scholarly journals Modified Ridge Regression Estimator with the Application of Peanut Production in Pakistan

2019 ◽  
pp. 1-8
Author(s):  
Asifa Mubeen ◽  
Nasir Jamal ◽  
Muhammad Hanif ◽  
Usman Shahzad
Keyword(s):  
Ridge Regression ◽  
Real Data ◽  
Production Data ◽  
Regression Estimator ◽  
Mean Square ◽  
Data Set ◽  
Regression Estimators ◽  
Peanut Production ◽  
Ridge Regression Estimator ◽  
The Mean

The main objective of the present study was to develop a new ridge regression estimator and fit the ridge regression model to the peanut production data of Pakistan. Peanut production data has been used to analyze the results. The data has been taken peanut production and growth rate of Pakistan. The mean square error of the proposed estimator is compared with some existing ridge regression estimators. In this study, we proposed a ridge regression estimator. The properties of proposed estimators are also discussed. The real data set of peanut production is used for assuming the performance of proposed and existing estimators. Numerical results of real data set show that proposed ridge regression estimator provides best results as compare to reviewed ones.

scholarly journals Regression Estimator Using Double Ranked Set Sampling

2002 ◽  
Vol 7 (2) ◽  
pp. 311
Author(s):  
Hani M. Samawi ◽  
Eman M. Tawalbeh
Keyword(s):  
Correlation Coefficient ◽  
Real Data ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Regression Estimator ◽  
Primary Analysis ◽  
Data Set ◽  
Population Mean ◽  
The Mean

The performance of a regression estimator based on the double ranked set sample (DRSS) scheme, introduced by Al-Saleh and Al-Kadiri (2000), is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS) or ranked set sampling (RSS) (Yu and Lam, 1997) regression estimator.  Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4) and DRSS for high correlation coefficient (at least 0.91.) The theory is illustrated using a real data set of trees.  

scholarly journals The Performance of Some Restricted Estimators In Restricted Linear Regression Model

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Bader Aboud ◽  
Mustafa Ismaeel Naif
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Regression Estimator ◽  
Mean Square ◽  
Ridge Regression Estimator ◽  
The Mean ◽  
Biased Estimators ◽  
Better Than

In the linear regression model, the restricted biased estimation as one of important  methods to addressing the high variance and the  multicollinearity problems. In this paper, we make the simulation study of the some restricted biased estimators. The mean square error (MME) criteria are used to make a comparison  among them. According to the simulation study we observe that, the performance of the restricted modified unbiased  ridge regression estimator (RMUR) was proposed by  Bader and Alheety (2020)  is better than  of these estimators. Numerical example have been considered to illustrate the performance of the estimators.

scholarly journals Predictive efficiency of ridge regression estimator

2017 ◽  
Vol 27 (2) ◽  
pp. 243-247
Author(s):  
Manoj Tiwari ◽  
Amit Sharma
Keyword(s):  
Least Squares ◽  
Ridge Regression ◽  
Ordinary Least Squares ◽  
Regression Estimator ◽  
Performance Properties ◽  
Regression Estimators ◽  
Ridge Regression Estimator ◽  
Predictive Efficiency

In this article we have considered the problem of prediction within and outside the sample for actual and average values of the study variables in case of ordinary least squares and ridge regression estimators. Finally, the performance properties of the estimators are analyzed.

scholarly journals Biased Adjusted Poisson Ridge Estimators-Method and Application

2020 ◽  
Vol 44 (6) ◽  
pp. 1775-1789
Author(s):  
Muhammad Qasim ◽  
Kristofer Månsson ◽  
Muhammad Amin ◽  
B. M. Golam Kibria ◽  
Pär Sjölander
Keyword(s):  
Poisson Regression ◽  
Regression Estimator ◽  
Mean Square ◽  
Poisson Regression Model ◽  
Negative Effects ◽  
Likelihood Estimator ◽  
Ridge Regression Estimator ◽  
The Mean ◽  
Almost Unbiased ◽  
Better Than

AbstractMånsson and Shukur (Econ Model 28:1475–1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Türkan and Özel (J Appl Stat 43:1892–1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE ($$ \hat{k}_{q4} $$ k ^ q 4 ) performed better than the rest in the presence of high to strong (0.80–0.99) multicollinearity situation.

scholarly journals Ridge regression and its applications in genetic studies

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0245376
Author(s):  
M. Arashi ◽  
M. Roozbeh ◽  
N. A. Hamzah ◽  
M. Gasparini
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Regression Estimator ◽  
Ridge Estimator ◽  
Data Set ◽  
Explanatory Variables ◽  
Ridge Estimation ◽  
Large Scale Data ◽  
Ridge Regression Estimator ◽  
Satisfactory Answer

With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings.

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

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