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两样本比例优势模型中实值参数的估计AI 解读

This paper considers efficient estimation of the Euclidean parameter $\theta$ in the proportional odds model $G(1 - G)^{-1} = \theta F(1 - F)^{-1}$ when two independent i.i.d. samples with distributions $F$ and $G$, respectively, are observed. The Fisher information $I(\theta)$ is calculated based on the solution of a pair of integral equations which are derived from a class of more general semiparametric models. A one-step estimate is constructed using an initial $\sqrt N$-c…

有限因变量联立方程的半参数估计：女性劳动供给案例AI 解读

Abstract We propose a general strategy to estimate semi‐parametrically simultaneous equations with limited dependent variables. First, each reduced form (RF) is estimated with various semi‐parametric methods. Second, the specification of each RF is tested to select an appropriate method. Third, the structural form (SF) equations are estimated using minimum distance methods and the restrictions among the SF and RF parameters. A case study of female labour supply is presented.

半参数回归泛函AI 解读

A regression method is developed for a general class of functionals. A semiparametric linear model is adopted, and the regression parameters are estimated by maximizing a profiled nonparametric or empirical likelihood based on a local estimate of the conditional distribution function. Simulated and real data examples are shown, including an application of quantile regression to censored survival data from a clinical trial for myeloma.

基于估计函数的滤波与平滑AI 解读

We consider the problem of filtering and smoothing in state-space models, which include nonlinear and non-Gaussian models. We do not make any distributional assumptions about the processes involved. Our approach to these problems is based on the theory of estimating functions. Filter and smoother are obtained as solutions of estimating equations that are optimal in appropriate classes. We illustrate our procedures by simulation studies of a model where the observational…

基于中位数回归模型的生存分析AI 解读

The median is a simple and meaningful measure for the center of a long-tailed survival distribution. To examine the covariate effects on survival, a natural alternative to the usual mean regression model is to regress the median of the failure time variable or a transformation thereof on the covariates. In this article we propose semiparametric procedures to make inferences for such median regression models with possibly censored observations. Our proposals can be…

利用验证数据估计线性与非线性变量测量误差模型AI 解读

Consistent estimators for linear and nonlinear regression models with measurement errors in variables in the presence of validation data are proposed. The estimation procedures are based on least squares methods with regression functions replaced by wide-sense conditional expectation functions. The methods do not depend on distributional assumptions and are robust against the misspecification of a measurement error model. They are computationally and analytically simpler…

缺失数据下多元回归模型的半参数效率AI 解读

We consider the efficiency bound for the estimation of the parameters of semiparametric models defined solely by restrictions on the means of a vector of correlated outcomes, Y, when the data on Y are missing at random. We show that the semiparametric variance bound is the asymptotic variance of the optimal estimator in a class of inverse probability of censoring weighted estimators and that this bound is unchanged if the data are missing completely at random. For this case…

缺失数据下重复结局半参数回归模型的分析AI 解读

We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not…

半参数回归泛函AI 解读

A regression method is developed for a general class of functionals. A semiparametric linear model is adopted, and the regression parameters are estimated by maximizing a profiled nonparametric or empirical likelihood based on a local estimate of the conditional distribution function. Simulated and real data examples are shown, including an application of quantile regression to censored survival data from a clinical trial for myeloma.

协变量含测量误差的半参数回归模型中的降维AI 解读

We consider a semiparametric estimation method for general regression models when some of the predictors are measured with error. The technique relies on a kernel regression of the "true" covariate on all the observed covariates and surrogates. This requires a nonparametric regression in as many dimensions as there are covariates and surrogates. The usual theory copes with such higher-dimensional problems by using higher-order kernels, but this is unrealistic for most…

密度积分泛函的估计AI 解读

Let $\varphi$ be a smooth function of $k + 2$ variables. We shall investigate in this paper the rates of convergence of estimators of $T(f) = \int\varphi(f(x), f'(x), \ldots, f^{(k)}(x), x) dx$ when $f$ belongs to some class of densities of smoothness $s$. We prove that, when $s \geq 2k + \frac{1}{4}$, one can define an estimator $\hat{T}_n$ of $T(f)$, based on $n$ i.i.d. observations of density $f$ on the real line, which converges at the semiparametric rate $1/ \sqrt n$.…

摘要暂缺

基于核删失的局部似然AI 解读

SUMMARY By drawing an analogy with likelihood for censored data, a local likelihood function is proposed which gives more weight to observations near a region of interest in the sample space. Resulting methods can be used for assessing local departures from a parametric model, and for semiparametric density estimation. Some theory, and three examples, is given.

