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