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

• Format: Hardcover
• Language: English
• ISBN: 3036509445
• ISBN13: 9783036509440
• Release Date: April 2021
• Publisher: Mdpi AG
• Length: 314 Pages
• Weight: 1.93 lbs.
• Dimensions: 1.0" x 6.7" x 9.6"