共查询到20条相似文献,搜索用时 34 毫秒
1.
Summary This paper considers different bootstrap procedures for investigating the estimation of the fractional parameter d in a particular
case of long memory processes, i.e. for ARFIMA models withd in (0.0, 0.5). We propose two bootstrap techniques to deal with semiparametric estimation methods of d. One approach consists
of the local bootstrap method for time frequency initially suggested for the ARMA case by Paparoditis and Politis (1999),
and the other consists of the bootstrapping in the residuals of the frequency-domain regression equation. Through Monte Carlo
simulation, these alternative bootstrap methods are compared, based on the mean and the mean square error of the estimators,
with the well-known parametric and nonparametric bootstrap techniques for time series models. 相似文献
2.
Yasunori Fujikoshi 《Journal of multivariate analysis》1985,17(1):27-37
This paper deals with two criteria for selection of variables for the discriminant analysis in the case of two multivariate normal populations with different means and a common covariance matrix. One is based on the estimated error rate of misclassification. The other uses Akaike's information criterion. The asymptotic distributions and error rate risks of the criteria are obtained. The result will prove that the two criteria are asymptotically equivalent in the sense of their asymptotic distributions and error rate risks being identical. 相似文献
3.
This work assumes that the small area quantities of interest follow a Fay–Herriot model with spatially correlated random area
effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared
error of the empirical best linear unbiased predictor (EBLUP). A simulation study based on the Italian Agriculture Census
2000 compares bootstrap and analytical estimates of the MSE and studies their robustness to non-normality. Results indicate
lower bias for the non-parametric bootstrap under specific departures from normality.
相似文献
4.
William C. Parr 《Statistics & probability letters》1985,3(2):97-100
The bootstrap, discussed by Efron (1979, 1981), is a powerful tool for the nonparametric estimation of sampling distributions and asymptotic standard errors. We demonstrate consistency of the bootstrap distribution estimates for a general class of robust differentiable statistical functionals. Our conditions for consistency of the bootstrap are variants of previously considered criteria for robustness of the associated statistics. A general example shows that, for almost any location statistic, consistency of the bootstrap variance estimator requires a tail condition on the distribution from which samples are taken. A modification of Efron's estimator of standard error is shown to circumvent this problem. 相似文献
5.
基于OLS估计残差,本文将Bootstrap方法用于空间误差相关性LM-Error检验,综合考虑Bootstrap模拟抽样次数、空间衔接结构以及样本量,研究并比较空间误差相关Bootstrap LM-Error检验与渐近检验的水平扭曲。大量Monte Carlo实验结果显示,当模型误差不满足独立正态分布的假设条件时,空间误差相关LM-Error渐近检验的水平扭曲较大,采用Bootstrap方法可以较好地降低该水平扭曲;不管模型误差是否满足独立正态分布的假设条件,Bootstrap方法均能够有效地降低LMError渐近检验的水平扭曲。 相似文献
6.
Hie-Choon Chung Chien-Pai Han 《Annals of the Institute of Statistical Mathematics》2000,52(3):544-556
We consider the problem of classifying a p× 1 observation into one of two multivariate normal populations when the training samples contain a block of missing observations. A new classification procedure is proposed which is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The new discriminant function is easy to use. We consider the estimation of error rate of the linear combination classification procedure by using the leave-one-out estimation and bootstrap estimation. A Monte Carlo study is conducted to evaluate the error rate and the estimation of it. A numerical example is given tof illustrate its use. 相似文献
7.
Bootstrap bandwidth selection in kernel density estimation from a contaminated sample 总被引:4,自引:0,他引:4
In this paper we consider kernel estimation of a density when the data are contaminated by random noise. More specifically
we deal with the problem of how to choose the bandwidth parameter in practice. A theoretical optimal bandwidth is defined
as the minimizer of the mean integrated squared error. We propose a bootstrap procedure to estimate this optimal bandwidth,
and show its consistency. These results remain valid for the case of no measurement error, and hence also summarize part of
the theory of bootstrap bandwidth selection in ordinary kernel density estimation. The finite sample performance of the proposed
bootstrap selection procedure is demonstrated with a simulation study. An application to a real data example illustrates the
use of the method.
This research was supported by ‘Projet d’Actions de Recherche Concertées’ (No. 98/03-217) from the Belgian government. Financial
support from the IAP research network nr P5/24 of the Belgian State (Federal Office for Scientific, Technical and Cultural
Affairs) is also gratefully acknowledged. 相似文献
8.
Chris Field John Robinson Elvezio Ronchetti 《Annals of the Institute of Statistical Mathematics》2008,60(1):225-227
We obtain marginal tail area approximations for the one-dimensional test statistic based on the appropriate component of the
M-estimate for both standardized and Studentized versions which are needed for tests and confidence intervals. The result is
proved under conditions which allow the application to finite sample situations such as the bootstrap and involves a careful
discretization with saddlepoints being used for each neighbourhood. These results are used to obtain second-order relative
error results on the accuracy of the Studentized and the tilted bootstrap. The tail area approximations are applied to a Poisson
regression model and shown to have very good accuracy.
An erratum to this article can be found at 相似文献
9.
Arnab Chakraborty 《Computational Statistics》2006,21(1):103-119
Summary Generating random samples from multivariate distributions is a common, requirement in many fields of study. Often the complete
joint distribution is not specified to the scientist. This paper addresses the situation where only the marginals and the
correlation matrix are specified. We suggest a deterministic algorithm, PERMCORR, to approximately achieve the required correlation structure that can be used to get good initial values to standard stochastic
algorithms. In many situations the output of PERMCORR is already accurate enough to preempt any need for running an expensive stochastic algorithm. We provide some theoretical
justification for our method as well as simulation studies. We also provide a bootstrap technique based on PERMCORR. 相似文献
10.
