New answers tagged

0 votes

How do you test persistence in an AR(p) regression?

The persistence is equal to the largest eigenvalue of the companion matrix (in absolute value). For example, an AR(3) process can be written as $$\left[ \begin{array}{c} Y_{t} \\ Y_{t-1} \\ Y_{t-2}%...
user avatar
  • 904
0 votes

How do you test persistence in an AR(p) regression?

In a simple AR(p) process, the so-called cumulative impulse response is given by 1/(1-p), where p is the sum of the autoregressive coefficients. Therefore, your intuition can be justified. See: ...
user avatar
  • 1
0 votes

How do I interpret these linear mixed model coefficients from r?

how do I interpret the estimate of 'LanguageTamil'(0.3926) when it is a categorical variable (French or Tamil) and there is no "1 unit increase" between the two. Categorical variables get ...
user avatar
1 vote

Do demographic variables need to be standardized when population size is included as a covariate?

You incorporate the population size as an offset in the regression For this type of count regression you would usually use the population size as an exposure variable, which is a variable that ...
user avatar
  • 97.6k
0 votes

Negative Binomial Coefficient Interpretation?

Yes, that is correct. In French, the mean number of propagations is .53 of the mean in Tamil, or 47% less. This comes from the link function in NB regression: $$\ln(\mu)=\beta_0+\beta_1x$$
user avatar
  • 533
0 votes

An inherent inconsistency in interpreting the indirect effect in a mediation analysis?

There seems to be a confusion that you can move variables only one at a time. You have to look at the whole pictures, not just part of it. There seems to also be a confusion on regression coefficients ...
user avatar
  • 414
1 vote

Out-of-sample predictive checks for Bayesian TVP models

You can find the answer in the Garratt et al.(2009)[1], section 4.3. The author explained why we use a ex-post out-of-sample prediction (which is your first procedure) instead of a real time out-of-...
user avatar
  • 11
5 votes

Discrimination/Slope Item Response Theory Models

Intuitions are useful, but it is worth understanding the math behind the model. We model the probability of answering correctly to $i$-th question by a person with the ability $\theta$, $p_i({\theta})$...
user avatar
  • 115k
5 votes
Accepted

Discrimination/Slope Item Response Theory Models

A steeper slope means a stronger relationship between ability and the question. It means that the item (and therefore the test) is more reliable - and reliability is the inverse of measurement error. ...
user avatar
  • 14.8k

Top 50 recent answers are included