Questions tagged [references]

Questions seeking external references (books, papers, etc.) about a particular subject. Always use a more specific tag in addition.

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2answers
52 views

Reference request - Computer Vision Book

What are the best books for obtaining a strong understanding of computer vision? From what I understand based on my undergraduate class, almost all current state-of-the-art computer vision is just ...
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0answers
31 views

Comparisons of targeting models

I need to compare two targeting models in an experiment. We will apply the same treatment to everyone who is targeted. The experimental set up is as follows: The total population is split into ...
0
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0answers
13 views

Reference for Inception-v2

Cross-posted from Data Science StackExchange. The "Rethinking" paper doesn't describe the actual implementation of the Inception-v3 model in Tensorflow: an accurate description is written in model....
7
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2answers
283 views

Argument on Interactions in The Book of Why

There is a paragraph on interactions in The Book of Why (Pearl & Mackenzie, 2018), Chapter 9 (I cannot share the page number because I have the book in epub format), where the authors argue that: ...
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0answers
13 views

Reference Request: Overall and Average Accuracy [duplicate]

I am looking for a source to cite on the definitions of overall and average accuracy. I have found many informal sources online, including here on CVSE, but the papers and textbooks I have found seem ...
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0answers
22 views

Summary statistics for bipartite networks

I have a large bipartite network that I would like to summarise. So far, I have found the following summary statistics: Degree centrality Graph density Modularity Nestedness I have not found a ...
1
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1answer
30 views

Theory on custom loss functions for GBDT and other ML

I'm looking for resources on the theory behind choosing a loss function for ML---I'm interested in GBDT but for deep learning would work as well. I'd like to get a better understanding of how the loss ...
2
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0answers
30 views

Reference request: Network/graph topology inference

I am a mathematician looking for a survey/book on methods for inference of graph/network topology (structure). Specifically, the kind of problem I am looking to study is as follows: Given a graph $...
0
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1answer
48 views

Standardized residuals GARCH models

Lets say I have a GARCH(1,1) model, First, I model the conditional MEAN, $$Y_t=\delta+\beta Y_{t-1}+\varepsilon_t$$ NextI gather the residuals $\varepsilon_t$ and model the conditional variance, ...
2
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2answers
55 views

Applying TF/IDF to non-text data?

I have a classification problem in which I am supposed to predict the end state of an object based on a set of events it experiences. There are about one thousand possible events and each object is ...
1
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1answer
53 views

Reference for continuous (multivariate) distributions?

A reference that not just enlist the formulas of continuous (multivariate) distributions but goes in details about them and maybe treat the relations between them (e.g. derivation/proofs, intuition ...
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0answers
11 views

MSE series derived from cross validation on time series

This answer suggests a way of doing leave-one-out cross-validation on time series data: An approach that's sometimes more principled for time series is forward chaining, where your procedure ...
5
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1answer
59 views

Name and interpretation of “$h(x)$” in exponential family

The exponential family is defined (in many sources) as: $$p(x | \theta) = h(x) \exp\{\theta^TT(x) - A(\theta)\}$$ where: $T(x)$ is a sufficient statistic, $\theta$ is a canonical parameter, and $A(...
4
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1answer
70 views

Distribution Reference $\gamma x^{\gamma-1}$

I have been unable to find resources regarding the family of distributions with pdf $$ f_\gamma(x) = \begin{cases} \gamma x^{\gamma-1}, & \text{if } 0 \leq x \leq 1 \\ 0, & \text{...
1
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1answer
37 views

Generalizing Bayesian methods by assuming a “distribution of distributions” instead of a prior

Bayesian methods assume a prior distribution with several hyperparameters. Unfortunately, this is asymptotically incorrect, because distributions in the real world are never exact. For example, the ...
1
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0answers
20 views

A book-chapter long nontechnical introduction / overview of neural networks

I will be teaching an introductory course in machine learning for students in management who have minimal quantitative skills. I am looking for a brief and gentle introduction to neural networks that ...
0
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1answer
24 views

applied papers on probabilistic generative models and inference engines

I am looking for applications papers where people choose some task on which they will do Bayesian inferencing and graphical modeling, and then build an inference engine to infer latent parameters. And ...
3
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0answers
51 views

