Questions tagged [selectiveinference]
For questions related to CRAN R package selectiveInference and high dimensional inference for LASSO regularized regression models
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Is valid inference possible after a model improvement suggested by model checking?
Scenario
Consider the following scenario in which you have some data $\mathcal D = \{(X_i,Y_i)\}_{i = 1,2,\dots, N}$ and a candidate model $M$ (for instance, for the conditional distribution $\text {...
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Statistical inference in lasso - does data splitting work?
There is something about statistical inference after (linear) model selection that I can’t wrap my head around.
Let’s take Lasso, for example; I am interested in doing hypothesis testing on the (...
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Selection of data analysis methods? [closed]
Say I want to classify a set of 10000 observations into 5 categories A, b, c, d, e. I have about 500 statistical tests to choose from for categorization but only want to use 3 statistical tests. If I ...
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Single/ multiple imputation in post-selection/-regularization context
Context of problem:
In some situations researchers face high-dimensional problems with $p > n$, where $p$ is the number of covariates to be considered in a regression model and $n$ is the sample ...
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Is Individual Coefficient Significance with Ridge or Lasso possible, when Amount of Variables exceeds Observations
First, to introduce you to my situation, I have a dataset containing n = 16 observations and p = 17 variables. My variable set contains 16 independent variables (14 variables I'm interested in and two ...
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Is post-selection inference a problem when robust tests are used?
It's pretty well acknowledged that error control via p-values fails when models are selected based on the data rather than decided on a priori. I've always viewed this as an issue of marginal vs ...
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Effect size after using elastic net and selective inference
I've employed Elastic net to fit a logistic model with predictors that displayed high degrees of correlation between themselves. I wanted to be able to see which predictors significantly influenced ...
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2
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1
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Bias in P-value of MM-type estimators or Cochrans Q Penalized Regression
There are a number of linear regression methods designed to limit the influence of outliers on estimates:
For example, Cochrans Q Penalised regression as described in [1] will do an initial linear ...
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How much of a problem is inference after model selection when few models are manually compared?
tl;dr: I found a better model than the one I first thought of while inspecting the data and performed a few steps of variable selection/model fine-tuning. I assume that this is a (mild) case of ...
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Hypothesis Testing on Coefficients post LASSO Variable Selection (in R)
Is it possible to run a hypothesis test to test for a significant difference in calculated coefficients of the same independent variable in two different subsets of one population, after you have ...
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Using regularization when doing statistical inference
I know about the benefits of regularization when building predictive models (bias vs. variance, preventing overfitting). But, I'm wondering if it is a good idea to also do regularization (lasso, ...
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Inference for quasibinomial GLM with LASSO penalty using selectiveInference package
I would like to carry out inference on a binomial LASSO model, but take into account the fact that my data are overdispersed and use the quasibinomial family instead.
R package ...
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Testing for coefficients significance in Lasso logistic regression
[A similar question was asked here with no answers]
I have fit a logistic regression model with L1 regularization (Lasso logistic regression) and I would like to test the fitted coefficients for ...
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