|
|
|
|
|
 |
Search published articles |
 |
|
Showing 3 results for Ridge Regression
, , ,
Volume 21, Issue 2 (3-2017)
Abstract
In this paper a new weighted fuzzy ridge regression method for a given set of crisp input and triangular fuzzy output values is proposed. In this regard, ridge estimator of fuzzy parameters is obtained for regression model and its prediction error is calculated by using the weighted fuzzy norm of crisp ridge coefficients. . To evaluate the proposed regression model, we introduce the fuzzy coefficient of determination (FCD). Fuzzy regression is compared with its ridge version by using mean predict error and FCD, numerically. It is evident from comparison results the proposed fuzzy ridge regression is superior to the non-ridge counterpar
Ms. Zahra Jafarian Moorakani, Dr. Heydar Ali Mardani-Fard,
Volume 27, Issue 1 (3-2023)
Abstract
The ordinary linear regression model is $Y=Xbeta+varepsilon$ and the estimation of parameter $beta$ is: $hatbeta=(X'X)^{-1}X'Y$. However, when using this estimator in a practical way, certain problems may arise such as variable selection, collinearity, high dimensionality, dimension reduction, and measurement error, which makes it difficult to use the above estimator. In most of these cases, the main problem is the singularity of the matrix $X'X$. Many solutions have been proposed to solve them. In this article, while reviewing these problems, a set of common solutions as well as some special and advanced methods (which are less favored by someone, but still have the potential to solve these problems intelligently) to solve them.
Dr Manije Sanei Tabass,
Volume 27, Issue 2 (3-2023)
Abstract
Regression analysis using the method of least squares requires the establishment of basic assumptions. One of the problems of regression analysis in this way
faces major problems is the existence of collinearity among the regression variables. Many methods to solve the problems caused by the existence of the same have been introduced linearly. One of these methods is ridge regression. In this article, a new estimate for the ridge parameter using generalized maximum Tsallis entropy is presented and we call it the Ridge estimator of generalized maximum Tsallis entropy. For the cement dataset
Portland, which have strong collinearity and since 1332, different estimators have been presented for these data, this estimator is calculated and
We compare the generalized maximum Tsallis entropy ridge estimator, generalized maximum entropy ridge estimator and the least squares estimator.
|
|
|
|
|
|
|
|
|
