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通过随机等度连续性研究半参数计量模型的渐近性质AI 解读

This paper provides a general framework for proving the "square root of" T-consistency and asymptotic normality of a wide variety of semiparametric estimators. The class of estimators considered consists of estimators that can be defined as the solution to a minimization problem based on a criterion function that may depend on a preliminary infinite dimensional nuisance parameter estimator. The method of proof exploits results concerning the stochastic equicontinuity of…

双重单位根的半参数检验AI 解读

In this article, I argue that if an explosive root is considered a serious alternative to an I(2) process, then joint testing for the number of unit roots along the lines of Hasza and Fuller is preferable instead of prior differencing as suggested by Dickey and Pantula. A semiparametric equivalent of the Hasza-Fuller test that permits rather general assumptions about the innovation process is developed. The test is a straightforward generalization of the Phillips Z test for…

基于枢轴估计函数的重抽样方法AI 解读

SUMMARY Suppose that, under a semiparametric model setting, one is interested in drawing inferences about a finite-dimensi onal parameter vector /? based on an estimating function. Generally a consistent point estimator /J for /?0, the true value for /J, can be easily obtained by finding a root of the corresponding estimating equation. To estimate the variance of ft, however, may involve complicated and subjective nonparametric functional estimates. In this paper, a general…

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约束拟似然估计AI 解读

For maximum likelihood or least squares parameter estimation subject to a constrained parameter, the standard approach is to use the method of Lagrange multipliers. In this paper it is shown that the same formal procedure applies very generally for constrained quasi-likelihood estimation even though there is ordinarily no objective function to maximize or minimize. This allows for optimal estimation in a wide range of problems where maximum likelihood cannot be used, such as…

二元生存函数中关联参数的半参数估计AI 解读

Clayton's model for association in bivariate survival data is both of intrinsic importance and an interesting example of a semiparametric estimation problem, that is, a problem where inference about a parameter is required in the presence of nuisance functions. The joint distribution of the two survival times in this model is absolutely continuous and a single parameter governs the association between the two survival times. In this paper we describe an algorithm to derive…

回归模型中的有效估计AI 解读

In this paper we consider the regression model with smooth regression function and smooth error and covariate distributions. We study how well one can estimate functionals of the regression function which may also depend on the distribution of the covariate. This is done by deriving the efficient influence functions of least dispersed regular estimators of such functionals under various assumptions on the parameters of our model. Then we demonstrate how efficient estimates…

2×2交叉试验中个体内变异的精确推断AI 解读

The comparison of primary interest in a 2 × 2 crossover trial typically concerns the effect of the treatments, say A and B, on the mean response level. This article deals with another important aspect, namely the within-subject response variability under A and B. Differences in drug formulation and/or administration may lead to considerable differences in within-subject variability, whatever the difference in terms of mean level. Consideration of both these aspects may,…

当前状态数据的回归分析：母乳喂养应用AI 解读

Current-status data are often collected for use in survival analyses, because this type of data generally is considered to be more reliable than retrospective reports of when an event occurred. Although techniques for calculating mean survival time from current-status data are well known, their use in multiple regression models is somewhat troublesome. Using data on current breast-feeding behavior, this article considers a number of techniques that have been suggested in the…

多事件时间数据的半参数贝叶斯分析AI 解读

Multiple event time data (e.g., carcinogenic growths in different times and locations, multiple attacks of cardiac arrest) arise in various medical studies. A Bayesian analysis of such data based on proportional intensity model of multiple event time data is presented in this paper. The Bayesian structure is somewhat analogous to that used by Kalbfleisch in a proportional hazard model. An unobserved random frailty component is used in the proportional intensity model to take…

L1 统计分析及相关方法AI 解读

Prologue. Thoughts on Real Data and Statistics (Y. Dodge). Some Statistical Tools for the Analysis of Complex Molecular Spectra (A.F. Ruckstuhl). Estimation, Testing and Property Characterization. The Gauss Markov Property for the Median (G.W. Basset, Jr.,). A Class of Estimators Based on Adaptative Convex Combinations of Two Estimation Procedures (Y. Dodge, J. Jureckova). Robustness of L1 Regression in the Light of Linear Programming (J. Dupacova). A Linear Model with a…

加权平均导数估计量与指数模型的效率AI 解读

Weighted average derivatives are useful parameters for semiparametric index models and nonparametric demand analysis. This paper gives efficiency results for average derivative estimators, including formulating estimators that have high efficiency. The authors derive the efficiency bound for weighted average derivatives of conditional location functionals, such as the conditional mean and median. They also derive the efficiency bound for semiparametric index models, where…

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若干半参数选择模型的效率界AI 解读

In this paper, we calculate semiparametric efficiency bounds for the regression coefficients in an 'outcome' equation subject to censoring under weak conditions on the form of the selectivity bias. It is assumed that the regression function for the binary selection variable which governs the censoring mechanism is nonparametric, as is the form of the selection correction term in the outcome equation. Our results extend those of Chamberlain (1986), who considered models with…

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单指标模型的半参数最小二乘与加权估计AI 解读

For the class of single-index models, I construct a semiparametric estimator of coefficients up to a multiplicative constant that exhibits 1 √n-consistency and asymptotic normality. This class of models includes censored and truncated Tobit models, binary choice models, and duration models with unobserved individual heterogeneity and random censoring. I also investigate a weighting scheme that achieves the semiparametric efficiency bound.

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模拟极端次序统计量的半参数自助法AI 解读

We propose a technique for simulating the joint distribution of the j largest order statistics of a very large sample. We assume that the parent population is in the domain of attraction of the Type 1 (Gumbel) extreme value distribution. The bootstrap variates are generated by resampling the normalized spacings of the k largest observed values in the original data where k is larger than j. We compare the bootstrap distribution to the fitted extremal distribution of Weissman.…

模拟极端次序统计量的半参数自助法AI 解读

We propose a technique for simulating the joint distribution of the j largest order statistics of a very large sample. We assume that the parent population is in the domain of attraction of the Type 1 (Gumbel) extreme value distribution. The bootstrap variates are generated by resampling the normalized spacings of the k largest observed values in the original data where k is larger than j. We compare the bootstrap distribution to the fitted extremal distribution of Weissman.…

非参数回归函数估计中的一致模型设定检验AI 解读

This paper proposes a general framework for specification testing of the regression function in a nonparametric smoothing estimation context. The same analysis can be applied to cases as varied as testing for omission of variables, testing certain nonlinear restrictions in the regressors, and testing the correct specification of some parametric or semiparametric model of interest, for example, testing a certain type of nonlinearity of the regression function. Furthermore,…

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