共查询到20条相似文献,搜索用时 656 毫秒
1.
In this paper,we propose a class of varying coefcient 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 efcient generalized least squares series estimation for the unknown coefcient functions.The consistency and asymptotic normality of the resulting estimators are established.In comparison with the ordinary least squares ones,the proposed estimators are more efcient with smaller asymptotical variances.Some simulation 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
2.
受实际问题研究的启发, 为减少模型偏差, 提出了一类半相依部分线性可加的半参数回归模型. 这类半相依模型中, 响应变量与
一部分解释变量之间的关系是线性的, 与另一部分解释变量之间的关系未知但具有可加结构, 各方程的误差之间是相关的. 将级
数逼近法、最小二乘法和同期相关的估计结合起来, 提出了用于估计模型参数分量的加权半参数最小二乘估计量(WSLSEs), 和用于估
计模型非参数分量的加权级数逼近估计量(WSEs). 证明了这些加权的估计量比相应的不加权的估计量渐近有效, 并导出了相应的渐近正态性.
另外, 还讨论了利用这些估计量的渐近性质来对模型的参数及非参数分量作统计推断. 用大量的模拟实验考察
了所提出的方法在有限样本情况下的表现, 并对美国的一个关于妇女工资问题的全国纵向调查(NLS)数据集进行了统计分析. 相似文献
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4.
Asymptotic properties of the least squares estimators of the parameters of the chirp signals 总被引:1,自引:0,他引:1
Chirp signals are quite common in different areas of science and engineering. In this paper we consider the asymptotic properties
of the least squares estimators of the parameters of the chirp signals. We obtain the consistency property of the least squares
estimators and also obtain the asymptotic distribution under the assumptions that the errors are independent and identically
distributed. We also consider the generalized chirp signals and obtain the asymptotic properties of the least squares estimators
of the unknown parameters. Finally we perform some simulations experiments to see how the asymptotic results behave for small
sample and the performances are quite satisfactory. 相似文献
5.
���ܻԡ���Ф��ʩ�ŷ� 《应用概率统计》2018,34(2):111-134
This paper concerns with the estimation of a fixed effects panel data partially linear regression model with the idiosyncratic errors being an autoregressive process. For fixed effects short time series panel data, the commonly used autoregressive error structure fitting method will not result in a consistent estimator of the autoregressive coefficients. Here we propose an alternative estimation and show that the resulting estimator of the autoregressive coefficients is consistent
and this method is workable for any order autoregressive error structure. Moreover, combining the B-spline approximation, profile least squares dummy variable (PLSDV) technique and consistently estimated the autoregressive error structure, we develop a weighted PLSDV estimator for the parametric component and a weighted B-spline series (BS) estimator for the nonparametric component. The weighted PLSDV estimator is shown to be asymptotically normal and more asymptotically efficient than the one which ignores the error autoregressive structure. In addition, this paper derives the asymptotic bias of the weighted BS estimator and establish its asymptotic normality as well. Simulation studies and an example of application are conducted to illustrate the finite sample performance of the proposed procedures. 相似文献
6.
Recently, Kundu and Gupta (Metrika, 48:83 C 97, 1998) established the asymptotic normality of the least squares estimators in the two dimensional cosine model. In this paper, we give the approximation to the general least squares estimators by using random weights which is called the Bayesian bootstrap or the random weighting method by Rubin (Annals of Statistics, 9:130 C 134, 1981) and Zheng (Acta Math. Appl. Sinica (in Chinese), 10(2): 247 C 253, 1987). A simulation study shows that this approximation works very well. 相似文献
7.
In this paper we study the problem of estimating the drift/viscosity coefficient for a large class of linear, parabolic stochastic partial differential equations (SPDEs) driven by an additive space-time noise. We propose a new class of estimators, called trajectory fitting estimators (TFEs). The estimators are constructed by fitting the observed trajectory with an artificial one, and can be viewed as an analog to the classical least squares estimators from the time-series analysis. As in the existing literature on statistical inference for SPDEs, we take a spectral approach, and assume that we observe the first N Fourier modes of the solution, and we study the consistency and the asymptotic normality of the TFE, as \(N\rightarrow \infty \). 相似文献
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9.
