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利用广义估计方程进行聚类数据的半参数回归AI 解读

We consider estimation in a semiparametric generalized linear model for clustered data using estimating equations. Our results apply to the case where the number of observations per cluster is finite, whereas the number of clusters is large. The mean of the outcome variable μ is of the form g(μ) = XTβ + θ(T), where g(·) is a link function, X and T are covariates, β is an unknown parameter vector, and θ(t) is an unknown smooth function. Kernel estimating equations proposed…

长记忆随机波动率模型中记忆参数的对数周期图回归估计AI 解读

We consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model. We study the estimator based on a log periodogram regression as originally proposed by Geweke and Porter-Hudak (1983, Journal of Time Series Analysis 4, 221–238). Expressions for the asymptotic bias and variance of the estimator are obtained, and the asymptotic distribution is shown to be the same as that obtained in recent literature for a Gaussian long memory series.…

摘要暂缺

存在纵向协变量测量误差的比例风险模型半参数估计AI 解读

A common objective in longitudinal studies is to characterise the relationship between a failure time process and time‐independent and time‐dependent covariates. Time‐dependent covariates are generally available as longitudinal data collected periodically during the course of the study. We assume that these data follow a linear mixed effects model with normal measurement error and that the hazard of failure depends both on the underlying random effects describing the…

多事件时间数据的边际比例风险模型AI 解读

The Wei et al. (1989) semiparametric approach to the analysis of multiple event‐time data assumes that each event time is related to covariates through a proportional hazards model with a completely unspecified baseline hazard function, but does not impose any constraint on the joint distribution of different event times. As a result of the order restriction, it is not clear whether or not event times can simultaneously satisfy their respective marginal proportional hazards…

通过扰动优化目标进行简单重抽样AI 解读

Suppose that under a semiparametric setting an estimator of a vector of parameters of interest is obtained by optimising an objective function which has a U‐process structure. The covariance matrix of the estimator is generally a function of the underlying density function, which may be difficult to estimate well by conventional methods. In this paper, we present a simple resampling method by perturbing the objective function repeatedly. Inferences of the parameters can then…

点过程的半参数变换模型AI 解读

In this article, we propose a family of semiparametric transformation models for point processes with positive jumps of arbitrary sizes. These models offer great flexibilities in formulating the effects of covariates on the mean function of the point process while leaving the stochastic structure completely unspecified. We develop a class of estimating equations for the baseline mean function and the vector-valued regression parameter based on censored point processes and…

部分线性删失回归模型的半参数估计AI 解读

In this paper we propose an estimation procedure for a censored regression model where the latent regression function has a partially linear form. Based on a conditional quantile restriction, we estimate the model by a two stage procedure. The first stage nonparametrically estimates the conditional quantile function at in-sample and appropriate out-of-sample points, and the second stage involves a simple weighted least squares procedure. The proposed procedure is shown to…

摘要暂缺

I(1) 过程统计推断的有限样本改进AI 解读

Abstract Robinson and Marinucci ( 1998 ) investigated the asymptotic behaviour of a narrow‐band semiparametric procedure termed Frequency Domain Least Squares (FDLS) in the broad context of fractional cointegration analysis. Here we restrict discussion to the standard case when the data are I(1) and the cointegrating errors are I(0), proving that modifications of the Fully Modified Ordinary Least Squares (FM‐OLS) procedure of Phillips and Hansen ( 1990 ) which use the FDLS…

摘要暂缺

纵向数据的半参数与非参数回归分析AI 解读

This article deals with the regression analysis of repeated measurements taken at irregular and possibly subject-specific time points. The proposed semiparametric and nonparametric models postulate that the marginal distribution for the repeatedly measured response variable Y at time t is related to the vector of possibly time-varying covariates X through the equations E{Y(t)|| X(t} = α0(t) + β′0X(t) and E{Y(t)||X(t)} = α0(t)+ β′0(t)X(t), where α0(t) is an arbitrary function…

异方差非参数回归模型的协方差分析方法AI 解读

We consider an ANCOVA design in which the relationship between the response Yi and the covariate Xi in cell (factor-level combination) i satisfies the model Yi = mi(Xi)+σi(Xi)ϵi, where the error term ϵi is assumed to be independent of Xi, and mi and σi are respectively a smooth (but unknown) regression and scale function. This model can be viewed as a generalization of the nonparametric ANCOVA model of Young and Bowman. As such it is a useful alternative for parametric or…

