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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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7 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
130 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
433 views

Vector autoregressive models in R

Can anyone recommend a good tutorial/blog/books which introduces the theory behind vector auto-regressive models but also shows some example code snippets in R?
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1answer
47 views

Circular smooths within a GAM-GEE framework

I have a predictor variable which I fit in a GAM as a circular smooth term: ...
0
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0answers
10 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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1answer
21 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 ...
4
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2answers
89 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 ...
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0answers
19 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 $...
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0answers
35 views

Very Crisp and Concise Statistics Book [closed]

I had been struggling with various Statistics books and online MOOCs but I keep losing interest because of the following reasons: Formulas are unnecessarily complicated. e.g. extra super/subscripts, ...
0
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1answer
17 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, ...
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0answers
28 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 ...
3
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0answers
234 views

Concerns about housing data mining

Can anyone suggest me good papers related to housing data mining. I have googled for a while and couldn't find any recent paper. I am assuming that people have stopped doing any mining related to ...
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0answers
10 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 ...
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1answer
49 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(...
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3answers
2k views

Good literature about cross validation

Does anybody know a good book/webpage to start learning the techniques of cross validation?
4
votes
1answer
68 views

What are best practices for visualizing/selecting visualizations for continuous data?

There appear to be a large number of rules of thumb for histogram bin size and kernel selection for density plots. Are histograms and/or density plots really the best visualization for a single ...
3
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0answers
65 views

Better Visualization for Correlation Plots

I'm currently working on an analysis of correlated variables involving +20 variables. I can create correlation (triangle) plots and that is quite helpful but I am wondering what other kinds of data ...
4
votes
1answer
69 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{...
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0answers
22 views

Latent Point Process Inference

I need to perform inference on a point process, by which I mean I need a statistical model from which I can (at least) estimate likelihoods. The underlying process appears to have a latent structure, ...
0
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0answers
24 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 ...
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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 ...
2
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0answers
137 views

References to learn how to efficiently use neural networks for time series prediction (textbooks, online courses, etc.) [duplicate]

Is there any educational material that focuses on introducing neural networks for time series prediction, ideally comparing to traditional models? It is ok if the references assume the reader knows ...
16
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2answers
8k views

Getting started with neural networks for forecasting

I need some resources to get started on using neural networks for time series forecasting. I am wary of implementing some paper and then finding out that they have greatly over stated the potential of ...
0
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1answer
23 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 ...
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0answers
7 views

Models for document segmentation

I have been tasked with writing an algorithm which can segment a PDF-document, or an image of said document, into segments of text, tables or image. However, I am not sure which models to use. It ...
0
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1answer
453 views

Gamma distribution and applications

I'm looking for references to read about gamma distribution and applications in industry or in quality control. I had a look at statistical methods for reliability data (Meeker and Escobar). It has ...
3
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0answers
48 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
16 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
23 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 $...
2
votes
4answers
8k views

I'm looking for solution manual “A first course in Bayesian statistical methods”

Im looking for a solution manual for Peter Hoff's A first course in Bayesian statistical methods. I cannot find it online, does anybody know whether there is a manual available? Alternatively does ...
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0answers
26 views

Alternatives to The Statistical Sleuth?

I am looking for a simple and concise book on statistics (t-test, ANOVA and all its variants, liner regression, etc.), centered on data analysis. I am not interested in theory or proofs, but just want ...
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0answers
18 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 ...
30
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5answers
149k views

AIC guidelines in model selection

I typically use BIC as my understanding is that it values parsimony more strongly than does AIC. However, I have decided to use a more comprehensive approach now and would like to use AIC as well. I ...
1
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1answer
22 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.
2
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3answers
160 views

Importance of complicated linear algebra for data scientist [closed]

I've already finished Andrew Ng's machine learning course and now working with textbook 'The Elements of Statistical Learning'. I'm successfully implementing equations and concepts described there ...
2
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2answers
76 views

Textbook for AIC, BIC, ANOVA, Eigenvalues and some other topics

I am going through Casella's statistical inference in one-semester standard statistic course and have mathematical background from Sheldon Axler's linear algebra done right and Louis Brand's advanced ...
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4answers
80k views

What references should be cited to support using 30 as a large enough sample size?

I have read/heard many times that the sample size of at least 30 units is considered as "large sample" (normality assumptions of means usually approximately holds due to the CLT, ...). Therefore, in ...
1
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1answer
24 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 ...
4
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2answers
138 views

Bayesian biostatistics book

I am looking for a good book on biostatistics from a Bayesian point of view. I am going to be starting some research in oncology and so books geared towards that would be great, but also just a ...
1
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0answers
29 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. ...
1
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1answer
206 views

Good Literature about Problems with R squared

A question from a newbie. Recently, I was told that R squared or adjusted R squared can not used as a criteria to select a good regression model (model selection) due to, for example, overfitting . I ...
0
votes
1answer
190 views

P Value interpretation in K fold Validation

I am validating a credit risk model. I did a k fold validation to check the stability of the estimates. The estimates of the model are quite stable but the variables now have a high P value(above 0.1) ...
6
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3answers
2k views

Books - Deep learning and recurrent neural networks

I have read with interest the Elements of Statistical Learning and Murphy's Machine Learning - a Probabilistic Perspective. The latter touches upon deep learning and deep / recurrent neural networks ...
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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 ...
1
vote
1answer
41 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 ...
2
votes
1answer
129 views

Books or resources for learning unified view of classical statistics?

I did a science PhD doing applied bayesian statistics, and I liked it so much that I decided to shift to statistics. For the last 2-3 years have been working at a firm that has more of a frequentist ...
11
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4answers
2k views

Good text on Clinical Trials?

I'm an undergraduate statistics student looking for a good treatment of clinical trials analysis. The text should cover the fundamentals of experimental design, blocking, power analysis, latin squares ...
3
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1answer
45 views

Book/resource recommendation for design and analysis of clinical trials

I will be transitioning from a role analyzing purely observational epidemiological data to a role analyzing and designing clinical trials. I was wondering if you may have any recommendations for books ...
1
vote
1answer
18 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 ...
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0answers
15 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 ...