In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
Bayesian models are supposedly well equipped to deal with high-dimensionality problems, and can handle sparse data well, too. But suppose I've created a model that estimate more parameters than there ...
I would like to use GLM and Elastic Net to select those relevant features + build a linear regression model (i.e., both prediction and understanding, so it would be better to be left with relatively ...
I have done a live cell imaging time course over 24 hours, and have a result for each hour. I have 3 experimental groups and 1 control group. What I want to know is if any of the experimental groups ...
SVM has relatively low classification rate for high-dimensional data even though 2-D projections show they are separable
I have another problem with 14000 features and 500 training samples. It is a binary classification problem and approximately in the form of an ellipse. My classification accuracy using the 2nd degree ...
Minimum sample size for PCA when the main goal is to estimate the first or second principal component?
If I have a dataset with $n$ observations and $p$ variables (dimensions), and generally $n$ is small (n=12-16), and $p$ may range from small (p = 4-10) or perhaps much larger (p= 30-50). I remember ...
I've been told (read) this many times, but I never understood why it's bad for the number of dimensions in your data, or the number of explanatory variables in your model to be higher than your ...