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随机生产前沿模型的半参数估计AI 解读

This paper extends the linear stochastic frontier model proposed by D. J. Aigner, C. A. K. Lovell, and P. Schmidt (1977) to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudo-likelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte…

失效时间变量间依赖强度的评估AI 解读

The correlation p between cumulative hazard variates is considered as a measure of dependence between pairs of failure time variates. Such correlation is readily estimated in the absence of censorship, but modelling assumptions are required for its estimation in the presence of independent right censorship. A semiparametric class of bivariate failure time models, in which a dependence parameter is in one-to-one correspondence with the cumulative hazard variate correlation p,…

劳动市场制度与工资分布，1973—1992：半参数方法AI 解读

This paper provides evidence that the decline in the real value of the minimum wage and in the rate of unionization account for a significant share of the increase in wage inequality in the United States between 1979 and 1988. The role of the minimum wage is particularly important for women, while deunionization has the largest impact on men. The authors develop a semiparametric procedure that applies kernel density methods to appropriately weighted samples. The procedure…

局部参数化的非参数密度估计AI 解读

This paper develops a nonparametric density estimator with parametric overtones. Suppose $f(x, \theta)$ is some family of densities, indexed by a vector of parameters $\theta$. We define a local kernel-smoothed likelihood function which, for each x, can be used to estimate the best local parametric approximant to the true density. This leads to a new density estimator of the form $f(x, \hat{\theta}(x))$, thus inserting the best local parameter estimate for each new value of…

协方差分析中的回归秩得分估计AI 解读

In semiparametric ANOCOVA (mixed-effects) models, the role of regression rank scores in robust estimation of fixed-effects parameters as well as covariate regression functionals is critically appraised, and the relevant asymptotic theory is presented.

半参数 Weibull 混合模型的一致估计速率AI 解读

This paper presents a uniform estimator for a finite-dimensional parameter in the semiparametric Weibull mixture model. The rates achieved by the estimator hold uniformly over shrinking sequences of models much more general than traditional sequences that are required to satisfy a Hellinger differentiable property. We show that these rates are optimal in a class of identified models constrained by a moment condition on the nonparametric mixing distribution.

位置混合模型中尺度参数的可识别性与估计速率AI 解读

In this paper we consider the problem of identifiability and estimation for the scale parameter $\theta$ in the location mixture model $\theta (X + Y)$, where X has a known distribution independent of the Y, whose distribution is unknown. Identification of $\theta$ is ensured by constraining Y based on the tail behavior of the distribution for X. Rates for estimation are described for those X which can be written as a square summable series of exponential variables. As a…

一致模型设定检验：遗漏变量与半参数函数形式AI 解读

By using nonparametric kernel estimation method and a central limit theorem for degenerate U-statistics of order higher than two, the authors develop several consistent model specification tests in the context of a nonparametric regression model. These include tests for omitted variables, tests for a partially linear model, and tests for a semiparametric single index model. The asymptotic normality of the test statistics are established under the respective null hypotheses…

二元混合效应模型中的估计效率AI 解读

We investigate the efficiency of likelihood methods for estimating the regression parameters of mixed-effects logistic regression models. One approach uses a conditional likelihood which eliminates the random intercept terms. A second uses the likelihood generated from the marginal distribution of the data where the random intercepts are integrated out. Parametric estimates result from assuming a parametric form for the intercept distribution, whereas we obtain…

随机区组设计的半参数贝叶斯模型AI 解读

A model is proposed for a Bayesian semiparametric analysis of randomised block experiments. The model is a hierarchical model in which a Dirichlet process is inserted at the middle stage for the distribution of the block effects. This model allows an arbitrary distribution of block effects, and it results in effective estimates of treatment contrasts, block effects and the distribution of block effects. An effective computational strategy is presented for describing the…

随机系数自回归模型中的自适应估计AI 解读

This paper proves the local asymptotic normality of a stationary and ergodic first order random coefficient autoregressive model in a semiparametric setting. This result is used to show that Stein's necessary condition for adaptive estimation of the mean of the random coefficient is satisfied if the distributions of the innovations and the errors in the random coefficients are symmetric around zero. Under these symmetry assumptions, a locally asymptotically minimax adaptive…

协变量含误差的病例对照研究：半参数混合方法AI 解读

Methods are devised for estimating the parameters of a prospective logistic model in a case-control study with dichotomous response D that depends on a covariate X. For a portion of the sample, both the gold standard X and a surrogate covariate W are available; however, for the greater portion of the data, only the surrogate covariate W is available. By using a mixture model, the relationship between the true covariate and the response can be modeled appropriately for both…

