All Questions
Tagged with bias regression
186
questions
1
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0
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17
views
Why is the threshold term incorporated into the weight vector in linear classifiers?
In the context of linear classifiers, such as the perceptron or logistic regression, I understand that the decision boundary is defined by a linear combination of input features and weights, plus a ...
0
votes
0
answers
27
views
How to prevent a regression model from overpredicting lower values and underpredicting higher ones
I'm trying to predict rental prices for houses that are listed for sale. My training set consists of houses that are listed for rent. With the predictions, my idea is to then compute an estimate of ...
1
vote
0
answers
45
views
Question on nonlinear least squares
Consider the following equation for $Y>0$:
$$
(1) \quad \log(Y)=\log(\gamma)+\log(\alpha+\beta X)+\epsilon.
$$
Assume that $E(\epsilon| X)=c\neq 0$. What are the consequences of this assumption on ...
- 889
0
votes
0
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6
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What are the conditions to specify the regressors in Heckman 2 step model
I have the issue of interpreting the STATA command Twostep Heckman model, and also adding fixed effects to the model.
My analysis is based on a panel dataset and I want to solve for the selection bias ...
6
votes
3
answers
470
views
Do autocorrelated residuals cause OLS coefficients to be biased?
I see different answers everywhere. Intuitively, I would think if residuals are autocorrelated then there is some information that you are not incorporating into your model and is a sign of a biased ...
- 372
3
votes
2
answers
119
views
Instrumental variable as a control variable
I understand that instrumental variable is used to address endogeneity bias since there could be correlation between the variable of interest and the error term.
Suppose now we want to see the ...
- 33
1
vote
0
answers
23
views
Multiplicative BIASES in Log-Log regression
When we try to estimate elasticities by regression, we usually estimate the following regression model:
$$ln(y) = \beta_0 + \beta_1 ln(x_1) + \dots + \epsilon$$
When we expect to have endogenous ...
- 205
3
votes
1
answer
120
views
How does non-collapsibility and the lack of an error term affect coefficients in regression
I have read from here that in nonlinear models such as the logit and Cox, because of a lack of an error term, coefficients may be biased (typically towards zero) when covariates are omitted; I see how ...
- 741
0
votes
0
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51
views
Why the MSE of the fitted data is not equal to the sum of the bias and the variance in R?
I use simple linear regression and I want to find the decomposition of MSE, that is as a sum of the bias, the variance and the variance of the error terms. I have the following code:
...
0
votes
0
answers
19
views
Statistical analysis to interpret beta effect size for two different elastic net model
I have two elastic net model and I want to compare their coefficient to say if they have any significant beta effect changes across these two models.
I thought of using Anova but realized since we don'...
1
vote
0
answers
52
views
Proof of attenuation bias in multiple linear regression model
Consider the case of measurement error with a single explanatory variable measured with error
\begin{equation}
y=\beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_k x^{\ast}_k + \nu
\label{...
- 309
2
votes
0
answers
12
views
Study design when exposure more likely to lead to test for outcome
I am doing an observational study looking at the association between a baseline exposure (binary) and the first instance of an abnormal blood test result (binary) among people with serial blood tests.
...
- 21
1
vote
0
answers
96
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OLS with $iid$ Cauchy errors still unbiased?
A comment to this question suggests that the OLS estimate of linear model parameters is unbiased, even when the error term is Cauchy. Given that Cauchy distributions lack an expected value, I am ...
- 65k
1
vote
0
answers
38
views
Conditioning group effect on values post-treatment variable
I'm relatively new to causal inference, so please be gentle.
I have the above DAG, which represents the following variables:
G: exposure variable, two factors (control and treatment)
S: Pre-...
- 31
2
votes
0
answers
13
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Regression and sampling issues : risk of selection bias when estimating gender gap?
I have been told to estimate a gender and nationality gap using a regression model for a dataset provided by a third party.
The dataset contains information about workers in a very specific sector.
A ...
- 21