[PDF] Top 20 Estimation of the slope parameter for linear regression model with uncertain prior information
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Estimation of the slope parameter for linear regression model with uncertain prior information
... The estimation of the slope parameter of the linear regression model with normal error is considered in this paper when uncertain prior information on the ... See full document
21
Improving statistical inference with uncertain non-sample prior information
... the slope parameter of the regression ...the estimation of the intercept parameter is more difficult than that of the slope ...the slope parameter is required in ... See full document
11
Shrinkage estimation of the slope parameters of two parallel regression lines under uncertain prior information
... The estimation of the slope parameter of two linear regression models with normal errors are considered, when it is apriori suspected that the two lines are ...The uncertain ... See full document
17
Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information
... that uncertain non-sample prior information on the value of the slope parameter, β is available, either from previous study or from practical experience of the researchers or ... See full document
18
Parameter estimation in biochemical systems models with alternating regression
... the slope-minus-degradation or -production expressions in steps {5} and {9} of the algorithm may become negative, thereby disallowing the necessary logarithmic transforma- ...same model. This sometimes ... See full document
11
Online Hidden Markov Model Parameter Estimation and Minimax Robust Quickest Change Detection in Uncertain Stochastic Processes
... in linear state-space and regression processes [46], these previous results are only established under linear (rather than polynomial) delay ...the parameter estimates inherent to GLR ... See full document
212
Test of hypotheses for linear regression models with non-sample prior information
... in parameter estimation and hypothesis ...non-sample prior information (NSPI) on the value of another related ...any parameter is likely to be uncertain (or ...intercept ... See full document
207
Robust optimization model for uncertain multiobjective linear programs
... The parameter values of optimization problems in real world are usually uncertain due to prediction errors, estimation errors, or lack of information at the time of ...such uncertain ... See full document
11
Combined Parameters Estimation Methods of Linear Regression Model with Multicollinearity and Autocorrelation
... its estimation using OLS estimator by regressing the extracted components (PCs) on the standardized dependent variable for the combined ...its estimation, the proposed combined estimators do not result back ... See full document
8
Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction
... logistic regression with incomplete data, where the missing data is found anywhere in the ...for model selection using a criterion based on a penalized version of the observed-data ...generalized ... See full document
30
Test of hypotheses for linear regression models with non-sample prior information
... in parameter estimation and hypothesis ...non-sample prior information (NSPI) on the value of another related ...any parameter is likely to be uncertain (or ...intercept ... See full document
12
Testing equality of two intercepts for the parallel regression model with non-sample prior information
... of estimation with NSPI there has been a lot of work, notably Ban- croft (1944, 1964), Hand and Bancroft (1968), and Judge and Bock (1978) intro- duced a preliminary test estimation of parameters to ... See full document
17
Estimation of the parameters of two parallel regression lines under uncertain prior information
... “nuisance parameter” is removed by ‘testing it ...of estimation of conditional mean for simple regression ...pre-test estimation for the multivariate Student-t regression ... See full document
16
The Relative Efficiency of the Conditional Root Square Estimation of Parameter in Inhomogeneous Equality Restricted Linear Model
... Restricted Linear Model, we show that it have smaller mean squares error than the conditional root squares Estimation, and give display solution of general- ize root squares estimation, ... See full document
5
Semiparametric estimation of the dependence parameter of the error terms in multivariate regression
... dependence parameter and the joint distribution of the error term in the multivariate linear regression ...the estimation of the regression ... See full document
30
Approximation results for parameter estimation in non-linear elastomers
... which again is dominated by the right side of (4.7). This completes the required arguments. We note that the above theorem gives a computationally tractable method to solve the parameter estimation problem ... See full document
16
The use of prior information in very robust regression for fraud detection
... specified in advance of the analysis. These results are in line with those from the monitoring of different forms of robust regression in Riani et al. (2014) which show, for three examples, just how sensitive the ... See full document
31
Some Properties of a Recursive Procedure for High Dimensional Parameter Estimation in Linear Model with Regularization
... for estimation of a high dimensional vector of parameters are presented and ...state/parameter estimation in a very high dimensional oceanic model is presented to demonstrate the efficiency of ... See full document
13
Wavelet regression using a Lévy prior model
... Despite the promising results of the wavelet entropic regularization, there are some aspects in this approach that need to be improved. A key focus of immediate future work is to develop an efficient estimation of ... See full document
155
Error Estimation Using Fuzzy Linear Regression Analysis
... Regression analysis is one of the areas in which fuzzy set theory is used frequently, since Tanaka [4] initiated research on fuzzy linear regression (FLR) analysis. This area is widely developed and ... See full document
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