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高崩溃点非线性回归参数的计算AI 解读

Most algorithms for estimating high breakdown regression estimators in linear regression rely on finding the least squares fit to many p-point elemental sets, where p is the dimension of the X matrix. Such an approach is computationally infeasible in nonlinear regression. This article presents a new algorithm for computing high breakdown estimates in nonlinear regression that requires only a small number of least squares fits to p points. The algorithm is used to compute…

用交互样条模型拟合多元回归函数AI 解读

SUMMARY In nonparametric multivariate regression analysis, we seek methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. The original additive models approximate a regression function by additive univariate functions, which overcome successfully the difficulty caused by the curse of dimensionality and have a clear interpretation. But since they ignore any interactions between regressors they are not…

随机系数回归中的系数分布估计AI 解读

Random coefficient regression models are important in representing linear models with heteroscedastic errors and in unifying the study of classical fixed effects and random effects linear models. For prediction intervals and for bootstrapping in random coefficient regressions, it is necessary to estimate the distributions of the random coefficients consistently. We show that this is often possible and provide practical representative estimators of these distributions.

α 混合随机变量三角阵列的一般重抽样方案及谱密度估计应用AI 解读

In 1989 Kunsch introduced a modified bootstrap and jackknife for a statistic which is used to estimate a parameter of the $m$-dimensional joint distribution of stationary and $\alpha$-mixing observations. The modification amounts to resampling whole blocks of consecutive observations, or deleting whole blocks one at a time. Liu and Singh independently proposed (in 1988) the same technique for observations that are $m$-dependent. However, many time-series statistics, notably…

用于验证混合分布分析的信息比率AI 解读

Determining the number of components in a mixture of distributions is an important but difficult problem. This article introduces a procedure called minimum information ratio estimation and validation (MIREV), which is based on a ratio of Fisher information matrices. The smallest eigenvalue of the information ratio matrix is used to determine the number of components. A measure of uncertainty may be obtained using a bootstrap technique. Simulations illustrate the…

总量冲击与宏观经济波动：贝叶斯方法AI 解读

Abstract This paper describes Bayesian techniques for analysing the effects of aggregate shocks on macroeconomic time‐series. Rather than calculate point estimates of the response of a time‐series to an aggregate shock, we calculate the whole probability density function of the response and use Monte‐Carlo or Gibbs sampling techniques to evaluate its properties. The proposed techniques impose identification restrictions in a way that includes the uncertainty in these…

Bootstrap 后的 Jackknife：标准误与影响函数AI 解读

SUMMARY This paper shows how to derive more information from a bootstrap analysis, information about the accuracy of the usual bootstrap estimates. Suppose that we observe data x = (x 1 x 2, . . ., xn), compute a statistic of interest s(x) and further compute B bootstrap replications of s, say s(x*1) s(x*2), . . ., s(x*B), where B is some large number like 1000. Various accuracy measures for s(x) can be obtained from the bootstrap values, e.g. the bootstrap estimates of…

用秩统计理论研究一般 Bootstrap 的一致性AI 解读

A general notion of a bootstrapped mean constructed by exchangeably weighting sample points is introduced. Consistency of this generalized bootstrapped mean, which includes proposals of Efron and Rubin among others, is proved by classical linear rank statistics theory. The consistency of generalized bootstrapped empirical and quantile processes is also established.

条件分布的Bootstrap估计AI 解读

Techniques are developed for bootstrap estimation of conditional distributions, with application to confidence intervals and hypothesis tests for one parameter, conditional on the value of an estimator of another. Both Monte Carlo and saddlepoint methods for approximating bootstrap distributions are considered, and empirical methods are suggested for implementing these techniques. For example, in the case of Monte Carlo methods, we suggest empirical techniques for selecting…

样本分位数的平衡重复复制法之渐近性质AI 解读

Inference, including variance estimation, can be made from stratified samples by selecting a balanced set of subsamples. This balanced subsampling method is generically called the balanced repeated replication method in survey data analysis, which includes McCarthy's balanced half-samples method and its extensions for more general stratified designs. We establish the asymptotic consistency of the balanced repeated replication variance estimators when the parameter of…

多元线性回归参数 M 估计量的自助法AI 解读

Consider a multiple linear regression model $Y_i = x'_i\beta + \varepsilon_i$, where the $\varepsilon_i$'s are independent random variables with common distribution $F$ and the $x_i$'s are known design vectors. Let $\bar\beta_n$ be the $M$-estimator of $\beta$ corresponding to a score function $\psi$. Under some conditions on $F, \psi$ and the $x_i$'s, two-term Edgeworth expansions for the distributions of standardized and studentized $\bar\beta_n$ are obtained. Furthermore,…

方向数据的排序：圆与球面上的数据深度AI 解读

Three notions of depth for directional data, angular simplicial depth (ASD), angular Tukey's depth (ATD) and arc distance depth (ADD), are developed and studied. The empirical versions of these depths give rise to center-outward rankings of angular data which may be regarded as extensions of the usual center-outward ranking on the line. Three medians derived from these depths are examined and compared. Applications in nonparametric classification and in implementing the…

