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Statistical intepretation of ratio of spectral norm of covariance matrix to its Frobenius norm
Frobenius norm tells you how correlated the variates are. Normalizing by trace instead of spectral norm comes up in estimation bounds, target is hard to estimate when your measurement vectors are ...
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Textbook on high-dimensional statistics
You mention interest in genomic problems, with 1000s of genes but 100s of samples. Brad Efron's "Large Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction" might ...
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Accepted
Textbook on high-dimensional statistics
The book "Fundamentals of High-Dimensional Statistics" by Lederer should be what you want: it is very well written, explains all details, and contains R labs. The first chapters explain the ...
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Pattern recognition and machine learning (Bishop) - Derivation of Evidence approximation
The approximation has nothing to do with Dirac delta function or uniformity of hyperpriors. Here the assumption is only "sharply peaked". Due to this assumption, the integrand $p(t|{\bf w},\...
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Choosing among recent references on Machine Learning
I would recommand the new (2022) version of the Probabilistic ML book from Kevin P. Murphy: https://probml.github.io/pml-book/book1.html.
I am a huge fan of the approach from the first version: it has ...
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Resources to understand the bootstrap
Bradley Efron is the statistician that introduced the bootstrap sampling technique. His original paper is Bootstrap Methods: Another Look at the Jacknife.
Efron also released Computer Age Statistical ...
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Difference between Univariate Linear Regression and Simple Linear Regression?
Univariate Model : 1) Simple : 1 DV & 1 IV
2) Multiple: 1 DV & many IV
Multivariate Model: 1) Simple Multivariate: many DV & 1 IV
<...
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Formal approach to message passing algorithms
Graphical Models, Exponential Families, and
Variational Inference by Martin Wainwright and Michael I. Jordan might fit the bill. Jordan is a very distinguished mathematician, and this book is ...
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Recommended books or articles as introduction to Cluster Analysis?
I am not sure what software you use, but this book is a good one if you are using R. It covers many common unsupervised learning algorithms with good examples!
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Recommended books or articles as introduction to Cluster Analysis?
From an statistical viewpoint there is Model-Based Clustering and Classification for Data Science which uses R for the examples. The book is by Charles Bouveyron ...
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Research methods book for PhD students
Not sure what you mean by advanced research methods, but I found the following books useful:
W. Lawrence Neuman Social Research Methods: Qualitative and Quantitative Approaches
Whitley, B. E., & ...
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Is there a name for a logical fallacy that uses irrelevant or unfamiliar statistics to make a point?
One is unreasonable averaging. And I have a great real-world example. This paper Staff Memo 4/2021 from the Norwegian Central bank. (link at the bottom).
To explain unreasonable averaging, here is a ...
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Parameters of the Nakagami Distribution given a known Gamma distribution
Per Wikipedia, given a gamma distributed $Y$ with shape $k$ and scale $\theta$, $X=\sqrt{Y}$ is Nakagami with parameters $m$ and $\Omega$, where
$$ k=m \quad\text{and}\quad\theta=\frac{\Omega}{m}$$
or
...
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Is there a name for a logical fallacy that uses irrelevant or unfamiliar statistics to make a point?
I think you could reasonably call this an instance of context-dropping --- in the present case the conclusion is a fallacious inference from the evidence, since the inference relies on a lack of ...
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What is industrial statistics?
I agree with Nick Cox, but if you'd like a definition this seems like a useful one:
Industrial Statistics is concerned with maintaining and improving the
quality of goods and services. It involves a ...
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2
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Term for the error in machine learning as a direct result of incorrectly labelled data?
Answered in comments by Sycorax:
This is sometimes called "label noise," the acknowledgement that the
labels themselves may be incorrect (for any reason, including human
error).
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Need a good book on Dirichlet Process
Better start with Tamara Broderick 2016
"Nonparametric Bayes Tutorial"
https://tamarabroderick.com/tutorial_2016_mlss_cadiz.html
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Reference for Logistic and Sigmoid Kernels
Kernel is basically a PDF of a respective distribution without parameters. Logistic distribution uses logistic function as CDF, and its PDF would be exactly as you stated in your question (you can ...
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Is Probabilistic Reasoning In Intelligent Systems a pre-requisite for Causality?
I have only read Causality (2nd ed) from Judea Pearl. I would say that, at a minimum. a graduate level course in probability and statistics is the only "pre-req". I would say each of Judea'...
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Resources for learning about lasso
I think the suggestions in the comments above will all be good references:
Introduction to Statistical Learning (ISL or ISLR, see statlearning.com) gives a "broad and less technical treatment of ...
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