All Questions
7
questions
1
vote
0
answers
171
views
Projection pursuit regression
Projection pursuit regression (PPR) is described in Hastie et al.'s The Elements of Statistical Learning in the chapter on neural networks. The algorithm was introduced by Friedman and Stuetzle (1981)....
2
votes
0
answers
327
views
Is kernalized linear regression parametric or nonparametric?
We know that for linear regression, we can predict:
$$\hat{y} = w^Tx +b$$
Where $w$ is the parameter that minimizes the square loss.
It is easy to prove that for the final solution using gradient ...
1
vote
1
answer
986
views
Estimating conditional probability with many samples
I am confused about the estimation of conditional probabilities. Suppose I want to predict a binary outcome variable $Y = 0,1$ given $n$ categorical features $X = (X_1, \ldots, X_n)$, i.e. to ...
2
votes
0
answers
133
views
Smooth regression algorithms that produce zero training error
I am looking to fit three regression functions $f_1, f_2, f_3:\mathbb{R}^2 \to \mathbb{R}$. For example, let's say $X_1$ is time, $X_2$ is geographic latitude, $f_1$ is the temperature, $f_2$ is the ...
0
votes
0
answers
402
views
Non-parametric non-linear regression with deep learning
I have a situation where I have an increasing list of real numbers $\vec a$ of variable length (generally about 50 numbers but sometimes more). It turns out that these numbers uniquely correspond to ...
8
votes
1
answer
1k
views
Nonparametric nonlinear regression with prediction uncertainty (besides Gaussian Processes)
What are state-of-the-art alternatives to Gaussian Processes (GP) for nonparametric nonlinear regression with prediction uncertainty, when the size of the training set starts becoming prohibitive for ...
1
vote
1
answer
236
views
Kernel nonparametric regression
One of the methods for nonparametric regression is using kernels.
My question is what are the conditions on the kernels functions in this method?
In other words how can I decide if a given function ...