复杂调查数据的重抽样程序AI 解读

In complex survey data, often the sampling design induces a non-iid structure to the data (e.g., without replacement sampling, stratification, multistage, or unequal probability of selection). Though techniques for variance estimation and confidence intervals do exist, they often are cumbersome to implement or do not extend to complex designs. It would be desirable to have resampling methods that reuse the existing estimation system repeatedly, using computing power to avoid…

Little Bootstrap 与回归维度选择：固定设计下的预测误差AI 解读

When a regression problem contains many predictor variables, it is rarely wise to try to fit the data by means of a least squares regression on all of the predictor variables. Usually, a regression equation based on a few variables will be more accurate and certainly simpler. There are various methods for picking “good” subsets of variables, and programs that do such procedures are part of every widely used statistical package. The most common methods are based on stepwise…

分位数过程弱收敛的初等方法及其在删失生存数据中的应用AI 解读

Let ξ be a continuously differentiable function with positive derivative, and let ξ n be a sequence of right-continuous increasing processes. We show that if n 1/2(ξ n − ξ) W, where W is continuous, then n 1/2(ξ−1 n − ξ−1) − W(ξ−1)/ξ′(ξ−1). This result is applied to classical processes such as the empirical distribution function, the Kaplan-Meier estimator, and some other situations. We also prove an analogous result for the bootstrapped version of n 1/2(ξ n − ξ) and show…

隐私保护自助法AI 解读

Methods for privacy protection of microdata include grouping and publication of data perturbed with random noise. The authors suggest a variant of the latter in which the noise is generated by bootstrapping from the original empirical distribution. The published data distribution then essentially consists of a convolution of a distribution with itself and the distribution can be recovered, although the individual observations remain protected. The authors explore the…

非参数回归中的 Bootstrap 置信区间AI 解读

Several authors have developed bootstrap methods for constructing confidence intervals in nonparametric regression. On each occasion a nonpivotal approach has been employed. Nonpivotal methods are still the overwhelmingly popular choice when statisticians use the bootstrap to compute confidence intervals, but they are not necessarily the most appropriate. In this paper we point out some of the theoretical advantages of pivoting. They include a reduction in the error of the…

U 统计量与 V 统计量的自助法AI 解读

Bootstrap distributional limit theorems for $U$ and $V$ statistics are proved. They hold a.s., under weak moment conditions and without restrictions on the bootstrap sample size (as long as it tends to $\infty$), regardless of the degree of degeneracy of $U$ and $V$. A testing procedure based on these results is outlined.

依赖变量分布的渐近展开与自助法：鞅方法AI 解读

The paper develops a one-step triangular array asymptotic expansion for continuous martingales which are asymptotically normal. Mixing conditions are not required, but the quadratic variations of the martingales must satisfy a law of large numbers and a central limit type condition. From this result we derive expansions for the distributions of estimators in asymptotically ergodic differential equation models, and also for the bootstrapping estimators of these distributions.

偏差估计对密度 Bootstrap 置信区间覆盖精度的影响AI 解读

The bootstrap is a poor estimator of bias in problems of curve estimation, and so bias must be corrected by other means when the bootstrap is used to construct confidence intervals for a probability density. Bias may either be estimated explicitly, or allowed for by undersmoothing the curve estimator. Which of these two approaches is to be preferred? In the present paper we address this question from the viewpoint of coverage accuracy, assuming a given number of derivatives…

协方差矩阵同质性检验的自助法临界值AI 解读

Bartlett's modified likelihood ratio statistic A is often suggested in multivariate analysis for testing equality of covariance matrices. Unfortunately, the χ 2-approximation to the null distribution of −2 log Λ is useful only when the data is normally distributed. This article presents a pooled bootstrap procedure that replaces the χ 2-approximation and makes Bartlett's statistic a useful tool for data analysis. This procedure also applies to most quadratic form test…

生物学中的随机化与蒙特卡洛方法AI 解读

Part 1 Randomization tests and confidence intervals: the idea of a randomization test examples of a randomization test aspects of randomization testing raised by the examples confidence intervals from randomization. Part 2 Monte Carlo and other computer intensive methods: Monte Carlo tests jackknifing bootstrapping bootstrap tests of significance and confidence intervals. Part 3 Some general considerations: power determining how many randomizations are needed determining a…

协方差矩阵同质性检验的 Bootstrap 临界值AI 解读

Bartlett's modified likelihood ratio statistic A is often suggested in multivariate analysis for testing equality of covariance matrices. Unfortunately, the χ 2-approximation to the null distribution of −2 log Λ is useful only when the data is normally distributed. This article presents a pooled bootstrap procedure that replaces the χ 2-approximation and makes Bartlett's statistic a useful tool for data analysis. This procedure also applies to most quadratic form test…

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