Estimation results for linear regression model
Efficient semiparametric estimation of a partially linear quantile regression model
... Asymptotic results given in previ- ous sections indicate that the AQR estimator is as efficient as the one-step es- timator for homoskedastic designs + Furthermore , the optimally weighted AQR estimator is ...
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Series estimation of functional-coefficient partially linear regression model
... local linear and one-step back 4 fitting approach with optimal choice of the smoothing parameters h and 1 h , see Wong et ...estimating regression function with series method, the CV approach is not only ...
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Estimation of the slope parameter for linear regression model with uncertain prior information
... may well have some superior statistical property in terms of another more popular statistical criterion, namely the mean square error. In this process, we define three biased estimators: the restricted estimator (RE) ...
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Estimation of the slope parameter for linear regression model with uncertain prior information
... may well have some superior statistical property in terms of another more popular statistical criterion, namely the mean square error. In this process, we define three biased estimators: the restricted estimator (RE) ...
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Combined Parameters Estimation Methods of Linear Regression Model with Multicollinearity and Autocorrelation
... Generalized Linear Estimator (CORC and ML) and Principal Components (PCs) Estimator that estimate the parameters of linear regression model when the two problems are in the ...A linear ...
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Comparing The ANN And Linear Regression In Estimation Of The Growth Model (The Case Of MENA)
... the results will become more precise and the non-linearity problem will be solved and appropriate functional form of movements of per capita income of countries toward their steady-state value will be ...OLS ...
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Sparse estimation in functional linear regression
... squares regression a b s t r a c t As a useful tool in functional data analysis, the functional linear regression model has become increasingly common and been studied extensively in recent ...
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The simple linear Regression Model
... negative estimation errors enter with the same weight due to the squaring down, it is therefore irrelevant whether the expected value for observation yi is underestimated or ...
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Estimation of the Bi-response Poisson Regression Model Based on Local Linear Approach
... Poisson regression model based on the bivariate Poisson distribution and the response variable is a count data that ...Poisson regression model can be estimate using the parametric approach ...
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Linear Regression Model for Gaussian Noise Estimation and Removal for Medical Ultrasound Images
... novel linear regression model for Gaussian representation of speckle noise in medical ultrasound ...proposed linear model for Gaussian noise estimation and ...experimental ...
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Estimation of Heteroscedasticity Effects in a Classical Linear Regression Model of a Cross-Sectional Data
... scientific model that would have been fully examined for heteroscedasticity, and would suffice for the fixing and periodic review of auditor remuneration without ...
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Weighted Maximum Median Likelihood Estimation For Parameters In Multiple Linear Regression Model
... We also propose Transformed Both Sides (TBS) Robust Based estimators, namely the TBS-WMML1 Based estimator, the TBS-WMML2 Based estimator and the TBS-WMML3 Based estimator for data sets with problems of outliers and non- ...
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On Local Linear Regression Estimation of Finite Population Totals in Model Based Surveys
... nonparametric regression is employed which provides an estimation of unknown finite population ...in model based inference is constructed using the procedure of local linear ...efficiency. ...
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3 Multiple linear regression: estimation and properties
... 1 2 1 3 2 t t t t t V P P P u (3-30) In the above expression the dots indicate that past expenditure on advertising continues to have an indefinite influence, although it is assumed that with a ...
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liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors
... Liu regression estimator is now a commonly used alternative to the conventional ordinary least squares estimator that avoids the adverse effects in the situations when there exists a considerable degree of ...
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Estimation of slope for linear regression model with uncertain prior information and student-t error
... linear combination of the mle and the RE, and the shrinkage estimator (SE) by using the preliminary test approach. We investigate the bias and the mean square error functions, both analytically and graphically to ...
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Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information
... the estimation of parameters has been introduced to ‘improve’ the quality of the ...sion model. However, the estimation of the intercept parameter is more difficult than that of the slope ...the ...
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Towards an Early Software Estimation Using Log-Linear Regression and a Multilayer Perceptron Model
... a model to predict software effort from use case diagrams. Our model can be used in the early stages of the software life ...cost estimation so that they can bid on ...UCP model. In this work, ...
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Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients
... in linear regression ...of regression metrics within a single ...in linear regression to be discussed here, such as dominance analysis (Azen & Budescu, 2003) and relative weights ...
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The Effect of Single-Axis Sorting on the Estimation of a Linear Regression
... mathematical model which is based on the theory of order statistics. This model made it possible to carry out the analytical investigation in Sections 3 and ...
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