Statistical Inference from High Dimensional Data

•Real-world problems can be high-dimensional, complex, and noisy •More data does not imply more information •Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information •A process with multidimensional information is not necessarily easy to interpret nor process •In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth •The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data •The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches •Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data