[PDF] Top 20 An improvement over regression method of estimation
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An improvement over regression method of estimation
... efficient estimation of the mean of the study variable when auxiliary variables are available for instance see (Diana 1993), (Upadhyaya and Singh, 1999), (Singh and Tailor, 2005), (Singh and Karpe, 2009), (Singh ... See full document
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<p>Estimation of the Clinical and Economic Impact of an Improvement in Adherence Based on the Use of Once-Daily Single-Inhaler Triple Therapy in Patients with COPD</p>
... the estimation of the improvement of adherence associated with a greater use of ...the improvement in adherence with SITT vs MITT has not been ...the estimation of adherence improvement ... See full document
12
Improvement of an elutriation method for estimation of weed seedbank in the soil
... for estimation of weed seedbank in the ...elutriation method on results of qualitative and quantitative estimation of weed seedbank densities has been found at any of the three locations differing in ... See full document
8
A simplified nonlinear regression method for human height estimation in video surveillance
... calibration method for estimating the human height in video ...proposed method, these parameters are directly calculated using a nonlinear regression model from the observed head and foot points of a ... See full document
9
A Method for Assessing Customer Harmonic Emission Level Based on the Iterative Algorithm for Least Square Estimation
... This method was simple to use, easy to the development of ...fluctuations method [5,6], the linear regression method [8,9] and the reference impedance method ...tions method and ... See full document
8
3D Human Pose Estimation from a Monocular Image Using Model Fitting in Eigenspaces
... pose estimation from monocular ...former method can estimate the poses rapidly but has the disadvantage of low estimation ...latter method is able to accurately estimate the poses, its ... See full document
7
Predicting CBR Values of Black Cotton Soil Stabilized with Cement and Waste Glass Admixture Using Regression Model
... multiple regression method for the estimation of soaked CBR by reliably correlating the soaked CBR value of cement stabilized Black cotton soil with Waste Glass (WG) admixture, using index properties ... See full document
6
Addressing multicollinearity in regression models: a ridge regression application
... ridge regression as an alternative to the ordinary least square method of estimation when there is multi-linearity between explanatory ...new method for choosing the ridge parameter and used ... See full document
21
Parametric Cost Estimation Model for Li-ion Battery Pack of E-motorcycle Conversion based on Activity Based Costing
... cost estimation model with activity-based cost assignments to estimate production costs through cost calculations for each ...cost estimation model to calculate the cost of each activity into the total cost ... See full document
20
Mathematical reflection approach to instrumental variable estimation method for simple regression model
... modified method uses the reflection of the manifest values of the independent variable to define IV ...IV method provides a much better estimator of the slope and intercept ...and regression ... See full document
21
Resolving intravoxel white matter structures in the human brain using regularized regression and clustering
... The proposed method utilizes regularized regression and clustering techniques for estimation of the principal direction of nerves within a voxel. The method’s robustness has been heavily relied on ... See full document
12
Using GLS to Generate Forecasts in Regression Models with Auto-correlated Disturbances with simulation and Palestinian Market Index Data
... linear regression models with autocorrelated disturbances is inspired by problems, which arise in meteorology and ...famous method of estimating and forecasting is the Ordinary Least Squares (OLS), it is ... See full document
12
A Comparative Study between Multiple Imputation Method and Regression Imputation Method of Estimation of Missing Data
... promising method is to use standard regression analysis to provide estimates of the missing data conditional on complete variables in the ...a regression model to explain Y by the remaining p ... See full document
10
Combined Estimation for Quantile Regression
... quantile regression, which model the conditional quantiles ...quantile regression is a natural thought and has several ...tile regression is robust against extreme returns and free of distributional ... See full document
103
Estimation and Selection in Regression Clustering
... In contrast, a partitioning method constructs a fixed number of clusters, say k. It classifies the data into k clusters, which together satisfy two requirements of a partition: (i) each cluster must contain at ... See full document
12
The Use Of Logit And Probit Regression Models In The Process Of Graduates’ Employment
... Quasi-Newton Estimation Method Hooke-Jeeves pattern moves Estimation Method Hooke-Jeeves and Quasi-Newton Estimation Method Rosenbrock pattern search Estimation ... See full document
8
A study of estimation in a normal heteroscedastic regression model with Type 1 censored data
... In this article, a similar issue is considered for a normal heteroscedastic regressIOn model with Type I censored data, and an estimation method analogous to generalized least squares is[r] ... See full document
26
Subsampling Method for Robust Estimation of Regression Models
... sampling method, the bootstrap method and the method of trimming ...subsampling method, we substitute analytical treatment of the outliers (such as the use of the functions in the ... See full document
16
Estimation in semiparametric spatial regression
... and regression, but no such development has taken place for spatial ...nonparametric regression. In this paper, a semiparametric spatial regression approach is proposed to avoid this ...An ... See full document
41
Adaptive Minimax Regression Estimation over Sparse $\ell_q$-Hulls
... unless stated otherwise) are given as initial estimates of the unknown true regression func- tion. The goal is to construct a linearly combined estimator using these estimates to pursue the performance of the best ... See full document
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