现时状态数据分析的半参数比例优势回归模型AI 解读

An approach to covariate analysis of current status data is described. The method is applicable when the logit of the conditional probability of survival given the covariates is some increasing function of time plus a linear combination of the covariates. The approach is based on approximating the infinite-dimensional nuisance parameter, the baseline log-odds of failure, with a step function, and carrying out a maximum likelihood procedure. The resulting finite dimensional…

劳动市场参与二元响应模型的参数与半参数估计AI 解读

This paper compares the familiar probit model with three semiparametric estimators of binary response models in an application to labour market participation of married women. This exercise is performed using two different cross-section data sets from Switzerland and Germany. For the Swiss data the probit specification cannot be rejected and the models yield similar results. In the German case the probit model is rejected, but the coefficient estimates do not vary…

劳动市场参与二元响应模型的参数与半参数估计AI 解读

This paper compares the familiar probit model with three semiparametric estimators of binary response models in an application to labour market participation of married women. This exercise is performed using two different cross-section data sets from Switzerland and Germany. For the Swiss data the probit specification cannot be rejected and the models yield similar results. In the German case the probit model is rejected, but the coefficient estimates do not vary…

附带干扰参数模型中估计量的渐近效率AI 解读

In this paper we show that the well-known asymptotic efficiency bounds for full mixture models remain valid if individual sequences of nuisance parameters are considered. This is made precise both for some classes of random (i.i.d.) and nonrandom nuisance parameters. For the random case it is shown that superefficiency of the kind given by an example of Pfanzagl can happen only with low probability. The nonrandom case deals with permutation-invariant estimators under…

半参数混合模型中的有效最大似然估计AI 解读

We consider maximum likelihood estimation in several examples of semiparametric mixture models, including the exponential frailty model and the errors-in-variables model. The observations consist of a sample of size n from the mixture density $\int p_{\theta}(x|z) d \eta(z)$. The mixing distribution is completely unknown. We show that the first component $\hat{\theta}_n$ of the joint maximum likelihood estimator , $(\hat{\theta}_n \hat{\eta}_n)$ is asymptotically normal and…

参数与半参数模型中的渐近一致最强检验AI 解读

Tests of hypotheses about finite-dimensional parameters in a semiparametric model are studied from Pitman's moving alternative (or local) approach using Le Cam's local asymptotic normality concept. For the case of a real parameter being tested, asymptotically uniformly most powerful (AUMP) tests are characterized for one-sided hypotheses, and AUMP unbiased tests for two-sided ones. An asymptotic invariance principle is introduced for multidimensional hypotheses, and AUMP…

区间删失比例风险模型的有效估计AI 解读

The maximum likelihood estimator (MLE) for the proportional hazards model with "case 1" interval censored data is studied. It is shown that the MLE for the regression parameter is asymptotically normal with $\sqrt{n}$ convergence rate and achieves the information bound, even though the MLE for the baseline cumulative hazard function only converges at $n^{1/3}$ rate. Estimation of the asymptotic variance matrix for the MLE of the regression parameter is also considered. To…

利用多元正态混合进行贝叶斯曲线拟合AI 解读

Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multidimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression functions have features similar to generalised kernel regression estimates, but the…

半参数二元回归的贝叶斯推断AI 解读

We propose a regression model for binary response data that places no structural restrictions on the link function except monotonicity and known location and scale. Predictors enter linearly. We demonstrate Bayesian inference calculations in this model. By modifying the Dirichlet process, we obtain a natural prior measure over this semiparametric model, and we use Polya sequence theory to formulate this measure in terms of a finite number of unobserved variables. We design a…

摘要暂缺

左删失持续时间模型中半参数效率界的实现AI 解读

Consider the estimation of unemployment duration when the statistician samples individuals from the pool of unemployed persons and later interviews them and asks how long they had been unmployed. This incurs the problem of left-censoring; ignoring left-censoring overestimates the mean duration since longer spells tend to be observed more frequently than shorter ones. Lancaster (1979) gives a simple and convenient solution to the problem by introducing a conditional maximum…

半参数回归模型中 MINPIN 估计量的 Edgeworth 近似AI 解读

We examine the higher order asymptotic properties of semiparametric regression estimators that were obtained by the general MINPIN method described in Andrews (1989, Semiparametric Econometric Models: I. Estimation, Discussion paper 908, Cowles Foundation). We derive an order n −1 stochastic expansion and give a theorem justifying order n −1 distributional approximation of the Edgeworth type.