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1.635 references questions.

I recently started teaching statistics in English and I didn't manage to get any good book recommendations. It is supposed to be an "advanced" class, but most of the students haven't had any ...

I am performing time series analysis with yearly frequently. However I need to regress a data compiled by calendar year against another compiled by fiscal year. Is it possible to deal with this? If ...

Let $X > 0$ be a random variable; let $P$ be the underlying probability measure; let $\delta > 0$. I wonder if there is already in probability literature a known result giving a sharper bound ...

What is Hat matrix and leverages in classical multiple regression? What are their roles? And Why do use them? Please explain them or give satisfactory book/ article references to understand them.

I have read some paper that expresses that "recent works" show we can use a VAR model with raw data I(1) but there has to be cointegration. This means that there is no reason to difference the data ...

I am teaching myself machine learning and I am looking to solve the following problem. My main issue is that I do not know the name of this (or a similar) problem so I do not know how to look for ...

I'm looking for any good reference that can help me to understand the following exercises about kernels and online learning. A training set $(x_1, y_1), ...,(x_m, y_m)$ is generic iff $\mathbf{x}_i = ...

I've been taking courses in Applied Statistics and through the homework problems I get to practice what I've learned. But, I find that the practice problems are always a bit contrived to fit within ...

Is there any paper which summarizes the mathematical foundation of deep learning? Now, I am studying about the mathematical background of deep learning. However, unfortunately I cannot know to what ...

I very much like the famous "ILSR" book by Hastie, Tibshirani et al! Lots of nice R experiments and good intuitive background is explained - but what the book in my opinion lacks though, is ...

I can't be the first person to think about estimating variance components of mixed models by gradient descent, and then computing BLUP's at each update. Googling, I find little on the topic. But the ...

Are there any free econometrics textbooks available online for individual use? I'm aware of Bruce Hansen's first year PhD Econometrics textbook, but I'd be interested to hear if there are any other ...

I'm trying to learn bayesian structural time series analysis. For a variety of reasons I need to use Python (mostly pymc3) not R so please do not suggest the ...

I know I have seen some research, perhaps in the contexts of time-varying topic models, on the popularity of Bayesian methods in statistics and machine learning over the last 20 years. Unfortunately ...

Can we report a Two-way ANOVA with count data? If Yes, What are your references? If No, Why? For example: Factor A in 4 level and Factor B in 3 level and our responses are number of patients.

Having graduated in econometrics I am now working as a Data Science Consultant. I would like to gain a deeper knowledge of (theory of) inferential statistics. Is there any recommendable online course/...

First of all, I have to admit that I am not statistician so some of my nomenclature could not be very rigorous and maybe a bit confusing; pleas ask me to clarify if necessary. The Problem Let's say ...

Statistics Jokes

72 answers, 188.291 views references humor
Well, we've got favourite statistics quotes. What about statistics jokes?

Many sources date the classic "box plot" design to John Tukey and his "schematic plot" of 1970. The design seems to have stayed relatively static since then, with Edward Tufte's cut-down version of ...

Assume we specify a state space model as $$Y_t = a X_t + W_t$$ and $$X_{t+1} = b X_t + V_t$$ where $b,a \in R$, $E[W_t] = E[V_t] = 0 \quad \forall{t }$ and $W_t $ and $V_t$ are indipendent for ...

The Scikit-Learn package in Python has two metrics: f1_score and fbeta_score. Each of these has a 'weighted' option, where the classwise F1-scores are multiplied by ...

I'm looking for good references to learn more about POMDPs, preferably from a more mathematical stand point. The only good reference I've been able to find so far is: http://www.cs.toronto.edu/~...

For mixed model, when we estimate variance component by restricted maximum likelihood estimation procedure, an iterative algorithm is required to solve the estimating equations for variance component. ...

If $X$ is a random variable, then it's moment generating function is the function $$ t \rightarrow E(\exp(tX)) $$ which takes values in $]0,+\infty[$. For a sequence of random variables $X_n$ point ...

