共查询到19条相似文献,搜索用时 46 毫秒
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本文首先讨论了纵向数据部分线性模型yij=xijβ+g(tij)+eij的可行广义最小二乘估计方法及其估计的渐近性质,然后通过统计模拟研究表明我们的估计方法在有限样本情形也有良好的效果.由该方法获得的估计量具有显示解,计算简便,便于实际应用. 相似文献
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作为部分线性模型与变系数模型的推广,部分线性变系数模型是一类应用广泛的半参数模型.文章主要研究该模型线性部分存在约束条件下的估计和检验问题,首先基于backfitting方法给出了常数系数以及变系数部分的约束估计,其次构造了检验统计量用于检验约束条件. 相似文献
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作为可加模型和部分线性模型的推广,部分线性可加模型是一类应用广泛的半参数模型。文章主要讨论了当线性部分的协变量测量含误差时模型的估计问题,我们基于profile全最小二乘法构造了参数分量和模型误差方差的估计,并证明了估计量的渐近正态性。 相似文献
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文章结合基函数逼近以及惩罚最小二乘技术,对响应变量随机缺失下的部分线性模型,给出了一个变量选择方法.并结合局部二次逼近,得到了一个迭代算法.数据模拟表明该变量选择方法是可行的. 相似文献
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文章首先对线性EV模型与正交最小一乘估计进行了简要介绍.对正交距离的绝对值作了一个近似处理,使得原先不可导的目标函数为一个光滑可导的函数,从而极大地方便了求解.并且通过与正交最小二乘估计、正交最小-乘估计及其算法比较,分析了这种光滑正交最小-乘的合理性和有用性. 相似文献
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本文将最小二乘支持向量机(LSSVM) 和二次推断函数法(QIF) 相结合,为个体内具有相关结构的固定效应部分线性变系数面板模型提供了一种新的快速估计方法;在一定的正则条件下,论证了参数估计量的渐近正态性和非参数估计量的收敛速度;采用Monte Carlo模拟考察了估计方法在有限样本下的表现并将估计技术应用于现实数据分析。该方法不仅保证了估计的有效性和统计推断力,而且程序运行速度得到较大幅度提升。 相似文献
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Sanzhi Shi 《统计学通讯:理论与方法》2013,42(10):1799-1816
In this article, we are concerned with whether the nonparametric functions are parallel from two partial linear models, and propose a test statistic to check the difference of the two functions. The unknown constant α is estimated by using moment method under null models. Nonparametric functions under both null and full models are estimated by using local linear method. The asymptotic properties of parametric and nonparametric components are derived. The test statistic under the null hypothesis is calculated and shown to be asymptotically normal. 相似文献
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As a compromise between parametric regression and nonparametric regression, partially linear models are frequently used in statistical modelling. This article considers statistical inference for this semiparametric model when the linear covariate is measured with additive error and some additional linear restrictions on the parametric component are assumed to hold. We propose a restricted corrected profile least-squares estimator for the parametric component, and study the asymptotic normality of the estimator. To test hypothesis on the parametric component, we construct a Wald test statistic and obtain its limiting distribution. Some simulation studies are conducted to illustrate our approaches. 相似文献
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A statistical test procedure is proposed to check whether the parameters in the parametric component of the partially linear spatial autoregressive models satisfy certain linear constraint conditions, in which a residual-based bootstrap procedure is suggested to derive the p-value of the test. Some simulations are conducted to assess the performance of the test and the results show that the bootstrap approximation to the null distribution of the test statistic is valid and the test is of satisfactory power. Furthermore, a real-world example is given to demonstrate the application of the proposed test. 相似文献
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Motivated by covariate-adjusted regression (CAR) proposed by Sentürk and Müller (2005) and an application problem, in this article we introduce and investigate a covariate-adjusted partially linear regression model (CAPLM), in which both response and predictor vector can only be observed after being distorted by some multiplicative factors, and an additional variable such as age or period is taken into account. Although our model seems to be a special case of covariate-adjusted varying coefficient model (CAVCM) given by Sentürk (2006), the data types of CAPLM and CAVCM are basically different and then the methods for inferring the two models are different. In this article, the estimate method motivated by Cui et al. (2008) is employed to infer the new model. Furthermore, under some mild conditions, the asymptotic normality of estimator for the parametric component is obtained. Combined with the consistent estimate of asymptotic covariance, we obtain confidence intervals for the regression coefficients. Also, some simulations and a real data analysis are made to illustrate the new model and methods. 相似文献
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This article considers statistical inference for the heteroscedastic partially linear varying coefficient models. We construct an efficient estimator for the parametric component by applying the weighted profile least-squares approach, and show that it is semiparametrically efficient in the sense that the inverse of the asymptotic variance of the estimator reaches the semiparametric efficiency bound. Simulation studies are conducted to illustrate the performance of the proposed method. 相似文献
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This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method. 相似文献
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In this article, we propose two test statistics for testing the underlying serial correlation in a partially linear single-index model Y = η(Z τα) + X τβ + ? when X is measured with additive error. The proposed test statistics are shown to have asymptotic normal or chi-squared distributions under the null hypothesis of no serial correlation. Monte Carlo experiments are also conducted to illustrate the finite sample performance of the proposed test statistics. The simulation results confirm that these statistics perform satisfactorily in both estimated sizes and powers. 相似文献
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Partially linear models are extensions of linear models that include a nonparametric function of some covariate allowing an adequate and more flexible handling of explanatory variables than in linear models. The difference-based estimation in partially linear models is an approach designed to estimate parametric component by using the ordinary least squares estimator after removing the nonparametric component from the model by differencing. However, it is known that least squares estimates do not provide useful information for the majority of data when the error distribution is not normal, particularly when the errors are heavy-tailed and when outliers are present in the dataset. This paper aims to find an outlier-resistant fit that represents the information in the majority of the data by robustly estimating the parametric and the nonparametric components of the partially linear model. Simulations and a real data example are used to illustrate the feasibility of the proposed methodology and to compare it with the classical difference-based estimator when outliers exist. 相似文献
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Chuanhua Wei 《统计学通讯:理论与方法》2013,42(7):1284-1298
This article considers statistical inference for partially linear varying-coefficient models when the responses are missing at random. We propose a profile least-squares estimator for the parametric component with complete-case data and show that the resulting estimator is asymptotically normal. To avoid to estimate the asymptotic covariance in establishing confidence region of the parametric component with the normal-approximation method, we define an empirical likelihood based statistic and show that its limiting distribution is chi-squared distribution. Then, the confidence regions of the parametric component with asymptotically correct coverage probabilities can be constructed by the result. To check the validity of the linear constraints on the parametric component, we construct a modified generalized likelihood ratio test statistic and demonstrate that it follows asymptotically chi-squared distribution under the null hypothesis. Then, we extend the generalized likelihood ratio technique to the context of missing data. Finally, some simulations are conducted to illustrate the proposed methods. 相似文献