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风险与收益关系的半参数估计AI 解读

This article proposes semiparametric generalized least-squares estimation of parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of parametric factors. A distinctive feature of our estimator is that it does not require a fully parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is nonstandard due to the presence of estimated factors.…

基于误差分布估计的参数混合效应模型拟合优度检验AI 解读

We address the problem of testing for a parametric function of fixed effects in mixed models. We propose a test based on the distance between two empirical error distribution functions, which are constructed from residuals calculated under the opposing hypotheses. The proposed test statistic has power against all alternatives, and its asymptotic distribution is derived. A simulation study shows that the test outperforms others in the literature. The test is applied to…

非参数前沿估计中的不可观测异质性与内生性AI 解读

In production theory, firm efficiencies are measured by their distances to a production frontier. In the presence of heterogeneous conditions (like environmental factors) that may influence the shape and the position of the frontier, traditional measures of efficiency obtained in the space of inputs/outputs are difficult to interpret, since they mix managerial inefficiency and shift of the frontier. This can be corrected by using nonparametric conditional efficiencies. In…

基于 Copula 多元模型的半参数条件分位数估计AI 解读

We consider a new approach in quantile regression modeling based on the copula function that defines the dependence structure between the variables of interest. The key idea of this approach is to rewrite the characterization of a regression quantile in terms of a copula and marginal distributions. After the copula and the marginal distributions are estimated, the new estimator is obtained as the weighted quantile of the response variable in the model. The proposed…

未知测量误差方差下的生产前沿估计AI 解读

Frontier estimation appears in productivity analysis. Firm’s performance is measured by the distance between its output and an optimal production frontier. Frontier estimation becomes difficult if outputs are measured with noise and most approaches rely on restrictive parametric assumptions. This paper contributes to nonparametric approaches, with unknown frontier and unknown variance of a normally distributed error. We propose a nonparametric method identifying and…

非参数位置—尺度模型中的前沿估计AI 解读

Conditional efficiency captures efficiency of firms facing heterogeneous environmental conditions. Traditional approaches estimate nonparametrically conditional distribution requiring smoothing techniques. We rather use a flexible nonparametric location-scale model to eliminate the dependence of inputs/outputs on these factors. These “pre-whitened” inputs/outputs define the optimal frontier function and a “pure” measure of efficiency more reliable to produce rankings, since…

病例对照数据二次分析中同方差回归的稳健估计AI 解读

Summary Primary analysis of case–control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case–control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case–control sampling, where the regression of Y on X is different from what it is in the population.…

相依持续期数据的经验似然置信区间AI 解读

Three types of confidence intervals are developed for a general class of functionals of a survival distribution based on censored dependent data. The confidence intervals are constructed via asymptotic normality (Wald’s method), the empirical likelihood (EL) method, and the blockwise EL method in which sample means over blocks of observations are used in place of the original data. Asymptotic results are derived to accurately calibrate the various procedures, and their…

拓展经验似然的适用范围AI 解读

This article extends the scope of empirical likelihood methodology in three directions: to allow for plug-in estimates of nuisance parameters in estimating equations, slower than $\sqrt{n}$-rates of convergence, and settings in which there are a relatively large number of estimating equations compared to the sample size. Calibrating empirical likelihood confidence regions with plug-in is sometimes intractable due to the complexity of the asymptotics, so we introduce a…

半参数变换模型的估计AI 解读

This paper proposes consistent estimators for transformation parameters in semiparametric models. The problem is to find the optimal transformation into the space of models with a predetermined regression structure like additive or multiplicative separability. We give results for the estimation of the transformation when the rest of the model is estimated non- or semi-parametrically and fulfills some consistency conditions. We propose two methods for the estimation of the…

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非参数回归中方差函数参数形式的新检验AI 解读

Summary In the common non-parametric regression model the problem of testing for the parametric form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes that are obtained from the standardized non-parametric residuals under the null hypothesis (of a specific parametric form of the variance function) and the alternative is introduced and its weak convergence established. This result is used for the…

单调非减风险率的检验AI 解读

A test of the null hypothesis that a hazard rate is monotone nondecreasing, versus the alternative that it is not, is proposed. Both the test statistic and the means of calibrating it are new. Unlike previous approaches, neither is based on the assumption that the null distribution is exponential. Instead, empirical information is used to effectively identify and eliminate from further consideration parts of the line where the hazard rate is clearly increasing; and to…

回归曲线及其导数的自助法置信带AI 解读

Confidence bands for regression curves and their first p derivatives are obtained via local pth order polynomial estimation. The method allows for multiparameter local likelihood estimation as well as other unbiased estimating equations. As an alternative to the confidence bands obtained by asymptotic distribution theory, we also study smoothed bootstrap confidence bands. Simulations illustrate the finite sample properties of the methodology.

目标函数不光滑时半参数模型的估计AI 解读

We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some nonparametric estimators that can themselves depend on the parameters to be estimated. Our results extend existing theories such as those of Pakes and Pollard (1989), Andrews (1994a), and Newey (1994). We also show that…

时间序列误差下非参数回归推断的差分方法AI 解读

Summary We show that difference-based methods can be used to construct simple and explicit estimators of error covariance and autoregressive parameters in nonparametric regression with time series errors. When the error process is Gaussian our estimators are efficient, but they are available well beyond the Gaussian case. As an illustration of their usefulness we show that difference-based estimators can be used to produce a simplified version of time series…

删失数据下二元分布与边际分布的估计AI 解读

Summary Consider a pair of random variables, both subject to random right censoring. New estimators for the bivariate and marginal distributions of these variables are proposed. The estimators of the marginal distributions are not the marginals of the corresponding estimator of the bivariate distribution. Both estimators require estimation of the conditional distribution when the conditioning variable is subject to censoring. Such a method of estimation is proposed. The weak…

基于二元删失数据的多元 M 估计推断AI 解读

Consider two random variables subject to random right censoring, for example, the survival times of twins or two recurrence times of a certain disease recorded on the same individual. Of interest is the estimation of a multivariate M functional of these two random variables. Asymptotic results for the proposed estimator are established under general conditions on the criterion function of the M estimator. The obtained results include the asymptotic normality of the…

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…

删失回归模型中的尾部信息迁移AI 解读

Consider a heteroscedastic regression model $Y = m(X) + \sigma(X)\varepsilon$, where the functions $m$ and $\sigma$ are “smooth,” and $\varepsilon$ is independent of $X$. The response variable $Y$ is subject to random censoring, but it is assumed that there exists a region of the covariate $X$ where the censoring of $Y$ is “light.” Under this condition, it is shown that the assumed nonparametric regression model can be used to transfer tail information from regions of light…