Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [model]

A formalization of relationships between stochastically (randomly) related variables in the form of mathematical equations. DO NOT USE THIS TAG BY ITSELF: always include a more specific one.

In mathematical terms, a statistical model is frequently thought of as a pair $(Y, P)$ where $Y$ is the set of possible observations and $P$ the set of possible probability distributions on $Y$ . It is assumed that there is a distinct element of $P$ which generates the observed data. Statistical inference enables one to make statements about which element(s) of this set are likely to be the true(s) one.

Reference: Wikipedia

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How much to pay? A practical problem

This is not a home work question but real problem faced by our company. Very recently (2 days ago) we ordered for manufacturing of 10000 product labels to a dealer. Dealer is independent person. He gets the labels manufactured from outside and…
Neeraj
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Should covariates that are not statistically significant be 'kept in' when creating a model?

I have several covariates in my calculation for a model, and not all of them are statistically significant. Should I remove those that are not? This question discusses the phenomenon, but does not answer my question: How to interpret…
A.M.
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Mixed Effects Model with Nesting

I have data collected from an experiment organized as follows: Two sites, each with 30 trees. 15 are treated, 15 are control at each site. From each tree, we sample three pieces of the stem, and three pieces of the roots, so 6 level 1 samples per…
Erik
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Why should we use t errors instead of normal errors?

In this blog post by Andrew Gelman, there is the following passage: The Bayesian models of 50 years ago seem hopelessly simple (except, of course, for simple problems), and I expect the Bayesian models of today will seem hopelessly simple, 50…
Potato
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Should parsimony really still be the gold standard?

Just a thought: Parsimonious models have always been the default go-to in model selection, but to what degree is this approach outdated? I'm curious about how much our tendency toward parsimony is a relic of a time of abaci and slide rules (or, more…
theforestecologist
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Is an overfitted model necessarily useless?

Assume that a model has 100% accuracy on the training data, but 70% accuracy on the test data. Is the following argument true about this model? It is obvious that this is an overfitted model. The test accuracy can be enhanced by reducing the…
Hossein
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How can you account for COVID-19 in your models?

How are you dealing with the coronavirus "event" in your machine learning models? Let's say you used to predict the number of sales each month. The virus affected your results last year and it will affect for at least a couple of months. So your…
dsbr__0
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In layman's terms what is the difference between a model and a distribution?

The answers (definitions) defined on Wikipedia are arguably a bit cryptic to those unfamiliar with higher mathematics/statistics. In mathematical terms, a statistical model is usually thought of as a pair ($S, \mathcal{P}$), where $S$ is the set…
AlanSTACK
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When forcing intercept of 0 in linear regression is acceptable/advisable

I have a regression model to estimate the completion time of a process, based on various factors. I have 200 trials of these processes, where the 9 factors being measured vary widely. When I perform a linear regression of the 9 factors (and all 2…
Zack Newsham
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What is a null model in regression and how does it relate to the null hypothesis?

What is the null model in regression and what's the relationship between the null model and the null hypothesis? From my understanding, does it mean Using "an average of the response variable" to predict the continuous response variable? Using the…
Haitao Du
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what happens when a model is having more parameters than training samples

In a simple neural network, say, for example, the number of parameters is kept small compared to number of samples available for training and this perhaps forces the model to learn the patterns in the data. Right? My question is that what…
Upendra01
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What would be an example of a really simple model with an intractable likelihood?

Approximate Bayesian computation is a really cool technique for fitting basically any stochastic model, intended for models where the likelihood is intractable (say, you can sample from the model if you fix the parameters but you cannot numerically,…
Rasmus Bååth
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What are real life examples of "non-parametric statistical models"?

I am reading the Wikipedia article on statistical models here, and I am somewhat perplexed as to the meaning of "non-parametric statistical models", specifically: A statistical model is nonparametric if the parameter set $\Theta$ is infinite…
Creatron
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What is the difference between a "statistical experiment" and a "statistical model"?

I am following A.W. van der Vaart, asymptotic statistics (1998). He talks of statistical experiments, claiming that they are different from a statistical model, but he defines neither. My question: What is a statistical experiment, a statistical…
Mikkel Rev
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Definition and delimitation of regression model

An embarrassingly simple question -- but it seems it has not been asked on Cross Validated before: What is the definition of a regression model? Also a support question, What is not a regression model? With regards to the latter, I am…
Richard Hardy
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