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随机筛似然与一般回归模型AI 解读

Consider a semiparametric regression model Y = f(θ, X, ϵ), where f is a known function, θ is an unknown vector, ϵ consists of a random error and possibly of some unobserved variables, and the distribution F(·) of (ϵ, X) is unspecified. This article introduces, in a general setting, new methodology for estimating θ and F(·). The proposed method constructs a profile likelihood defined on random-level sets (a random sieve). The proposed method is related to empirical likelihood…

含未观测异质性的比例风险模型半参数估计AI 解读

The proportional hazard model with unobserved heterogeneity gives the hazard function of a random variable conditional on covariates and a second random variable representing unobserved heterogeneity. This paper shows how to estimate the baseline hazard function and the distribution of the unobserved heterogeneity nonparametrically. The baseline hazard function and heterogeneity distribution are assumed to satisfy smoothness conditions but are not assumed to belong to known,…

跨国增长回归中的非线性：半参数方法AI 解读

In this paper we employ an additive semiparametric partially linear model to uncover the way that initial output and schooling levels affect growth rates. Our results based on marginal integration allow for graphical representation of the non-linearities that characterize the effects that these variables have on growth rates and suggest the presence of multiple regimes (equilibria). Our findings seem to be in agreement with those of Durlauf and Johnson (1995) and Hansen…

长程依赖时间序列的宽频带对数周期图回归AI 解读

This paper discusses the properties of an estimator of the memory parameter of a stationary long-memory time-series originally proposed by Robinson. As opposed to ‘‘narrow-band’’ estimators of the memory parameter (such as the Geweke and Porter-Hudak or the Gaussian semiparametric estimators) which use only the periodogram ordinates belonging to an interval which degenerates to zero as the sample size $n$ increases, this estimator builds a model of the spectral density of…

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回归中响应选择与缺失数据问题的半参数方法AI 解读

Summary Suppose that data are generated according to the model f(y|x; θ) g(x), where y is a response and x are covariates. We derive and compare semiparametric likelihood and pseudolikelihood methods for estimating θ for situations in which units generated are not fully observed and in which it is impossible or undesirable to model the covariate distribution. The probability that a unit is fully observed may depend on y, and there may be a subset of covariates which is…

随机面板前沿的半参数方法及其银行业应用AI 解读

This article introduces new modeling and estimation methods designed to help mitigate problems of endogeneity and misspecification. The authors use an output distance function to model the technology of a multioutput firm. They also use results from B. U. Park and L. Simar (1995) and B. U. Park, R. C. Sickles, and L. Simar (1998) on semiparametric efficient estimation of panel models to estimate multioutput stochastic distance frontiers for which the distribution of the…

竞争风险子分布的比例风险模型AI 解读

With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is…

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水平序列记忆参数估计中的长短记忆条件异方差AI 解读

Semiparametric estimates of long memory seem useful in the analysis of long financial time series because they are consistent under much broader conditions than parametric estimates. However, recent large sample theory for semiparametric estimates forbids conditional heteroskedasticity. We show that a leading semiparametric estimate, the Gaussian or local Whittle one, can be consistent and have the same limiting distribution under conditional heteroskedasticity as under the…

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偏好异质性与需求系统的秩AI 解读

This article examines the effect of preference heterogeneity on nonparametric and parametric tests of the rank of demand systems. Using samples of households drawn from the U.K. Family Expenditure Survey, the authors find that preference heterogeneity increases the rank of demand systems. When the effects of household characteristics on demand are removed by semiparametric techniques, a rank-three demand system appears to be an adequate empirical specification for all…

半参数选择偏差模型的最大似然估计及其艾滋病疫苗试验应用AI 解读

The following problem is treated: given s possibly selection biased samples from an unknown distribution function, and assuming that the sampling rule weight functions for each of the samples are mathematically specified up to a common unknown finite-dimensional parameter, how can we use the data to estimate the unknown parameters? We propose a simple maximum partial likelihood method for deriving the semiparametric maximum likelihood estimator. A discussion of assumptions…

指数族半参数混合模型中的信息AI 解读

Z In a class of semiparametric mixture models, the score function (and consequently the effective information) for a finite-dimensional parameter can be made arbitrarily small depending upon the direction taken in the parameter space. This result holds for a broad range of semiparametric mixtures over exponential families and includes examples such as the gamma semiparametric mixture, the normal mean mixture, the Weibull semiparametric mixture and the negative binomial…

泊松过程同质性的调整得分检验AI 解读

Tests of homogeneity are being increasingly used for the analysis of event time data, but relatively little attention has been paid to their distributional properties in settings with small to moderate sample sizes. Here we consider tests of homogeneity for recurrent event data in which the null model is a Poisson process and the alternative is a mixed Poisson process. We examine score and adjusted score statistics in the context of parametric and semiparametric regression…

基于加权经验生存函数与风险函数的删失中位数回归AI 解读

For median regression models that regress the median of the survival time or a transform thereof on the covariates, some semi-parametric estimators that include the intercept component are introduced when the survival time may be censored. These new median regression estimators do not require estimating the censoring distributions. They can be viewed as an extension of the sample median to the censored regression model. These estimators are based on some weighted empirical…

比例优势回归模型中的半参数推断AI 解读

For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have…

含随机效应与测量误差的重复事件半参数回归AI 解读

Statistical methodology is presented for the regression analysis of multiple events in the presence of random effects and measurement error. Omitted covariates are modeled as random effects. Our approach to parameter estimation and significance testing is to start with a naive model of semiparametric Poisson process regression, and then to adjust for random effects and any possible covariate measurement error. We illustrate the techniques with data from a randomized clinical…

泊松过程同质性的调整得分检验AI 解读

Tests of homogeneity are being increasingly used for the analysis of event time data, but relatively little attention has been paid to their distributional properties in settings with small to moderate sample sizes. Here we consider tests of homogeneity for recurrent event data in which the null model is a Poisson process and the alternative is a mixed Poisson process. We examine score and adjusted score statistics in the context of parametric and semiparametric regression…

比例优势回归模型的半参数推断AI 解读

For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have…

含随机效应与测量误差的重复事件半参数回归模型AI 解读

Statistical methodology is presented for the regression analysis of multiple events in the presence of random effects and measurement error. Omitted covariates are modeled as random effects. Our approach to parameter estimation and significance testing is to start with a naive model of semiparametric Poisson process regression, and then to adjust for random effects and any possible covariate measurement error. We illustrate the techniques with data from a randomized clinical…

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二元选择模型中位置参数的半参数估计AI 解读

This paper considers the estimation of a location parameter in the binary choice model with some weak distributional assumptions imposed on the error term in the latent regression model. Two estimators are proposed here, both of which are two-step estimators; in the first step, the slope parameters are consistently estimated by existing methods; in the second step, the location parameter is consistently estimated based on a moment condition. The estimators are shown to be…

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