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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Can an asymptotically efficient estimator be biased?
In "Theory of point estimation" by Lehmann and Casella (1998) there is the following definition:
It is also said that
So terms of the asymptotically normal sequence of estimators can be ...
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Transformation bias with non-linear functions
This a more general question: I often deal with experimental data (subject to uncertainties in the measurements) that have to be transformed using a function, to calculate a parameter (which can, for ...
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Do Oscar winners live longer? How account for length and time-dependent bias
I read a bunch of references on the bias that one should beware of when analyzing survival times. One classic example is the one of Oscar winners: a miss-specification of the model leads to two kind ...
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Bias Variance tradeoff from a Bayesian perspective
I know the general question about bias variance has been asked before. I understand the frequentist approach and the concept of model selection and the impact of bias and variance on "accuracy" of a ...
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R packages that work with biased samples
I'm working with a biased sample of web users. I'm only able to track responses of users who have navigated my site in a certain way, and I'd like to run an analysis to determine how certain factors (...
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Fitting a fixed effect model to the residuals from a mixed effects model
In some statistical analyses (ie genetics), it may makes sense to perform a two-step regression analysis. In this analysis, the dependent variable is regressed against several independent variables. ...
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Bias Variance Decomposition for Mean Absolute Error
The mean squared error of an estimator $\hat{\theta}$ with respect
to an unknown parameter $\theta$ is defined as
$$
MSE(\hat{\theta})=E[(\hat{\theta}-\theta)^{2}].
$$
It is well known that there is ...
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Direction of Bias in OLS with systematic measurement error
I am looking at a study that wants to measure the effect of $x_t$ on $Y_t$, but the true values of the $x_t$ are not observed. Instead, what is observed is a minimum value for $x_t$. In effect, what ...
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Bias correcting penalized maximum likelihood / maximum a posteriori estimates
Suppose an estimator $\hat\theta_T$ is defined as the value of $\theta$ maximizing:
$$\sum_{t=1}^T{l(y_t|\theta)}+\mu_T g(\theta),$$
where $l(y_t|\theta)$ is the log-likelihood of observation $t$, $\...
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Relation between bias and R-square
I am trying to understand relation between bias and R-squared value in linear regression.
High bias means that the model is underfit. By this I am assuming that the R-square d will be less.
So my ...
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Recent Example of the Consequences of Bad Data in Business/Commercial Domain
Preface
I work at an ecommerce saas company. I have been asked to perform an analysis on the relationship between behaviors of potential customers during free trials and their conversion to paying ...
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Relationship between Total Over/Under scores and actual total scores in sports
I have a data set of actual scores from sporting games (Australian Rules football matches), matched with the bookmaker's Total Over/Under Score (O/U Score) and the odds the bookmaker was offering that ...
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Can unbalanced classes introduce bias in a Random Forest model?
I am working on a classification problem using Random Forest.
The training set has 600 instances and 16 attributes. The final class is an Yes/No answer. The ratio of "Yes" to "No" in the training ...
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bias adjusted confidence interval for correlation coefficient
I stumbled across confidence intervals for $\rho$ in a statistics textbook. This was not part of the class, but I found it interesting. My textbook has a formula for this with a Fisher transformation, ...
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How to calculate MSE in a quantile regression simulation study
I am working on a simulation study on quantile regression. So what I did is to simulate data based on a given model, which is different from the true underlying model of the data, in other words, a ...
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