Articles with "regression estimator" as a keyword



Photo by munibsaad from unsplash

The small sample properties of the restricted principal component regression estimator in linear regression model

Sign Up to like & get
recommendations!
Published in 2017 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2015.1024867

Abstract: ABSTRACT In regression analysis, to deal with the problem of multicollinearity, the restricted principal components regression estimator is proposed. In this paper, we compared the restricted principal components regression estimator, the principal components regression estimator,… read more here.

Keywords: restricted principal; regression; regression estimator; components regression ... See more keywords
Photo by martenbjork from unsplash

On the efficiency of ratio estimator over the regression estimator

Sign Up to like & get
recommendations!
Published in 2017 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2015.1100741

Abstract: ABSTRACT It is well known that the ratio and product estimators have the limitation of having efficiency not exceeding that of the linear regression estimator. This paper develops a new approach to ratio estimation that… read more here.

Keywords: regression estimator; efficiency; estimator; ratio estimator ... See more keywords
Photo by campaign_creators from unsplash

On a Mixed Poisson Liu Regression Estimator for Overdispersed and Multicollinear Count Data

Sign Up to like & get
recommendations!
Published in 2022 at "The Scientific World Journal"

DOI: 10.1155/2022/8171461

Abstract: The mixed Poisson regression models are commonly employed to analyze the overdispersed count data. However, multicollinearity is a common issue when estimating the regression coefficients by using the maximum likelihood estimator (MLE) in such regression… read more here.

Keywords: regression; count data; regression estimator; estimator ... See more keywords
Photo by martindorsch from unsplash

Generalized Ridge Regression Estimator in High Dimensional Sparse Regression Models

Sign Up to like & get
recommendations!
Published in 2018 at "Statistics, Optimization and Information Computing"

DOI: 10.19139/soic.v6i3.581

Abstract: Modern statistical analysis often encounters linear models with the number of explanatory variables much larger than the sample size. Estimation in these high-dimensional problems needs some regularization methods to be employed due to rank deficiency… read more here.

Keywords: high dimensional; regression; ridge regression; estimator ... See more keywords