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 …

Multicollinearity among the predictor variables is a serious problem in regression analysis.
There are some classes of biased estimators for solving the problem in statistical literature …

When multicollinearity exists in the context of robust regression, ridge rank regression
estimator can be used as an alternative to the rank estimator. Performance of the ridge rank …

This book presents some post-estimation and predictions strategies for the host of useful
statistical models with applications in data science. It combines statistical learning and …

Some linear stochastic constraints may occur during real data set modeling, based on either
additional information or prior knowledge. These stochastic constraints often cause some …

Nowadays, high-dimensional data appear in many practical applications such as
biosciences. In the regression analysis literature, the well-known ordinary least-squares …

In classical regression analysis, the ordinary least–squares estimation is the best strategy
when the essential assumptions such as normality and independency to the error terms as …

In order to down-weight or ignore unusual data and multicollinearity effects, some alternative
robust estimators are introduced. Firstly, a ridge least trimmed squares approach is …

In statistics, regression analysis is a method for predicting a target variable by establishing
the optimal linear relationship between the dependent and independent variables. The …

Craniofacial reconstruction is one of the dominating research domains having vital
significance towards forensic purposes as well as archaeological investigation needs. With …