共查询到20条相似文献,搜索用时 31 毫秒
1.
This paper proposes a method to estimate the conditional quantile function using an epsilon-insensitive loss in a reproducing kernel Hilbert space. When choosing a smoothing parameter in nonparametric frameworks, it is necessary to evaluate the complexity of the model. In this regard, we provide a simple formula for computing an effective number of parameters when implementing an epsilon-insensitive loss. We also investigate the effects of the epsilon-insensitive loss. 相似文献
2.
R. Farnoosh S.J. Mortazavi 《Nonlinear Analysis: Theory, Methods & Applications》2011,74(17):6358-6370
The first-order nonlinear autoregressive model is considered and a semiparametric method is proposed to estimate regression function. In the presented model, dependent errors are defined as first-order autoregressive AR(1). The conditional least squares method is used for parametric estimation and the nonparametric kernel approach is applied to estimate regression adjustment. In this case, some asymptotic behaviors and simulated results for the semiparametric method are presented. Furthermore, the method is applied for the financial data in Iran’s Tejarat-Bank. 相似文献
3.
A. W. Bowman M. C. Jones I. Gijbels 《Journal of computational and graphical statistics》2013,22(4):489-500
Abstract This article provides a test of monotonicity of a regression function. The test is based on the size of a “critical” bandwidth, the amount of smoothing necessary to force a nonparametric regression estimate to be monotone. It is analogous to Silverman's test of multimodality in density estimation. Bootstrapping is used to provide a null distribution for the test statistic. The methodology is particularly simple in regression models in which the variance is a specified function of the mean, but we also discuss in detail the homoscedastic case with unknown variance. Simulation evidence indicates the usefulness of the method. Two examples are given. 相似文献
4.
Yong Zhou Dao-ji Li 《应用数学学报(英文版)》2006,22(3):353-368
In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models (GLMs), when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the (conditional) variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the (conditional) variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance. 相似文献
5.
Minggen Lu 《Journal of multivariate analysis》2010,101(10):2528-2542
We study a spline-based likelihood method for the partly linear model with monotonicity constraints. We use monotone B-splines to approximate the monotone nonparametric function and apply the generalized Rosen algorithm to compute the estimators jointly. We show that the spline estimator of the nonparametric component achieves the possible optimal rate of convergence under the smooth assumption and that the estimator of the regression parameter is asymptotically normal and efficient. Moreover, a spline-based semiparametric likelihood ratio test is established to make inference of the regression parameter. Also an observed profile information method to consistently estimate the standard error of the spline estimator of the regression parameter is proposed. A simulation study is conducted to evaluate the finite sample performance of the proposed method. The method is illustrated by an air pollution study. 相似文献
6.
Rob J. Hyndman David M. Bashtannyk Gary K. Grunwald 《Journal of computational and graphical statistics》2013,22(4):315-336
Abstract We consider the kernel estimator of conditional density and derive its asymptotic bias, variance, and mean-square error. Optimal bandwidths (with respect to integrated mean-square error) are found and it is shown that the convergence rate of the density estimator is order n –2/3. We also note that the conditional mean function obtained from the estimator is equivalent to a kernel smoother. Given the undesirable bias properties of kernel smoothers, we seek a modified conditional density estimator that has mean equivalent to some other nonparametric regression smoother with better bias properties. It is also shown that our modified estimator has smaller mean square error than the standard estimator in some commonly occurring situations. Finally, three graphical methods for visualizing conditional density estimators are discussed and applied to a data set consisting of maximum daily temperatures in Melbourne, Australia. 相似文献
7.
We consider the nonparametric estimation problem of conditional regression quantiles with high-dimensional covariates. For
the additive quantile regression model, we propose a new procedure such that the estimated marginal effects of additive conditional
quantile curves do not cross. The method is based on a combination of the marginal integration technique and non-increasing
rearrangements, which were recently introduced in the context of estimating a monotone regression function. Asymptotic normality
of the estimates is established with a one-dimensional rate of convergence and the finite sample properties are studied by
means of a simulation study and a data example. 相似文献
8.
本文在{Xr,t∈N)是一个严平稳过程的假设下,用核估计的方法对未来状态XN+T的条件密度进行估计.在假设{Xt,t∈N)是α-混合过程的情况下,讨论了过程有限维密度核估计的期望与方差,以及过程条件密度核估计的偏及均方误差.在一定条件下,证明了估计的弱收敛性. 相似文献
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11.
