Sample (material)

Mathematics

Noise (video)

Algorithm

Image (mathematics)

Data mining

Statistics

Engineering

Estimator

Artificial intelligence

Systems engineering

Set (abstract data type)

Chemistry

Programming language

Software

Computer science

Task (project management)

Data set

Model selection

Chromatography

Selection (genetic algorithm)

Least-squares function approximation

Selecting the best model from a set of candidates for a given set of data is obviously not an easy task. In this paper, we propose a new criterion that takes into account a larger penalty when adding too many coefficients (or estimated parameters) in the model from too small a sample in the presence of too much noise, in addition to minimizing the sum of squares error. We discuss several real applications that illustrate the proposed criterion and compare its results to some existing criteria based on a simulated data set and some real datasets including advertising budget data, newly collected heart blood pressure health data sets and software failure data.

A New Criterion for Model Selection