Questions tagged [fused-lasso]

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variance estimation for fused lasso

In the paper THE SOLUTION PATH OF THE GENERALIZED LASSO the authors derived degrees of freedom for the generalised lasso. Assume that $y\in\mathcal{N}(\mu,\Sigma)$, with $\mu\in\mathbb{R}^{n}$ and $\...
ABK's user avatar
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alternative solution to fussed lasso

The question is related to strange result from fused lasso estimator Let us consider fussed lasso estimator: $$ \hat{\beta}^{FL} = \underset{\beta \in \mathbb{R}^{n}}{\arg \min} [(y_{i} - \beta_{i})^{...
AnTlr's user avatar
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strange result from fused lasso estimator

Let us consider the following estimator: $$ \hat{\beta}^{F} = \underset{\beta \in \mathbb{R}^{n}}{\arg \min} (y_{i} - \beta_{i})^{2} + \lambda_{1} \sum_{i=1}^{n-1}|\beta_{i} - \beta_{i+1}|, $$ which ...
AnTlr's user avatar
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Optimal penalty for finding changepoints with the fused lasso, assuming some probabilistic model?

I am interested in detecting changepoints in a signal using the fused lasso (as implemented here for example). I am in particular interested in getting estimates of changepoints which are close to the ...
user54038's user avatar
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2 votes
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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 ...
nithin's user avatar
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Xgboost / Boosted decision trees: Representing categorical id numbers as continuous integer variable

I've been reading through some kernels at for a sales forecasting competition, and noticed that a lot of people using Xgboost are feeding it categorial ID variables, represented as ...
djfinnoy's user avatar
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5 votes
2 answers

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 ...
AlexConfused's user avatar
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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 ...
user152503's user avatar
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5 votes
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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.
user90772's user avatar
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0 votes
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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) ...
i_a_n's user avatar
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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 ...
roccomay's user avatar
3 votes
1 answer

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 \...
yannick's user avatar
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11 votes
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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 ...
Bert Carremans's user avatar
2 votes
1 answer

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 ...
gregorp's user avatar
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3 votes
1 answer

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). ...
Haitao Du's user avatar
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3 votes
3 answers

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 ...
Georg Heiler's user avatar
5 votes
1 answer

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 ...
inmybrain's user avatar
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4 votes
2 answers

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 ...
Adam Ryczkowski's user avatar
7 votes
3 answers

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?
Victor's user avatar
  • 375
54 votes
4 answers

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 ...
Matteo Fasiolo's user avatar
97 votes
2 answers

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 ...
NPE's user avatar
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