What are the main approaches to the foundation of statistics without probability

The frequentist, likelihood and, to an even greater extent, Bayesian approaches to statistics are all based on probability. Without probability, it seems difficult to use a data sample ("seen" cases), ...
2
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0answers
20 views

Probability of sparse spectrum

Consider a vector $v$ such that $v \sim \mathrm{Unif}(\mathbb{S}^{d-1})$, the uniform distribution on the unit sphere in $d$ dimensions. Question: is there an upper bound on the probability that $v$ ...
3
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0answers
33 views

Convergence rate of the inverse covariance matrix [closed]

I am trying to find results regarding the convergence rate of the inverse covariance matrix in the case where the number of observations $n$ is larger than the number of dimensions $p$. Assume that $...
0
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2answers
83 views

Alternatives to The Statistical Sleuth?

I am looking for a simple and concise book on statistics (t-test, ANOVA and all its variants, linear regression, etc.), centered on data analysis. I am not interested in theory or proofs, but just ...
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0answers
20 views

Dimension of a probability distribution function

Consider the following statement We want to sample from a complex high dimensional distribution which is intractable. What is meant by a dimension of distribution here? Does each random variable ...
1
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1answer
25 views

How much matrix differentiation background for Seber and Lee's Linear Regression Analysis

How self-contained is Seber & Lee's textbook 'Linear Regression Analysis'? It seems to assume knowledge of matrix differentiation.
1
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1answer
26 views

Does a more powerful test mechanically lead to more type III errors then?

"Type III error occurs when you correctly conclude that the two groups are statistically different, but you are wrong about the direction of the difference. Say that a treatment increases some ...
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0answers
38 views

Literature on conceptual understanding of beta regression

I am researching beta regression models to decide if they are appropriate for my data. My very first search yielded this basic introduction, that also describes the zero-one inflated beta regression. ...
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0answers
17 views

Literature recommendation for convolutional neural nets

I am looking for a good book or an article concearning convolutional neural nets, especially their architecture. I like the http://deeplearningbook.org but it does not provide any information on the ...
2
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1answer
55 views

Targeted Maximum Likelihood Estimation for dummies?

I have tried to get my head around the concept of TMLE, but most references seem to be written by people who despise being understood (or maybe I am just hebetudinous). I have tried to read the paper ...
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0answers
18 views

References for this ARMA two step method estimation

I was doing some survey on ARMA parameters estimation methods. While on that, I found these lecture notes: http://www.phdeconomics.sssup.it/documents/Lesson12.pdf There, the author describes a two ...
1
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1answer
19 views

Gradually increasing reinforcement learning environment complexity

The problem of robotic arm control has different levels of complexity ranging from simulation to real-world application. For example, a simulation may not model friction of the joints, which becomes ...
0
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1answer
25 views

When kernels are not useful in SVM?

In SVM using kernels we map the original features to the higher, transformer space (feature mapping) and then perform linear SVM in this higher space. But when kernels are not useful? I could not find ...
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0answers
34 views

Mysteries of the Normal Distribution [duplicate]

I have been studying ML for over a year and am actually a Bachelors of Statistics myself and am sick of not knowing the beauty of the Gaussian distribution and why it is so prevalent in nature. I've ...
1
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1answer
57 views

Why is using keras ImageDataGenerator for data augmentation relevent?

I have used keras ImageDataGenerator to generate more data in my neural networks as I have had really small datasets and it has proven itself. As far as I ...
2
votes
1answer
160 views

Analysis of count data with percentages

for my master thesis I count and identify sediment grains. In total I have 82 samples from 3 different gravity cores. I divided the sediment components in 11 groups (Quarz, Mica, Opaque, Aggregate, ...
4
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0answers
24 views

How to combine noisy and noise-free datasets to train a model

Overview Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
4
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2answers
110 views

Are GLMs just glorified WLS regressions?

