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半参数变换治愈模型AI 解读

A general class of semiparametric transformation cure models is studied for the analysis of survival data with long‐term survivors. It combines a logistic regression for the probability of event occurrence with the class of transformation models for the time of occurrence. Included as special cases are the proportional hazards cure model (Farewell, 1982; Kuk & Chen, 1992; Sy & Taylor, 2000; Peng & Dear, 2000) and the proportional odds cure model. Generalised estimating…

病例队列删失数据半参数变换模型的加权估计方程AI 解读

SUMMARY In a case-cohort design introduced by Prentice (1986), covariates are assembled only for a subcohort randomly selected from the entire cohort, and any additional cases outside the subcohort. Semiparametric transformation models are considered here for failure time data from the case-cohort design. Weighted estimating equations are proposed for esti mation of the regression parameters. The estimation procedure of survival probability at given covariate levels is also…

双重删失数据的半参数回归分析AI 解读

We analyse doubly censored data using semiparametric transformation models. We provide inference procedures for the regression parameters and derive the asymptotic distributions of the proposed estimators. Procedures for model checking and model selection are also discussed. We illustrate our approach with a viral‐load dataset from a recent AIDS clinical trial.

基于局部 L2 拟合的半参数密度估计AI 解读

This article examines density estimation by combining a parametric approach with a nonparametric factor. The plug-in parametric estimator is seen as a crude estimator of the true density and is adjusted by a nonparametric factor. The nonparametric factor is derived by a criterion called local L2-fitting. A class of estimators that have multiplicative adjustment is provided, including estimators proposed by several authors as special cases, and the asymptotic theories are…

半参数回归中的一致协变量选择与模型选择后推断AI 解读

This paper presents a model selection technique of estimation in semiparametric regression models of the type $Y_{i}={\beta}^{\prime}\underline{X}_{i}+f(T_{i})+W_{i}$ , i=1,…,n. The parametric and nonparametric components are estimated simultaneously by this procedure. Estimation is based on a collection of finite-dimensional models, using a penalized least squares criterion for selection. We show that by tailoring the penalty terms developed for nonparametric regression to…

响应随机缺失时的半参数回归分析AI 解读

We develop inference tools in a semiparametric partially linear regression model with missing response data. A class of estimators is defined that includes as special cases a semiparametric regression imputation estimator, a marginal average estimator, and a (marginal) propensity score weighted estimator. We show that any of our class of estimators is asymptotically normal. The three special estimators have the same asymptotic variance. They achieve the semiparametric…

The partially linear model Y = XTβ + ν(Z) + ϵ has been studied extensively when data are completely observed. In this article, we consider the case where the covariate X is sometimes missing, with missingness probability π depending on (Y, Z). New methods are developed for estimating β and ν(·). Our methods are shown to outperform asymptotically methods based only on the complete data. Asymptotic efficiency is discussed, and the semiparametric efficient score function is…

二次损失下组合估计问题的半参数基础AI 解读

When there is uncertainty concerning the appropriate statistical model-estimator to use in representing the data sampling process, we consider a basis for optimally combining estimation problems. The objective is to produce natural adaptive estimators that are free of subjective choices and tuning parameters. In the context of two competing multivariate linear statistical models–estimators, we demonstrate a semiparametric Stein-like (SPSL) estimator, , that, under quadratic…

当前状态数据半参数回归模型的筛最大似然估计AI 解读

In a randomized controlled clinical trial study where the response variable of interest is the time to occurrence of a certain event, it is often too expensive or even impossible to observe the exact time. However, the current status of the subject at a random time of inspection is much more natural, feasible, and practical in terms of cost-effectiveness. This article considers a semiparametric regression model that consists of parametric and nonparametric regression…

同时建模条件异方差与尺度变化AI 解读

This paper proposes a semiparametric approach by introducing a smooth scale function into the standard generalized autoregressive conditional heteroskedastic (GARCH) model so that conditional heteroskedasticity (CH) and scale change in financial returns can be modeled simultaneously. An estimation procedure combining kernel estimation of the scale function and maximum likelihood estimation of the GARCH parameters is proposed. Asymptotic properties of the estimators are…

