scholarly journals PENERAPAN METODE GENERALIZED RIDGE REGRESSION DALAM MENGATASI MASALAH MULTIKOLINEARITAS

2013 ◽  
Vol 2 (1) ◽  
pp. 54
Author(s):  
NI KETUT TRI UTAMI ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Ridge Regression ◽  
Estimation Method ◽  
Minimum Variance ◽  
Least Square ◽  
Ordinary Least Square ◽  
Parameter Estimation Method ◽  
Alternative Method ◽  
Highly Correlated

Ordinary least square is parameter estimation method for linier regression analysis by minimizing residual sum of square. In the presence of multicollinearity, estimators which are unbiased and have a minimum variance can not be generated. Multicollinearity refers to a situation where regressor variables are highly correlated. Generalized Ridge Regression is an alternative method to deal with multicollinearity problem. In Generalized Ridge Regression, different biasing parameters for each regressor variables were added to the least square equation after transform the data to the space of orthogonal regressors. The analysis showed that Generalized Ridge Regression was satisfactory to overcome multicollinearity.

scholarly journals Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi

2019 ◽  
Vol 1 (2) ◽  
pp. 61
Author(s):  
Aditya Setyawan R ◽  
Mustika Hadijati ◽  
Ni Wayan Switrayni
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Linear Regression ◽  
Statistical Method ◽  
Estimation Method ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Regression Parameters ◽  
Independent Variable

Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.

scholarly journals KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS

2014 ◽  
Vol 3 (4) ◽  
pp. 146
Author(s):  
HANY DEVITA ◽  
I KOMANG GDE SUKARSA ◽  
I PUTU EKA N. KENCANA
Keyword(s):  
Linear Correlation ◽  
Ridge Regression ◽  
Minimum Variance ◽  
Sum Of Squares ◽  
Least Square ◽  
Bias Parameter ◽  
Ridge Estimator ◽  
Ordinary Least Square ◽  
Parameter Estimations

Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity.  Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can  reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.

A novel parameter estimation method for the Weibull distribution on heavily censored data

2019 ◽  
pp. 1748006X1988764 ◽  
Author(s):  
Renyan Jiang
Keyword(s):  
Parameter Estimation ◽  
Censored Data ◽  
Estimation Method ◽  
Least Square ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Distribution Models ◽  
Data Set ◽  
Hybrid Censoring ◽  
Parameter Estimation Method

It is desired to build the life distribution models of critical components (which are assumed to be non-repairable) of a repairable system as early as possible based on field failure data in order to optimize the operation and maintenance decisions of the components. When the number of the systems under observation is large and the observation duration is relatively short, the samples obtained for modeling are large and heavily censored. For such samples, the classical parameter estimation methods (e.g. maximum likelihood method and least square method) do not provide robust estimates. To address this issue, this article develops a hybrid censoring index to quantitatively describe censoring characteristics of a data set, proposes a novel parameter estimation method based on information extracted from censored observations, and evaluates the accuracy and robustness of the proposed method through a numerical experiment. Its applicable range in terms of the hybrid censoring index is determined through an accuracy analysis. The experiment results show that the proposed approach provides much accurate estimates than the classical methods for heavily censored data. A real-world example is also included.

scholarly journals Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-wei Yang ◽  
Man-feng Dou ◽  
Zhi-yong Dai
Keyword(s):  
Parameter Estimation ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Estimation Method ◽  
Trust Region ◽  
Fault Tolerant Control ◽  
Trust Region Algorithm ◽  
Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

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