I was trying to find some "good" reading books about Bayesian Model selection. So is there any recommendations? To be specific, I was trying to understand the Bayesian Information Criterion (BIC), the ...

I'm using R and the manuals on the R site are really informative. However, I'd like to see some more examples and implementations with R which can help me develop my knowledge faster. Any suggestions?

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 ...

Say M periodic signals, then one can safely say using AR-M model can achieve the perfect prediction. But how about further, in a more general sense, is there any publications on this?

I'm looking for good references explaining how to design and analyze simulation studies (and, just to be clear, by this I mean the typical study you'd find in many modern papers, where you repeatedly ...

I'm about to learn statistical reliability theory for the first time. I'm told that Barlow and Proschan's book entitled "Statistical theory of reliability and life testing" is a classic on this topic. ...

I'm looking for a book that provides deep, rigorous coverage of probability theory, but with an emphasis on material that is mostly useful outside of a mathematics department. I've heard "The Theory ...

As a former mathematics student, when reading any math-related materials I tend to care about their mathematical rigour very much. Such high attention to mathematical details might be a good habitat ...

I am slowly learning Python, and I am curious if anyone has come across some easy to follow tutorials or books explaining how to program simple to intermediate statistical methods, from basic ...

What is the difference between a Bayesian Network and a Markov process? I believed I understood the principles of both, but now when I need to compare the two I feel lost. They mean almost the same ...

Given $X_1, \dots, X_n, \dots \sim \mathscr{N}(0,1)$, consider the random variables $$ Z_n := \max_{1 \le i \le n} X_i\,. $$ Question: What is the most "important" result about these random ...

I'm taking an introductory graduate course in statistical programming. I usually like to read textbooks along with my coursework, but the professor doesn't have any suggestions for books matching the ...

The Fréchet–Hoeffding upper bound applies to the copula distribution function and it is given by $$C(u_1,...,u_d)\leq \min\{u_1,..,u_d\}.$$ Is there a similar (in the sense that it depends on the ...

I know Havard's Stat 110 is freely available online and covers the first four to five chapters, but is there another video series or even a set of lecture notes that would complement chapters 7 and ...

Are there any online resources or books where probability and statistics concepts are explained in schematic pictures and plots? What I am looking for is: let's say while explaining marginal, a 2d ...

Background: A lot of the modern research in the past ~4 years (post alexnet) seems to have moved away from using generative pretraining for neural networks to achieve state of the art classification ...

I am currently reading this RNN blog, where it talks about Backprop through time. I am struggling to derive it and don't understand how to go about such derivations in general. Stuff like this ends ...

Quick question: can anyone give me a citation that I can use to justify using ML when doing model comparisons? Background: I am fitting some multilevel models in R using lme4, and I do a series of ...

The stochastic process $(X_t)_{t \in T}$ is a Gaussian process if the marginal distribution of $X_{t_1}, \ldots, X_{t_n}$ is a multivariate Gaussian distribution for all $t_1, \ldots, t_n \in T$. Let ...

I need to study Markov Chain Monte Carlo methods, to be more specific I need to study Metropolis Hastings algorithm and all about it like convergence criteria. Who can prescribe me a book, or a paper,...

Does anybody know any good papers or software that use Monte Carlo techniques to estimate the probability of certain hands or winning/losing a hand in Texas Holdem? Ideally I'd like to have some ...

Can someone please point me to a textbook or lecture notes that explains what variance stabilising transformations are? I can only find bits and pieces on google. I don't know a lot of statistics, ...

I am looking for a reference of the above fact in the title where $r$ is the usual sample correlation estimate and $\rho$ is the true correlation coefficient. I think the result is valid only for ...

I would like to read the originally published papers to see how the structure of the equations is justified. I would especially like to read Holt, Charles C. (1957). "Forecasting Trends and Seasonal ...

I mean ‘duality’ in the strict mathematical sense, not merely as a synonym for ‘symmetry’.

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