# Tag Info

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Textbook: Czado "Analyzing Dependent Data with Vine Copulas: A Practical Guide With R" (2019). R-vines are presented in chapters 5-9. Slides: Kramer & Schepsmeier "Introduction to vine copulas" (2011) Slides: Haff "How To Select A Good Vine" (2016) Wikipedia: quite detailed.

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Yes, it is basically TRUE. Mean is a first moment, the variance a second moment. Assuming moment estimates, the variance of an empirical mean is a second moment. The variance of an empirical variance is a fourth moment. So to ascertain the uncertainty in a variance estimate, you take fourth powers of your data! That will end to magnify errors quite a bit ... ...

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This playlist is a great explanation and is based on the paper by Rabiner mentioned in the answer above. - https://www.youtube.com/watch?v=J_y5hx_ySCg&list=PLix7MmR3doRo3NGNzrq48FItR3TDyuLCo This above playlist is a 12 series lecture which begins with explanation of Markov Chains/ observable Markov Models and then moves on to HMMs

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Circular statistics are useful to describe random processes with a rotational symmetry or a modular arithmetic such that multiple additions can result in an identity. E.g. for a clock, if you add twelve times an hour then you return at the same point. This is not relevant for 'the lateral angle of the internal acoustic meatus' (you might want to simplify ...

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See the 2019 preprint Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer by Alessandretti, Baronchelli & He. Here is the Abstract: Empirical analysis is often the first step towards the birth of a conjecture. This is the case of the Birch-Swinnerton-Dyer (BSD) Conjecture describing the rational points on an elliptic curve, ...

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I think the word 'introductory' should be banned in statistics. Not many without a strong background in statistics will find topics such as vector autoregressive models or ARDL to be introductory nor the Hamilton work and many others mentioned. There is a a huge gap between academic and practitioner audiences in this topic I feel. Having looked hard as a ...

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What the term tensor means depends on the context it is used in: Field Meaning Machine learning Multi-dimensional array (usually numeric)1 2 Maths an algebraic object describing a (multilinear) relationship between sets of algebraic objects The machine learning term is inspired by the fact that in a fixed basis/coordinate system, a tensor can be ...

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This is not an answer providing a canonical example. However, this answer provides another occurrence of the same type of fallacy mistake in a slightly different context. This makes it interesting to be placed as an answer. (I do not want to add it to the question which would make it too much cluttered) It also shows that this type of fallacy is more ...

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Looking at the first page (first edition) with set.seed() commands, I was able to reproduce the outcome, using R version 3.4.4 (2018-03-15). (R studio produces exactly the same.) > set.seed(1303) > rnorm(10) [1] -1.1439763145 1.3421293656 2.1853904757 0.5363925179 0.0631929665 [6] 0.5022344825 -0.0004167247 0.5658198405 -0.5725226890 -1....

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The seed should make your analysis fully reproducible. I would much rather assume that differences in R (and package) versions, or a bug in your (or the book's) code, or possibly even machine/OS differences would be responsible for any discrepancies. (Converted from a comment.)

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I would like the share the results of staring at a diagram. I promise to do almost no calculation (and the calculations that are performed involve only multiplications by $0,$ $1,$ and $-1$ along with additions). Start by re-expressing $X$ as $$X = A-B \mod 1,$$ which is just the fractional part of $A-B.$ This function is defined on the entire $(A,B)$ ...

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I am able to show that $X,Y$ are id Unif(0,1). My problem is showing they are iid (i.e. I'm missing the independent). Intuitively: Besides $X \sim U(0,1)$ and $Y \sim U(0,1)$ you can also show the independence from $B$ like $X|B \sim U(0,1)$ and $Y|B \sim U(0,1)$. $B$ is the only common variable in the equations for $X$ and $Y$ The distributions of $X$ and ...

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Yes, you can do structural equation models without latent variables. Regression, t-tests (paired and unpaired) can all be considered to be SEMs without latent variables. In addition, things like mediation analysis, or cross-lagged regression analysis, can also be done as SEMs, without (or with) latent variables. (Well, in a sense that's not true. Almost all ...

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There are different concepts and some of them overlap. Also I think the main once are already been mention : ). I think these are also interesting in terms of time series and analysis. 'spurious regression' high R2 values and high t-ratios yielding results with no economic meaning. This could happed ussually in a) just silly correlations as in https://www....

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Hansen & Lunde "Does Anything Beat a GARCH(1,1)?" compare a large number of parametric volatility models in an extensive empirical study. They find that no other model provides significantly better forecasts than the GARCH(1,1) model. (This is probably the best known paper of GARCH model comparisons. It is however already 20 years old, so you ...

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