"References" is our generic tag for questions seeking information about books, papers, presentations, videos of lectures, on-line tutorials, etc., regarding any subject matter that is on-topic for Cross Validated.

learn more… | top users | synonyms (9)

0
votes
0answers
4 views

Posterior pointwise uncertainty of variational mixture of Gaussians

Given a variational mixture of Gaussians (as per, e.g., Chapter 10 of Bishop, 2006), we can compute the posterior predictive pdf: $$ \left\langle p(x|\alpha,\beta,\nu,\mu,V) \right\rangle $$ where ...
0
votes
0answers
8 views

Reference for forecasting nonstationary variables

My goal is to extrapolate / forecast data up to 10, 20, 100 years depending on certain independent variables. Is there like a publication or a book that I could follow that specifically pertains to ...
0
votes
0answers
7 views

Intraclass correlation coefficient in Bayesian statistics

I need some references about intraclass correlation coefficient in Bayesian statistics and hypothesis testing. I already take a look in A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian ...
4
votes
0answers
41 views

Bayesian inference via approximate data likelihood

Suppose that we have a very large i.i.d. sample $x_1,...,x_n$ and a data likelihood defined by $$p(x | \theta,\beta) = \prod_ip(x_i | \theta,\beta)$$. Further suppose that $\theta$ is the parameter ...
0
votes
0answers
15 views

Validating regression - common and best practice

Is there a reference setting out a best practice way to validate a regression (such as Lasso, but in general any automated regression), and what is done in practice? My motivation for the question is ...
0
votes
0answers
11 views

Factor Analysis with low sample size

Does anyone know of references to support conducting EFA with a low sample size?
0
votes
0answers
4 views

Modern Introduction to Orthogonal Density Estimators [on hold]

I've found an abundance of papers about the subject, but most of them are rather dated, Most Notably [1]. There's a good review from 1991 [2], but it is very brief and insufficient for research level ...
3
votes
1answer
27 views

convergence of geometric mean/harmonic mean

Does any one know papers regarding the convergence of geometric mean or harmonic mean in probability, parallel to central limit theorem?
0
votes
0answers
9 views

“*usefulness*” is a bivariate property used in Regression and Anova. Has a generalization (trivariate) analogon been discussed?

Just for selfstudy/exercising of algorithms I looked at the computation of the "usefulness"-measure in multiple regression, which means the part of variance which one independent item contributes to ...
3
votes
1answer
67 views

Learning functional analysis for studying kernels

I'm trying to learn more about kernel machine theory and I've discovered that I need to learn a lot of background math, and so I'm looking for some good resources for this. In particular: I've got ...
0
votes
1answer
43 views

Text to read to prepare for course in advanced data analysis (second-level data analysis graduate course)

I have some free time this summer and would like to read something to prepare for the "Advanced Data Analysis" course I will be taking. This second level course, according to the course catalog, ...
1
vote
1answer
36 views

References for learning about online random forests

I am new to concepts of random forest. Can someone provide relevant sites where I could get learn more about using random forests to learn incoming data like an online algorithm?
1
vote
0answers
18 views

Treatment changing over time

I have a problem with the identification technique of my research paper. I would like to estimate the causal effect of a policy (the introduction of a budget balance rule) on the composition of ...
0
votes
1answer
17 views

References on ARDL model

Please suggest books/references on ARDL model and ARDL bounds test approach to study.
0
votes
0answers
20 views

How to predict link using rooted pagerank

I am studying link prediction in social networks and I am trying to implement algorithms like common neighbors. I can't understand rooted Pagerank; it's an algorithm who calculate the similarity ...
1
vote
0answers
14 views

Reference summarizing various machine learning algorithms' computational complexity

For example, suppose you train a linear regression model using the Normal Equation, on a training set $\mathbf{X}$ containing $m$ instances and $n$ features. The Normal Equation requires computing ...
0
votes
0answers
7 views

General strategy of confounding design

Example: Suggest a confouding scheme for a $2^8$ experiment in 16 blocks of 16, assuming that all 2-factor and 3-factor interactions are to be estimated. Please find the confounding design. Question: ...
1
vote
0answers
31 views

Where did sublinear tf-idf originate?

