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受试者工作特征曲线的非参数与半参数估计AI 解读

The receiver operating characteristic (ROC) curve describes the performance of a diagnostic test used to discriminate between healthy and diseased individuals based on a variable measured on a continuous scale. The data consist of a training set of m responses $X_1, \dots, X_m$ from healthy individuals and n responses $Y_1, \dots, Y_n$ from diseased individuals. The responses are assumed i.i.d. from unknown distributions F and G, respectively. We consider estimation of the…

因变量未知变换回归模型的半参数估计AI 解读

This paper shows how to estimate a model in which an unknown transformation of the dependent variable is a linear function of explanatory variables plus an unobserved random variable, U, whose distribution is unknown. The model nests many familiar parametric and semiparametric models, including models with Box-Cox transformed dependent variables and proportional hazards models with and without unobserved heterogeneity. The paper develops root-n consistent, asymptotically…

面板数据回归模型的半参数估计AI 解读

Linear models with error components are widely used to analyse panel data. Some applications of these models require knowledge of the probability densities of the error components. Existing methods handle this requirement by assuming that the densities belong to known parametric families of distributions (typically the normal distribution). This paper shows how to carry out nonparametric estimation of the densities of the error components, thereby avoiding the assumption…

度假支出的参数与半参数建模AI 解读

We analyse several limited dependent variable models explaining the budget share that Dutch families spend on vacations. To take account of the substantial number of zero shares, two types of models are used. The first is the single-equation censored regression model. We estimate and test several parametric and semiparametric extensions of the Tobit model. Second, we consider two-equation models, in which the participation decision and the decision on the amount to spend are…

度假支出的参数与半参数建模AI 解读

We analyse several limited dependent variable models explaining the budget share that Dutch families spend on vacations. To take account of the substantial number of zero shares, two types of models are used. The first is the single-equation censored regression model. We estimate and test several parametric and semiparametric extensions of the Tobit model. Second, we consider two-equation models, in which the participation decision and the decision on the amount to spend are…

存在依赖删失时的半参数回归估计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.…

异质处理效应下半参数两样本模型的经验过程方法AI 解读

SUMMARY Two types of semiparametric model are proposed for comparing two samples with possibly heterogeneous treatment effects. One model is a two-sample location-scale model which assumes that the Q-Q-plot of the two distributions involved is linear; the other is the class of two-sample transformation models which assume parametric forms for a probability plot—the receiver operating characteristic curve. The empirical process approach is used to construct strong…

含离散解释变量的非参数与半参数估计AI 解读

This note is concerned with nonparanetric and semiparametric inference in regression models where regressors are not continuous. In economic practice, few explanatory variables are continuous. Many of the are dummics, qualitative variables, or counts; and others though continuous in nature, are recorded at intervals and can be treated as discrete.

计数过程一般加性—乘性风险模型的半参数分析AI 解读

The additive-multiplicative hazard model specifies that the hazard function for the counting process associated with a multidimensional covariate process $Z = (W^T, X^T)^T$ takes the form of $\lambda(t\mid Z) = g\{\beta^T_0 W(t)\} + \lambda_0(t)h\{\gamma^T_0X(t)\}$, where $\theta_0 = (\beta^T_0, \gamma^T_0)^T$ is a vector of unknown regression parameters, $g$ and $h$ are known link functions and $\lambda_0$ is an unspecified "baseline hazard function." In this paper, we…

惩罚似然问题的变分解与平滑曲线估计AI 解读

Usual nonparametric regression estimators often show many little wiggles which do not appear to be necessary for a good description of the data. The new "Wp" smoother is a maximum penalized likelihood (MPL) estimate with a novel roughness penalty. It penalizes a relative change of curvature. This leads to disjoint classes of functions, each with given number, $n_w$, of inflection points. For a "Wp" estimate, $f"(x) = \pm (x - w_1)\cdots (x - w_{n_w}) \cdot \exp h_f(x)$,…

长程依赖的高斯半参数估计AI 解读

Assuming the model $f(\lambda) \sim G\lambda^{1-2H}$, as $\lambda \rightarrow 0 +$, for the spectral density of a covariance stationary process, we consider an estimate of $H \in (0, 1)$ which maximizes an approximate form of frequency domain Gaussian likelihood, where discrete averaging is carried out over a neighbourhood of zero frequency which degenerates slowly to zero as sample size tends to infinity. The estimate has several advantages. It is shown to be consistent…

