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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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1answer
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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.
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1answer
20 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
27 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
16 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 ...
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1answer
37 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
13 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 ...
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1answer
16 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
24 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
27 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
31 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
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1answer
131 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, ...
3
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0answers
17 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 ...
3
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1answer
63 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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2answers
34 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 ...
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0answers
18 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 ...
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1answer
28 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
47 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
34 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
9 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 ...
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1answer
266 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 ...
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0answers
37 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
10 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 ...
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0answers
11 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 ...
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2answers
82 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 ...
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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 ...
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0answers
22 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) ...
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1answer
53 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 ...
1
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1answer
32 views

Scientific papers using “entry” level Econometric procedures

I am studying Econometrics on a Masters' level. I have a pretty good grasp of the theoretical aspect of different processes, from dummy variables to time series, stationarity or simultaneous models (...
1
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1answer
42 views

Books for self-learning about statistic Simulation?

Preferably an introductory book, i.e. for undergrad (or notes or something like that) that explains concepts with detail and with lots of examples, without losing the formality. That covers the ...
0
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1answer
46 views

Best book for statistical inference (Self-study) [duplicate]

I want to develop some skills in statistical inference for a career in data science or machine learning. I purchased the book "All of Statistics" which is a good book, but there are not answer keys ...
3
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1answer
23 views

Reference request: initializing big neural networks with small neural networks

I am currently trying some meta-algorithms on training neural networks. Start with a small but expressive enough network for training and after several epochs, initialize a larger neural network with ...
3
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0answers
24 views

Are there extant deep learning analogs to random coefficient (aka mixed) models?

Random coef models, applied to longitudinal data, capture response heterogeneity by cross-sectional unit. I've got a longitudinal prediction problem, in which I know that some "features" (or ...
4
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1answer
37 views

Seminal works in deep learning [closed]

I'm compiling a list of 7 seminal works in deep learning to work on during 14 week semester course. I'd appreciate if you suggested papers for the list. I'm looking for the papers that impacted the ...
0
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1answer
23 views

Kolmogorov Distribution D statistics

As far as I have searched the cumulative distribution function of 𝐾, asymptotically (kolmogorov distribution) is given by Pr(𝐾≤𝑥)=1−2∑∞𝑘=1(−1)𝑘−1𝑒−2𝑘2𝑥2=2𝜋√𝑥∑∞𝑘=1𝑒−(2𝑘−1)2𝜋2/(8𝑥2). But ...
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0answers
51 views

Does High Dimensional Data effects SVM?

As we move into higher dimensions, we will find even more corners. This will make an ever increasing percentage of the total space available. Now imagine we have data spread across some ...
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0answers
63 views

What are some introductions to classical statistics that emphasize unifying principles? [duplicate]

I'd like to know an introduction to classical statistics, that: Emphasizes connections and unifying principles (I checked this question and the links posted therein, but didn't find an introduction ...
1
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1answer
33 views

Variable Importance for Logistic regression with categorical data?

If I run the logistic regression with X variables containing categorical data. (I do one-hot encoding on categorical data) How do I evaluate the variable importance? Is there any methods or literature ...
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0answers
7 views

Fusion gene detection from TCGA or ICGC data

My PhD project involves fusion gene detection from cancer data of TCGA and ICGC portals. I find that the RNAseq files (fastq,bam formats) are mainly closed access, whereas the clinical or expression ...
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0answers
18 views

Clearing out errors from a data set

Sorry for the vagueness of the title, I am having a hard time even coming up with sort of problem I am facing (if there is a specific name for it....) In a nutshell, I have a time series of points, ...
4
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2answers
72 views

What are the best books to study Neural Networks from a purely mathematical perspective?

I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation functions, ...
2
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2answers
65 views

In hypothesis testing why do we need to use the reject null hypothesis approach but not the other way round?

In hypothesis testing, the common approach is to first set a null hypothesis and a hypothesis we want to test. Then apply some statistical techniques and see whether the observation is likely to ...
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0answers
63 views

Using a surrogate model for the solution space of an optimization problem

I have an optimization problem: Given a complex $n\times n$ covariance matrix $C$ one must find a complex $n$-vector $v_C^\ast$ which (approximately) minimizes an objective $f_C(v)$ over all space. $...
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0answers
15 views

Is there an archive of closed-form mutual information among the “famous” distributions?

I'm looking for a document or compilation table of closed-form mutual information as a function of their parameters, for known distributions such as normal, gamma, Poisson distributions. At least, I ...
7
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1answer
166 views

Multiple comparisons correction for dependent comparisons

In this blog post the authors discuss simultaneously estimating quantiles, and constructing a simultaneous confidence envelope for the estimation which covers the whole quantile function. They do this ...
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0answers
34 views

Are there any generally simple examples on Tabu Search in R?

I am looking for any examples of implementing Tabu Search in R. I know there is a package, but I would like to see if there are any good instructive examples where the code is built up and used to ...
0
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1answer
19 views

Maximum Likelihood estimator for GARCH with jump (papers on this topic)

Does anyone know a reference to a paper that would show an actual calibration of GARCH(1,1) model with jumps to a historical time series?
6
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1answer
68 views

Do there exist adaptive step size methods for Newton-Raphson optimization?

Stochastic/Mini-batch gradient descent, caused by interest in deep learning, has made lots of advances in adaptive step sizes. For example, Adam, Nadam, Adamax, ..., are all improvements to the ...
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0answers
22 views

Ridge, Lasso or Elastic nets used in Accounting Research

I am trying to come up with ideas for my master's thesis and was wondering why literature on the above mentioned regression methods within Accounting Research is non-existent? I felt like the ...
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0answers
47 views

Introductory books on bayesian statistics with focus on normal distribution

I am searching for introductory books on bayesian statistics. Which Focus on normal distribution (Most of the books I came across through this answer focus on binomial distribution) Practical ...