I have often come across this weighting scheme for tf-idf (term frequency - inverse document frequency) in text mining. I am wondering where it came from (for citations). I've searched very ...
0
votes
0answers
9 views

Counting repetitions

I would like to count how many times a pattern occurs in a signal and I'm confused what is the best method to do that. As I see it I could train a classifier to recognize the pattern that I'm ...
0
votes
0answers
11 views

Presenting finite sample examination of asymptotics

From statistical theory, we often obtain results such as $\sqrt n (\theta - \hat \theta) \rightarrow_d N(0, \sigma)$ ie we have a normal limiting distribution. Because this formula says nothing ...
0
votes
0answers
8 views

Reference Request: Kernel density estimation for classification problems

I'm looking for resources dealing with problems like in this question: We have data with a continuous independent variable $X$ and a discrete dependent variable $Y$ (with values $y_1, y_2$). How (and ...
0
votes
0answers
12 views

Multivariate Linear Regression with Linear Constraints

Consider random samples $z_1,\ldots, z_N\in \mathbb{R}^n$ distributed according to $z_i\sim\mathcal{N}(T\cdot q_i, \Sigma)$ with $T,\Sigma\in\mathbb{R}^{n\times n}$ and $q_i\in\mathbb{R}^n$. If the ...
1
vote
0answers
7 views

Where can I find ressouce for big multi valued time series? [closed]

for my job I need to find timeseries datas to fit our algorithms. I have encoutered many links for Keogh archive and his work in general, but provided datas are "too clean" i.e datas are aligned, ...
0
votes
1answer
40 views

AIC, model selection and overfitting

I am looking for references that specifically show that Akaike's Information Criterion (AIC), or its corrected form (AICc), can in some practical applications -- that is, not in the asymptotic regime ...
4
votes
1answer
129 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 ...
0
votes
0answers
11 views

Unbiased proportion estimate for a 2-component Gaussian mixture model?

Suppose I have a 2-component Gaussian mixture model of dimension $p$, where each mixture has the same covariance matrix: $$ \pi_1 \mathcal{N}(\mu_1, \Sigma) + \pi_0 \mathcal{N}(\mu_0, \Sigma) $$ ...
-1
votes
0answers
53 views

Data science interview book

I am working as a quant in the area of finance, and when preparing for the interview I have used books "Heard on the Street" by Crack and "Quant Job Interview: Questions and Answers" by Joshi et al. I ...
2
votes
1answer
16 views

Deviation due to conditioning

Let $A$ and $B$ be random variables. Can we upper-bound the following expression? $$ \mathbb{E}\Big[\Big(\mathbb{E}[A|B] - \mathbb{E}[A]\Big)^2\Big] $$ The above looks classical research. However, I ...
0
votes
0answers
6 views

Estimating cross-spectrum of random fields

There is a cross-periodogram (and it's smoothed versions) for estimating cross-spectral density of stationary bivariate process ($\xi_t = (X_t,Y_t), t\in \mathbf R) $. Is there a cross-periodogram for ...
0
votes
0answers
13 views

weak classifier with weak features

I have a set of weak features, and I am looking to create a weak classifier based on them. I am only trying to be right in ~60% of the case (that's enough for my problem). Is there a literature on ...
1
vote
1answer
19 views

Reference to Statistical Control Quality

This semester I'm taking a Green Belt course. I'm really enjoying it and I want to go more deeper in that, I already take this book to start to study Introduction to Statistical Quality Control does ...
0
votes
0answers
14 views

Regression with continous outcome and binary and count independent variables; looking for info/references

Is there a name for a regression model (borrowing R formula syntax) $$y \sim b + N$$ where $y$ is a continous (possibly non-normal) outcome measure, $b$ is a binary variable (0, 1) and $N$ is a ...
3
votes
2answers
821 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?
0
votes
1answer
55 views

Convergence diagnostic of Markov chain that converge to uniform

Let $\Omega$ be a finite state space, $(X_t)_{t\in\mathbb{N}}$ be a discrete-time Markov chain that converges to the uniform distribution, and $P$ be its transition matrix. I'm looking for different ...
4
votes
0answers
46 views