修正病例队列设计下半参数变换回归模型的拟合AI 解读

We consider the problem of fitting semiparametric transformation regression models to data from a modified case‐cohort study in which the censoring times of all the censored subjects in the cohort are observed. We propose to maximise a conditional profile likelihood to obtain the estimator of the regression parameter. Under the assumption that the censoring is independent of the covariates, the estimator is shown to be consistent and asymptotically normally distributed.…

非线性结构型测量误差模型的半参数估计AI 解读

In the nonlinear structural errors-in-variables model, we propose a consistent estimator of the unknown parameter using a modified least squares criterion. We give an upper bound of its rate of convergence which is strongly related to the regularity of the regression function and is generally slower than the parametric rate of convergence n-1/2. Nevertheless, the rate is of order n-1/2 for some particular analytic regression functions. For instance, when the regression…

针对光滑备择的广义线性与半参数模型检验AI 解读

Summary We propose goodness-of-fit tests for testing generalized linear models and semiparametric regression models against smooth alternatives. The focus is on models having both continous and factorial covariates. As a smooth extension of a parametric or semiparametric model we use generalized varying-coefficient models as proposed by Hastie and Tibshirani. A likelihood ratio statistic is used for testing. Asymptotic expansions allow us to write the estimates as linear…

离散持续时间模型的贝叶斯半参数估计：Dirichlet 过程先验应用AI 解读

Abstract This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non‐parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline…

样本选择模型的参数与半参数估计：葡萄牙女性劳动力的实证应用AI 解读

Abstract This paper applies both parametric and semiparametric methods to the estimation of wage and participation equations for married women in Portugal. The semiparametric estimators considered are the two‐stage estimators proposed by Newey ( 1991 ) and Andrews and Schafgans ( 1998 ). The selection equation results are compared using the specification tests proposed by Horowitz ( 1993 ), Horowitz and Härdle ( 1994 ), and the wage equation results are compared using a…

双变量现状数据的关联性评估AI 解读

Assuming that the two failure times of interest with bivariate current status data follow a bivariate copula model, we propose a two‐stage estimation procedure to estimate the association parameter which is related to Kendall's tau. Asymptotic properties of the proposed semiparametric estimator show that, although the first‐stage marginal estimators have a convergence rate of only n1/3, the resulting parameter estimator still converges to a normal random variable with the…

成组失效时间数据的半参数回归分析AI 解读

Inference procedures based on the partial likelihood function for the Cox proportional hazards model have been generalised to the case in which the data consist of a large number of independent small groups of correlated failure time observations (Lee, Wei [amp ] Amato, 1992; Liang, Self [amp ] Chang, 1993; Cai [amp ] Prentice, 1997). However, the Cox model may not fit the data well. A class of linear transformation models, which includes the proportional hazards and odds…

预防 HIV-1 母婴垂直传播的随机试验分析AI 解读

We analyze data from a randomized trial of breastfeeding versus formula feeding in HIV-1-infected mothers and their infants. Estimates of infant HIV-1-free survival and the timing of vertical transmission of HIV-1 are complicated by the fact that the infants' HIV-1 status at death may be unknown, the time of HIV-1 infection may be interval censored, and the tests used to detect HIV-1 infection in the infants may be less than 100% sensitive or specific. To address these…

预防 HIV-1 母婴传播随机试验的分析AI 解读

We analyze data from a randomized trial of breastfeeding versus formula feeding in HIV-1-infected mothers and their infants. Estimates of infant HIV-1-free survival and the timing of vertical transmission of HIV-1 are complicated by the fact that the infants' HIV-1 status at death may be unknown, the time of HIV-1 infection may be interval censored, and the tests used to detect HIV-1 infection in the infants may be less than 100% sensitive or specific. To address these…

重复事件均值函数与发生率函数的半参数回归AI 解读

Summary The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate…

股市交易量的长记忆性AI 解读

This article examines consistent estimation of the long-memory parameters of stock-market trading volume and volatility. The analysis is carried out in the frequency domain by tapering the data instead of detrending them. The main theoretical contribution of the article is to prove a central limit theorem for a multivariate two-step estimator of the memory parameters of a nonstationary vector process. Using robust semiparametric procedures, the long-memory properties of…