K. Y. Cheung Stephen M. S. Lee 《Annals of the Institute of Statistical Mathematics》2005,57(2):279-290
We consider the problem of estimating the variance of a sample quantile calculated from a random sample of sizen. Ther-th-order kernel-smoothed bootstrap estimator is known to yield an impressively small relative error of orderO(n
−r/(2r+1)
). It nevertheless requires strong smoothness conditions on the underlying density function, and has a performance very sensitive
to the precise choice of the bandwidth. The unsmoothed bootstrap has a poorer relative error of orderO(n
−1/4), but works for less smooth density functions. We investigate a modified form of the bootstrap, known as them out ofn bootstrap, and show that it yields a relative error of order smaller thanO(n
−1/4) under the same smoothness conditions required by the conventional unsmoothed bootstrap on the density function, provided
that the bootstrap sample sizem is of an appropriate order. The estimator permits exact, simulation-free, computation and has accuracy fairly insensitive
to the precise choice ofm. A simulation study is reported to provide empirical comparison of the various methods.
Supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU
7131/00P). 相似文献
11.
Random weighting method for Cox’s proportional hazards model 总被引:1,自引:0,他引:1
Variance of parameter estimate in Cox’s proportional hazards model is based on asymptotic variance. When sample size is small, variance can be estimated by bootstrap method. However, if censoring rate in a survival data set is high, bootstrap method may fail to work properly. This is because bootstrap samples may be even more heavily censored due to repeated sampling of the censored observations. This paper proposes a random weighting method for variance estimation and confidence interval estimation for proportional hazards model. This method, unlike the bootstrap method, does not lead to more severe censoring than the original sample does. Its large sample properties are studied and the consistency and asymptotic normality are proved under mild conditions. Simulation studies show that the random weighting method is not as sensitive to heavy censoring as bootstrap method is and can produce good variance estimates or confidence intervals. 相似文献
12.
Bootstrapping Log Likelihood and EIC, an Extension of AIC 总被引:1,自引:0,他引:1
Makio Ishiguro Yosiyuki Sakamoto Genshiro Kitagawa 《Annals of the Institute of Statistical Mathematics》1997,49(3):411-434
Akaike (1973, 2nd International Symposium on Information Theory, 267-281,Akademiai Kiado, Budapest) proposed AIC as an estimate of the expected loglikelihood to evaluate the goodness of models fitted to a given set of data.The introduction of AIC has greatly widened the range of application ofstatistical methods. However, its limit lies in the point that it can beapplied only to the cases where the parameter estimation are performed bythe maximum likelihood method. The derivation of AIC is based on theassessment of the effect of data fluctuation through the asymptoticnormality of MLE. In this paper we propose a new information criterion EICwhich is constructed by employing the bootstrap method to simulate the datafluctuation. The new information criterion, EIC, is regarded as an extensionof AIC. The performance of EIC is demonstrated by some numerical examples. 相似文献
13.
A. Muñoz-Reyes J. L. Moreno-Rebollo M. D. Jiménez-Gamero J. Munoz-Garcia 《Computational Statistics》2005,20(1):105-118
Summary Since it is not always possible to calculate bootstrap estimators, they are usually approximated by simulation. In this article,
we propose a bootstrap bias estimator for smooth functions of sample means that has less mean squared error, due to the simulation
process, than the ordinary bootstrap. The estimator is based on shrinking the bootstrap mean towards the original sample mean.
It can easily be implemented while demanding almost no additional computational effort. 相似文献
14.
Peter Hall Brett Presnell Berwin A. Turlach 《Annals of the Institute of Statistical Mathematics》2000,52(3):507-518
Jackknife and bootstrap bias corrections are based on a differencing argument which does not necessarily respect the sign of the true parameter value. Depending on sampling variability they can over-correct, producing a final estimator that is negative when one knows on physical grounds that it should be positive. To overcome this problem we suggest a simple, alternative bootstrap approach, based on biased-bootstrap methods. Our technique has similar properties to the standard uniform-bootstrap method in cases where the latter does not endanger sign, but it respects sign in a canonical way when the standard method disregards it. 相似文献
15.
16.
A bootstrap procedure useful in latent class, or more general mixture models has been developed to determine the sufficient number of latent classes or components required to account for systematic group differences in the data. The procedure is illustrated in the context of a multidimensional scaling latent class model, CLASCAL. Also presented is a bootstrap technique for determining standard errors for estimates of the stimulus co‐ordinates, parameters of the multidimensional scaling model. Real and artificial data are presented. The bootstrap procedure for selecting a sufficient number of classes seems to correctly select the correct number of latent classes at both low and high error levels. At higher error levels it outperforms Hope's (J. Roy. Statist. Soc. Ser B 1968; 30 : 582) procedure. The bootstrap procedures to estimate parameter stability appear to correctly re‐produce Monte Carlo results. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
17.
Arnold Janssen 《Annals of the Institute of Statistical Mathematics》2005,57(3):507-529
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. 相似文献
18.
This paper introduces a method of bootstrap wavelet estimation in a nonparametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes. 相似文献
19.
WANG Qihua 《数学物理学报(B辑英文版)》2000,20(4)
In this paper,the author studies the asymptotic accuracies of the one-term Edgeworth expansions and the bootstrap approximation for the studentized MLE from randomly censored exponential population.It is shown that the Edgeworth expansions and the bootstrap approximation are asymptotically close to the exact distribution of the studentized MLE with a rate. 相似文献
20.
Heteroscedasticity checks for regression models 总被引:1,自引:0,他引:1
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in
parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the
choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null
distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional,
the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap
approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap
is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed.
A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the
literature. The approach may readily be extended to handle partial linear, and linear autoregressive models. 相似文献