Tagged Questions

6
votes
3answers
227 views

In R, find function F(x) to transform values in a vector to a normal distribution?

I have a PDF (Probability Density Function) generated from a vector of 1,000,000 empirical values. This empirical PDF is heavily skewed to the right. In this form, I can't make accurate predictions ...
0
votes
1answer
70 views

Parameter learning of Markov random field

Given a Markov random field $\mathcal{G} = (\mathcal{V},\mathcal{E})$, the corresponding density function to which is expressed by $f(x) \propto \prod_{x\in\mathcal{V}} \psi_u(x) ...
2
votes
1answer
105 views

Standard deviation of a particular dimension in a multivariate Gaussian distribution

I have a set (cluster) of vectors in dimension d. From this I have calculated the sample mean and covariance matrix ( I make the assumption that they are from a multivariate Gaussian). My question ...
0
votes
1answer
62 views

Parameter estimation with GMM

I have estimated the parameters of normal distribution with GMM and got the follwing results : mean = -0.01168 , p-value = 0.83519 i'm bit confused in interpreting the result. can i say that the ...
3
votes
1answer
152 views

Why would predicted values be normally-distributed when the actual values are uniform?

I'm building a supervised learning model where the target variable is a uniformly-distributed continuous value ranging from 0-1 (originally a rank value from 1-38000, then scaled down to 0-1). The 20 ...
4
votes
1answer
383 views

Linear regression prediction interval

If the best linear approximation (using least squares) of my data points is the line $y=mx+b$, how can I calculate the approximation error? If I compute standard deviation of differences between ...
0
votes
1answer
605 views

Improving transformation of dependent variable and robust regression

In a multiple regression with 16k cases 2 IV (non-normally distributed) and one dependent variable that is also not normally distributed. DV see below: I've tried three ways of transforming the DV ...
1
vote
0answers
55 views

Interpreting odds ratios with log-transformed continuous variables in a logistic regression [duplicate]

Possible Duplicate: Interpretation of log transformed predictors in logistic regression Should quantitative predictors be transformed to be normally distributed? I was hoping for some ...
2
votes
4answers
353 views

How to increase variance in Gaussian Process regression?

I'm currently experimenting with Gaussian processes. I decided to use matlab + gpml (http://www.gaussianprocess.org/gpml/code/) for playing around with Gaussian processes a bit. I'd like to do ...
0
votes
1answer
57 views

Conditional on Gaussian, need clarification

I'm reading Andrew Ng's notes on machine learning, and on page 12 of this document, he makes a step in his proof that I'm trying to decipher: Let $\textbf{x} = \left( 1 , x_1 , x_2 , \cdots , x_n ...
1
vote
1answer
170 views

Probit ordered model for non-normal distribution of outcomes

I have the following Y outcomes distribution with the normal density function represented by the superimposed red line: I need to develop a regression methodology to predict $Y$ given a number of ...
6
votes
1answer
355 views

How can I prove the experiment data follows heavy-tail distribution?

I have several test results of server response delay. According to our theory analysis, the delay distribution (The probability distribution function of response delay) should have heavy-tail ...
1
vote
3answers
624 views

Is using a questionnaire score (EuroQol's EQ-5D) with a bimodal distribution as outcome in linear regression a problem?

There is currently a debate whether the EQ-5D score that has a ceiling problem and a bimodal distribution can be used in a linear regression model or not. Background The score is very simple and ...
3
votes
2answers
192 views

Adjusting for tilt of the earth

I have rewritten the old question (below) to hopefully make things a bit clearer. Basically I think that the temperature of the earth should be normally distributed but is not due to the ‘seasonal ...
0
votes
1answer
318 views

Assuming $u\sim N(0,\sigma^2)$ when y is highly skewed

does it make sense to assume $u\sim N(0,\sigma^2)$ when I know from a histogram that $y$ is highly skewed. Because from the assumption $u\sim N(0,\sigma^2)$ it follows that $y\sim N(x\beta,\sigma^2)$ ...