Regression is the procedure that attempts to relate a$p$-dimensional vector of predictors $Xb$ with a response variable$Y$. Frequently, we deal with regression problems that have a largeamount of predictors. In those cases, we try to reduce thedimension of our predictor vector. The reason we are...

Parameter identifiability is very useful if one wishes to make inferences in a statistical model. There are two important nonidentifiabilities in finite mixture models : boundary nonidentifiability and label nonidentifiability. Although parameters are not identifiable in the strict sense, in...