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1.
关于变点个数及位置的检测和估计   总被引:10,自引:1,他引:9  
本文根据信息论准则研究变点问题,在模型选择的框架下,研究变点个数和变点位置的检测,证明当方差不同时,均值向量变点个数及变点位置估计的强相合性。  相似文献
2.
Sensitivity of a posterior quantity (f, P) to the choice of the sampling distribution f and prior P is considered. Sensitivity is measured by the range of (f, P) when f and P vary in nonparametric classes f and P respectively. Direct and iterative methods are described which obtain the range of (f, P) over f f when prior P is fixed, and also the overall range over f f and P P . When multiple i.i.d. observations X 1,...,X k are observed from f, the posterior quantity (f, P) is not a ratio-linear function of f. A method of steepest descent is proposed to obtain the range of (f, P). Several examples illustrate applications of these methods.  相似文献
3.
Evolving Time Series Forecasting ARMA Models   总被引:3,自引:0,他引:3  
Time Series Forecasting (TSF) allows the modeling of complex systems as black-boxes, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.  相似文献
4.
基于蒙特卡洛-马尔科夫链(MCMC)的ARMA模型选择   总被引:2,自引:0,他引:2  
AIC与SIC等准则函数方法是ARMA模型选择过程中经常使用的方法。但是,当模型的阶数很高时,无法计算并比较每一个备选模型的准则函数值。本文提出了一个基于蒙特卡洛-马尔科夫链方法的随机模型生成方法,以产生准则函数值最小的备选模型。实际应用表明本文的方法在处理拥有大量备选模型的ARMA模型选择问题时有很好的效果。  相似文献
5.
孙道德 《数学杂志》2001,21(1):71-78
线性回归模型的建立,一个很重要的过程就是自变元的选择,它直接决定着模型的优劣。本文给出了“宜取回归方程”的逐步回归方法,在一组适当的条件下,证明了这种方法的强相合性。  相似文献
6.
Bootstrapping Log Likelihood and EIC, an Extension of AIC   总被引:1,自引:0,他引:1  
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
7.
Model selection for regression on a fixed design   总被引:1,自引:0,他引:1  
We deal with the problem of estimating some unknown regression function involved in a regression framework with deterministic design points. For this end, we consider some collection of finite dimensional linear spaces (models) and the least-squares estimator built on a data driven selected model among this collection. This data driven choice is performed via the minimization of some penalized model selection criterion that generalizes on Mallows' C p . We provide non asymptotic risk bounds for the so-defined estimator from which we deduce adaptivity properties. Our results hold under mild moment conditions on the errors. The statement and the use of a new moment inequality for empirical processes is at the heart of the techniques involved in our approach. Received: 2 July 1997 / Revised version: 20 September 1999 / Published online: 6 July 2000  相似文献
8.
一种通用的基于梯度的SVM核参数选取算法   总被引:1,自引:0,他引:1  
核函数的选取是SVM分类器选取的核心问题.核函数的自动选取既可以提高分类器的性能,又可以减少人为的干预.因此如何自动选取核函数已经成为SVM的热点问题,但是这个问题并没有获得很好的解决.近年来对核函数参数的自动选取的研究,特别是对基于梯度的优化算法的研究取得了一定的进展.提出了一种基于梯度的核函数选取的通用算法,并进行了实验.  相似文献
9.
10.
Estimating copula densities, using model selection techniques   总被引:1,自引:0,他引:1  
Recently a new way of modeling dependence has been introduced considering a sequence of parametric copula models, covering more and more dependency aspects and thus giving a closer approximation to the true copula density. The method uses contamination families based on Legendre polynomials. It has been shown that in general after a few steps accurate approximations are obtained. In this paper selection of the adequate number of steps is considered, and estimation of the unknown parameters within the chosen contamination family is established, thus obtaining an estimator of the unknown copula density. There should be a balance between the complexity of the model and the number of parameters to be estimated. High complexity gives a low model error, but a large stochastic or estimation error, while a very simple model gives a small stochastic error, but a large model error. Techniques from model selection are applied, thus letting the data tell us which aspects are important enough to capture into the model. Natural and simple estimators of the involved Fourier coefficients complete the procedure. Theoretical results show that the expected quadratic error is reduced by the selection rule to the same order of magnitude as in a classical parametric problem. The method is applied on a real data set, illustrating that the new method describes the data set very well: the error involved in the classical Gaussian copula density is reduced with no fewer than 50%.  相似文献
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