摘要暂缺

一类光滑半参数估计量的极限定理AI 解读

We consider an econometric model based on a set of moment conditions which are indexed by both a finite-dimensional vector of interest, θ, and an infinite-dimensional parameter, h, which in turn depends upon both θ and another infinite-dimensional parameter, τ. The population moment conditions equal zero at θ = θ0. Estimators of θ0 are obtained by forming nonparametric estimates of h and τ, substituting them into the sample analog of the moment conditions, and choosing that…

删失数据下变换模型的分析AI 解读

In this paper we consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linearly related to the covariates with various completely specified error distributions. This class of regression models includes the proportional hazards and proportional odds models. Inference procedures derived from a class of generalised estimating equations are proposed to examine the covariate effects with censored observations.…

依赖删失下的半参数回归估计AI 解读

We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured prior to start of follow-up, in the presence of dependent censoring given X. The proposed estimators are consistent when the data are ‘missing at random’ but not ‘missing completely at random’ (Rubin, 1976), and do not require full specification of the complete data likelihood.…

多元分布族依赖参数的半参数估计程序AI 解读

This paper investigates the properties of a semiparametric method for estimating the dependence parameters in a family of multivariate distributions. The proposed estimator, obtained as a solution of a pseudo-likelihood equation, is shown to be consistent, asymptotically normal and fully efficient at independence. A natural estimator of its asymptotic variance is proved to be consistent. Comparisons are made with alternative semiparametric estimators in the special case of…

比例风险模型的分组持续时间分析：最小卡方估计与设定检验AI 解读

This article develops a semiparametric, minimum chi-squared estimation method of the proportional hazard model for the case when durations are grouped and covariates are categorical. The proposed estimator is easy to compute, yet asymptotically as efficient as the maximum likelihood estimator. This article also suggests simple specification tests for the proportional hazard model. If proportionality holds, then two sets of minimum chi-squared estimators, one from a further…

基于似然模型的半参数回归AI 解读

A weighted likelihood is used to estimate the parameters in a semiparametric model involving two covariates and allowing an association between the covariates. The development is for arbitrary but specified densities of the observations. The estimators are consistent and asymptotically normal. Hypothesis testing of the parametric component can be performed using a Wald test. Simulations and analysis of data with Bernoulli observations demonstrate the estimators' application.…

衰减偏差的半参数校正AI 解读

A correction method is proposed for models including the generalized linear model when the covariate is measured with error. The method requires a separate validation data set that consists of the surrogate W and the true covariate X or an unbiased estimate X⊃ of X. We do not require the classical additive measurement error model in which the surrogate is unbiased for the true covariates. We first obtain an estimate of E(X|W) by using nonparametric kernel regression of X or…

分组持续期比例风险模型：最小卡方估计与设定检验AI 解读

This article develops a semiparametric, minimum chi-squared estimation method of the proportional hazard model for the case when durations are grouped and covariates are categorical. The proposed estimator is easy to compute, yet asymptotically as efficient as the maximum likelihood estimator. This article also suggests simple specification tests for the proportional hazard model. If proportionality holds, then two sets of minimum chi-squared estimators, one from a further…

测量误差衰减偏误的半参数修正AI 解读

A correction method is proposed for models including the generalized linear model when the covariate is measured with error. The method requires a separate validation data set that consists of the surrogate W and the true covariate X or an unbiased estimate X⊃ of X. We do not require the classical additive measurement error model in which the surrogate is unbiased for the true covariates. We first obtain an estimate of E(X|W) by using nonparametric kernel regression of X or…

基于似然模型的半参数回归AI 解读

A weighted likelihood is used to estimate the parameters in a semiparametric model involving two covariates and allowing an association between the covariates. The development is for arbitrary but specified densities of the observations. The estimators are consistent and asymptotically normal. Hypothesis testing of the parametric component can be performed using a Wald test. Simulations and analysis of data with Bernoulli observations demonstrate the estimators' application.…