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摘要暂缺

子抽样 MCMC 估计的广义后分层与重要性抽样AI 解读

Benchmark estimation is motivated by the goal of producing an approximation to a posterior distribution that is better than the empirical distribution function. This is accomplished by incorporating additional information into the construction of the approximation. We focus here on generalized poststratification, the most successful implementation of benchmark estimation in our experience. We develop generalized poststratification for settings where the source of the…

函数型方差过程AI 解读

We introduce the notion of a functional variance process to quantify variation in functional data. The functional data are modeled as samples of smooth random trajectories observed under additive noise. The noise is assumed to be composed of white noise and a smooth random process—the functional variance process—which gives rise to smooth random trajectories of variance. The functional variance process is a tool for analyzing stochastic time trends in noise variance. As a…

相依删失下加速寿命模型的秩估计AI 解读

Under independent censoring, estimation of the covariate effects in the accelerated lifetime model may be based on censored data rank tests. Similar rank methodology has been developed with bivariate accelerated lifetime models for dependent censoring but uses artificial censoring, which may lead to substantial information loss. We present a new artificial censoring technique using pairwise ranking and establish the asymptotic properties of a pairwise rank estimator.…

半参数多元 Copula 模型的有效估计AI 解读

We propose a sieve maximum likelihood estimation procedure for a broad class of semiparametric multivariate distributions. A joint distribution in this class is characterized by a parametric copula function evaluated at nonparametric marginal distributions. This class of distributions has gained popularity in diverse fields due to its flexibility in separately modeling the dependence structure and the marginal behaviors of a multivariate random variable, and its…

二元右删失数据的局部有效估计AI 解读

Estimation of the survival curve for independently right-censored bivariate failure time data is a problem that has been studied extensively over the past 20 years. In this article we propose a new class of estimators for the bivariate survivor function based on locally efficient (LE) estimation. The LE estimator takes bivariate estimators Fn and Gn of the distributions of the time variables (T1, T2) and the censoring variables (C1, C2), and maps them to the resulting…

基于消费的等价尺度半参数估计：德国案例AI 解读

Abstract Consumption‐based equivalence scales are estimated by applying the extended partially linear model (EPLM) to the 1998 German Income and Consumption Survey (EVS). In this model the equivalence scales are identified from nonlinearities in household demand. The econometric framework should not therefore impose strong restrictions on the functional forms of household expenditure shares. The chosen semi‐parametric specification meets this requirement. It is flexible, it…

含单整协变量的半参数二元选择模型AI 解读

This paper studies a semiparametric nonstationary binary choice model. Imposing a spherical normalization constraint on the parameter for identification purposes, we find that the maximum score estimator and smoothed maximum score estimator are at least [square root of n]-consistent. Comparing this rate to the convergence rate of the parametric maximum likelihood estimator (MLE), we show that when a normalization restriction is imposed on the parameter, the Park and Phillips…

气温序列趋势的半参数估计与检验AI 解读

The application of a partially linear model to global and hemispheric temperature series is proposed. Partially linear modelling allows the trend to take a very general form rather than imposing the restriction of linearity seen in existing studies. The removal of the linearity restriction is based on the fact that it is well accepted that a significant trend is present in global temperature series. The model will allow for the data to "speak for themselves" with regard to…

具有异质线性趋势的二元选择面板模型的一致估计AI 解读

This paper presents an extension of fixed effects binary choice models for panel data, to the case of heterogeneous linear trends. Two estimators are proposed: a Logit estimator based on double conditioning and a semiparametric, smoothed maximum score estimator based on double differences. We investigate small-sample properties of these estimators with a Monte Carlo simulation experiment, and compare their statistical properties with standard fixed effects procedures. An…

摘要暂缺

利用已实现测度对随机波动率模型进行半参数比较AI 解读

This paper proposes a procedure to test for the correct specification of the functional form of the volatility process within the class of eigenfunction stochastic volatility models. The procedure is based on the comparison of the moments of realized volatility measures with the corresponding ones of integrated volatility implied by the model under the null hypothesis.We first provide primitive conditions on the measurement error associated with the realized measure, which…