In this paper, two new tests for heteroscedasticity in nonparametric regression are presented and compared. The first of these
tests consists in first estimating nonparametrically the unknown conditional variance function and then using a classical
least-squares test for a general linear model to test whether this function is a constant. The second test is based on using
an overall distance between a nonparametric estimator of the conditional variance function and a parametric estimator of the
variance of the model under the assumption of homoscedasticity. A bootstrap algorithm is used to approximate the distribution
of this test statistic. Extended versions of both procedures in two directions, first, in the context of dependent data, and
second, in the case of testing if the variance function is a polynomial of a certain degree, are also described. A broad simulation
study is carried out to illustrate the finite sample performance of both tests when the observations are independent and when
they are dependent. 相似文献
12.
??In this paper, semiparametric estimation of a regression function in the third order partially linear autoregressive model with first order autoregressive errors is mainly studied. We suppose that the regression function has a parametric framework, and use the conditional least squares method to obtain the parameter estimators. Then semiparametric estimators of the regression function can be given by combining with the nonparametric kernel function adjustment. Furthermore, under certain conditions, the consistency of the estimators is proved. Finally, simulation research is presented to evaluate the
effectiveness of the proposed method. 相似文献
13.
In this paper, a fixed design regression model where the errors follow a strictly stationary process is considered. In this model the conditional mean function and the conditional variance function are unknown curves. Correlated errors when observations are missing in the response variable are assumed. Four nonparametric estimators of the conditional variance function based on local polynomial fitting are proposed. Expressions of the asymptotic bias and variance of these estimators are obtained. A simulation study illustrates the behavior of the proposed estimators. 相似文献
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This paper studies the estimation of change point in mean and variance function of a non-parametric regression model based on kernel estimation and wavelet method. First, kernel estimation of mean function is developed and it is used to estimate the position and jump size of mean change. Second, wavelet methods are applied to derive the variance estimator which is used to estimate the location and jump size of the change point in variance. The asymptotic properties of these estimators are proved. Finally, the results from a numerical simulations and comparison study show that validate the effectiveness of our method. 相似文献
16.
Aboubacar Amiri 《Statistical Inference for Stochastic Processes》2013,16(2):81-96
The main purpose of this paper is to estimate the regression function by using a recursive nonparametric kernel approach. We derive the asymptotic normality for a general class of recursive kernel estimate of the regression function, under strong mixing conditions. Our purpose is to extend the work of Roussas and Tran (Ann Stat 20:98–120, 1992) concerning the Devroye–Wagner estimate. 相似文献
17.
基于非参数函数的核估计,构造了部分线性自回归模型中误差四阶矩的相合估计,从而给出了误差方差核估计的渐近正态性,并通过模拟算例和实例说明了其应用. 相似文献
18.
Modal regression based on nonparametric quantile estimator is given. Unlike the traditional mean and median regression, modal regression uses mode but not mean or median to represent the center of a conditional distribution, which helps the model to be more robust for outliers, asymmetric or heavy-taileddistribution. Most of solutions for modal regression are based on kernel estimation of density. This paper studies a new solution for modal regression by means of nonparametric quantile estimator. This method builds on the fact that the distribution function is the inverse of the quantile function, then the flexibility of nonparametric quantile estimator is utilized to improve the estimation of modal function. The simulations and application show that the new model outperforms the modal regression model via linear quantile function estimation. 相似文献
19.
C. Durot 《Mathematical Methods of Statistics》2008,17(4):327-341
In this paper we study the nonparametric least squares estimator of a regression function in a random design setting under
the constraint that this function is monotone, say, nonincreasing. The errors are not assumed conditionally i.i.d. given the
observation points. In particular, this includes the case of conditional heteroscedasticity and the case of the current status
model. The -error is shown to be of order n
−p/3 and asymptotically Gaussian with explicit asymptotic mean and variance.
相似文献
20.
Jacobo de Uña-Álvarez M. Carmen Iglesias-Pérez 《Annals of the Institute of Statistical Mathematics》2010,62(2):323-341
In this paper we consider the problem of estimating a conditional distribution function in a nonparametric way, when the response
variable is nonnegative, and the observational procedure is length-biased. We propose a proper adaptation of the estimate
to right-censoring provoked by limitation in following-up. Large sample analysis of the introduced estimator is given, including
rates of convergence, limiting distribution, and efficiency results. We show that the length-bias model results in less variance
in estimation, when compared to methods based on observed truncation times. Practical performance of the proposed estimator
is explored through simulations. Application to unemployment data analysis is provided. 相似文献