日期精度：到日 1012 篇 · 到月 958 篇 · 到年 101 篇
利用重抽样方法估计分布端点
The problem of estimating an endpoint of a distribution is revisited, using the bootstrap and random subsample methods. Contrary to an example in Bickel and Freedman (1981) suggesting that these methods do not work here, it is shown that one can in fact construct asymptotically valid confidence intervals in some situations.
线性回归预测误差的Bootstrap与交叉验证估计
Different estimates of the mean squared error of prediction for linear regression models are derived by the bootstrap and cross-validation approaches. A comparison is made under normal error distributions, especially by the biases and the mean square errors. The results indicate that the bias corrected bootstrap estimator is best unbiased and should be the first choice, while its simulated variant has approximately the same behaviour. On the other hand, if only a comparison…
时间序列之间条件线性依赖与反馈的度量
Measures of linear dependence and feedback for two multiple time series conditional on a third are defined. The measure of conditional linear dependence is the sum of linear feedback from the first to the second conditional on the third, linear feedback from the second to the first conditional on the third, and instantaneous linear feedback between the first and second series conditional on the third. The measures are non-negative and may be expressed in terms of measures of…
非嵌套线性模型的比较
I consider the usual linear-model situation, except that there are two possible linear subspaces that may contain the true mean vector, and neither of the two subspaces is nested within the other. Approximate confidence intervals are developed for the difference in mean squared error (MSE) of prediction using the two models, not assuming that either model is necessarily correct. The confidence intervals are based on parametric bootstrap methods, applied to Mallows's Cp…
非嵌套线性模型的比较
I consider the usual linear-model situation, except that there are two possible linear subspaces that may contain the true mean vector, and neither of the two subspaces is nested within the other. Approximate confidence intervals are developed for the difference in mean squared error (MSE) of prediction using the two models, not assuming that either model is necessarily correct. The confidence intervals are based on parametric bootstrap methods, applied to Mallows's Cp…
回归问题中 Bootstrap 的若干注记
Editor's Note: A new Short Communications section has been added to JBES. This section will include short papers and notes on topics of interest to readers, including measurement problems, revisions of major business and economic time series, forecasting experiments, computer programs, experience with applications of new methods, and comments on previously published papers. To submit papers to be considered for Short Communications, send four copies prepared in accord with…
关于样本中位数自助法的一则说明
Efron (1979, 1982), in his treatment of the bootstrap, discusses its use for estimation of the asymptotic variance of the sample median, in the sampling situation of independent and identically distributed random variables with common distribution function $F$ having a positive derivative continuous in a neighborhood of the true median $\mu$. The natural conjecture that the bootstrap variance estimator converges almost surely to the asymptotic variance is shown by an example…
平稳线性模型中两阶段最小二乘估计的自助法
For models similar to those used in econometric work, under suitable regularity conditions, the bootstrap is shown to give asymptotically valid approximations to the distribution of errors in coefficient estimates.
