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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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193
votes
8answers
85k views

Algorithms for automatic model selection

I would like to implement an algorithm for automatic model selection. I am thinking of doing stepwise regression but anything will do (it has to be based on linear regressions though). My problem ...
248
votes
149answers
130k views

Famous statistical quotations

What is your favorite statistical quote? This is community wiki, so please one quote per answer.
99
votes
14answers
71k views

Books for self-studying time series analysis?

I started by Time Series Analysis by Hamilton, but I am lost hopelessly. This book is really too theoretical for me to learn by myself. Does anybody have a recommendation for a textbook on time ...
57
votes
17answers
10k views

Machine learning cookbook / reference card / cheatsheet?

I find resources like the Probability and Statistics Cookbook and The R Reference Card for Data Mining incredibly useful. They obviously serve well as references but also help me to organize my ...
192
votes
36answers
110k views

What is the best introductory Bayesian statistics textbook?

Which is the best introductory textbook for Bayesian statistics? One book per answer, please.
54
votes
6answers
16k views

Introduction to statistics for mathematicians

What is a good introduction to statistics for a mathematician who is already well-versed in probability? I have two distinct motivations for asking, which may well lead to different suggestions: I'd ...
56
votes
6answers
30k views

L2 regularization is equivalent to Gaussian Prior

I keep reading this and intuitively I can see this but how does one go from L2 regularization to saying that this is a Gaussian Prior analytically? Same goes for saying L1 is equivalent to a Laplacean ...
11
votes
3answers
4k views

$P[X=x]=0$ when $X$ is continuous variable

I know that for continuous variable $P[X=x]=0$. But i can't visualize that if $P[X=x]=0$, there is infinite number of possible $x$'s. And also why do their probabilities get infinitely small ?
94
votes
31answers
33k views

What book would you recommend for non-statistician scientists?

What book would you recommend for scientists who are not statisticians? Clear delivery is most appreciated. As well as the explanation of the appropriate techniques and methods for typical tasks: ...
41
votes
4answers
83k 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 ...
119
votes
27answers
36k views

Free statistical textbooks

Are there any free statistical textbooks available?
77
votes
9answers
11k views

Mathematician wants the equivalent knowledge to a quality stats degree

I know people love to close duplicates so I am not asking for a reference to start learning statistics (as here). I have a doctorate in mathematics but never learned statistics. What is the shortest ...
78
votes
21answers
41k views

Free resources for learning R

I'm interested in learning R on the cheap. What's the best free resource/book/tutorial for learning R?
171
votes
9answers
34k views

Why the sudden fascination with tensors?

I've noticed lately that a lot of people are developing tensor equivalents of many methods (tensor factorization, tensor kernels, tensors for topic modeling, etc) I'm wondering, why is the world ...
31
votes
2answers
4k views

Theory behind partial least squares regression

Can anyone recommend a good exposition of the theory behind partial least squares regression (available online) for someone who understands SVD and PCA? I have looked at many sources online and have ...
55
votes
9answers
17k views

Advanced statistics books recommendation

There are several threads on this site for book recommendations on introductory statistics and machine learning but I am looking for a text on advanced statistics including, in order of priority: ...
28
votes
5answers
49k views

Real-life examples of common distributions

I am a grad student developing an interest for statistics. I like the material over-all, but I sometimes have a hard time thinking about applications to real life. Specifically, my question is about ...
50
votes
8answers
21k views

Book for reading before Elements of Statistical Learning?

Based on this post, I want to digest Elements of Statistical Learning. Fortunately it is available for free and I started reading it. I don't have enough knowledge to understand it. Can you recommend ...
9
votes
2answers
14k views

Logistic regression and ordinal independent variables

I have found this post: Yes. The coefficient reflects the change in log odds for each increment of change in the ordinal predictor. This (very common) model specification assumes the the predictor ...
26
votes
4answers
10k views

Internal vs external cross-validation and model selection

My understanding is that with cross validation and model selection we try to address two things: P1. Estimate the expected loss on the population when training with our sample P2. Measure and report ...
26
votes
11answers
18k views

Book recommendations for multivariate analysis

I'm interested in getting some books about multivariate analysis, and need your recommendations. Free books are always welcome, but if you know about some great non-free MVA book, please, state it.
13
votes
2answers
627 views

do(x) operator meaning?

I have seen the $do(x)$ operator everywhere in some literature review I am doing on Causality (see, for instance this wikipedia entry). However, I cannot find a formal and general definition of this ...
94
votes
5answers
32k views

Comprehensive list of activation functions in neural networks with pros/cons

Are there any reference document(s) that give a comprehensive list of activation functions in neural networks along with their pros/cons (and ideally some pointers to publications where they were ...
45
votes
2answers
62k views

Linear kernel and non-linear kernel for support vector machine?

