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函数型与纵向数据分析中主成分方法的性质AI 解读

The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of “functional data analysis,” it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analysis a random function typically represents a patient, or subject, who is observed…

半参数空间回归中的估计AI 解读

Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given its closest neighbors requires a four-dimensional nonparametric regression. In this paper a semiparametric spatial regression approach is proposed to avoid this problem. An estimation…

两成分混合模型的半参数估计AI 解读

Suppose that univariate data are drawn from a mixture of two distributions that are equal up to a shift parameter. Such a model is known to be nonidentifiable from a nonparametric viewpoint. However, if we assume that the unknown mixed distribution is symmetric, we obtain the identifiability of this model, which is then defined by four unknown parameters: the mixing proportion, two location parameters and the cumulative distribution function of the symmetric mixed…

含误分类解释变量的回归模型：识别与估计AI 解读

This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is nonparametrically identified in the presence of an additional random variable that is correlated with the unobserved true underlying variable but unrelated to the measurement error. Identification for semiparametric and parametric…

解决 Berkson 测量误差问题的非参数方法AI 解读

Summary It is common, in errors-in-variables problems in regression, to assume that the errors are incurred ‘after the experiment’, in that the observed value of the explanatory variable is an independent perturbation of its true value. However, if the errors are incurred ‘before the experiment’ then the true value of the explanatory variable equals a perturbation of its observed value. This is the context of the Berkson model, which is increasingly attracting attention in…

面向半参数假设的定制拟合优度检验AI 解读

We introduce a new framework for constructing tests of general semiparametric hypotheses which have nontrivial power on the n−1/2 scale in every direction, and can be tailored to put substantial power on alternatives of importance. The approach is based on combining test statistics based on stochastic processes of score statistics with bootstrap critical values.

平滑二元分位数回归AI 解读

Abstract This paper extends results regarding smoothed median binary regression to general smoothed binary quantile regression, discusses the interpretation of the resulting estimators under alternative assumptions, and shows how they may be used to obtain semiparametric estimates of counterfactual probabilities. The estimators are applied to a model of labour force participation of married women in the USA. We find that the elasticity with respect to non‐labour income is…

跨期定价与价格歧视：个人电脑市场的半参数特征价格分析AI 解读

Abstract We apply a smooth coefficient semiparametric model to a unique high‐frequency data set to examine the intertemporal pricing of personal computers. Furthermore, we test whether firms charge differential component prices for their top performance personal computers and whether premium firms charge both a premium for all their computers and a premium for their top performance ones. We find nonlinear effects in the pricing of personal computer components. We also find…

病例队列研究的半参数变换模型AI 解读

A general class of semiparametric transformation models is studied for analysing survival data from the case-cohort design, which was introduced by Prentice (1986). Weighted estimating equations are proposed for simultaneous estimation of the regression parameters and the transformation function. It is shown that the resulting regression estimators are asymptotically normal, with variance-covariance matrix that has a closed form and can be consistently estimated by the usual…

删失临床数据中两阶段随机化设计的生存分布半参数有效估计AI 解读

Two-stage randomisation designs are useful in the evaluation of combination therapies where patients are initially randomised to an induction therapy and then, depending upon their response and consent, are randomised to a maintenance therapy. In this paper we derive the best regular asymptotically linear estimator for the survival distribution and related quantities of treatment regimes. We propose an estimator which is easily computable and is more efficient than existing…

异方差部分线性模型的有效半参数估计量AI 解读

We study the heteroscedastic partially linear model with an unspecified partial baseline component and a nonparametric variance function. An interesting finding is that the performance of a naive weighted version of the existing estimator could deteriorate when the smooth baseline component is badly estimated. To avoid this, we propose a family of consistent estimators and investigate their asymptotic properties. We show that the optimal semiparametric efficiency bound can…

