5
votes
1answer
45 views
+50

Finding quality of fit for two discrete variables with low statistics

I have data from an experiment which I am trying to explain using a model. I do not have an analytic formula for the prediction of the model but instead I got its prediction through a simulation. The ...
9
votes
0answers
212 views
+50

Help in finding the estimator : which technique to apply

Question: problem statement is similar to Example 9.5 from Chapter 9: Fundamentals of Statistical Signal processing by Steven Kay. $\theta = \sum_n r_n^d$ $y_n = \theta + \eta_n$ AND $f_R(r;k,d) = ...
10
votes
1answer
107 views
+200

Why does adding a lag effect increase mean deviance in a Bayesian hierarchical model?

Background: I'm currently doing some work comparing various Bayesian hierarchical models. The data $y_{ij}$ are numeric measures of well-being for participant $i$ and time $j$. I have around 1000 ...
2
votes
2answers
46 views
+50

Why do one-versus-all multi class SVMs need to be calibrated?

On the wiki page for multi-class support vector machines (https://en.wikipedia.org/wiki/Support_vector_machine#Multiclass_SVM) it states that "it is important that the output functions be calibrated ...
12
votes
2answers
725 views
+50

Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients

I am wondering what the exact relationship between partial $R^2$ and coefficients in a linear model is and whether I should use only one or both to illustrate the importance and influence of factors. ...
8
votes
1answer
76 views
+50

How to select the best fit without over-fitting data? Modelling a bimodal distribution with N normal functions, etc

I have an obviously bimodal distribution of values, which I seek to fit. The data can be fit well with either 2 normal functions (bimodal) or with 3 normal functions. Additionally, there is a ...
9
votes
1answer
127 views
+50

Why is controlling FDR less stringent than controlling FWER?

I have read that controlling FDR is less stringent than controlling FWER, such as in Wikipedia: FDR controlling procedures exert a less stringent control over false discovery compared to ...
1
vote
0answers
25 views
+50

Factorizable time evolution in a dynamic stochastic process

I have a stationary dynamic system which at each given time $t$ is in state $x_t \in \mathcal{X}$. The set of states $\mathcal{X}$ is assumed to be finite but too large to be enumerated by a practical ...
3
votes
1answer
46 views
+50

Maximum number of alternatives in a discrete choice model

We are modeling a discrete choice scenario, with alternative-specific coefficients. We also break the assumption of independence of irrelevant alternatives. To model this, we are using an ...
2
votes
0answers
32 views
+50

How to use information about likelihood of classes in a classifier?

General question: How can information about the likelihood of classes be used to improve a classifier? Suppose that the probability of each class is known quite precisely (from a very large sample), ...
4
votes
1answer
140 views
+50

Sufficient statistics for $\mu_1 - \mu_2$

If $ X_1, ..., X_n$ is a random sample from $ X \sim N(\mu_1, \sigma^2)$ and $Y_1,..., Y_n$ is a random sample from $Y \sim N(\mu_2, \sigma^2),$ if the samples are independent and $ \sigma^2$ is ...
0
votes
0answers
20 views
+50

Can I use deviance to compare the fit of a model to different datasets?

I'm using R's nls to fit different datasets to the same model. I've read that using R-squared is usually not correct for ...
1
vote
0answers
39 views
+100

Convergence from the EM Algorithm with bivariate mixture distribution

I have a mixture model which I want to find the maximum likelihood estimator of given a set of data $x$ and a set of partially observed data $z$. I have implemented both the E-step (calculating the ...