# Tagged Questions

Is a form of regularization used in estimation of regression coefficients which shrinks coefficient estimates by penalizing their absolute value (i.e. the $L_1$ norm of the estimates). The LASSO is equivalent to the Bayesian estimation problem where iid standard Laplacian prior is used for the ...

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### Why does regularization of coefficient magnitude improve the generalization of linear regression?

What is the basic argument upon which ridge and lasso regression are based on? I went through Tikhonov regularization wiki where it was mentioned that In many cases, tikhonov matrix is chosen as ...
112 views

### glmnet, categorical variable, group lasso?

I am using glmnet for LASSO. My data set contains several continuous variables and one categorical variable (it has four levels). I wondered if I could treat three dummy variables as other continuous ...
98 views

### R package (relaxed lasso for Cox's proportional hazards model)

Is there any R package to implement relaxed lasso for Cox's proportional hazards model? Thank you.
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### Problem with BootCV for coxph in pec after feature selection with glmnet (lasso)

I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I ...
69 views

### Does it make sense to use Lasso regression for recommendation systems?

I'm really intrigued by Lasso Regression, and it seems like a potential candidate for estimating “objective” ratings for items based on the users who watched them. The user data is sparse, and the ...
87 views

### How to check the features which are selected by LASSO

I am using LASSO (glmnet) to do feature selection. However, how can I check which features are selected?
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### Does a less sparse matrix give less accurate estimation using cross-validation?

I am using a simple regression example $$Y=X\beta+\epsilon$$ $X$: 10 by 15 data matrix. $\beta$: 15 by 1 vector. $Y$: 10 by 1 vector. I am using beta1 and beta2 to compare the results against ...
29 views

### Confusion related to the derivation of a dual of a problem

I have this confusion related to the derivation of the dual. I was referring to these lecture slides. I didn't get how the dual was derived. I didn't get how the dual was derived. I am ok up to the ...
37 views

### How to select model when different models are preferred with different seeds?

I am trying to apply the lasso or ridge regression to my data set for the feature selection, but different random seeds produce different models. What is a good or universal way to obtain the final ...
111 views

### Is there LASSO type model in which only some of the regressors are regulated?

I am a beginner with LASSO. Is there any way (paper/code) to perform a LASSO type model in which only some of the explanatory variables are regularized? Or imposing different regulation parameters to ...
35 views

### how to change fused lasso regression depict image

We performed aCGH and depict the result using fused lasso regression. We got similar image as shown in below: Since we have more samples, the left side of so called legend (Gain or loss) came for ...
79 views

### Existence theorem for LASSO

In case of ridge regression we have the theorem that always exists such a shrinking parameter that Mean Squared Error (MSE) of ridge is less than that of least squares. But do we have a similar ...
61 views

### Penalized methods for categorical data: combining levels in a factor

Penalized models can be used to estimate models where the number of parameters is equal to or even greater than the sample size. This situation can arise in log-linear models of large sparse tables of ...
36 views

### Upper bound for the Cardinal of the Active Set in Lasso

I am searching for a proof to: With probability tending to one, we have that the cardianl of the active set in the Lasso estimator is bounded by $C\frac{n}{\lambda_n^2}$, ($C$ is some constant) i.e.; ...
52 views

### Estimating sparse inverse covariance matrix in high dimensional data

I am trying to estimate the graph in very high dimensional data, I mean with million nodes. Up to now all the papers that I have found, they are limited to few thousands. All of them like graphical ...
142 views

### Why isn't the Dantzig selector popular in applied statistics?

Lasso-like methods have become pretty common in applied statistics but the Dantzig selector remains unpopular despite having great properties (minimax optimality). Why hasn't it become more popular?
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### Using LASSO from lars (or glmnet) package in R for variable selection

Sorry if this question comes across a little basic. I am looking to use LASSO variable selection for a multiple linear regression model in R. I have 15 predictors, one of which is categorical(will ...
53 views

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### Stepwise regression vs. elastic net

I understand that Stepwise regression analysis has lots of limitations, including the assumption that the predictors are not highly correlated with each other. In fact, this limitation was the most ...
88 views

### Using standardized Y in Elastic Net

I have an Elastic Net model that is selecting a number of variables from X, for prediction of Y. The assumption for Elastic Net is that X is standardized (I'm using Z-Scores), and Y is centered around ...
160 views

### Calculating R-square for Elastic Net

I am trying to do "variable selection" using Elastic Net method (Matlab Lasso function with alpha of 0.5). I have 75 predictors in total (some are correlated with each other, hence using Elastic Net ...
450 views

### Non negative lasso implementation in R

I am looking for some open source or an existing library I can use. As far as I tell the glmnet package isn't very easily extensible to cover the non negative case. I may be wrong, Any one with any ...