When using support vector machine, are there any guidelines on choosing linear kernel vs. nonlinear kernel, like RBF? I once heard that non-linear kernel tends not to perform well once the number of ...
17
votes
4answers
3k views

Good resources (online or book) on the mathematical foundations of statistics

Before I ask my question, let me give you a bit of background about what I know about statistics so that you have a better sense of the types of resources that I'm looking for. I'm a graduate student ...
16
votes
3answers
3k views

Idea and intuition behind quasi maximum likelihood estimation (QMLE)

Question(s): What is the idea and intuition behind quasi maximum likelihood estimation (QMLE; also known as pseudo maximum likelihood estimation, PMLE)? What makes the estimator work when the actual ...
9
votes
1answer
6k views

ML estimate of exponential distribution (with censored data)

In Survival Analysis, you assume the survival time of a r.v. $X_i$ to be exponentially distributed. Considering now that I have $x_1,\dots,x_n$ "outcomes" of i.i.d r.v.'s $X_i$. Only some proportion ...
7
votes
2answers
2k views

Beta as distribution of proportions (or as continuous Binomial)

Beta distribution is related to binomial being also the distribution for order statistics. Probability mass function of binomial distribution is $$ f(k) = {n \choose k} p^k (1-p) ^{n-k} \tag{1} $$ ...
166
votes
76answers
208k views

Statistics Jokes

Well, we've got favourite statistics quotes. What about statistics jokes?
24
votes
13answers
28k views

Econometrics textbooks?

Which good econometrics textbooks would you recommend? Edit: there are quite a few books out there, with varying levels of mathematical sophistication. It would be good to get some idea of how ...
15
votes
4answers
5k views

Is the logit function always the best for regression modeling of binary data?

I've been thinking about this problem. The usual logistic function for modeling binary data is: $$ \log\left(\frac{p}{1-p}\right)=\beta_0+\beta_1X_1+\beta_2X_2+\ldots $$ However is the logit function,...
7
votes
7answers
2k views

Is there a GLM bible?

Is there consensus in the field of statistics that one book is the absolute best source and completely covering every aspect of GLM - detailing everything from estimation to inference?
50
votes
16answers
21k views

Recommended books on experiment design?

What are the panel's recommendations for books on design of experiments? Ideally, books should be still in print or available electronically, although that may not always be feasible. If you feel ...
23
votes
5answers
2k views

Introduction to causal analysis

What are good books that introduce causal analysis? I'm thinking of an introduction that both explains the principles of causal analysis and shows how different statistical methods could be used to ...
25
votes
1answer
58k views

Comparing levels of factors after a GLM in R

Here is a little background about my situation: my data refer to the number of prey successfully eaten by a predator. As the number of prey is limited (25 available) in each trial, I had a column "...
16
votes
1answer
537 views

What does it mean that AUC is a semi-proper scoring rule?

A proper scoring rule is a rule that is maximized by a 'true' model and it doesn't allow 'hedging' or gaming the system (deliberately reporting different results as is the true belief of the model to ...
43
votes
7answers
6k views

Neural network references (textbooks, online courses) for beginners

I want to learn Neural Networks. I am a Computational Linguist. I know statistical machine learning approaches and can code in Python. I am looking to start with its concepts, and know one or two ...
37
votes
2answers
41k views

When and how to use standardized explanatory variables in linear regression

I have 2 simple questions about linear regression: When is it advised to standardize the explanatory variables? Once estimation is carried out with standardized values, how can one predict with new ...
29
votes
1answer
21k views

How are the standard errors computed for the fitted values from a logistic regression?

When you predict a fitted value from a logistic regression model, how are standard errors computed? I mean for the fitted values, not for the coefficients (which involves Fishers information matrix). ...
9
votes
3answers
13k views

A normal divided by the $\sqrt{\chi^2(s)/s}$ gives you a t-distribution — proof

let $Z \sim N(0,1)$ and $W \sim \chi^2(s)$. If $Z$ and $W$ are independently distributed then the variable $Y = \frac{Z}{\sqrt{W/s}}$ follows a $t$ distribution with degrees of freedom $s$. I am ...
14
votes
2answers
3k views

Best suggested textbooks on Bootstrap resampling?

I just wanted to ask which are in your opinion the best available books on bootstrap out there. By this I don't necessarily only mean the one written by its developers. Could you please indicate ...
9
votes
1answer
744 views

What should a graduate course in experimental design cover?

I have been asked to propose a course in experimental design for advanced graduate students in agronomy and ecology. I have never taken such a course, and was surprised to find that the course might ...
13
votes
1answer
9k views

What is the long run variance?

How is long run variance in the realm of time series analysis defined? I understand it is utilized in the case there is a correlation structure in the data. So our stochastic process would not be a ...
10
votes
0answers
1k views

Machine learning self-learning book? [duplicate]

Possible Duplicate: Machine learning cookbook / reference card / cheatsheet? I wonder if there is a good self-learning textbook for machine learning? I am particularly looking for those in ...
96
votes
4answers
59k views

How to intuitively explain what a kernel is?

Many machine learning classifiers (e.g. support vector machines) allow one to specify a kernel. What would be an intuitive way of explaining what a kernel is? One aspect I have been thinking of is ...
28
votes
7answers
12k views

Good sources for learning Markov chain Monte Carlo (MCMC)

Any suggestions for a good source to learn MCMC methods?
17
votes
12answers
32k views

Best books for an introduction to statistical data analysis?

I bought this book: How to Measure Anything: Finding the Value of Intangibles in Business and Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions What ...
22
votes
3answers
2k views

Benefits of using QQ-plots over histograms

In this comment, Nick Cox wrote: Binning into classes is an ancient method. While histograms can be useful, modern statistical software makes it easy as well as advisable to fit distributions to ...
16
votes
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 ...
11
votes
2answers
3k views

Reference for $\mathrm{Var}[s^2]=\sigma^4 \left(\frac{2}{n-1} + \frac{\kappa}{n}\right)$?

In his answer to my previous question, @Erik P. gives the expression $$ \mathrm{Var}[s^2]=\sigma^4 \left(\frac{2}{n-1} + \frac{\kappa}{n}\right) \>, $$ where $\kappa$ is the excess kurtosis of the ...