All Questions

1
vote
1answer
98 views
+50

Probabilistic Interpretation of Radial Basis Function

I was wondering if someone could flesh out the probabilistic interpretation of using the Radial Basis Function to compute the probability between an observation and some reference value. My question ...
9
votes
7answers
258 views
+50

Avoiding social discrimination in model building

I have questions inspired from the Amazon recent recruitment scandal, where they were accused of discrimination against women in their recruitment process. More info here: Amazon.com Inc's machine-...
7
votes
3answers
196 views
+50

Why care so much about expected utility?

I have a naive question about decision theory. We calculate the probabilities of various outcomes assuming particular decisions and assign utilities or costs to each outcome. We find the optimal ...
1
vote
1answer
40 views
+100

Alternative to Chow test in the case of heavy tailed residual distribution

I would like to check if two subpopulations of my data have the same parameters in a model. Model 1 is based on subpopulation 1 and Model 2 is based on subpopulation 2. Model 1: $y=x^\alpha + \...
1
vote
1answer
88 views
+50

Credibility evaluation - how to model conditional continuous density from multiple variables of various types?

I recently got dataset for 37000 households with declared income and a few dozens of other variables of various types: continuous, discrete, binary. The task is to automatically (unsupervised) ...
0
votes
0answers
34 views
+100

Spectral Clustering of a skipgram model

I have a model where I'm applying Spectral Clustering to frequencies of words. My pipeline consists in TF-IDF, followed by a <...
5
votes
0answers
108 views
+50

Effects of model selection and misspecification testing on inference: Probabilistic Reduction approach (Aris Spanos)

This question is about pre-test bias, inference after model selection and data snooping within the Probabilistic Reduction (PR) methodology by Aris Spanos (which is related to the Error Statistics ...
0
votes
0answers
44 views
+200

Can one usefully specify a multilevel-model with a partially-nested, partially non-nested structure?

Background Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models includes an example in section 13.5 of how to model non-nested data. The second example in this section ...
3
votes
0answers
116 views
+100

Delta Method Average Marginal Effects Multinomial Logit

Following the incredible demonstration in Statalist by Jeff Pitblado on how to calculate - using the Delta Method - the Standard Errors for Average Marginal Effects of a Logit Model. Q: What would ...
1
vote
2answers
66 views
+50

Bayesian inference for non-Gaussian errors

Following from a previously unanswered question, regression tasks involving measurements with normally distributed noise apply Gaussian processes. But are there any recommended approaches for ...
3
votes
1answer
88 views
+50

Are unbiased efficient estimators stochastically dominant over other (median) unbiased estimators?

General description Does an efficient estimator (which has sample variance equal to the Cramér–Rao bound) maximize the probability for being close to the true parameter $\theta$? Say we compare the ...
2
votes
0answers
37 views
+100

Estimating Spline curve by OLS. Is a good idea to fix the knots at Chebyshev sites?

I am writing my master's degree thesis on a novel method for fixing knots in an adaptive way and while reading the literature I've found many references to the so-called Chebyshev sites. This sites or ...
1
vote
0answers
31 views
+50

Accounting for errors in independent variable through Gaussian process regression

In Gaussian process regression (GPR), one applies a kernel (i.e. covariance function) to describe the similarity between observed and predicted data in the domain. The diagonal of the covariance ...