Li-ping ZHU~ Zhou YU Department of Statistics East China Normal University Shanghai China 《中国科学A辑(英文版)》2007,50(9):1289-1302
The dimension reduction is helpful and often necessary in exploring the nonparametric regression structure.In this area,Sliced inverse regression (SIR) is a promising tool to estimate the central dimension reduction (CDR) space.To estimate the kernel matrix of the SIR,we herein suggest the spline approximation using the least squares regression.The heteroscedasticity can be incorporated well by introducing an appropriate weight function.The root-n asymptotic normality can be achieved for a wide range choice of knots.This is essentially analogous to the kernel estimation.Moreover, we also propose a modified Bayes information criterion (BIC) based on the eigenvalues of the SIR matrix.This modified BIC can be applied to any form of the SIR and other related methods.The methodology and some of the practical issues are illustrated through the horse mussel data.Empirical studies evidence the performance of our proposed spline approximation by comparison of the existing estimators. 相似文献
10.
Chin-Tsang Chiang 《Annals of the Institute of Statistical Mathematics》2005,57(4):637-653
In this paper, we study the properties of the simultaneous and componentwise splines for the varying coefficient model with
repeatedly measured (longitudinal) dependent variable and time invariant covariates. The proposed simultaneous smoothing spline
estimators are mainly obtained from the penalized least squares with adjustment for the variations of covariates in the penalized
terms. We do this mainly to avoid the penalized terms being influenced by the scales of the covariates and the random smoothing
parameters appearing in the estimators, which complicates the derivation of the asymptotic properties of the estimators. It
is shown in this study that our estimators have smaller variances than the componentwise ones. Through a Monte Carlo simulation
and two empirical examples, the simultaneous smoothing splines are all found to be more accurate in the variances. 相似文献
11.
In many real-world problems, observations are usually described by approximate values due to fuzzy uncertainty, unlikeprobabilistic uncertainty that has nothing to do with experimentation. The combination of statistical model and fuzzy set theory is helpful to improve the identification and analysis of complex systems. As an extension ofstatistical techniques, this study is an investigation of the relationship between fuzzy multiple explanatory variables and fuzzy response with numeric coefficients and the fuzzy random error term. In this work we describe a parameter estimation procedure carrying out the least-squares method in a
complete metric space of fuzzy numbers to determine the coefficients based on the extension principle. We demonstrate how the fuzzy least squares
estimators present large sample statistical properties, including asymptotic normality, strong consistency and confidence region. The estimators are also examined via asymptotic relative efficiency concerning traditional least squares estimators. Different from the construction of error term in Kim et
al.\cite{21}, it is more reasonable in the proposed model since the problems of inconsistency in referring to fuzzy variable and producing the negative spreads may be avoided. The experimental study verifies that the proposed fuzzy least squares estimators achieve the meaning consistent with the theory identification for large sample data set and better generalization regarding one single variable model. 相似文献
12.
本文提出对季节性时间序列利用加权对称估计量的单位根检验,导出相应统计量的极限分布。用MonteCarlo方法计算经验百分位数及检验势,并对最小平方估计量,简单对称估计量和加权对称估计量的经验检验势作了比较。 相似文献
13.
Varying index coefficient models (VICMs) proposed by Ma and Song (J Am Stat Assoc, 2014. doi: 10.1080/01621459.2014.903185) are a new class of semiparametric models, which encompass most of the existing semiparametric models. So far, only the profile least squares method and local linear fitting were developed for the VICM, which are very sensitive to the outliers and will lose efficiency for the heavy tailed error distributions. In this paper, we propose an efficient and robust estimation procedure for the VICM based on modal regression which depends on a bandwidth. We establish the consistency and asymptotic normality of proposed estimators for index coefficients by utilizing profile spline modal regression method. The oracle property of estimators for the nonparametric functions is also established by utilizing a two-step spline backfitted local linear modal regression approach. In addition, we discuss the bandwidth selection for achieving better robustness and efficiency and propose a modified expectation–maximization-type algorithm for the proposed estimation procedure. Finally, simulation studies and a real data analysis are carried out to assess the finite sample performance of the proposed method. 相似文献
14.
