Varying Coefficient Models are used to reveal properties of multidimensional data among nonparametric regression models. Since these models allow the coefficients to change over the variable called the "index variable", ...

Support Vector Machines were introduced by Vladamir Vapnik in 1995 as a new-generation technique, learning from training set which is high-dimensional and whose sample size is much small, by creating and solving a quadratic ...

Repeated measures data describe multiple measurements taken from the same experimental unit under different treatment conditions. In particular, researches with repeated measures data in various fields such as health and ...

The classic Shewhart control charts are generally used for monitoring the process mean and variability in the characteristics of a random quality variable of interest and are based on the normality assumptions. For skewed ...

A common shock Poisson process is used to model dependent event frequencies. In this process, a shock may be a big car accident, a particular hurricane, a medical mistake with consequences in a hospital. The common shock ...

Numerous classical distributions are used in the modeling of acquired data in engineering, actuarial, environmental and medical sciences, demography, economics, finance, insurance, biological studies, life analysis and ...

In this study, a robust loss function and binary boosting classification method are proposed. The purpose of classification methods is to obtain classifier function with generalization ability, i.e., high prediction ...

In the literature, a very important problem in deep neural networks is the determination of hyper-parameters. There are many combinations that can be tried for the selection of hyper-parameters to be used in data analysis. ...

In square contingency tables, analysis of agreement between the row and column classifications is of interest. For nominal categories, kappa coefficient is used to summarize the degree of agreement between two raters. For ...