Questions tagged [terminology]
Usage and meaning of specific technical words/concepts in statistics.
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Why is density function of survival time called instantaneous failure rate
Why density function of survival time is called instantaneous failure rate. I am confused why density function is a failure rate for survival time.
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Definition of 'independence' of comparisons in Multiple-Comparison - Bonferroni Adjusted Standardized Residuals for Chi-Squared Test
I have provided a hypothetical example to illustrate my question. Assume an alternate hypothesis for a difference between two variables of interest (arbitrary for the example), analyzed with a chi-...
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Why use Kernel trick if soft margin SVM works for non-linearly separable data? [duplicate]
Most articles and textbooks say that soft margin SVM is used as the data is messy/not linearly separable. We introduce slack variables to make the data linearly separable. Kernels are used when the ...
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Is "skewing the data" and "skewing the results" just selection bias?
I recall various conversations with biologists, ecologists, and foresters that I neglected to ask for clarification on at the time. It doesn't occur in any of my statistics references.
Sometimes in ...
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What is Bayesian PCA and its cousin?
When I think of the phrase "Bayesian PCA" I think of two things, but these two things are what I have contrived rather than conventional notions. I would appreciate guidance on what these ...
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Purpose of distribution "over" a variable notation
The deep learning book (Goodfellow, Bengio, and Courville, 2016)'s notation section says $\mathcal{N}(\mathbf{x}; \mathbf{\mu}, \mathbf{\Sigma})$ represents a normal distribution over $\mathbf{x}$, ...
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Do testability and falsifiability have statistical definitions?
Psychology: the Core Concepts says
Psychology differs from the pseudosciences in that it employs
the scientific method to test its ideas empirically. The scientific
method relies on ...
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How do you refer to data that's not part of train/test/validation?
The purpose of creating a machine learning model is to deploy in live situation, e.g., classification in the wild.
The model is trained on the training set, tuned on the validation set, and evaluated ...
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No Pooling approach is being applied from a fixed effect
Hello I was reading about no pooling and partial pooling.
I understoond that a mixed model is using partial pooling approach when we talking about hierarchical data.
Is it correct to say that fixed ...
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What are differences, or relationships, among "relative probability", "relative frequency", "probability density" and "probability"? [duplicate]
ORIGINAL QUESTION
What are the differences, or relationships, among
"relative probability",
"relative frequency",
"probability density" and
"probability"?
A ...
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Random Slopes - Categorical Variables
I know that a a "random slopes" model usually has both intercepts and slopes models varying randomly across groups.
Was reading the classic example with sleepstudy data. I understood that ...
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What is pseudocomplete data?
Skimming Huang et al 2019 I see references to a term "pseudocomplete-data" that I am not familiar with. It looks like they are dealing with a data censoring problem which they approach with ...
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Random Effect Model vs Random Intercept Model
I was reading in Brown book the theory of a random effect model and then I started searching about how can I apply it in R.
I read that when I am modeling a mixed model using R, I am referring to ...
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Inverse Gaussian and normal-inverse Gaussian distributions synonymous
On Wikipedia, there is a page for the inverse Gaussian distribution and the normal-inverse Gaussian distribution. And I observed some confusion in other websites: they say normal-inverse Gaussian ...
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Formal terminology: metafeatures then groupby (GROUP BY) operation
In ML training and other analytics I often combine features to produce a 'metafeature' and then perform a 'groupby' (pandas) or 'GROUP BY' (SQL) query.
What is the technical term for this operation?
...
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Terminology for distribution of posterior mean before seeing data
Imagine we are performing Bayesian inference with normal-normal conjugate priors. We have some prior:
$$
\mu \sim N(\mu_0, \sigma_0^2).
$$
We know we will collect some normally distributed data $x$ ...
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Histogram normalisation: a question about the correct terminology
Given a histogram and the following quantities
...
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What is "cohort likelihood" and where is it from?
this is my very first post and I would consider myself a beginner in statistics, but I couldn't find anything about this in other asks.
I am learning about the Self-controlled case series (SCCS) model ...
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Definition of "unpredictable"
How do we rigorously define the term "unpredictable" in cases of point and density prediction?
The term "unpredictable" is employed in various contexts, e.g.
"the outcome of ...
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Why it is called "BatchNorm" not "Batch Standardize"?
Regarding the differences between "Normalization" and "Standardization," I found that:
Normalization: Is the process of making a dataset having a specified range, probably [0,1] ...
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Does this type of metric have a common name?
I tend to use metrics like the following:
"80% of all parcels are delivered within the given 60 min time period" or
"In 90% of all emergency calls, the rescue service is on the scene ...
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Confusion about the notation in Horvitz-Thompson estimator
I am a bit confused about the terminology used in the context of sampling of populations. The Horvitz-Thompson estimator, as well as the Hansen-Hurwitz estimator, for example, are examples of ...
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Why do we use term "arm" to express different treatment "group" in clinical trials?
I'm reading papers about randomization in clinical trials. I'm curious why we use the term "arm" such as in "two-arm trials" to express "treatment and control groups".
I ...
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Name for matrices with $M_{i,j} = M_{n-i+1, n-j+1}$ symmetry (eg. 2D autocorrelation)?
Covariance matrices are symmetric positive definite matrices. The 'Symmetric' part of this means that the strict upper elements are redundant with the strict lower elements, i.e $M_{i,j} = M_{j,i}$. ...
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Clarification: confidence interval of the (slope of the) regression line
When confidence intervals are referenced in the regression context, I often see them mentioned (generally speaking) as "of the slope of the regression line," or as "of the regression ...
