Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [fused-lasso]

20 questions
93
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 predictive accuracy.
NPE
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53
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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 influence of these points. So far what I did is…
Matteo Fasiolo
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10
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2 answers

Preprocess categorical variables with many values

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 sklearn. The categorical variables have many…
Bert Carremans
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7
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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
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5
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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 vectors $x_1,\dots,x_N, \beta \in \mathbb{R}^n, y_i \in…
AlexConfused
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5
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1 answer

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
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5
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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 ideas about the origin or the meaning of 'fused'?
inmybrain
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4
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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 build such model?
Adam Ryczkowski
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4
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1 answer

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 similar for Ridge regression. We can also add a…
user152503
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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: $y_i \sim NB(\mu_i,\theta)$ , where $\log \mu = \log m…
yannick
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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). However, is there any related work to add special…
Haitao Du
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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 robust linear regression but my problem is that the…
Georg Heiler
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2 answers

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 locations. But I don't know how to use this in my…
nithin
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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 response variable vary too much across segments.…
gregorp
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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 real changepoints (as opposed to being…
user54038
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