删失数据的最小二乘回归AI 解读

The semiparametric accelerated failure time model relates the logarithm of the failure time linearly to the covariates while leaving the error distribution unspecified. The present paper describes simple and reliable inference procedures based on the least-squares principle for this model with right-censored data. The proposed estimator of the vector-valued regression parameter is an iterative solution to the Buckley–James estimating equation with a preliminary consistent…

不依赖比例风险假设的单指标风险函数半参数估计AI 解读

This research develops a semiparametric kernel-based estimator of hazard functions which does not assume proportional hazards. The maintained assumption is that the hazard functions depend on regressors only through a linear index. The estimator permits both discrete and continuous regressors, both discrete and continuous failure times, and can be applied to right-censored data and to multiple-risks data, in which case the hazard functions are risk-specific. The estimator is…

一般重复测量问题中的半参数估计AI 解读

Summary The paper considers a wide class of semiparametric problems with a parametric part for some covariate effects and repeated evaluations of a nonparametric function. Special cases in our approach include marginal models for longitudinal or clustered data, conditional logistic regression for matched case–control studies, multivariate measurement error models, generalized linear mixed models with a semiparametric component, and many others. We propose profile kernel and…

序列与非序列符号—秩统计量：渐近表示与渐近正态性AI 解读

The classical theory of rank-based inference is entirely based either on ordinary ranks, which do not allow for considering location (intercept) parameters, or on signed ranks, which require an assumption of symmetry. If the median, in the absence of a symmetry assumption, is considered as a location parameter, the maximal invariance property of ordinary ranks is lost to the ranks and the signs. This new maximal invariant thus suggests a new class of statistics, based on…

惩罚最大似然与半参数二阶效率AI 解读

We consider the problem of estimation of a shift parameter of an unknown symmetric function in Gaussian white noise. We introduce a notion of semiparametric second-order efficiency and propose estimators that are semiparametrically efficient and second-order efficient in our model. These estimators are of a penalized maximum likelihood type with an appropriately chosen penalty. We argue that second-order efficiency is crucial in semiparametric problems since only the…

基于 Copula 的半参数时间序列模型估计AI 解读

This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric invariant (or marginal) distributions and parametric copula functions that capture the temporal dependence of the processes; the implied transition distributions are all semiparametric, and a member in this class can be expressed as a generalized semiparametric regression transformation model. One advantage of this copula…

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平均处理效应匹配估计量的大样本性质AI 解读

Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for…

经济发展与人力资本回报：平滑系数半参数方法AI 解读

Abstract This paper investigates the impact of human capital on the process of economic growth by allowing the contribution of traditional inputs (capital and labour) as well as that of human capital to vary both across countries and time. The former is accomplished by constructing an index of TFP growth for traditional inputs, while the latter through semiparametric methods. We derive estimates of the output elasticity and social return to human capital for 51 countries at…

失效原因缺失的线性变换竞争风险模型：回归系数的半参数估计AI 解读

We consider the problem of estimating the regression coefficients in a competing risks model, where the relationship between the cause-specific hazard for the cause of interest and covariates is described using linear transformation models and when cause of failure is missing at random for a subset of individuals. Using the theory of Robins et al. (1994) for missing data problems and the approach of Chen et al. (2002) for estimating regression coefficients for linear…

聚类数据广义部分线性模型的稳健估计AI 解读

In this article we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear models (GPLMs) for longitudinal data or clustered data in general. We approximate the nonparametric function in the GPLM by a regression spline, and use bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers. We show that the regression spline approach avoids some of the intricacies associated…

含协变量误差的半参数生存回归：伪偏似然方法AI 解读

This article presents an estimator for the regression coefficient vector in the Cox proportional hazards model with covariate error. The estimator is obtained by maximizing a likelihood-type function similar to the Cox partial likelihood. The likelihood function involves the cumulative baseline hazard function, for which a simple estimator is substituted. The method is capable of handling general covariate error structures; it is not restricted to the independent additive…