methods for testing the validity of the assumptions underlying the censored regression of the Tobit model were presented. This paper questions the numerical results presented there. Indeed, my results concerning the tests when applied to Fair's well‐known model for…

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