稳健非平稳回归AI 解读

This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed that allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities, and they…

分层两样本设计的非参数方法及多中心临床试验应用AI 解读

Motivated by some problems arising from multiclinic trials, we consider stratified two-sample designs. Nonparametric effects are defined and nonparametric hypotheses are formulated in a design where treatment, centers (strata), and interactions are assumed to be fixed factors. The interpretation of the nonparametric effects and hypotheses is analyzed in two classes of semiparametric models: the linear models and models with Lehmann alternatives. The case where centers and…

半参数广义线性模型中的渐近有效估计AI 解读

We use the method of maximum likelihood and regression splines to derive estimates of the parametric and nonparametric components of semiparametric generalized linear models. The resulting estimators of both components are shown to be consistent. Also, the asymptotic theory for the estimator of the parametric component is derived, indicating that the parametric component can be estimated efficiently without under-smoothing the nonparametric component.

协变量缺失或误测时条件密度的半参数有效估计AI 解读

SUMMARY Pepe and Fleming, and Carroll and Wand have recently proposed estimators in a parametric model for the density of a random variable Y conditional on a vector of covariates (X, V) when data on one of the regressors X is missing for some study subjects. We propose a new class of estimators that remains consistent and asymptotically normal even when the probability that X is missing depends on the observed V and Y, includes an estimator whose asymptotic variance attains…

半参数模型中的最优 B 稳健影响函数AI 解读

Bounded influence functions are used for robust estimation in semiparametric models. In this paper, we generalize Hampel's variational problem to semiparametric models and define the optimal B-robust influence function as the one solving the variational problem. We identify the lowest bounds for influence functions and establish the existence and uniqueness of the optimal influence functions in general semiparametric models. Explicit optimal influence functions are given for…

半参数方差函数模型与异方差检验AI 解读

We propose a general semiparametric variance function model in a fixed design regression setting. In this model, the regression function is assumed to be smooth and is modelled nonparametrically, whereas the relation between the variance and the mean regression function is assumed to follow a generalized linear model. Almost all variance function models that were considered in the literature emerge as special cases. Least-squares-types estimates for the parameters of this…

以参数估计为起点的非参数密度估计AI 解读

The traditional kernel density estimator of an unknown density is by construction completely nonparametric in the sense that it has no preferences and will work reasonably well for all shapes. The present paper develops a class of semiparametric methods that are designed to work better than the kernel estimator in a broad nonparametric neighbourhood of a given parametric class of densities, for example, the normal, while not losing much in precision when the true density is…

尺度变换随机效应模型的生存分析AI 解读

Frailty models are effective in broadening the class of survival models and inducing dependence in multivariate survival distributions. In proportional hazards, the random effect multiplies the hazard function. The scale-change model incorporates unobserved heterogeneity through a random effect that enters the baseline hazard function to change the time scale. We interpret this random effect as frailty, or other unobserved risks that create heterogeneity in the population.…

非参数经验贝叶斯生长曲线分析AI 解读

Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

基于时间尺度变换随机效应模型的生存分析AI 解读

Frailty models are effective in broadening the class of survival models and inducing dependence in multivariate survival distributions. In proportional hazards, the random effect multiplies the hazard function. The scale-change model incorporates unobserved heterogeneity through a random effect that enters the baseline hazard function to change the time scale. We interpret this random effect as frailty, or other unobserved risks that create heterogeneity in the population.…

非参数经验贝叶斯生长曲线分析AI 解读

Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

用于半参数模型的非参数核估计AI 解读

This paper presents a number of consistency results for nonparametric kernel estimators of density and regression functions and their derivatives. These results are particularly useful in semiparametric estimation and testing problems that rely on preliminary nonparametric estimators, as in Andrews (1994, Econometrica 62, 43–72). The results allow for near-epoch dependent, nonidentically distributed random variables, data-dependent bandwidth sequences, preliminary estimation…

半参数平均导数估计量的正态近似AI 解读

With the same normalization as that for standard parametric statistics, and centered at a parameter of interest, many semiparametric estimates based on n observations have been shown to be root-n-consistent and asymptotically normal. In the context of semiparametric averaged derivative estimates, the author goes further by showing that the rate of convergence of the finite-sample distribution to the normal limit distribution can equal that of standard parametric statistics.…