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name for joint density as a function both of data and parameters
A common point of confusion in introductory math stats classes is the difference between the joint density (formally a function of the data parameterized by the parameters) and the likelihood (...
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Equivalent of proper scoring rule for point forecasts
Proper scoring rule is a concept used for evaluating density forecasts. What would be an equivalent for evaluating point forecasts? E.g. mean squared error seems like a proper metric for evaluating ...
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Correct qualifier (terminology) for this kind of statistics
I am doing an analysis of a population of animals that died during the first year of their life. I am comparing them a) to all animals that died, ever, after how-ever long life; and b) animals that ...
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Is there a formal definition of the term "generalized" when used with distributions?
I am interested in peak models that are observed in instrumental analysis. The term "generalized" is commonly used in the context of statistical distributions, referring to a class of ...
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Vaccination acceptance by subregion and country of residence visualization name and instrument to build the similar one
I've found a pretty interesting visualization in the article "High monkeypox vaccine acceptance among male users of smartphone-based online gay-dating apps in Europe, 30 July to 12 August 2022&...
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Incorrectly Using the Word "Causal" to Describe a Regression Model?
Suppose we take the classical linear regression model:
$$y_i = \beta_0 + \beta_1 x_i + \epsilon_i$$
Over the years, I have heard so many people say that such an interpretation can be drawn from this ...
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Is there a collective name for non-ensemble MCMC methods?
There is a class of MCMC algorithms which are called "ensemble samplers", so-called because they use an ensemble of walkers whose positions depend on each other to sample from the posterior ...
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Relative risk but using the full population and a subset thereof
Consider the probability $p$ of an event in the full population, and the probability $p_0$ of the same event for a subset of the population. In other words, $p_0$ is a conditional probability (...
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Is density estimation the same as parameter estimation?
I was studying parameter estimation from Sheldon Ross' probability and statistics book. Here the task of parameter estimation is described as follows:
Is this task the same of density estimation in ...
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Is there a term used to refer to the total number of positive predictions?
I'm not sure how else to put it, but I often use the sklearn.metrics.classification_report function in order to measure the performance of various classification ...
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What a stochastic process with constant variance is called?
Consider a stochastic process $\left( x_t \right)_{t \in \mathbb{R}}$ with auto-covariance function $k(t, t') = \mathbb{E}[(x_t - \mu_t) (x_{t'} - \mu_{t'})]$ where $\mu_{t} = \mathbb{E}[x_t]$.
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Why is the E-step in the EM algorithm called this way?
Yes, this has been asked before here, but for different reasons. In the E-Step nothing is calculated, we simply define the function, yet once it is defined it is defined once and for all. We could ...
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Odds "ratio" in logistic regression?
Odds ratio, as the word itself demonstrates, refers the ratio of odds. Hence, we need 2 events in computing odds ratio.
But in simple logistic regression, given that we are interested in estimating ...
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Does my use of the notation $P(\cdot|\Omega)$ make sense?
I'm new to probability theory.
Let's say that I have the following situation:
Three identical boxes have different collections of doughnuts in them. The box on the
left ($L$) has 2 plain ($p$) ...
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Iterated mean as a variant of the quantile function
Suppose you want to compute the 25% quantile of some random variable. You can do this by first computing the median, and then computing the median of everything less than the median. You can repeat ...
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Technical Term for choosing distribution of starting point of a time series in a way such that the time series is stationary
In Time Series Analysis there is this idea of choosing the distribution of the starting point of a time series in a way such that the time series is stationary. Let for example $\{X_t\}_{t=0}^T%$ be a ...
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Experiment terminology: control participants invertedly receive treatment
I have spent a solid hour to remember with the technical term for this. Suppose you have a between-subjects lab design with one control and two treatment conditions. What is the term to describe when ...
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Name of a property of characteristic functions (Fourier transforms)
I'm a scientist but not a professional mathematician and in this question, I asked about a possible typographical error in an article on round-off error that I've been reading in the journal ...
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Use of $p_1/(p_1+p_2)$ to compare two probabilities $p_1$, $p_2$?
Let's say we want to compare two probabilities $p_1$ and $p_2$, not necessarily referred to the same population. For example, $p_1$ may be the probability of getting a certain disease conditioned on ...
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What is likelihood actually?
I have been pretty confused about maximum likelihood as expressed by my question here. But this question is not about MLE.
It occurs to me my confusion may have been because the likelihood function ...
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What are Dense and Sparse Models?
I'm watching an explanation of the SHAP algorithm by its author S Lundberg.
At 22:45 in the video, one of the audience members asks a question about the models behind his graphs. He explains that one ...
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What's the difference between a Permutation and a Perturbation?
I frequently come across the terms Permutation and Perturbation within the field of explainable AI.
I understand that both terms refer to methods that make changes to a sample's features. However, I'm ...
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Understanding the advantages of CRD experiments
I am self studying an introductory course on Designed Experiments and have come across the notion of a Completely Randomised Design (CRD) defined as follows:
Completely Randomised Design: the ...
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SEM: Difference between unidentified, underidentified, and underdetermined models?
It's been asserted to me that there is a difference between underidentified and unidentified models in that:
Underidentified models can still be estimated and solutions can be
obtained, but they are ...
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What is the name of a model that is not saturated?
Is there a standard way to refer to a statistical model that is not saturated?
It is for paper writing. For the moment, I am using the term "non-saturated model" but I think there must be ...
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