检索结果-南通市图书馆

Testing heteroscedasticity in nonparametric regression models based on residual analysis

在线全文

学校读者我要写书评

暂无评论

Applied Mathematics(A Journal of Chinese Universities) 2008年第3期23卷 265-272页

作者： ZHANG Lei MEI Chang-lin School of Science Xi＇an Jiaotong University Xi＇an 710049 China Xinhua News Agency Beijing100803 China School of Science Xi＇an Jiaotong University Xi＇an 710049 China

The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this paper, a simple test for heteroscedasticity is proposed in nonparametric regression based on residual analysis. Furthermore, some simulations with a comparison with Dette and Munk＇s method are conducted to evaluate the performance of the proposed test. The results demonstrate that the method in this paper performs quite satisfactorily and is much more powerful than Dette and Munk＇s method in some cases.

关键词： heteroscedasticity nonparametric regression residual analysis

Robust Depth-Weighted Wavelet for nonparametric regression Models

在线全文

学校读者我要写书评

暂无评论

Acta Mathematica Sinica,English Series 2005年第3期21卷 585-592页

作者： Lu LIN School of Mathematics and Systems Science Shandong University Ji'nan 250100 P. R. China

In the nonparametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.

关键词： nonparametric regression Wavelet Statistical depth Robustness

OPTIMAL　GLOBAL　RATES　OF　CONVERGENCE　OF　M－ESTIMATES　FOR　MULTIVARIATE　nonparametric　regression

在线全文

学校读者我要写书评

暂无评论

Acta Mathematicae Applicatae Sinica 1995年第2期11卷 139-151页

作者：施沛德王学仁 Institute of Systems Science the Chinese Academy of Sciences Beijing China Institute of Applied Statistics Yunnan University Kunming China

Consider the nonparametric regression model Y=go(T)+u, where Y is real-valued, u is a random error, T is a random d-vector of explanatory variables ranging over a nondegenerate d-dimensional compact set C, and go(·) is the unknown smooth regression function, which is m (0) times continuously differentiable and its mth partial derivatives satisfy the Hǒlder condition with exponent γ∈(0,1], where i1, . . . , id are nonnegative integers satisfying ik=m. The piecewise polynomial estimator of go based on M-estimates is considered. It is proved that the rate of convergence of the underlying estimator is Op () under certain regular conditions, which is the optimal global rate of convergence of least square estimates for nonparametric regression studied in [10-11] .

关键词： nonparametric regression global rate of convergence piecewise polynomial Mestimates

Using Evolutionary Computation to Solve Problems in nonparametric regression

在线全文

学校读者我要写书评

暂无评论

Wuhan University Journal of Natural Sciences 1998年第1期3卷 27-31页

作者： Ding Lixin Kang Lishan Chen Yuping Pan Zhengjun

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.

关键词： nonparametric regression Nearest Neighbour Estimate evolutionary computation nonhomogeneous selection adaptive model-building

ASYMPTOTICS OF THE RESIDUALS DENSITY ESTIMATION IN nonparametric regression UNDER m(n)-DEPENDENT SAMPLE

在线全文

学校读者我要写书评

暂无评论

Applied Mathematics(A Journal of Chinese Universities) 1996年第1期11卷 59-76页

作者： QIN GENGSHENG SHI SUNJUAN CHAI GENXIANG Department of Mathematics, Sichuan University Chengdu 610064 Department of Mathematics, Sichuan Educational College, Chengdu 610061 Department of Applied Mathematics, Tongji University Shanghai 200092.

Let Y_i=M(X_i)+ei, where M(x)=E(Y|X=x) is an unknown realfunction on B(? R), {(X_1,Y_i)} is a stationary and m(n)-dependent sample from(X, Y), the residuals {e_i} are independent of {X_i} and have unknown common densityf(x). In [2] a nonparametric estimate f_n(x) for f(x) has been proposed on the basisof the residuals estimates. In this paper, we further obtain the asymptotic normalityand the law of the iterated logarithm of f_n(x) under some suitable conditions. Theseresults together with those in [2] bring the asymptotic theory for the residuals densityestimate in nonparametric regression under m(n)-dependent sample to completion.

关键词： nonparametric regression residuals asymptotic normality iterated logarithm m(n)-dependent sample

Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, nonparametric regression

在线全文

学校读者我要写书评

暂无评论

Open Journal of Statistics 2016年第6期6卷 1138-1154页

作者： Sarah Pyeye Charles K. Syengo Leo Odongo George O. Orwa Romanus O. Odhiambo Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Statistics and Actuarial Science Kenyatta University Nairobi Kenya Department of Statistics and Actuarial Science Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

The study focuses on the imputation for the longitudinal survey data which often has nonignorable nonrespondents. Local linear regression is used to impute the missing values and then the estimation of the time-dependent finite populations means. The asymptotic properties (unbiasedness and consistency) of the proposed estimator are investigated. Comparisons between different parametric and nonparametric estimators are performed based on the bootstrap standard deviation, mean square error and percentage relative bias. A simulation study is carried out to determine the best performing estimator of the time-dependent finite population means. The simulation results show that local linear regression estimator yields good properties.

关键词： Longitudinal Survey Nonmonotone Nonresponse Imputation nonparametric regression

Floating Car Data Based nonparametric regression Model for Short-Term Travel Speed Prediction

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Southwest Jiaotong University(English Edition) 2007年第3期15卷 223-230页

作者：翁剑成扈中伟于泉任福田 Beijing Key Lab of Traffic Engineering Beijing University of Technology

A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.

关键词： K-Nearest neighbor Short-term prediction Travel speed nonparametric regression Intelligence transportation system（ ITS ） Floating car data （FCD）

Data-driven Transient Stability Assessment Based on Kernel regression and Distance Metric Learning

在线全文

学校读者我要写书评

暂无评论

Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 27-36页

作者： Xianzhuang Liu Yong Min Lei Chen Xiaohua Zhang Changyou Feng the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China State Grid Jibei Electric Power Company Beijing 100053China State Grid Corporation of China Beijing 100031China

Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power flow.TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples.Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

关键词： Transient stability assessment(TSA) critical clearing time(CCT) conservativeness level distance metric learning Nadaraya-Watson kernel regression Mahalanobis distance nonparametric regression data-driven

Optimal sequence or ordinary sequence? A uni ed framework for variance estimation in nonparametric regression

在线全文

学校读者我要写书评

暂无评论

Optimal sequence or ordinary sequence? A uni ed framework fo...