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NRH

Copenhagen, Denmark

Researcher, broadly interested in probability theory, mathematical, computational and applied statistics.

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Top Questions
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78
On the importance of the i.i.d. assumption in statistical learning
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63
How to understand degrees of freedom?
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57
Why is sample standard deviation a biased estimator of $\sigma$?
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46
Is there an intuitive interpretation of $A^TA$ for a data matrix $A$?
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43
Why are regression problems called "regression" problems?
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42
How to sample from a normal distribution with known mean and variance using a conventional programming language?
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40
How to interpret an inverse covariance or precision matrix?
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34
Is machine learning less useful for understanding causality, thus less interesting for social science?
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33
Is there any gold standard for modeling irregularly spaced time series?
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31
What is the objective function of PCA?
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28
Can I use Kolmogorov-Smirnov to compare two empirical distributions?
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28
Bootstrap-based confidence interval
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28
What is the distribution of the Euclidean distance between two normally distributed random variables?
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26
How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
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24
Is AR(1) a Markov process?
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23
Significance testing or cross validation?
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23
Differences between heavy tail and fat tail distributions
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23
Definition of autocorrelation time (for effective sample size)
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22
Overdispersion and Underdispersion in Negative Binomial/Poisson Regression
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22
What is empirical entropy?
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20
Are $t$ test and one-way ANOVA both Wald tests?
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20
Who invented k-fold cross-validation?
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19
Are the random variables $X$ and $f(X)$ dependent?
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19
Why are Gaussian "discriminant" analysis models called so?
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19
What are some illustrative applications of empirical likelihood?
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18
Why is a projection matrix of an orthogonal projection symmetric?
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17
Is there any reason to prefer the AIC or BIC over the other?
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17
Why does my bootstrap interval have terrible coverage?
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16
Biased bootstrap: is it okay to center the CI around the observed statistic?
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16
What are the pros and cons of learning about a distribution algorithmically (simulations) versus mathematically?
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