Peter J. Brockwell Richard A. Davis A. Alexandre Trindade 《Journal of multivariate analysis》2004,90(2):1934-347
We establish consistency and derive asymptotic distributions for estimators of the coefficients of a subset vector autoregressive (SVAR) process. Using a martingale central limit theorem, we first derive the asymptotic distribution of the subset least squares (LS) estimators. Exploiting the similarity of closed form expressions for the LS and Yule–Walker (YW) estimators, we extend the asymptotics to the latter. Using the fact that the subset Yule–Walker and recently proposed Burg estimators satisfy closely related recursive algorithms, we then extend the asymptotic results to the Burg estimators. All estimators are shown to have the same limiting distribution. 相似文献
15.
We focus on nonparametric multivariate regression function estimation by locally weighted least squares. The asymptotic behavior for a sequence of error processes indexed by bandwidth matrices is derived. We discuss feasible data-driven consistent estimators minimizing asymptotic mean squared error or efficient estimators reducing asymptotic bias at points where opposite sign curvatures of the regression function are present in different directions. 相似文献
16.
Adaptive Unified Biased Estimators of Parameters in Linear Model 总被引:1,自引:0,他引:1
HuYang Li-xingZhu 《应用数学学报(英文版)》2004,20(3):425-432
To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biasedestimators such as the time-honored Stein estimator,the ridge and the principal component estimators havebeen studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some sufficient conditions are proposed in the literature.In this paper,we propose a unified framework toformulate a class of adaptive biased estimators.This class includes all existing biased estimators and some newones.A sufficient condition for outperforming the least squares estimator is proposed.In terms of selectingparameters in the condition,we can obtain all double-type conditions in the literature. 相似文献
17.
In this paper, we consider a family of feasible generalised double k-class estimators in a linear regression model with non-spherical disturbances. We derive the large sample asymptotic distribution of the proposed family of estimators and compare its performance with the feasible generalized least squares and Stein-rule estimators using the mean squared error matrix and risk under quadratic loss criteria. A Monte-Carlo experiment investigates the finite sample behaviour of the proposed family of estimators. 相似文献
18.
In this paper, we consider the statistical inference for the partially liner varying coefficient model with measurement error in the nonparametric part when some prior information about the parametric part is available. The prior information is expressed in the form of exact linear restrictions. Two types of local bias-corrected restricted profile least squares estimators of the parametric component and nonparametric component are conducted, and their asymptotic properties are also studied under some regularity conditions. Moreover, we compare the efficiency of the two kinds of parameter estimators under the criterion of Lo?ner ordering. Finally, we develop a linear hypothesis test for the parametric component. Some simulation studies are conducted to examine the finite sample performance for the proposed method. A real dataset is analyzed for illustration. 相似文献
19.
A generalization of classical linear models is varying coefficient
models, which offer a flexible approach to modeling nonlinearity between covariates. A
method of local weighted composite quantile regression is suggested to estimate the
coefficient functions. The local Bahadur representation of the local estimator is derived
and the asymptotic normality of the resulting estimator is established. Comparing to the
local least squares estimator, the asymptotic relative efficiency is examined for the local
weighted composite quantile estimator. Both theoretical analysis and numerical simulations
reveal that the local weighted composite quantile estimator can obtain more efficient than
the local least squares estimator for various non-normal errors. In the normal error case,
the local weighted composite quantile estimator is almost as efficient as the local least
squares estimator. Monte Carlo results are consistent with our theoretical findings. An
empirical application demonstrates the potential of the proposed method. 相似文献
20.
本文研究了一类半参数回归模型,利用稳健补偿最小二乘估计法,得到了稳健补偿最小二乘估计量,以及它们的影响函数及渐近方差一协方差,对结果的分析表明了该法优于补偿最小二乘法,而且具有稳定性. 相似文献