共查询到20条相似文献,搜索用时 0 毫秒
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本文探讨具有不同光滑变量的变系数模型的建模、估计和估计的渐近性.首先,从实际出发建立模型;然后,使用局部线性方法给出模型中未知函数的初始估计,再使用平均方法,给出它们的平均估计;进—步,给出这些平均估计的渐近正态性.两个模拟例子说明这一估计方法是有效的. 相似文献
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A new estimation procedure based on modal regression is proposed for single-index varying-coefficient models. The proposed method achieves better robustness and efficiency than that of Xue and Pang (2013). We establish the asymptotic normalities of proposed estimators and evaluate the performance of the proposed method by a numerical simulation. 相似文献
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Varying coefficient error-in-covariables models are considered with surrogate data and validation sampling. Without specifying any error structure equation, two estimators for the coefficient function vector are suggested by using the local linear kernel smoothing technique. The proposed estimators are proved to be asymptotically normal. A bootstrap procedure is suggested to estimate the asymptotic variances. The data-driven bandwidth selection method is discussed. A simulation study is conducted to evaluate the proposed estimating methods. 相似文献
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In this paper we consider the problem of estimating an unknown joint distribution which is defined over mixed discrete and continuous variables. A nonparametric kernel approach is proposed with smoothing parameters obtained from the cross-validated minimization of the estimator's integrated squared error. We derive the rate of convergence of the cross-validated smoothing parameters to their ‘benchmark’ optimal values, and we also establish the asymptotic normality of the resulting nonparametric kernel density estimator. Monte Carlo simulations illustrate that the proposed estimator performs substantially better than the conventional nonparametric frequency estimator in a range of settings. The simulations also demonstrate that the proposed approach does not suffer from known limitations of the likelihood cross-validation method which breaks down with commonly used kernels when the continuous variables are drawn from fat-tailed distributions. An empirical application demonstrates that the proposed method can yield superior predictions relative to commonly used parametric models. 相似文献
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In this paper jackknifing technique is examined for functions of the parametric component in a partially linear regression model with serially correlated errors. By deleting partial residuals a jackknife-type estimator is proposed. It is shown that the jackknife-type estimator and the usual semiparametric least-squares estimator (SLSE) are asymptotically equivalent. However, simulation shows that the former has smaller biases than the latter when the sample size is small or moderate. Moreover, since the errors are correlated, both the Tukey type and the delta type jackknife asymptotic variance estimators are not consistent. By introducing cross-product terms, a consistent estimator of the jackknife asymptotic variance is constructed and shown to be robust against heterogeneity of the error variances. In addition, simulation results show that confidence interval estimation based on the proposed jackknife estimator has better coverage probability than that based on the SLSE, even though the latter uses the information of the error structure, while the former does not. 相似文献
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本文讨论在数据是强相依的情况下函数系数部分线性模型的估计.首先,采用局部线性方法,给出该模型函数项函数的估计;然后,使用两阶段方法给出系数函数的估计.并且讨论了函数项函数估计的渐近正态性,以及系数函数估计的弱相合性和渐近正态性.模拟研究显示,这些估计是较为理想的. 相似文献
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GUI HengjianDepartment of Mathematics Beijing Normal University Beijing China 《中国科学A辑(英文版)》2004,47(4):628-639
We consider a robust estimator (t-type regression estimator) of multiple linear regression model by maximizing marginal likelihood of a scaled t-type error t-distribution. The marginal likelihood can also be applied to the de-correlated response when the within-subject correlation can be consistently estimated from an initial estimate of the model based on the independent working assumption. This paper shows that such a t-type estimator is consistent. 相似文献
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Pavel Čížek 《Applications of Mathematics》2008,53(3):267-279
The paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator
which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals
obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves
robust properties of the initial robust estimate. However, contrary to the existing methods, the first-order asymptotic behavior
of 2S-LWS is fully independent of the initial estimate under mild conditions. We propose data-adaptive weighting schemes that
perform well both in the cross-section and time-series data and prove the asymptotic normality and efficiency of the resulting
procedure. A simulation study documents these theoretical properties in finite samples. 相似文献
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The quantile estimation methods are proposed for functional-coefficient partially linear regression (FCPLR) model by combining
nonparametric and functional-coefficient regression (FCR) model. The local linear scheme and the integrated method are used
to obtain local quantile estimators of all unknown functions in the FCPLR model. These resulting estimators are asymptotically
normal, but each of them has big variance. To reduce variances of these quantile estimators, the one-step backfitting technique
is used to obtain the efficient quantile estimators of all unknown functions, and their asymptotic normalities are derived.