Central Limit Theorem when the dimension size increases with the sample size

Let $X_1, X_2,\ldots, X_n \in \mathcal{R}^d$ and be zero-mean, unit variance random variables. Here the dimension ($d$) is a function of the sample size($n$) i.e, $d=f(n)$. For example $d = \sqrt{n}$. ...
0
votes
0answers
22 views

Estimating a parameter which itself is a sample from a parametric distribution

I am trying to model an engineering problem in the following way: Suppose $\theta_1$ is an unknown parameter lying in some set $A$, and let $\mathcal{P} = \{P_{\theta_1}: \theta_1 \in A \}$`be a ...
5
votes
1answer
80 views

Reproducible benchmarks for the performance of statistical prediction methods?

There are many statistical models used for predictive modelling. These include famous methods such as naive Bayes, knn, SVM, random forest etc. I am looking for reproducible examples (preferably in ...
1
vote
0answers
26 views

Looking for “Data gathering is hard and expensive”

I've finished some data mining and machine learning courses and in most of them at the very beginning has been said: "Collecting and pre-processing data is the most expensive and hardest part of the ...
0
votes
0answers
22 views

Event Study - Event Induced Volatility for One Firm

Normally, in a stock price event study, we assume that the daily variance in the estimation period is the same as that during the event window. The event-induced volatility literature (eg, Boehmer, ...
0
votes
0answers
21 views

Classifiers which only answer when they are confident?

It is often true that a classifier (or regression) can give some kind of confidence in its answers, e.g. through bootstrapping. However, this is often viewed as an afterthought: "here is the answer, ...
0
votes
0answers
18 views

multicollinearity and signficance confused

If explanatory variables are correlated, what is it have to do with some being individually insignificant or significant? What is that supposed to mean? If adding additional variable causes other ...
0
votes
0answers
26 views

Outliers detections in time-series

I am searching algorithms for detecting outliers in a time-series data. I see that there are a lot of algorithms and they have an implementation in R. But i don't find any explanation on how they ...
0
votes
0answers
18 views

Mixture Distribution of Multiples

Is there a specific name for a mixture distribution composed of a random variable and its multiples? Suppose we start with a "atomic" random variable $A$. I want to know what one calls the ...
0
votes
0answers
49 views

What are examples where only a single sample is needed?

Consider the following setup: Let $\Omega$ be a finite (but humongous) state space and $\pi:\Omega\to[0,1]$ be a probability mass function. It seems to me that when people want to "sample" in this ...
0
votes
1answer
25 views

Who came up with the term “complete spatial randomness?”

I've seen the term "complete spatial randomness" widely used in literature but can't figure out who was the first to coin it. Who originated it, and is there a paper it can be cited to?
2
votes
1answer
37 views

Panel data model with two-way fixed effects and individual-specific slopes

I have the following panel-data model: $$ y_{it} = \alpha_i + \lambda_t + \beta_i X_{it} + \varepsilon_{it}. $$ It contains individual-specific intercept $\alpha_i$, time-specific intercept ...
1
vote
1answer
32 views

Reference for known issues with histograms (binning, anchor point)

I'm looking for references on the known issues that arise when working with histograms, i.e.: the choice of the number of bins, and the choice of the origin point. The WP entry on Multivariate kernel ...
3
votes
1answer
35 views

Searching for a good book on count data

I am seeking recommendations for a good book on count data, with clear explanations of topics like Poisson regression. The level of the book should be suitable for a graduate math and statistics ...
0
votes
1answer
28 views

Tikhonov regularization in the context of deconvolution

I came across "Tikhonov regularization" and I have bare knowledge on it. It seems that it is a type of regularization that is important for deconvolution. Are there any good resources and examples? ...
2
votes
0answers
37 views

MCMC efficiency and nonlinear reparametrizations

The efficiency (e.g., effective sample size per density evaluation) of most MCMC methods depends on the parametrization. However, so far I have come across little work in the MCMC literature that ...