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时间序列线性的非参数检验AI 解读

We introduce tests of linearity for time series based on nonparametric estimates of the conditional mean and the conditional variance. The tests are compared to a number of parametric tests and to nonparametric tests based on the bispectrum. Asymptotic expressions give bad approximations, and the null distribution under linearity is constructed using resampling of the best linear approximation. The new tests perform well on the examples tested.

多重假设检验的逐步重抽样方法AI 解读

This article introduces a method of multiple hypothesis testing that combines the idea of sequential multiple testing procedures with the structure of resampling methods. The method can be seen as an alternative to the analytic method of Dunnett and Tamhane, which requires a specific distributional form. Resampling incorporates the covariance structure of the data without the need for distributional assumptions. Recent work by Westfall and Young has shown that a step-down…

线性模型中的一致变量选择AI 解读

A method of estimating linear model dimension and variable selection is proposed. This new criterion, which generalizes the Cp criterion, the Akaike information criterion (AIC), the Bayes information criterion, and the phiv criterion and is consistent under certain conditions, is based on a new class of penalty functions and a procedure of sorting covariates based on t-statistics. In the course of introducing this method, we discuss the important role of the penalty function…

摘要暂缺

分位数回归估计量的 BootstrapAI 解读

The asymptotic variance matrix of the quantile regression estimator depends on the density of the error. For both deterministic and random regressors, the bootstrap distribution is shown to converge weakly to the limit distribution of the quantile regression estimator in probability. Thus, the confidence intervals constructed by the bootstrap percentile method have asymptotically correct coverage probabilities.

计量经济学中的随机化检验AI 解读

Numerous shufflings of data produce a distribution of test-statistic values that can be used to assess the degree to which the test-statistic value produced by the actual data is unusual. Because this controversial randomization-testing methodology, made practical by the computer revolution, has begun to appear in applied econometric studies, econometricians should become familiar with its mechanics, rationale, and interpretation, all of which are quite different from the…

依赖数据自助法的分块选择规则AI 解读

SUMMARY: We address the issue of optimal block choice in applications of the block bootstrap to dependent data. It is shown that optimal block size depends significantly on context, being equal to n1/3, n1/4 and n1/5 in the cases of variance or bias estimation, estimation of a onesided distribution function, and estimation of a two-sided distribution function, respectively. A clear intuitive explanation of this phenomenon is given, together with outlines of theoretical…

非线性自回归的可加性检验AI 解读

Additivity is commonly used in the statistical literature to simplify data analysis, especially in analysis of variance and in multivariate smoothing. In this paper, we propose three procedures for testing additivity in nonlinear time series analysis. The first procedure combines some smoothing techniques with analysis of variance, the second is a Lagrange multiplier test using nonparametric estimation, and the third is a permutation test which uses smoothing techniques to…

时间序列线性的非参数检验AI 解读

We introduce tests of linearity for time series based on nonparametric estimates of the conditional mean and the conditional variance. The tests are compared to a number of parametric tests and to nonparametric tests based on the bispectrum. Asymptotic expressions give bad approximations, and the null distribution under linearity is constructed using resampling of the best linear approximation. The new tests perform well on the examples tested.

基于线性模型估计方程的自助法AI 解读

This paper presents a method of bootstrap estimation for the linear model. Rather than resampling from the original sample, as is conventional, the proposed method resamples summands in an estimating function used to produce the original estimate. The method is computationally simpler then existing competitors. However, its main advantage lies in the robustness to heteroscedasticity of the estimators it produces. These estimators are shown to have desirable asymptotic…

一般重抽样算法及其在分布估计中的表现AI 解读

Recent work of several authors has focussed on first-order properties (e.g., consistency) of general bootstrap algorithms, where the numbers of times that data values are resampled form an exchangeable sequence. In the present paper we develop second-order properties of such algorithms, in a very general setting. Performance is discussed in the context of distribution estimation, and formulae for higher-order moments and cumulants are developed. Arguing thus, necessary and…

最小假设下基于子样本的大样本置信区域AI 解读

In this article, the construction of confidence regions by approximating the sampling distribution of some statistic is studied. The true sampling distribution is estimated by an appropriate normalization of the values of the statistic computed over subsamples of the data. In the i.i.d. context, the method has been studied by Wu in regular situations where the statistic is asymptotically normal. The goal of the present work is to prove the method yields asymptotically valid…

