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非参数平滑回归中无关联假设的随机化检验AI 解读

When a linear regression function is estimated by ordinary least squares, the null hypothesis of no relationship between the response and the design variable can be tested by the normal theory F test. This article describes a new test for use in the case in which a smooth regression function is estimated by a nonparametric procedure such as kernel estimation or local regression. The test is constructed as an approximation to an exact permutation test. The test statistic is…

更高效的Bootstrap计算AI 解读

This article concerns computational methods for the bootstrap that are more efficient than the straightforward Monte Carlo methods usually used. The bootstrap is considered in its simplest form: in a one-sample nonparametric problem, where the goal is to estimate the bias or variance of some statistic by bootstrap sampling, or to set approximate confidence intervals for a parameter of interest in terms of various percentiles of the bootstrap distribution. The methods of this…

截断时间与失效时间独立性假设的检验AI 解读

For data subject to random censorship, it is well known that the assumption of independence between censoring time and failure time cannot be tested nonparametrically. For data subject to random truncation, however, it is possible to test independence of truncation time and failure time. A statistic generalizing Kendall's tau is proposed for testing the independence assumption, and asymptotic properties under the null hypothesis are derived. A conditional permutation test is…

爆炸性自回归过程的 BootstrapAI 解读

Asymptotic validity of the bootstrap is established for the least squares estimate of the parameter of an explosive first-order autoregressive process. It is noted that nonnormal limit distributions are obtained for both the traditional and the bootstrap estimates. The theoretical bootstrap validity results are supported by appropriate simulation.

回归模型自助法的 Edgeworth 展开AI 解读

The asymptotic performance of the bootstrap in linear regression models is studied. Edgeworth expansions show that asymptotically, the bootstrap is always at least as good as, and in some cases better than, the classical normal approximation. The performances of both the bootstrap and the normal approximation depend on the rate of increase in the elements of the design matrix.

经验函数的自助法AI 解读

We develop the complete bootstrapped parallel to the asymptotic theory of weighted empirical and quantile processes. Utilizing this parallel theory, we present a general body of techniques to establish the asymptotic validity of the bootstrap method of constructing confidence bands for statistical functions. These techniques are demonstrated to be applicable to the construction of asymptotic bootstrap confidence bands for a variety of concrete functions.

步态分析与 BootstrapAI 解读

This paper is about random coefficient trigonometric regression models and their use in gait analysis. Here gait analysis means free-speed walking on a level surface. Our study focuses on bootstrap-based prediction regions for the angular rotation curves of test children, when the relevant training data are gathered from normal children of comparable ages. Considerations that led to our choice of model and use of the bootstrap are given. Prediction regions and empirical…

方向数据的自助法置信区域AI 解读

Methods are proposed for constructing bootstrap confidence regions for the mean direction of a random p-dimensional unit vector X with an arbitrary unimodal distribution on the p sphere. The approach of this article differs from that of other authors in that it is based on pivotal statistics. A general pivotal method is introduced that produces a wide variety of confidence regions on general p-dimensional spheres; included are confidence cones and likelihood-based regions.…

使用 t 分布进行稳健统计建模AI 解读

The t distribution provides a useful extension of the normal for statistical modeling of data sets involving errors with longer-than-normal tails. An analytical strategy based on maximum likelihood for a general model with multivariate t errors is suggested and applied to a variety of problems, including linear and nonlinear regression, robust estimation of the mean and covariance matrix with missing data, unbalanced multivariate repeated-measures data, multivariate modeling…

方向数据的Bootstrap置信区域AI 解读

Methods are proposed for constructing bootstrap confidence regions for the mean direction of a random p-dimensional unit vector X with an arbitrary unimodal distribution on the p sphere. The approach of this article differs from that of other authors in that it is based on pivotal statistics. A general pivotal method is introduced that produces a wide variety of confidence regions on general p-dimensional spheres; included are confidence cones and likelihood-based regions.…

摘要暂缺

自然指数族的预测拟合AI 解读

The paper examines predictive distributions, concentrating on measuring their fit to the true distribution by average KulLback–Leibler divergence. The notion of an ‘averaged bootstrap’ predictive distribution is introduced. This predictive distribution is shown to be asymptotically superior to the estimative distribution, in terms of average Kullback–Leibler divergence, when the true distribution is in a natural exponential family. Small-sample results are presented for the…

