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Questions tagged [fused-lasso]

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83
votes
2answers
38k views

When to use regularization methods for regression?

In what circumstances should one consider using regularization methods (ridge, lasso or least angles regression) instead of OLS? In case this helps steer the discussion, my main interest is improving ...
50
votes
4answers
10k views

Fast linear regression robust to outliers

I am dealing with linear data with outliers, some of which are at more the 5 standard deviations away from the estimated regression line. I'm looking for a linear regression technique that reduces the ...
8
votes
2answers
11k views

Preprocess categorical variables with many values [duplicate]

I have a dataset that consists of only categorical variables and a target variable. I want to predict the (binary) target variable with the categorical variables. I am trying to do this in Python and ...
5
votes
2answers
4k views

Tools to detect jumps in a linear time series

I have a financial time series that has a linear down trend, but sometimes a jump happens (see image below). What statistical methods can I use to detect these jumps as early as possible?
5
votes
2answers
261 views

Solving Linear Regression with Fused Lasso Regularization by MLE

I am currently reading a paper stating the following regression problem $$\text{min} \sum_{i=1}^N ||\beta\cdot x_i-y_i||\\ \text{subject to} \sum_{j=2}^M ||\beta_{j}-\beta_{j-1}|| \leq S $$ for ...
4
votes
1answer
646 views

The name of 'Fused' Lasso

As many of you know, the Fused Lasso is one of well known penalized methods, which is introduced by Tibshirani, 2005. However, I don't get to the meaning of how it is called. Could anyone give any ...
4
votes
1answer
404 views

Is there a “fused” version Ridge regression?

we know there is a fused version of LASSO. Fused LASSO adds a further regularizer demanding the smoothness of \beta. More details could be found here I am wondering why I cannot find something ...
3
votes
3answers
181 views

predicting nearly constant data

How do you predict data that contains multiple levels of nearly constant data? Simple linear models even with weights (exponential) did not cut it. I experimented with some clustering and then ...
3
votes
1answer
519 views

Group lasso for generalized linear models

Are there any references on using (sparse) group Lasso for GLMs? I am interested in Gamma GLMs. Any examples of software to perform this would also be appreciated.
3
votes
1answer
285 views

Regularized linear model: adding special constraints to the coefficient

I understand we can add $L_1$ or $L_2$ regularization to linear regression (Lasso and Ridge regression). In addition, it is possible to restrict the coefficient to be integers (see this post). ...
2
votes
2answers
389 views

Regression model selection when there are more variables than cases

I have a database with 200+ variables and less then 50 cases. I need to choose an optimal model that predicts one dependent variable. Are stepwise/lasso regressions still appropriate methods to ...
2
votes
1answer
241 views

2D sparse fused lasso with negative binomial

I am looking for a very specific model, and I am not sure it exists (yet). It is the 2D sparse fused lasso in a negative binomial regression setting. That means Negative binomial observations: $y_i \...
1
vote
1answer
381 views

Constraint GLM coefficients

I am using a generalized linear model in R with categorical independent variables. The model is calibrated and validated, but the results are not of good practical use, because the differences in the ...
1
vote
0answers
105 views

Xgboost / Boosted decision trees: Representing categorical id numbers as continuous integer variable

I've been reading through some kernels at kaggle.com for a sales forecasting competition, and noticed that a lot of people using Xgboost are feeding it categorial ID variables, represented as ...
1
vote
0answers
29 views

Variable selection and merging categories after the fact

Notwithstanding the issues with certain variable selection procedures like stepwise regression, suppose you run some sort of variable selection to find significant categorical covariates in a ...
0
votes
0answers
17 views

What's the rationale behind multiple response LASSO?

I understand that, with LASSO, the regularization term puts a constraint on the complexity of our regression model. Usually, for prediction applications, regularization makes the model perform better ...
0
votes
1answer
66 views

How can I make use of zip codes when I am building a model for fraud detection

I have gone through few articles but I am not convinced on what should I do with these. I know from business standpoint it might be good to consider fraudulent transactions happening from unknown ...
0
votes
0answers
9 views

Degree of freedom of estimated sotuioin to the total variation problem from the ADMM algorithm

The study of the total variation problem is to solve the following problem: $$ \text{minimize} ~ \frac{1}{2}||x - b||_2^2 + \lambda * \sum_i^N |x_{i+1} - x_i| $$ where $x$ is the unknown, $b$ in $R^...
0
votes
1answer
67 views

threshold rule for two penalty terms

Can any one recommend some reference or advice for getting a (hard) thresholding rule combining $\lambda_1$ and $\lambda_2$ for solving an optimization problem with the two penalty (regularization) ...