A Review on Statistical Method for Neural Network

With the advent of computers and the information age, statistical problems have exploded both in size and complexity. Vast amounts of data are being generated in many fields, and the statistician’s job is to make sense of it all: to extract important patterns and trends, and understand, what the data says. This is called learning from data. The challenges in learning from data have led to a revolution in the statistical sciences. Since computation plays such a key role, it is not surprising that much of this new development has been done by researchers in other fields such as computer science and engineering. This article is attempt to bring together the important new ideas (neural network) in learning, and explain it in a statistical framework. Neural networks is efficiently being used as an alternative of statistical methods for different problems like estimation, classification, clustering analysis, sample recognition and etc. Here a review on neural networks according to the work of Hastie et al (2008) is given. The results show that, neural network is powerful and very general approach for regression and classification. Training a neural network implies solving a non-convex optimization problem with a large number of parameter: computer-intensive approach. Neural networks are especially effective in settings where prediction without interpretation is the goal.

Keywords:

Neural Network , Regression , Clustering.