动态非线性模型预测量的大样本性质AI 解读

The large-sample behavior of one-period-ahead and multiperiod-ahead predictors for a dynamic nonlinear simultaneous system is examined in this paper. Conditional on final values of the endogenous variables, the asymptotic moments of the deterministic, closed-form, Monte Carlo stochastic, and several variations of the residual-based stochastic predictor are analyzed. For one-period-ahead prediction, the results closely parallel our previous findings for static nonlinear…

摘要暂缺

关于指数编制随机方法的一则说明AI 解读

In a recent article, Clements and Izan (1987) used the stochastic approach to index-number theory to estimate the rate of inflation and its standard error. Selvanathan (1988) extended their approach to the prices of groups of goods and to prices within groups. In this note, I apply the within-group results to the U.K. alcohol data. Simulation results show that the estimates are unbiased, but the asymptotic standard errors understate the true sampling variability of the…

自回归估计量偏差的不动点刻画AI 解读

Least squares estimators of the coefficients of an autoregression of known, finite order are biased to order $1/T$, where $T$ is the sample length, unless the observed time series is generated by a unique model for that order. The coefficients of this special model are the fixed point of a linear mapping defined by the bias of the least squares estimator. Separate results are given for models with known mean and unknown mean. The "fixed point models" for different orders of…

高维对数线性模型最大似然估计量的 BootstrapAI 解读

The notion of a bootstrap estimator of the distribution of the maximum likelihood estimator in log-linear models is defined for common sampling models. It is shown that the bootstrap estimator is consistent under assumptions which allow the dimension of the model to increase to infinity. Such an approach allows treatment of large, sparse contingency tables.

无限方差情形下样本均值的自助法AI 解读

Athreya showed that the bootstrap distribution of a sum of infinite variance random variables did not (with probability 1) tend weakly to a fixed distribution but instead tended in distribution to a random distribution. In this paper, we give a different proof of Athreya's result motivated by a heuristic large sample representation of the bootstrap distribution.

一般平稳观测下的刀切法与自助法AI 解读

We extend the jackknife and the bootstrap method of estimating standard errors to the case where the observations form a general stationary sequence. We do not attempt a reduction to i.i.d. values. The jackknife calculates the sample variance of replicates of the statistic obtained by omitting each block of $l$ consecutive data once. In the case of the arithmetic mean this is shown to be equivalent to a weighted covariance estimate of the spectral density of the observations…

统计信息与似然：批判性论文集AI 解读

I Information and Likelihood.- I Recovery of Ancillary Information.- 0. Notes.- 1. Introduction.- 2. The Sample Size Analogy.- 3. A Logical Difficulty.- 4. Conceptual Statistical Experiments.- II Statistical Information and Likelihood Part I: Principles.- 0. Notes.- 1. Statistical Information.- 2. Basic Definitions and Relations.- 3. Some Principles of Inference.- 4. Information as a Function.- 5. Fisher Information.- 6. The Likelihood Principle.- III Statistical Information…

高效自助法模拟AI 解读

It is shown that three new, efficient bootstrap algorithms are asymptotically equivalent. This is done in two ways. First, asymptotic formulae for variances and mean squared errors are derived, and shown to be identical. Secondly, it is demonstrated that two of the methods may be viewed as approximations to the third. The three algorithms considered are the balanced bootstrap and the linear approximation method proposed by Davison, Hinkley & Schechtman (1986), and a centring…

重要性抽样与嵌套 bootstrapAI 解读

The nested bootstrap is a computer-intensive technique for reducing the statistical error in the ordinary bootstrap. We show how the Monte Carlo technique of importance sampling can be used to substantially reduce the amount of computation needed in a simple double bootstrap confidence limit method.

摘要暂缺

平滑与自助法AI 解读

Recent attention has focussed on possible improvements in performance of estimators which might flow from using the smoothed bootstrap. We point out that in a great many problems, such as those involving functions of vector means, any such improvements will be only second-order effects. However, we argue that substantial and significant improvements can occur in problems where local properties of underlying distributions play a decisive role. This situation often occurs in…