This paper considers the problem of simultaneous predicted response and prediction of average value of the study variable in a linear regression model when some prior exact restrictions are available, which bind the r...
详细信息
The conventional ordinary least squares (OLS) variance-covariance matrix estimator for a linear regression model under heteroscedastic errors is biased and inconsistent. Accordingly, several estimators have so far bee...
详细信息
The conventional ordinary least squares (OLS) variance-covariance matrix estimator for a linear regression model under heteroscedastic errors is biased and inconsistent. Accordingly, several estimators have so far been proposed by various researchers. However, none of these perform well under the finite-sample situation. In this paper, the powerful optimization technique of Genetic algorithm (GA) is used to modify these estimators. Properties of these newly developed estimators are thoroughly studied by Monte Carlo method for various sample sizes. It is shown that GA-versions of the estimators are superior to corresponding non-GA versions as there are significant reductions in the Total relative bias as well as Total root mean square error.
This paper overviews home and abroad time estimation technology and methods for vehicle travel, then it puts forward trip time estimation model for buses based on urban bus travel information service platform. Conside...
详细信息
ISBN:
(纸本)9781424420124
This paper overviews home and abroad time estimation technology and methods for vehicle travel, then it puts forward trip time estimation model for buses based on urban bus travel information service platform. Considered walking time, waiting time and travel time, the linear regression model was built to predict travel time. As an example, Beijing Bus 300 is illustrated. The result shows that the model has superior prediction precision;therefore it would be valuable to integrate this forecast result to travel information service platform. Moreover, the algorithm and structure of this model is very simple, it could be transplanted to other rapid transit.
Background: Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refi...
详细信息
Background: Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches. Results: A robust linearregression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time. Conclusion: Analysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.
In order to obtain a low computational cost method (or rough classification) for automatic handwritten characters recognition, this paper proposes a combined system of two feature representation methods based on a vec...
详细信息
ISBN:
(纸本)0780388194
In order to obtain a low computational cost method (or rough classification) for automatic handwritten characters recognition, this paper proposes a combined system of two feature representation methods based on a vector field: one is autocorrelation matrix, and another is a low frequency Fourier expansion. In each method, the similarity is defined as a weighted sum of the squared values of the inner product between input pattern feature vector and the reference pattern ones that are normalized eigenvectors of KL (Karhunen-Loeve) expansion. This paper also describes a way of deciding the weight coefficients using a simple linear regression model, and shows the effectiveness of the proposed method by illustrating some experimentation results for 3036 categories of handwritten Japanese characters.
Thirty-six provenances of Pinus densiflora were evaluated for stability and adaptability for height growth at 11 test sites in Korea. The data were obtained from measurements at age 6 and analyzed using linear regress...
详细信息
Thirty-six provenances of Pinus densiflora were evaluated for stability and adaptability for height growth at 11 test sites in Korea. The data were obtained from measurements at age 6 and analyzed using linear regression model and AMMI (additive main effect and multiplicative interaction) model. There was significant provenance by site interaction effect (p < 0.011). The interaction term explained 7.1% of total variation. While the regressionmodel accounted for 15.8% of GxE interaction term, the AMMI model accounted for 74.9% with four PCA values. Most of the provenances were not significantly different from the unity (b = 1.0), except for Inje (1), Jungsun (4), Bongwha (5), Koryung (26), Hamyang (30) and Seoguipo (36). Adaptability of provenances to the test sites was estimated with mean height growth and first AMMI component scores (IPCA 1). Inje (1), Bongwha (5), Taean (20) and Seoguipo (36) were specifically adapted to the high yielding environments. Considering the first and second AMMI components (IPCA 1 and IPCA 2, respectively) scores, Whachun (2), Samchuk (10), Joongwon (14) and Buan (29) provenances were more stable than others. The implication of GxE interaction was discussed in view of seed transfer and delineation of seed zones.
Simple and multiple linear regression models are considered between variables whose "values" are convex compact random sets in R-p, (that is, hypercubes, spheres, and so on). We analyze such models within a ...
详细信息
Simple and multiple linear regression models are considered between variables whose "values" are convex compact random sets in R-p, (that is, hypercubes, spheres, and so on). We analyze such models within a set-arithmetic approach. Contrary to what happens for random variables, the least squares optimal solutions for the basic affine transformation model do not produce suitable estimates for the linear regression model. First, we derive least squares estimators for the simple linear regression model and examine them from a theoretical perspective. Moreover, the multiple linear regression model is dealt with and a stepwise algorithm is developed in order to find the estimates in this case. The particular problem of the linearregression with interval-valued data is also considered and illustrated by means of a real-life example.
For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. In case that it exists, the quality of these records is not as good as it should be for most purposes and s...
详细信息
For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. In case that it exists, the quality of these records is not as good as it should be for most purposes and so an estimate of its values is desirable. To achieve such a task, an artificial neural network (ANN) model has been proposed to predict diffuse fraction (K-D) in hourly and daily scale. A comparison between the performances of the ANN model with that of two linear regression models has been reported. An attempt was also done to describe the ANN outputs in terms of first order polynomials relating K-D with clearness index (K-T) and sunshine fraction (S/S-0). If care is taken in considering the corresponding regional climatic differences, these correlations can be generalized and transferred to other sites. The results hint that the ANN model is more suitable to predict diffuse fraction in hourly and daily scales than the regressionmodels in the plain areas of Egypt. (c) 2006 Elsevier Ltd. All rights reserved.
In this article, we provide a procedure to select the significant covariates of the linear regression models in which some or all covariates are measured with errors. The proposed method is based on the combination of...
详细信息
In this article, we provide a procedure to select the significant covariates of the linear regression models in which some or all covariates are measured with errors. The proposed method is based on the combination of a non concave penalization and a corrected least squares, and it simultaneously selects significant covariates and estimates the unknown regression coefficients. Same as Fan and Li (2001), we show the resulted estimator has an oracle property with a proper choice of regularization parameters and penalty function. Some simulation studies are conducted to illustrate the finite sample performance of the proposed method.
暂无评论