观察性研究中的条件置换检验与倾向得分
In observational studies, the distribution of treatment assignments is unknown, and therefore randomization tests are not generally applicable. However, permutation tests that condition on sample information about the treatment assignment mechanism can be applicable in observational studies, provided treatment assignment is strongly ignorable. These tests use the conditional distribution of the treatment assignments given a sufficient statistic for the unknown parameter of…
分层抽样中的渐近正态性与 Bootstrap
This paper is about the asymptotic distribution of linear combinations of stratum means in stratified sampling, with and without replacement. Both the number of strata and their size is arbitrary. Lindeberg conditions are shown to guarantee asymptotic normality and consistency of variance estimators. The same conditions also guarantee the validity of the bootstrap approximation for the distribution of the $t$-statistic. Via a bound on the Mallows distance, situations will be…
不等右删失下比较生存曲线的精确检验
It is shown that through the use of conditional tests after artificial censoring one may produce analogues of standard nonparametric tests, such as the log rank and generalized Wilcoxon, that are exact even in the presence of unequal right censoring where standard permutation tests fail. The artificial censoring leads to a loss in power, but in important special cases, as for example in the study of rare diseases, this can be minor. The power loss is investigated through a…
计量经济模型的 Bootstrap：若干经验结果
The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation, based on a nonparametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric model describing the demand for capital, labor, energy, and materials. The model is fitted by three-stage least squares. In sharp contrast with previous results, the coefficient estimates and the estimated standard errors perform very…
用 Jackknife 近似 Bootstrap 估计
Let $\hat{T}_n$ be an estimate of the form $\hat{T}_n = T(\hat{F}_n)$, where $\hat{F}_n$ is the sample $\operatorname{cdf}$ of $n \operatorname{iid}$ observations and $T$ is a locally quadratic functional defined on $\operatorname{cdf's}$. Then, the normalized jackknife estimates for bias, skewness, and variance of $\hat{T}_n$ approximate closely their bootstrap counterparts. Each of these estimates is consistent. Moreover, the jackknife and bootstrap estimates of variance…
变量测量误差问题中的影响函数
This paper focuses on two aspects of the errors in variables problem--variance estimation of the classical estimators of slope and intercept, and the detection of influential observations. The behaviour of the jackknife, bootstrap, normal theory and influence function estimators of variability is examined under a number of sampling situations by Monte Carlo methods. In the multivariate case, perturbation analysis is used to calculate the influence function of the estimator…
回归方程的自助法：若干实证结果
The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation based on a nonparametric estimate of the underlying error distribution. The main object of this article is to present the bootstrap in the context of an econometric equation describing the demand for energy by industry. As it turns out, the conventional asymptotic formulas for estimating standard errors are too optimistic by factors of nearly three,…
不等右删失下比较生存曲线的若干精确检验
It is shown that through the use of conditional tests after artificial censoring one may produce analogues of standard nonparametric tests, such as the log rank and generalized Wilcoxon, that are exact even in the presence of unequal right censoring where standard permutation tests fail. The artificial censoring leads to a loss in power, but in important special cases, as for example in the study of rare diseases, this can be minor. The power loss is investigated through a…
报告显著性水平的非随机解释
Tests of significance are often made in situations where the standard assumptions underlying the probability calculations do not hold. As a result, the reported significance levels become difficult to interpret. This article sketches an alternative interpretation of a reported significance level, valid in considerable generality. This level locates the given data set within the spectrum of other data sets derived from the given one by an appropriate class of transformations.…
We study the asymptotic accuracy of the bootstrap approximation to the distribution of a $k$-sample studentized mean.
置换检验功效的 Edgeworth 展开
A randomization model for a two-sample situation with additive treatment effects is considered. Edgeworth expansions for the power of the usual permutation test are derived, under some conditions on the unit errors, from previously obtained expansions under the null hypothesis of no treatment effect. A general error structure is considered and conditions for the validity of the expansions for both conditional and unconditional power are examined. The results are shown to…
预测规则错误率的估计：对交叉验证的改进
We construct a prediction rule on the basis of some data, and then wish to estimate the error rate of this rule in classifying future observations. Cross-validation provides a nearly unbiased estimate, using only the original data. Cross-validation turns out to be related closely to the bootstrap estimate of the error rate. This article has two purposes: to understand better the theoretical basis of the prediction problem, and to investigate some related estimators, which…
不等删失下对数秩置换检验的表现
This note studies the extent to which the type I error of the permutation test based on the log rank score departs from its nominal value in the presence of unequal right censoring. For the asymptotic case, situations are identified that lead to conservative and to nonconservative behaviour, and the amount of conservatism is computed for some specific constant hazard situations. A small Monte Carlo study is used to support the relevance of the asymptotic results in a finite…
关于 jackknife、bootstrap 与 delta 方法的偏差和方差估计
The jackknife, bootstrap and delta method all provide estimators of bias and variance for an extremely wide class of statistics. Simulation results and asymptotic expansions show that the three methods coincide, at least to first-order terms. The absence of skewness leads to second-order coincidence of the jackknife and delta methods.