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Jan
26
comment If my goal is to show very low correlation, how should I check for statistical significance?
Are these two variables normally distributed?
Jan
25
comment Software performace
Thank you for the question, but it is not clear what you testing? Are you comparing software? Are you calculating the variance for multiple runs of the same software?
Jan
25
comment Probit Model: Interpretation of marginal effects if explanatory variables are proportions
Sorry, I misread the original question, @Fuca26 is entirely correct. It is important to note that the marginal effect is a linearization, and only meaningful around the current parameter value.
Jan
25
revised How high dimensional t-test in Feng's article has been obtained?
Appropriate Titles for Authors
Jan
25
comment How high dimensional t-test in Feng's article has been obtained?
I think you need to clarify what your specific question is, what do you understand? what don't you understand?
Jan
25
suggested approved edit on How high dimensional t-test in Feng's article has been obtained?
Dec
16
comment Gradient Boosting for Linear Regression - why does it not work?
I like the answer, but to be a bit pedantic, $\beta$ from regression is the best linear unbiased estimator. Dropping unbiasedness may allow you to do a bit better particularly under high multicollinearity, something you eluded to at the end.
Dec
15
comment How do H compare multiple runs of K-means?
The Rand index and the adjusted rand index provide a classical approach, this statistic is simply based on pairwise counts between clusterings. en.wikipedia.org/wiki/Rand_index
Sep
5
revised Discretizing a Continuous Input for an Artificial Neural Network
ANN was stated several times without defining, the neural-network tag was specified but it is unclear how much this will help with google searching.
Sep
5
suggested approved edit on Discretizing a Continuous Input for an Artificial Neural Network
Aug
16
comment Is there a multiple testing problem when performing t-tests for multiple coeffcients in linear regression?
Sorry I don't have time right now to give you a better answer, but the results from the regression are answering a particular hypothesis test (comparing distributions under the inclusion or removal of a particular variable. A Bonferroni-like adjustment would instead be more appropriate if the null hypothesis was more complicated including a vector of parameter. This is the case in multiple comparisons.
Jun
9
comment Fitting the differences between two curves
Let us continue this discussion in chat.
Jun
9
comment Fitting the differences between two curves
I'm still not happy with the definition; maybe a better statement would be along the lines of testing that the difference between two 'lines' are decreasing over a fixed interval. So what is generating these 'lines'?
Jun
9
comment Fitting the differences between two curves
I think you really need to flesh out what convergence means, in explicit language. If two things cross and remain close they can be parallel for a sufficiently small difference between each other, you could also have to coincident vertical lines, they clearly converge to each other, but diverge in several definitions of convergence. Is anything here random?
Jun
9
comment Fitting the differences between two curves
Can you define converge and diverge in a slightly less visual sense, and what the properties of these curves are? Remember convergence is defined as one object becoming another object in some sense such as the convergence of a sequence of values to another value, or convergence in distribution of a random variable to another random variable.
Jun
3
comment Likelihood-based hypothesis testing
You might want to clarify this a bit, you first state that you know the parameters, and you don't have an estimate. Since you know the parameters you already know if they are the same or not, so why are you doing a test? Then you start talking about CI's for parameters, unless you are doing something Bayesian, your parameters are not stochastic. Remember that you do tests based on the distribution of your estimator, and these tests should reflect the chance that the estimator comes from some null distribution.
Jun
3
comment Using SVD or PCA for reducing dimensionality
I would check out the links specified by @amoeba. What you have done here is reduce the column space or rank of $A$, but you haven't changed its dimension. What you want to do is to project your $d$-dimensional space into a $b$-dimensional space $d > b$. This can be done with eigenvectors/PCA, but you might loose some interpretability in your analysis.
May
31
answered How a statistical package like SAS analyses market risk without any calculus support
May
31
comment Best classifier for data with text among features
You might want to avoid using the word 'best', as it implies optimal which is unlikely to be achieved in your case (this word has very specific meaning in statistics, usually optimal under a squared error loss function). Furthermore you might want to give an example of a few observations including features and the label; in particular I find "label 2 of the features is a text" what does this mean, and what is 'text"?
May
22
comment Feature extraction based on correlations
How many voxel's are you looking at this could quickly get computationally intractable, quite quickly. Efficiency in this context is also a function of how much time are you willing to wait, and what computational resources you have access too. Intuitively I don't see what you are really trying to do with these correlations, what does the PCA of these things do for you?