Resampling student'st-type statistics

The present paper establishes conditional and unconditional central limit theorems for various resampling procedures for thet-statistic. The results work under fairly general conditions and the underlying random variables need not to be independent. Specific examples are then them(n) (double) bootstrap out ofk(n) observations, the Bayesian bootstrap and two-samplet-type permutation statistics. In case whenm(n)/k(n) is bounded away from zero and infinity necessary and sufficient conditions for the conditional central limit law of the bootstrapt-statistics are established. For high resampling intensity whenm(n)/k(n) tends to infinity the following general result is obtained. Without further other assumptions the bootstrap makes the resampledt-statistic automatically normal. The results are based on a general conditional limit theorem for weighted resampling statistics which is of own interest.     

Resampling a coverage pattern

The possibility of resampling (bootstrapping) a spatial pattern is investigated. It is suggested that resampling provides a unified approach to consistemt inference in a wide range of coverage problems. Nevertheless, resampling distorts some of the interactions in the problem, and so introduces biases. The sizes of bias and standard deviation are investigated in the case of estimating sampling variance via resampling.     

The Deficiency Introduced by Resampling

When the classical nonparametric bootstrap is implemented by a Monte-Carlo procedure one resamples values from a sequence of, typically, independent and identically distributed ones. But what happens when a decision has to be taken based on such resampled values? One way to quantify the loss of information due to this resampling step is to consider the deficiency distance, in the sense of Le Cam, between a statistical experiment of n independent and identically distributed observations and the one consisting of m observations taken from the original n by resampling with replacement. By comparing with an experiment where only subsamplingwith a random subsampling size has been performed one can bound the deficiency in terms of the amount of information contained in additional observations. It follows for certain experiments that the deficiency distance is proportional to the expected fraction of observations missed when resampling.     

Parallel Resampling in the Particle Filter

Modern parallel computing devices, such as the graphics processing unit (GPU), have gained significant traction in scientific and statistical computing. They are particularly well-suited to data-parallel algorithms such as the particle filter, or more generally sequential Monte Carlo (SMC), which are increasingly used in statistical inference. SMC methods carry a set of weighted particles through repeated propagation, weighting, and resampling steps. The propagation and weighting steps are straightforward to parallelize, as they require only independent operations on each particle. The resampling step is more difficult, as standard schemes require a collective operation, such as a sum, across particle weights. Focusing on this resampling step, we analyze two alternative schemes that do not involve a collective operation (Metropolis and rejection resamplers), and compare them to standard schemes (multinomial, stratified, and systematic resamplers). We find that, in certain circumstances, the alternative resamplers can perform significantly faster on a GPU, and to a lesser extent on a CPU, than the standard approaches. Moreover, in single precision, the standard approaches are numerically biased for upward of hundreds of thousands of particles, while the alternatives are not. This is particularly important given greater single- than double-precision throughput on modern devices, and the consequent temptation to use single precision with a greater number of particles. Finally, we provide auxiliary functions useful for implementation, such as for the permutation of ancestry vectors to enable in-place propagation. Supplementary materials are available online.     

Resampling DEA estimates of investment fund performance

Data envelopment analysis (DEA) is attractive for comparing investment funds because it handles different characteristics of fund distribution and gives a way to rank funds. There is substantial literature applying DEA to funds, based on the time series of funds’ returns. This article looks at the issue of uncertainty in the resulting DEA efficiency estimates, investigating consistency and bias. It uses the bootstrap to develop stochastic DEA models for funds, derive confidence intervals and develop techniques to compare and rank funds and represent the ranking. It investigates how to deal with autocorrelation in the time series and considers models that deal with correlation in the funds’ returns.     

Resampling time series using missing values techniques

Several techniques for resampling dependent data have already been proposed. In this paper we use missing values techniques to modify the moving blocks jackknife and bootstrap. More specifically, we consider the blocks of deleted observations in the blockwise jackknife as missing data which are recovered by missing values estimates incorporating the observation dependence structure. Thus, we estimate the variance of a statistic as a weighted sample variance of the statistic evaluated in a “complete” series. Consistency of the variance and the distribution estimators of the sample mean are established. Also, we apply the missing values approach to the blockwise bootstrap by including some missing observations among two consecutive blocks and we demonstrate the consistency of the variance and the distribution estimators of the sample mean. Finally, we present the results of an extensive Monte Carlo study to evaluate the performance of these methods for finite sample sizes, showing that our proposal provides variance estimates for several time series statistics with smaller mean squared error than previous procedures.     

经验似然重抽样下回归模型的Bootstrap逼近

本文提出用经验似然重抽样来bootstrap逼近线性回归模型中的学生化最小二乘估计．我们证明了该方法具有一般s－2项Edgeworth展开，它是二阶相合的而且比经典的方法损失更小．     

Nonparametric Resampling for Homogeneous Strong Mixing Random Fields

Künsch (1989, Ann. Statist.17 1217-1241) and Liu ane Singh (1992, in Exploring Limits of Bootstrap (R. Le Page and L. Billard, Eds.), pp. 225-248, Wiley, New York) have recently introduced a block resampling method that is successful in deriving consistent bootstrap estimates of distribution and variance for the sample mean of a strong mixing sequence. Raïs and Moore (1990, in Interface ′90) and Raïs (1992, Ph.D. Thesis, University of Montreal) extended the results of Künsch and Liu and Singh in the case of the sample mean of a homogeneous strong mixing random field in two dimensions (n = 2). In this paper, the general case (n Z⁺) is considered, and a resampling technique for strong mixing random fields is formulated, which is an extension of the "blocks of blocks" resampling scheme for sequences in Politis and Romano (1992, Ann. Statist.20 (4) 1985-2007). The "blocks of blocks" method can be used to construct asymptotically correct confidence intervals for parameters of the whole (infinite-dimensional) joint distribution of the random field, for example, the spectral density at a point. A variation of the "blocks of blocks" resampling scheme that involves "wrapping" the data around on a torus will also be studied, in view of its property to yield an unbiased bootstrap distribution.     