Two simulated examples are carried out to illustrate the proposed estimation methodology. 相似文献
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ZHOU Xiuqing & WANG Jinde School of Mathematics Computer Science Nanjing Normal University Nanjing China. Department of Mathematics Nanjing University Nanjing China 《中国科学A辑(英文版)》2005,48(7):880-897
The least absolute deviations (LAD) estimation for nonlinear regression models with randomly censored data is studied and the asymptotic properties of LAD estimators such as consistency, boundedness in probability and asymptotic normality are established. Simulation results show that for the problems with censored data, LAD estimation performs much more robustly than the least squares estimation. 相似文献
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In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporaneous correlations into account, we propose an efficient generalized least squares series estimation for the unknown coefficient functions. The consistency and asymptotic normality of the resulting estimators are established. In comparison with the ordinary/east squares ones, the proposed estimators are more efficient with smaller asymptotical variances. Some simulgtlon'studies and a real application are presented to demonstrate the finite sample performance of the proposed methods. In addition, based on a B-spline approximation, we deduce the asymptotic bias and variance of the proposed estimators. 相似文献
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We treat with the r-k class estimation in a regression model, which includes the ordinary least squares estimator, the ordinary ridge regression estimator and the principal component regression estimator as special cases of the r-k class estimator. Many papers compared total mean square error of these estimators. Sarkar (1989, Ann. Inst. Statist. Math., 41, 717–724) asserts that the results of this comparison are still valid in a misspecified linear model. We point out some confusions of Sarkar and show additional conditions under which his assertion holds. 相似文献
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The authors consider various procedures for testing the hypotheses of independence of two sets of variables and certain regression coefficients are zero under multivariate regression model. Various properties of these procedures and the asymptotic distributions associated with these procedures are also considered. 相似文献
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We focus on the problem of simultaneous variable selection and estimation for nonlinear models based on modal regression (MR), when the number of coefficients diverges with sample size. With appropriate selection of the tuning parameters, the resulting estimator is shown to be consistent and to enjoy the oracle properties. 相似文献
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Jie Li DING Xi Ru CHEN 《数学学报(英文版)》2006,22(6):1679-1686
For generalized linear models (GLM), in case the regressors are stochastic and have different distributions, the asymptotic properties of the maximum likelihood estimate (MLE) β^n of the parameters are studied. Under reasonable conditions, we prove the weak, strong consistency and asymptotic normality of β^n 相似文献
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A great deal of effort has been devoted to the inference of additive model in the last decade. Among existing procedures,
the kernel type are too costly to implement for high dimensions or large sample sizes, while the spline type provide no asymptotic
distribution or uniform convergence. We propose a one step backfitting estimator of the component function in an additive
regression model, using spline estimators in the first stage followed by kernel/local linear estimators. Under weak conditions,
the proposed estimator’s pointwise distribution is asymptotically equivalent to an univariate kernel/local linear estimator,
hence the dimension is effectively reduced to one at any point. This dimension reduction holds uniformly over an interval
under assumptions of normal errors. Monte Carlo evidence supports the asymptotic results for dimensions ranging from low to
very high, and sample sizes ranging from moderate to large. The proposed confidence band is applied to the Boston housing
data for linearity diagnosis.
Supported in part by NSF awards DMS 0405330, 0706518, BCS 0308420 and SES 0127722. 相似文献
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Ritei Shibata 《Annals of the Institute of Statistical Mathematics》1986,38(1):459-474
Summary A generalized Final Prediction Error (FPEα)_ criterion is considered. Based onn observations, the numberk of regression variables is selected from a given range 0≦k≦K, so as to minimize
. It is shown that if α tends to infinity withn, the selection is consistent but the maximum of the mean squared error of estimates of parameters diverges to infinity with
the same order of divergence as that of α. A meaningful minimax choice of α exists for a regret type mean squared error, while
for simple mean squared error it is trivially 0. The minimax regret choice of α converges to a constant, approximately 3.5
forK≧8 ifn−K increases simultaneously withn, otherwise it diverges to infinity withn. 相似文献
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In this paper we propose nonparametric estimates of the regression function and its derivative when it is only assumed a weak error's structure. We study their local and global asymptotic behaviour when we observe dependent trajectories. 相似文献