半参数等价尺度的估计AI 解读

Abstract Within the semiparametric framework introduced by Pendakur ( 1999 ) we introduce a new loss function to estimate equivalence scales. This loss function uses all available information from the total expenditures of both the reference and nonreference households and as such it produces more reliable estimates. Using Canadian family expenditure data for 1996 we apply this loss function to obtain equivalence scale estimates for a variety of expenditure share categories…

高维协变量下的切片逆回归AI 解读

Sliced inverse regression is a promising method for the estimation of the central dimension-reduction subspace (CDR space) in semiparametric regression models. It is particularly useful in tackling cases with high-dimensional covariates. In this article we study the asymptotic behavior of the estimate of the CDR space with high-dimensional covariates, that is, when the dimension of the covariates goes to infinity as the sample size goes to infinity. Strong and weak…

完全非参数协方差分析模型中的协变量效应检验AI 解读

Traditional inference questions in the analysis of covariance mainly focus on comparing different factor levels by adjusting for the continuous covariates, which are believed to also exert a significant effect on the outcome variable. Testing hypotheses about the covariate effects, although of substantial interest in many applications, has received relatively limited study in the semiparametric/nonparametric setting. In the context of the fully nonparametric analysis of…

空间相关生存数据的半参数正态变换模型AI 解读

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiologic studies. In this article we propose a new class of semiparametric normal transformation models for right-censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal…

含治愈比例的生存数据半参数变换模型AI 解读

We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and…

二元数据模型检验中的自助法近似AI 解读

Consider a binary regression model in which the conditional expectation of a binary variable given an explanatory variable belongs to a parametric family. To check whether a sequence of independent and identically distributed observations belongs to such a parametric family, we use Kolmogorov–Smirnov and Cramér–von Mises type tests based on a marked empirical process introduced by Stute. We propose and study a new resampling scheme for a bootstrap in this setup to…

病例测量缺失的两阶段队列研究：生存估计量的设定与实现AI 解读

Since 1986, we have been studying a cohort of individuals from a region in China with epidemic rates of gastric cardia cancer and have conducted numerous two-stage studies to assess the association of various exposures with this cancer. Two-stage studies are a commonly used statistical design. Stage one involves observing the outcomes and accessible baseline covariate information on all cohort members, and stage two involves using the stage one observations to select a…

二元纵向数据半参数动态模型的推断AI 解读

This article deals with the analysis of a hierarchical semiparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N3), where N is the total number of observations in the sample. Therefore, nonparametric estimation is largely infeasible when the sample size is large, as in typical in the…

具有边际比例风险的重复事件模型AI 解读

Semiparametric methods were proposed by Wei et al. (1989) to analyse recurring event-time data. They modelled the marginal distribution of each event time with a Cox proportional hazards model without imposing any constraint on the joint distribution of different event times. Therefore, it is unclear whether or not event times can simultaneously satisfy their respective marginal proportional hazards assumptions, while having continuous joint distribution. Often this leads to…

删失数据下的线性预期剩余寿命回归AI 解读

In the statistical literature, life expectancy is usually characterised by the mean residual life function. Regression models are thus needed to study the association between the mean residual life functions and their covariates. In this paper, we consider a linear mean residual life model and develop inference procedures in the presence of potential censoring. The new model and inference procedures are applied to the Stanford heart transplant data. Semiparametric efficiency…

部分线性单指标模型的经验似然置信区域AI 解读

Summary Empirical-likelihood-based inference for the parameters in a partially linear single-index model is investigated. Unlike existing empirical likelihood procedures for other simpler models, if there is no bias correction the limit distribution of the empirical likelihood ratio cannot be asymptotically tractable. To attack this difficulty we propose a bias correction to achieve the standard χ2-limit. The bias-corrected empirical likelihood ratio shares some of the…

正则化半参数模型识别：未知大分子基线下的核磁共振信号定量AI 解读

Summary We formulate and solve a semiparametric fitting problem with regularization constraints. The model that we focus on is composed of a parametric non-linear part and a nonparametric part that can be reconstructed via splines. Regularization is employed to impose a certain degree of smoothness on the nonparametric part. Semiparametric regression is presented as a generalization of non-linear regression, and all important differences that arise from the statistical and…