风险率函数的平均导数AI 解读

This paper develops semiparametric kernel-based estimators of risk-specific hazard functions for competing risks data. Both discrete and continuous failure times are considered. The maintained assumption is that the hazard function depends on explanatory variables only through an index. In contrast to existing semiparametric estimators, proportional hazards is not assumed. The new estimators are asymptotically normally distributed. The estimator of index coefficients is…

半参数非线性时间序列的模型选择AI 解读

Summary Semiparametric time series regression is often used without checking its suitability, resulting in an unnecessarily complicated model. In practice, one may encounter computational difficulties caused by the curse of dimensionality. The paper suggests that to provide more precise predictions we need to choose the most significant regressors for both the parametric and the nonparametric time series components. We develop a novel cross-validation-based model selection…

脑成像中重复非均匀点模式的空间强度建模AI 解读

Summary Pharmacological experiments in brain microscopy study patterns of cellular activ- ation in response to psychotropic drugs for connected neuroanatomic regions. A typical ex- perimental design produces replicated point patterns having highly complex spatial variability. Modelling this variability hierarchically can enhance the inference for comparing treatments. We propose a semiparametric formulation that combines the robustness of a nonparametric kernel method with…

The purpose of this note is to show how semiparametric estimators with a small bias property can be constructed. The small bias property (SBP) of a semiparametric estimator is that its bias converges to zero faster than the pointwise and integrated bias of the nonparametric estimator on which it is based. We show that semiparametric estimators based on twicing kernels have the SBP. We also show that semiparametric estimators where nonparametric kernel estimation does not…

半参数广义可加模型中的 Bootstrap 推断AI 解读

Semiparametric generalized additive models are a powerful tool in quantitative econometrics. With response Y, covariates X,T, the considered model is E(Y |X;T) = G{XTβ + α + m1(T1) + ··· + md(Td)}. Here, G is a known link, α and β are unknown parameters, and m1,…,md are unknown (smooth) functions of possibly higher dimensional covariates T1,…,Td. Estimates of m1,…,md, α, and β are presented, and asymptotic distributions are given for both the nonparametric and the parametric…

带固定效应的面板比例风险模型的半参数估计AI 解读

This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finitedimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also…

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利用误测协变量重复测量的半参数失效时间回归AI 解读

This article proposes a general strategy for the regression analysis of univariate and multivariate failure time data when a subset of covariates cannot be measured precisely but replicate measurements of their surrogates are available. Multivariate failure time data include recurrent events and clustered survival data. The number of replicate measurements can vary from subject to subject and can even depend on the failure time. No parametric assumption is imposed on the…

基于加权伽马过程的乘法强度模型计算方法AI 解读

We develop computational procedures for a class of Bayesian nonparametric and semiparametric multiplicative intensity models incorporating kernel mixtures of spatial weighted gamma measures. A key feature of our approach is that explicit expressions for posterior distributions of these models share many common structural features with the posterior distributions of Bayesian hierarchical models using the Dirichlet process. Using this fact, along with an approximation for the…

用于多维疾病分类流行病学研究的两阶段回归模型AI 解读

Polytomous logistic regression is commonly used to analyze epidemiological data with disease subtype information. In this approach effects of exposures on different disease subtypes are studied through separate exposure odds ratios comparing different case groups to the common control group. This article considers the situation where disease subtypes can be defined using multiple characteristics of a disease. For efficient analysis of such data, a two-stage modeling approach…

用半参数惩罚样条估计公司债利率期限结构AI 解读

This article provides a new methodology for estimating the term structure of corporate debt using a semiparametric penalized spline model. The method is applied to a case study of AT&T bonds. Typically, very few data are available on individual corporate bond prices, too little to find a nonparametric estimate of term structure from these bonds alone. This problem is solved by “borrowing strength” from Treasury bond data. More specifically, we combine a nonparametric model…

The local Whittle (or Gaussian semiparametric) estimator of long range dependence, proposed by Kunsch (1987) and analyzed by Robinson (1995a), has a relatively slow rate of convergence and a finite sample bias that can be large. In this paper, we generalize the local Whittle estimator to circumvent these problems. Instead of approximating the short-run component of the spectrum, phi(lambda), by a constant in a shrinking neighborhood of frequency zero, we approximate its…

Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes…

半参数持续时间模型AI 解读

estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too restrictive to describe financial durations accurately. Therefore, we consider semiparametric extensions of the standard specification that allow for arbitrary kinds of dependencies between the…