Questions tagged [bias]
The difference between the expected value of a parameter estimator & the true value of the parameter. Do NOT use this tag to refer to the [bias-term] / [bias-node] (ie the [intercept]).
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Bias and variance in leave-one-out vs K-fold cross validation
How do different cross-validation methods compare in terms of model variance and bias?
My question is partly motivated by this thread: Optimal number of folds in $K$-fold cross-validation: is leave-...
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What does "Scientists rise up against statistical significance" mean? (Comment in Nature)
The title of the Comment in Nature Scientists rise up against statistical significance begins with:
Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to ...
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Intuitive explanation of the bias-variance tradeoff?
I am looking for an intuitive explanation of the bias-variance tradeoff, both in general and specifically in the context of linear regression.
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When is a biased estimator preferable to unbiased one?
It's obvious many times why one prefers an unbiased estimator. But, are there any circumstances under which we might actually prefer a biased estimator over an unbiased one?
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(Why) do overfitted models tend to have large coefficients?
I imagine that the larger a coefficient on a variable is, the more ability the model has to "swing" in that dimension, providing an increased opportunity to fit noise. Although I think I've got a ...
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When is the bootstrap estimate of bias valid?
It is often claimed that bootstrapping can provide an estimate of the bias in an estimator.
If $\hat t$ is the estimate for some statistic, and $\tilde t_i$ are the bootstrap replicas (with $i\in\{1,...
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What are the most common biases humans make when collecting or interpreting data?
I am an econ/stat major. I am aware that economists have tried to modify their assumptions about human behavior and rationality by identifying situations in which people don't behave rationally. For ...
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Intuitive reasoning behind biased maximum likelihood estimators
I have a confusion on biased maximum likelihood (ML) estimators. The mathematics of the whole concept is pretty clear to me but I cannot figure out the intuitive reasoning behind it.
Given a certain ...
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Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares regression
I have a question about omitted variable bias in logistic and linear regression.
Say I omit some variables from a linear regression model. Pretend that those omitted variables are uncorrelated with ...
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Deep learning : How do I know which variables are important?
In terms of neural network lingo (y = Weight * x + bias) how would I know which variables are more important than others?
I have a neural network with 10 inputs, 1 hidden layer with 20 nodes, and 1 ...
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Incidental parameter problem
I always struggle to get the true essence of the incidental parameter problem. I read in several occasions that the fixed effects estimators of nonlinear panel data models can be severely biased ...
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Intuitive understanding of the difference between consistent and asymptotically unbiased [duplicate]
I am trying to to get an intuitive understanding and feel for the difference and practical difference between the term consistent and asymptotically unbiased. I know their mathematical/statistical ...
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Why does a decision tree have low bias & high variance?
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Does it depend on whether the tree is shallow or deep? Or can we say this irrespective of the depth/levels of the tree?
Why is bias low & variance high? Please explain intuitively and ...
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What intuitively is "bias"?
I'm struggling to grasp the concept of bias in the context of linear regression analysis.
What is the mathematical definition of bias?
What exactly is biased and why/how?
Illustrative example?
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Bias of moment estimator of lognormal distribution
I am doing some numerical experiment that consists in sampling a lognormal distribution $X\sim\mathcal{LN}(\mu, \sigma)$, and trying to estimate the moments $\mathbb{E}[X^n]$ by two methods:
Looking ...
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