When performing weighted least squares $L = \frac{1}{2} \sum_i w_i r_i^2$, Aitken showed that one ought to weight each sample by the inverse of its variance $w_i=1/\sigma_i^2$. This leads to gradients ...
0
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2answers
38 views

Dealing with extreme outliers in administrative data (in R)

I work with some data that includes some "extreme outliers". E.g. timestamps that are totally unreasonable (surgery took 20 days when most take 1 hour). Is there a set of principles one can use to ...
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0answers
6 views

Risk of Spurious Relationships As a Function of 3 Magnitudes: # Times Sampled, # Individuals Sampled and # Variables Measured

I've seen some literature that quantifies the risk of spurious relationships in terms of sample size vs number of variables but I've not seen literature that quantifies the risk based on all 3 ...
3
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0answers
31 views

Fisher information matrix and gradients

I'm a math Ph.D. without formal training in statistics. Quite a few papers on normalization methods in deep learning mention the Fisher information matrix and how it's related to the Riemannian metric ...
0
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1answer
33 views

Suggested books to study statistics [closed]

I am doing a research that requires me collecting and analyzing data samples in order to identify if there is correlation or no with respect to some parameter. I am looking for the best resources to ...
4
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2answers
51 views

Best resources on imputation in R [duplicate]

This is my first question at stats. I need to impute some factors and numbers in my data set in R. What are my best options regarding packages and also a source to read more about the theory.
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0answers
40 views

Where to learn the theory behind common statistical techniques [duplicate]

I'm a college student and pursuing (in part, at least) a statistics and data science track. Much of my coursework beyond the introductory statistics sequence has involved topics like multiple ...
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0answers
18 views

Wilks' lambda's exact distribution when one of the parameters is 1 or 2

Citing Wikipedia, From the relations between a beta and an F-distribution, Wilks' lambda can be related to the F-distribution when one of the parameters of the Wilks lambda distribution is either 1 ...
5
votes
1answer
273 views

Statistical analysis applied to methods coming out of Machine Learning [closed]

Most of the recent famous methods coming out of the machine learning, are supervised learning methods like Decision Trees, Random Forests, Deep Learning, SVMs. The more traditional supervised ...
3
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0answers
41 views

Looking to identify a book by a top statistician with a chapter on Simpson's Paradox

It was more than 20 years ago. I had just gotten acquainted with Simpson's paradox. I was browsing in a bookstore and saw a book by an eminent statistician -- eminent in the sense that I had come ...
0
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1answer
21 views

marchenko pastur for Correlation

It has been suggested to me that if I construct a covariance or correlation matrix using factor model then I can use the Marchenko-Pastur distribution to highlight significant correlations (or ...
0
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0answers
20 views

Phase marginal for a multivariate complex Gaussian density

Suppose $z$ is a random variable taking values in $\mathbb{C}^n$ and admitting the complex Gaussian density $p(z;W) \propto \exp{(-\frac{1}{2}z^*Wz)}$, where $W$ is Hermitian. Let $r$ be the vector of ...
1
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2answers
444 views

Finding the MLE of Poisson in R [closed]

I'm trying to determine the MLE of $\lambda$ in a Poisson distribution using R. I'm aware that the MLE is $\hat{\lambda}=\bar{x}$ but I want to demonstrate this using Rmarkdown. My experience with R ...
0
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0answers
12 views

(Existence part of) Neyman-Pearson via weak-* convergence

I would like a ask whether there is any statistical reference containing the following functional analytic argument for the existence part of Neyman-Pearson: Let $(R, \mathcal{F}, \mu)$ be a measure ...
1
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0answers
31 views

Correspondence between time series models in continuous vs. discrete time

I am interested in an overview over the connection and correspondence between time series models in continuous vs. discrete time in finance. E.g. take ARMA(p,q) or GARCH(s,r) or ARMA(p,q)-GARCH(s,r) ...
2
votes
1answer
70 views

Best Course of Study for Data-Science/Statistician Interviews [closed]

This is my first question here, so please pardon my gaffes. I am currently working as a Data-Scientist, a position which I worked up from Junior Analyst position.My bachelors is in Computer Science ...