Questions tagged [bias-correction]
The bias-correction tag has no usage guidance.
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Bias estimation (after reversing the log transformation) in parameter estimation of fractional Brownian motion
I am learning these two literature,
1 [SIMULATION AND IDENTIFICATION OF THE FRACTIONAL B ROWNIAN MOTION : A BIBLIOGRAPHICAL AND COMPARATIVE STUDY COEURJOLLY Jean-François]
2 [Estimating the Parameters ...
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What is the difference between using logistf and brglm2 when dealing with complete separation in a logistic regression?
I am trying to looking at how the three factors A (5 levels, a-e), B (2 levels, a and b) and C (2 levels, a and b) affect the likelihood of event Y (1 = occured, 0 = did not occur). I initially ran a ...
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What to show as error-bar if the bootstrap distribution is biased?
Say I have a sample, of finite size $N$, and I compute some statistic $\theta$ from it. I want to plot this sample estimate, $\hat{\theta}$, with an error-bar.
To compute the error, I am using ...
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Methods to level spatiotemporal data when simultaneous measurements of the same physical quantity are different
I have data of (simulated) measurements of the density content of ionized ozone in the atmosphere with three different satellites. Specifically, I have a unique set of observations x1,x2,x3,...xN for ...
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Why does the jackknife reduce bias? [duplicate]
Given a sample $x = (x_1, \ldots, x_n)$, define $x_{(-i)}$ as the sample values excluding sample $x_i$. That is,
$$
x_{(-i)} = (x_1, \ldots, x_{i-1}, x_{i+1}, \ldots x_n).
$$
Now given estimator $T(x)$...
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For a biased estimator, how does one call the point for which the expected value of the estimator is equal to the observed sample estimate? [closed]
Let $\hat{\theta}$ be a biased estimator whose bias depends on the true value $\theta_0$, such that $E[\hat\theta|\theta_0]= f(\theta_0)\neq \theta_0$. Let $t_{sample}$ be a sample realization of $\...
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Is it possible to use poststratification when some observations have missing values on the variables used as strata?
This is a theoretical question, so I don't have data to share.
Let's say I know the percentage of men and women in my population of interest, as well as the distribution of occupations and age ...
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How to avoid bias/avoid overfitting when choosing a machine learning model? [closed]
My typical workflow in the past, when creating machine learning models, has been to do the following:
Decide on some candidate model families for the task at hand.
Divide dataset into train and test ...
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How can I take into account multiple individuals with multiple observations across a data?
I have a set of data as this:
ID V1 V2 V3
A 12 10 8
A 11 9 10
B 7 10 8
C 13 10 9
C 10 12 6
This dataset is from health data, where each individual takes ...
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Correcting for spillover bias in experiments?
Suppose I have an experiment designed where I'm interested in observing the effect of daily walking on self-reported joint pain in elderly adults. The population of interest are elderly adults who ...
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How to estimate bias-corrected variance of a half-normal distribution?
Wikipedia says that for a given numbers $\{x_i\}_{i=1}^{n}$ drawn from a half-normal distribution, the variance of that distribution can be estimated by sample variance $\hat\sigma^2 = \frac{1}{n} \...
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How to address 'immortal time bias' using R - equivalent to Stata stset?
We have a dataset on cancer patients who have consented to join a study after their diagnosis, which could be months or even years later. After some follow-up, an event occurs. We can fit this data ...
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How to account for bias in experiment data when quantifying treatment efficacy?
Say my randomization wasn't very effective, so I have two groups, each with 100 individuals and the difference in success rates is 3% before the treatment has ever been administered.
I want to know ...
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Implementing bias-adjustion for step3 latent profile analysis in R [closed]
I am identifying a latent profile model with the Mclust package in R. After identifying an optimal number of cluster I would like to identify possible covariates and distal outcomes via logistic/...
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Suggestions on dealing with outliers when sample size is very small AND you must order the results
I run competitive events. In our normal event, we have 8 adjudicators split between to categories. Skill and Artistry.
For each category we throw out the high and low scores and average the remaining ...
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