Order Determination for Multivariate Autoregressive Processes Using Resampling Methods

LetX₁, …, X_nbe observations from a multivariate AR(p) model with unknown orderp. A resampling procedure is proposed for estimating the orderp. The classical criteria, such as AIC and BIC, estimate the orderpas the minimizer of the function[formula]wherenis the sample size,kis the order of the fitted model, Σ²_kis an estimate of the white noise covariance matrix, andC_nis a sequence of specified constants (for AIC,C_n=2m²/n, for Hannan and Quinn's modification of BIC,C_n=2m²(ln ln n)/n, wheremis the dimension of the data vector). A resampling scheme is proposed to estimate an improved penalty factorC_n. Conditional on the data, this procedure produces a consistent estimate ofp. Simulation results support the effectiveness of this procedure when compared with some of the traditional order selection criteria. Comments are also made on the use of Yule–Walker as opposed to conditional least squares estimations for order selection.     

Resampling method in nonparametric estimation related to renewal processes

Let the statistical data be a collection of n piecesx ₁, ...,x _n of simple renewal processes. The random times between renewals have an unknown cumulative hazard function (c.h.f.) H₀(·). Let τ(H(·)) be the value of some functional τ(·), given c.h.f. H(·). The value τ₀=τ(H₀(·)) is a parameter of interest. If is an estimator for τ₀, then its quality can be characterized by the distribution law of normed deviations . These laws L_n are unknown, but they can be consistently estimated, by using the resampling method, as n→∞. In the case considered, the resampling method can be justified as a special case of the central limit resampling theorem. Supported by the Bank of Sweden Tercentenary Foundation. Proceedings of the XVII Seminar on Stability Problems for Stochastic Models, Kazan, Russia, 1995, Part I.     

重抽样方法在投资组合理论中的应用与实证分析

在投资组合过程中,由于不了解投资对象的总体分布,可能会过高估计风险回报率.利用重抽样方法可以查看高估程度,采用Shrinkage方法可以改善模型,找到最优的分配权重,帮助投资人确定投资金额分配.基于均值-方差模型与重抽样方法,在不允许卖空的情况下,运用R软件得到了有效的投资组合.     

The multiple hybrid bootstrap — Resampling multivariate linear processes

The paper reconsiders the autoregressive aided periodogram bootstrap (AAPB) which has been suggested in Kreiss and Paparoditis (2003) [18]. Their idea was to combine a time domain parametric and a frequency domain nonparametric bootstrap to mimic not only a part but as much as possible the complete covariance structure of the underlying time series. We extend the AAPB in two directions. Our procedure explicitly leads to bootstrap observations in the time domain and it is applicable to multivariate linear processes, but agrees exactly with the AAPB in the univariate case, when applied to functionals of the periodogram. The asymptotic theory developed shows validity of the multiple hybrid bootstrap procedure for the sample mean, kernel spectral density estimates and, with less generality, for autocovariances.     

一定条件下样本函数极值的重采样方法

本文对一定条件下样本函数极值的重采样方法进行了研究,给出了γ~2估计的几种方法,包括减-d折刀法、减-1折刀法和自助法；并对得到的统计量的性质进行了分析．     

关于正方形序列覆盖正方形的注记

本文研究正方形序列覆盖正方形问题中涉及的一个函数的下界问题·设{Q_i}为闭正方形序列, f(x)=sup{a:Q为正方形,其边长为a,{Q_i}覆盖{Q},本文给出了f(x)的若干下界.     

More with the Lemke complementarity algorithm

In the case that the matrix of a linear complementarity problem consists of the sum of a positive semi-definite matrix and a co-positive matrix a general condition is deduced implying that the Lemke algorithm will terminate with a complementarity solution. Applications are presented on bi-matrix games, convex quadratic programming and multi-period programs.Contributed to the XXIII TIMS Meeting, Athens, July 1977.     

Adaptive Filtering: Once More with Gusto

Adaptive filtering, when used as a forecasting method, proposes to be able to distinguish a "signal pattern" of a time series instead of just smoothing out the random noise introduced by the data. Adaptive filtering is claimed by its creators to "...always do as well if not better than either moving averages, exponential smoothing,...". In order to see whether this claim could be substantiated, the author has taken the approach of a casual user of forecasting methods and has sought to determine whether adaptive filtering is useful, or not, as a forecasting method. The method was used to compute forecasts for ten sets of data on monthly insurance payments in a Finnish insurance company, and the experience gained from this work is compared with criticisms of the method expressed by a number of writers. It is shown that the method performs quite well for practical purposes, despite the fact that it has some major theoretical shortcomings.     

Supersaturated Design with More Than Two Levels

gO. IntroductionA fractional factorial design is called supersaturated if the number of factors includedexceeds the limitation for keeping all the factors estimable, ignoring interactions. Sincesupersaturated designS can accommodate large nUInbers of factors, they are thought usefulin screening experhoellts.The research work on tWO-level supersaturated design was st8lted in the late fifties andearly staies (see [1,101). The subject has been gaining more and more renewed interest inthe last …