回归误差相关性的置换检验AI 解读

This article is about tests for serial, spatial, or other correlation in e, the error vector in the linear model Y = Xβ + e. The errors need not come from a particular parametric distribution such as the normal. P values are computed using permutations of the regression residuals. In the special cases that Y = e or X is a vector of Is, the tests are valid and consistent. In general, regression residuals are not exchangeable, so the tests are only approximate. It is shown,…

Cox模型中若干Bootstrap置信区间的比较AI 解读

We study bootstrap confidence intervals for three types of parameters in Cox's proportional hazards model: the regression parameter, the survival function at fixed time points, and the median survival time at fixed values of a covariate. Several types of bootstrap confidence intervals are studied, and the type of interval is determined by two factors. One factor is the method of drawing the bootstrap sample. We consider three such methods: (1) ordinary resampling from the…

平稳自助法AI 解读

This article introduces a resampling procedure called the stationary bootstrap as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on weakly dependent stationary observations. Previously, a technique based on resampling blocks of consecutive observations was introduced to construct confidence intervals for a parameter of the m-dimensional joint distribution of m consecutive observations, where m is fixed. This…

有限总体的 Bootstrap 方法AI 解读

We show that the familiar bootstrap plug-in rule of Efron has a natural analog in finite population settings. In our method a characteristic of the population is estimated by the average value of the characteristic over a class of empirical populations constructed from the sample. Our method extends that of Gross to situations in which the stratum sizes are not integer multiples of their respective sample sizes. Moreover, we show that our method can be used to generate…

摘要暂缺

审计差错金额的组合界：Hoeffding 不等式与自助法AI 解读

This paper describes a simulation study of a nonparametric bound on the total error amount in an auditing population. The bound is a combination of a bootstrap approximation of the bound generated by Hoeffding's inequality and a modified nonparametric bootstrap-t method. For the 95 percent confidence level and size 120, the combined bound is reliable over a wide range of simulated study populations and is on average 13 percent smaller than the benchmark Stringer bound based…

Bootstrap 样本循环利用：嵌套 Bootstrap 的蒙特卡洛替代方案AI 解读

A Monte Carlo algorithm is described that can be used in place of the nested bootstrap. It is particularly advantageous when there is a premium on the number of bootstrap samples, either because samples are hard to generate or because expensive computations are applied to each sample. This recycling algorithm is useful because it enables inference procedures like prepivoting and bootstrap iteration in models where nested bootstrapping is computationally impractical.…

多元密度估计的交叉验证AI 解读

In recent years, the focus of study in smoothing parameter selection for kernel density estimation has been on the univariate case, while multivariate kernel density estimation has been largely neglected. In part, this may be due to the perception that calibrating multivariate densities is substantially more difficult. In this article, we explicitly derive and compare multivariate versions of the bootstrap method of Taylor, the least-squares cross-validation method developed…

右删失两样本问题中位移函数的推断及应用AI 解读

For two distribution functions, F and G, the shift function is defined by Δ(t) = G −1 · F(t) - t. The shift function is the distance from the 45° line and the quantity plotted in Q-Q plots. In the analysis of lifetime data, A represents the difference between two treatments. The shift function can also be used to find crossing points of two distribution functions. The large-sample distribution theory for estimates of Δ is studied for right-censored data. It turns out that…

Bootstrap：统计推断的非参数方法AI 解读

PART ONE: INTRODUCTION Traditional Parametric Statistical Inference Bootstrap Statistical Inference Bootstrapping a Regression Model Theoretical Justification The Jackknife Monte Carlo Evaluation of the Bootstrap PART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAP Bias Estimation Bootstrap Confidence Intervals PART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALS Confidence Intervals for Statistics With Unknown Sampling Distributions Inference When Traditional…

右删失两样本问题中位移函数的推断及应用AI 解读

For two distribution functions, F and G, the shift function is defined by Δ(t) = G −1 · F(t) - t. The shift function is the distance from the 45° line and the quantity plotted in Q-Q plots. In the analysis of lifetime data, A represents the difference between two treatments. The shift function can also be used to find crossing points of two distribution functions. The large-sample distribution theory for estimates of Δ is studied for right-censored data. It turns out that…

Bootstrap 循环利用：嵌套 Bootstrap 的蒙特卡洛替代方案AI 解读

A Monte Carlo algorithm is described that can be used in place of the nested bootstrap. It is particularly advantageous when there is a premium on the number of bootstrap samples, either because samples are hard to generate or because expensive computations are applied to each sample. This recycling algorithm is useful because it enables inference procedures like prepivoting and bootstrap iteration in models where nested bootstrapping is computationally impractical.…